IBM IBM Optimization Technical Mastery

IBM Db2 question Optimization using AI | killexams.com Real Questions and Pass4sure dumps

In September 2018, IBM introduced a brand new product, IBM Db2 AI for z/OS. This artificial intelligence engine monitors data access patterns from executing SQL statements, makes use of computing device studying algorithms to choose gold standard patterns and passes this assistance to the Db2 question optimizer to be used by subsequent statements.

computing device discovering on the IBM z Platform

In might also of 2018, IBM introduced edition 1.2 of its desktop discovering for z/OS (MLz) product. here is a hybrid zServer and cloud utility suite that ingests efficiency records, analyzes and builds models that signify the health popularity of quite a lot of indicators, monitors them over time and offers true-time scoring capabilities.

a few aspects of this product offering are geared toward assisting a neighborhood of model builders and bosses. as an instance:

It helps diverse programming languages such as Python, Scala and R. This permits statistics modelers and scientists to make use of a language with which they are familiar;

A graphical consumer interface known as the visual mannequin Builder courses mannequin builders without requiring enormously-technical programming skills;

It contains numerous dashboards for monitoring model results and scoring services, in addition to controlling the device configuration.

This laptop studying suite turned into in the beginning aimed toward zServer-primarily based analytics applications. one of the first evident decisions was zSystem efficiency monitoring and tuning. equipment management Facility (SMF) facts that are immediately generated by using the working system provide the uncooked data for system resource consumption such as vital processor usage, I/O processing, memory paging and so on. IBM MLz can assemble and shop these facts over time, and construct and teach fashions of system habits, score those behaviors, establish patterns now not conveniently foreseen by using people, enhance key performance warning signs (KPIs) and then feed the model outcomes back into the device to have an effect on device configuration alterations that may increase efficiency.

The subsequent step changed into to enforce this suite to investigate Db2 efficiency information. One answer, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the laptop studying technology to Db2 operational records to benefit an figuring out of Db2 subsystem health. it may well dynamically build baselines for key efficiency symptoms, give a dashboard of these KPIs and provides operational personnel true-time perception into Db2 operations.

whereas established Db2 subsystem performance is a vital aspect in standard software health and performance, IBM estimates that the DBA help team of workers spends 25% or extra of its time, " ... fighting entry route problems which trigger performance degradation and service impact.". (See Reference 1).

AI comes to Db2

agree with the plight of contemporary DBAs in a Db2 environment. In modern day IT world they should assist one or greater massive facts functions, cloud utility and database features, software setting up and configuration, Db2 subsystem and software efficiency tuning, database definition and management, catastrophe recovery planning, and extra. query tuning has been in existence on the grounds that the origins of the database, and DBAs are usually tasked with this as well.

The heart of query path evaluation in Db2 is the Optimizer. It accepts SQL statements from functions, verifies authority to access the records, reviews the locations of the objects to be accessed and develops a list of candidate information access paths. These entry paths can consist of indexes, desk scans, a variety of desk join strategies and others. within the information warehouse and large information environments there are usually extra decisions obtainable. One of these is the existence of summary tables (once in a while called materialized query tables) that include pre-summarized or aggregated records, for that reason permitting Db2 to stay away from re-aggregation processing. an additional choice is the starjoin entry route, usual in the facts warehouse, the place the order of table joins is modified for efficiency causes.

The Optimizer then stories the candidate entry paths and chooses the access course, "with the bottom charge." cost in this context ability a weighted summation of useful resource utilization together with CPU, I/O, memory and different elements. at last, the Optimizer takes the lowest cost access course, stores it in reminiscence (and, optionally, in the Db2 listing) and begins access path execution.

large records and statistics warehouse operations now consist of application suites that allow the company analyst to use a graphical interface to build and manipulate a miniature records model of the records they need to analyze. The applications then generate SQL statements in response to the clients’ requests.

The problem for the DBA

in an effort to do good analytics for your assorted data stores you want a very good figuring out of the information necessities, an realizing of the analytical capabilities and algorithms accessible and a high-efficiency records infrastructure. lamentably, the quantity and placement of facts sources is increasing (both in dimension and in geography), facts sizes are growing, and purposes continue to proliferate in number and complexity. How should still IT managers guide this ambiance, certainly with essentially the most experienced and mature group of workers nearing retirement?

remember additionally that a huge a part of decreasing the overall can charge of ownership of these programs is to get Db2 functions to run faster and extra effectively. This constantly translates into the usage of fewer CPU cycles, doing fewer I/Os and transporting less records across the community. considering it's commonly problematic to even determine which purposes might advantage from performance tuning, one approach is to automate the detection and correction of tuning issues. this is where laptop getting to know and synthetic intelligence will also be used to super impact.

Db2 12 for z/OS and synthetic Intelligence

Db2 edition 12 on z/OS makes use of the machine researching facilities mentioned above to gather and save SQL question textual content and access course particulars, in addition to actual efficiency-related ancient guidance comparable to CPU time used, elapsed instances and influence set sizes. This offering, described as Db2 AI for z/OS, analyzes and shops the records in machine learning fashions, with the model analysis results then being scored and made available to the Db2 Optimizer. The next time a scored SQL commentary is encountered, the Optimizer can then use the model scoring records as input to its access route alternative algorithm.

The influence may still be a discount in CPU consumption as the Optimizer makes use of mannequin scoring input to choose more advantageous entry paths. This then lowers CPU costs and speeds software response times. a major capabilities is that the use of AI utility doesn't require the DBA to have facts science talents or deep insights into question tuning methodologies. The Optimizer now chooses the premier access paths based now not simplest on SQL question syntax and statistics distribution data but on modelled and scored historic performance.

This will also be above all important if you save facts in distinct areas. as an instance, many analytical queries towards large statistics require concurrent access to certain data warehouse tables. These tables are often referred to as dimension tables, and they comprise the facts facets continually used to handle subsetting and aggregation. for example, in a retail environment trust a table known as StoreLocation that enumerates each keep and its location code. Queries in opposition t keep income facts might also want to mixture or summarize revenue by way of location; hence, the StoreLocation desk should be used by way of some large information queries. during this atmosphere it is average to take the dimension tables and replica them regularly to the huge facts utility. in the IBM world this vicinity is the IBM Db2 Analytics Accelerator (IDAA).

Now think about SQL queries from both operational applications, records warehouse clients and massive information business analysts. From Db2's point of view, all these queries are equal, and are forwarded to the Optimizer. however, within the case of operational queries and warehouse queries they should definitely be directed to access the StoreLocation table in the warehouse. nonetheless, the question from the company analyst in opposition t large statistics tables should likely entry the copy of the desk there. This results in a proliferations of skills entry paths, and extra work for the Optimizer. fortuitously, Db2 AI for z/OS can provide the Optimizer the guidance it should make sensible entry path selections.

the way it Works

The sequence of movements in Db2 AI for z/OS (See Reference 2) is commonly right here:

all through a bind, rebind, put together or explain operation, an SQL statement is passed to the Optimizer;

The Optimizer chooses the statistics access path; because the choice is made, Db2 AI captures the SQL syntax, entry course option and question performance statistics (CPU used, etc.) and passes it to a "researching task";

The discovering assignment, which will also be executed on a zIIP processor (a non-standard-intention CPU core that does not factor into application licensing expenses), interfaces with the laptop researching software (MLz mannequin features) to keep this tips in a model;

because the volume of records in every mannequin grows, the MLz Scoring carrier (which also can be achieved on a zIIP processor) analyzes the model records and ratings the conduct;

all through the next bind, rebind, put together or explain, the Optimizer now has entry to the scoring for SQL models, and makes appropriate changes to access route selections.

There are additionally a considerable number of consumer interfaces that supply the administrator visibility to the repute of the collected SQL remark efficiency facts and model scoring.

summary

IBM's desktop getting to know for zOS (MLz) providing is getting used to super effect in Db2 version 12 to improve the efficiency of analytical queries as well as operational queries and their linked functions. This requires management attention, as you should examine that your enterprise is ready to consume these ML and AI conclusions. How will you measure the costs and benefits of the use of computer researching? Which IT help personnel should be tasked to reviewing the outcome of model scoring, and perhaps approving (or overriding) the outcomes? How will you evaluate and justify the assumptions that the application makes about entry path choices?

In other phrases, how smartly were you aware your statistics, its distribution, its integrity and your current and proposed entry paths? this may check where the DBAs spend their time in assisting analytics and operational application efficiency.

# # #

Reference 1

John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: enhance IBM Db2 application performance with computer studying"https://www.worldofdb2.com/pursuits/ibm-db2-ai-for-z-os-boost-ibm-db2-software-efficiency-with-ma

Reference 2

Db2 AI for z/OShttps://www.ibm.com/assist/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

See all articles by way of Lockwood Lyon

ABB and Dassault Systèmes Enter world software Partnership for Digital Industries | killexams.com Real Questions and Pass4sure dumps

ZURICH--(enterprise WIRE)--Regulatory information:

ABB and Dassault Systèmes (Paris:DSY) nowadays announced a large spanning world partnership to present purchasers in digital industries a special software solutions portfolio ranging from product lifestyles cycle administration to asset health solutions. the two organizations will provide purchasers an conclusion-to-end offering of superior open digital options, bettering competitiveness of business organizations, while expanding flexibility, velocity and productivity of their items' lifecycles, manufacturing and operations.

The partnership will combine the strengths of ABB skill™ digital options and Dassault Systèmes' 3DEXPERIENCE platform, and build on both groups’ robust put in base, deep area abilities and global customer access. ABB has already adopted the 3DEXPERIENCE platform to mannequin and simulate its options before providing them to its customers. With this partnership, ABB will increase and provide customers with superior digital twins, enabling valued clientele to run ABB’s solutions and their operations with more desirable average effectivity, flexibility and sustainability.

The organizations will, in a staged approach, focal point on factory automation and robotics, technique industry automation, in addition to electrification options for wise structures. the primary joint solutions can be showcased on the upcoming industrial Hannover Messe change reasonable in Germany, April 1-5, 2019.

"This online game-altering partnership will serve our shoppers to steer in innovation and boom, basically reworking their entire price chain to tap the massive opportunities of commercial digitalization. together, we are providing an open, end-to-conclusion digital portfolio - from digital twin to asset health - that gives our customers a competitive area, constructing on our combined providing, domain knowledge and international attain,” pointed out ABB CEO Ulrich Spiesshofer. “ABB is adding Dassault Systèmes to its mighty partner community for industrial digitalization, together with Microsoft, HPE and IBM. We appear very an awful lot ahead to working with the entire global Dassault Systèmes group to power innovation and customer cost.”

“The business of the 21st century isn't any longer decided simply by way of the potential to fabricate goods. these days’s leaders may be decided by superior mastery of technical knowledge. here's the brand new aggressive differentiator and it’s happening now because of a convergence of digital technologies which are reworking each aspect of industrial business,” noted Bernard Charlès, vp and CEO, Dassault Systèmes. “in this business renaissance, a platform approach allows the actual and digital worlds to inform and fortify one yet another. Our partnership with ABB will draw from decades of combined knowledge to aid shoppers take advantage of this potent and dynamic fashion.”

In contemporary incredibly automatic industries, digital manufacturing unit modeling and versatile, robotized manufacturing programs aid groups to generate more design iterations at a quicker price with greater strong designs. This, in flip, helps to accelerate the shift from mass production to mass customization, where goods are manufactured in a greater diversity and in smaller batches and in shorter product life cycles. for a lot of manufacturers, the can charge of downtime has dramatically accelerated in contemporary years as just-in-time delivery has become the norm. An hour of downtime at a latest construction site can can charge more than $1 million.

ABB has already a powerful digital solution offering within the business through its providing ABB AbilityTM. It turned into launched in 2017 and presents greater than 210 digital solutions to plot, construct and function industrial operations with bigger productivity and security at reduce charges.

Dassault Systèmes works with agencies of all sizes in eleven industries to aid them meet new challenges in today’s trade Renaissance. The 3DEXPERIENCE platform integrates the entire applied sciences and capabilities that leverage capabilities and knowledge into one cohesive digital innovation environment that gives you digital continuity from theory to manufacturing to ownership and lower back. Industrial businesses can integrate the platform’s 3D applications to create a digital twin that captures insights and knowledge from throughout their whole ecosystem, to measure, assess and predict the efficiency of an industrial asset and help optimize its operation in an intelligent method.

The ABB – Dassault Systèmes partnership will originally focus on:

factory Automation and Robotics

Digital twin experiences for end-to-conclusion optimization of methods and techniques, mixed with the pliability of robotics automation, will give factories the agility to adapt to increasingly dynamic markets. This contains competent-to-operate manufacturing options and capabilities, together with joint consulting for industrial enterprise transformation, to optimize and pace the launch of latest products. Electronics makers can raise the construction of latest but brief-lived items straight away, while meals processors can alternate between in the community tailored seasonal choices whereas producing at excessive velocity. In enormously automatic industries such as automobile, the digital twin event of factories enables an integrated design and manufacturing environment to help new meeting procedures with bendy and reconfigurable cells. It additionally makes it viable to hyperlink separate methods, reminiscent of connecting logistics automation systems to robots at work on manufacturing traces that rely on actual constituents birth for most beneficial creation performance.

wise constructions

The digital partnership between Dassault Systèmes and ABB around digital twin systems will enable a seamless workflow all the way through design, engineering and operation of constructions, as well as connected sustainable transportation solutions. The accessible tips, in aggregate with Dassault Systèmes’ virtual universe 3DEXPERIENCE®, will additionally permit better customer interaction during the design specification phases and operation.

process industries: Mining example

competitive drive in process industries, such as mining, requires businesses to always look for new ways to increase security, productiveness and energy efficiency of websites, whereas decreasing costs and risk of day by day operations. A digital model of the underground atmosphere, in connection with mine planning and handle systems, would enable to optimize power consumption and mine automation, as well as allow mine operators to computer screen and optimize creation in real-time, while running digital simulations of future eventualities.

###

ABB (ABBN: SIX Swiss Ex) is a pioneering expertise chief in vigour grids, electrification items, industrial automation and robotics and action, serving consumers in utilities, trade and transport & infrastructure globally. carrying on with a history of innovation spanning greater than one hundred thirty years, ABB today is writing the future of industrial digitalization with two clear price propositions: bringing electricity from any vigour plant to any plug and automating industries from herbal components to accomplished products. As title accomplice in ABB formula E, the absolutely electric powered overseas FIA motorsport class, ABB is pushing the boundaries of e-mobility to contribute to a sustainable future. ABB operates in more than a hundred nations with about 147,000 employees. www.abb.com

Dassault Systèmes, the 3DEXPERIENCE business, provides company and americans with virtual universes to think about sustainable innovations. Its world-leading options seriously change the manner products are designed, produced, and supported. Dassault Systèmes’ collaborative options foster social innovation, increasing possibilities for the digital world to improve the precise world. The neighborhood brings price to over 250,000 customers of all measurement, in all industries, in more than one hundred forty nations. For more guidance, consult with www.3ds.com.

3DEXPERIENCE, the Compass logo and the 3DS emblem, CATIA, SOLIDWORKS, ENOVIA, DELMIA, SIMULIA, GEOVIA, EXALEAD, 3D by the use of, BIOVIA, NETVIBES and 3DEXCITE are registered trademarks of Dassault Systèmes or its subsidiaries in the US and/or other countries.

The future of AI Is here: Reflections on IBM believe | killexams.com Real Questions and Pass4sure dumps

posted: February 26, 2019

The IBM think conference in San Francisco this month was a massive adventure, taking on the SoMa district, interrupting traffic, demanding the locals, and riding up resort prices to stratospheric ranges. regardless of a reasonably respectable rainstorm that pounded the metropolis all over the weeklong convention, thousands of attendees swarmed throughout the Moscone middle and different nearby venues, intent to be taught, share, promote, and community. among a whole lot of sessions, displays, and demos, one theme came via evidently: for tremendous firms particularly, the AI-pushed future for which we’ve been told to put together is already here.

IBM is of route the domestic of Watson, which most of us doubtless recognize for its associations with Jeopardy, or the healthcare business, or the weather Channel. but there’s way more to the Watson laptop learning platform than can also be without problems surmised from its greater standard public appearances. truly, business corporations are the usage of Watson applied sciences today to tackle a myriad of challenges inherent in the scale of those organizations.

To take a number of examples: Petco is using Watson’s natural Language figuring out, Assistant, and other equipment to give speedy access to deliver chain counsel for a large number of business stakeholders. Carhartt makes use of a device known as Commerce Insights to give actual-time records on company performance to marketers and merchandisers, who're able to restock items or modify give to capitalize on present developments. container lets clients automatically tag documents with critical ideas and themes, making them more easily searchable. Smarttech uses Watson tools to enhance its cybersecurity efforts and boost timeliness of response to threats. AutoGlass body restoration used Watson visible focus to construct a tool that makes it possible for valued clientele to upload photographs of automobile damage for fast evaluation.

These are only a couple of of the applications showcased at think. The major takeaway is that any enterprise that relies on significant quantities of records kept in digital documents, spreadsheets, databases, photographs, or other media, or who need to manipulate complex workflows that contain a large number of smaller decisions including as much as significant enterprise have an effect on, can probably advantage from technology whereby machines are educated to extract and organize significant content. In other phrases, the advantage functions of AI are infinite.

Let me illustrate with an additional example that indicates the multifaceted function AI is enjoying in an age-historic industry. Gaurav Rao, executive director of engineering at Watson OpenScale, described an insurance business that uses AI to automate the review of claims. in this example, 9 out of 10 claims can be processed instantly the usage of a heuristic that takes into consideration a lot of components together with the substance of the claim itself as neatly because the applicant’s policy guidance, variety of prior claims, age, and different add-ons. One aim of providing oversight through OpenScale is to enrich the automation technique and additional reduce the want for human intervention in claims processing.

even more exciting, OpenScale became capable of highlight advantage bias in claims choices. Rao walked via an illustration where the OpenScale dashboard uncovered capabilities factors and counseled fixes for a bias that negatively impacted older applicants. In some circumstances, tweaking the heuristic comprises tradeoffs which need to be cautiously considered. for instance, Rao defined that as equity improves, accuracy can also somewhat decline. people need to be latest at the controls to verify the appropriate steadiness of priorities.

Gartner has claimed that in response to its research, AI will generate $2.9 trillion in business price through 2021 and may recover 6.2 billion hours of productiveness. corporations are recognizing this skills, with 85% viewing AI as a strategic possibility, in line with IBM. For those already the use of AI, seventy two% see extended earnings and 28% see can charge discount rates as the fundamental benefit.

notwithstanding many forward-thinking corporations are already using AI options, people that have greatly incorporated AI of their choices or strategies represent most effective a twentieth of all companies, based on a analyze from MIT Sloan administration evaluate. limitations to entry include lack of required technical team of workers, referred to as a priority with the aid of sixty three% of respondents in an IBM survey, in addition to regulatory concerns, mentioned by using 60 p.c. for a lot of industries comparable to insurance and healthcare, AI solutions will most effective make experience in the event that they are educated to work within stringent regulatory necessities.

regardless of those concerns, organizations which have already embraced AI are beginning to recognize its strategic merits. One such enterprise, Daimler AG, verified its Mercedes-Benz virtual assistant at consider. The assistant, aptly named Mercedes, appears to conclusion users in a number of distinctive guises. One is an Alexa-like in-car adventure for the business’s E- and S-type sedans. yet another absolutely distinctive deployment, created through Mercedes-Benz economic capabilities, makes use of a chatbot interface to assist borrowers pay off their latest automobile loans and finance new Mercedes purchases.

To create the assistant, the Mercedes group used Watson together with a textual content evaluation visualization device called Quid. The crew examined more than one hundred fifty,000 chat messages between buyers and human brokers, trying to cluster linked subject matters and to trap the numerous diversifications through which buyers communicate their needs. They discovered, for example, that buyers ask for personal loan payoff suggestions in as many as 200 distinctive notice and phrase mixtures. Armed with these variations, the Mercedes group become able to educate the Watson Assistant tool to respond accurately to nearly all of queries.

Gartner has claimed that eighty% of the realm’s facts is unstructured. I’ve tended to suppose of this observation as that means that facts is hidden away in files, PDFs, Excel spreadsheets, and other types that are unimaginable to collate and normalize using traditional strategies. That’s certainly a part of the story. however the discussions at feel mutually painted broader and extra nuanced definitions of “structure” and “statistics,” suggesting that enterprise techniques themselves, from deliver chain management to client service workflows to pricing fluctuations based on market conditions, are all discipline to analysis, automation, and optimization through ability of AI.

As I’m finishing this piece, news is breaking on CNN concerning the nonprofit analysis firm OpenAI, which has announced that it's withholding the latest unlock of its text-composing know-how from the general public because of issues that it so convincingly mimics prose written by using people that it may be used maliciously. Public perception concerning the future of AI has grew to become a little bitter in easy of such dystopian stories. That’s one reason I found it clean to be trained  at IBM suppose in regards to the many purposeful makes use of of AI for company and business. One company may also choose to withhold its expertise for now, but in gigantic half the equipment to build our AI-pushed future have already arrived.

if you’re drawn to some of the competencies affects of AI on native search and reputation management, I’ve shared some concepts on these subjects over on the Brandify weblog.

Damian Rollison writes the Streets ahead column for street battle. he is VP of product at Brandify, and can be reached via Twitter. Brandify is the publisher of road fight.

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IBM Optimization Technical Mastery Test v1

Back to Microservices with Istio (Part 1) | killexams.com real questions and Pass4sure dumps

Istio is an Open Source project developed in partnership between teams from Google, IBM, and Lyft and it provides a solution to the complexities of microservice based application, to name a few:

Traffic management: Timeouts, retries, load balancing,

Security: End-user Authentication and Authorization,

Observability: Tracing, monitoring, and logging.

All of these can be solved in the application layer, but your services don’t end up being so ‘Micro’ anymore, all the additional effort for implementing these is a strain in the company’s resources, resources that can be used to deliver business value. Let’s take an example:

PM: How long will it take to add a feedback feature?

Dev: Two sprints.

PM: What…? That’s just a CRUD!

Dev: Creating the CRUD is the easy part but then we need to authenticate and authorize users and services. And because the network is not reliable we need to implement retries, and circuit breakers in the clients, and to make sure that we do not take the whole system down we need Timeouts and Bulkheads, additionally to detect issues we need monitoring, tracing […]

PM: Let’s just stick it in the Product Service then. Jeez!

You get the idea, all the ceremony, and effort that must go in, for us to add one service is enormous. In this article, we’ll showcase how Istio removes all the above-mentioned cross-cutting concerns from our services.

Figure 1. The Ceremony of a Microservice

Note: This article assumes that you have a working knowledge of Kubernetes. If it’s not the case I recommend you to read my introduction to Kubernetes, and then continue with this article.

The Idea of Istio

In a world without Istio one service makes direct requests to another and in cases of failure, the service needs to handle it by retrying, timeouting, opening the circuit breaker etc.

Figure 2. Network traffic in Kubernetes

To resolve this, Istio provides an ingenious solution by being completely separated from the services and act only by intercepting all network communication. And doing so it can implement:

Fault Tolerance — Using response status codes it understands when a request failed and retries.

Canary Rollouts — Forward only the specified percentage of requests to a new version of the service.

Monitoring and Metrics — The time it took for a service to respond.

Tracing and Observability — It adds special headers in every request and traces them in the cluster.

Security — Extracts the JWT Token and Authenticates and Authorizes users.

To name a few (for real just a few) and get you intrigued! Let’s get to the Technical details!

Istio’s Architecture

Istio intercepts all network traffic and applies a set of rules by injecting an intelligent proxy as a sidecar in every pod. The proxies that enable all the features comprise The Data Plane and those are dynamically configurable by The Control Plane.

The Data Plane

The injected proxies enable Istio to easily achieve our requirements. For an example let’s check out the retrying and Circuit breaking functionalities.

Figure 3. How envoys implement Retries & CircuitBreaking

To summarize:

Envoy sends the request to the first instance of service B and it fails.

The Envoy Sidecar retries. (1)

Returns a failed request to the calling proxy.

Which opens the Circuit Breaker and calls the next Service on subsequent requests. (2)

This means that you don’t have to use another Retry library, you don’t have to develop your own implementation of Circuit Breaking and Service Discovery in Programming Language X, Y or Z. All of those and more are provided out of the box by Istio and NO code changes are required.

Great! Now you want to join the voyage with Istio, but you still have some doubts, some open questions. Is this a One-Size-Fits-All-Solution, which you’re suspicious about, as it always ends up being One-Size-Fits None solution!

You finally mutter the question: “Is this configurable?”

Welcome to the cruise my friend and let’s get introduced to the Control Plane.

The Control Plane

Is composed of three components: The Pilot, the Mixer, and the Citadel that in combination configure Envoys to route traffic, enforce policies and collect telemetry data. Visually presented in the image below.

Figure 4. Control Plane in relation to Data Plane

The envoys (i.e. the data plane) are configured using Kubernetes Custom Resource Definitions defined by Istio and specialized for this purpose. Which means that for you it’s just another Kubernetes Resource with a familiar syntax. Which after being created will be picked up by the control plane that applies it to the envoys.

Relation of Services to Istio

We described the relation of Istio to our Services, but not the other way around, what’s the relation of our Services to Istio?

Frankly, our services have as much knowledge of Istio’s presence, as fish do of water, they will ask themselves “What the hell is water?”.

Drawing by Victoria Dimitrakopoulos

This means that you can pick a working cluster and after deploying the components of Istio, the services within will continue to work and in the same manner, you can remove the components and everything will be just fine. Understandably, you would lose the capabilities provided by Istio.

We had enough of theory, let’s put this knowledge into practice!

Istio in Practice

Istio requires a Kubernetes Cluster with at least 4 vCPU and 8 GB of RAM. To quickly set up a cluster and follow up with this article, I recommend using the Google Cloud Platform, which provides new users with a $300 free trial.

After creating the cluster and configuring access with the Kubernetes command line tool we’re ready to install Istio using the Helm Package manager.

Installing Helm

Install the Helm client on your computer as explained in the official docs. Which we will use to generate the Istio installation templates in the next section.

Installing Istio

Download Istio’s resources from the latest release, extract the contents into one directory that we will refer to as [istio-resources].

To easily identify the Istio resources create a namespace istio-system in your Kubernetes Cluster:

$ kubectl create namespace istio-system

Complete the installation by navigating to [istio-resources] directory and executing the command below:

$ helm template install/kubernetes/helm/istio \--set global.mtls.enabled=false \--set tracing.enabled=true \--set kiali.enabled=true \--set grafana.enabled=true \--namespace istio-system > istio.yaml

The above command prints out the core components of Istio into the file istio.yaml. We customized the template using the following parameters:

global.mtls.enabled is set to false to keep the introduction focused.

tracing.enabled enables tracing of requests using jaeger.

kiali.enabled installs Kiali in our cluster for Visualizing Services and Traffic

grafana.enabled installs Grafana to visualize the collected metrics.

Apply the generated resources by executing the command:

$ kubectl apply -f istio.yaml

This marks the completion of the Istio installation in our cluster! Wait until all pods in the istio-system namespace are in Running or Completed state by executing the command below:

$ kubectl get pods -n istio-system

Now we’re ready to continue with the next section, where we will get the sample application up and running.

Sentiment Analysis Application Architecture

We will use the same microservice application used in the Kubernetes Introduction article, it’s complex enough to showcase Istio’s features in practice.

The App is composed of four microservice:

SA-Frontend service: Serves the frontend Reactjs application.

SA-WebApp service: Handles requests for Sentiment Analysis.

SA-Logic service: Performs sentiment analysis.

SA-Feedback service: Receives feedbacks from the users about the accuracy of the analysis.

Figure 6 Sentiment Analysis microservices

In figure 6, besides the services we see the Ingress Controller which in Kubernetes routes incoming requests to the appropriate services, Istio uses a similar concept called Ingress Gateway, which will be introduced in continuation of the article.

Running the Application with Istio Proxies

To follow up with this article clone the repository istio-mastery, containing the application and the manifests for Kubernetes and Istio.

Sidecar Injection

The injection is done Automatically or Manually. To enable automatic sidecar injection, we need to label the namespace with istio-injection=enabled, by executing the command below:

$ kubectl label namespace default istio-injection=enablednamespace/default labeled

From now every pod that gets deployed into the default namespace will get the sidecar injected. To verify this let’s deploy the sample application by navigating to the root folder of the [istio-mastery] repository and executing the following command:

$ kubectl apply -f resource-manifests/kubepersistentvolumeclaim/sqlite-pvc createddeployment.extensions/sa-feedback createdservice/sa-feedback createddeployment.extensions/sa-frontend createdservice/sa-frontend createddeployment.extensions/sa-logic createdservice/sa-logic createddeployment.extensions/sa-web-app createdservice/sa-web-app created

With the services deployed verify that the pods have two containers (the service and the sidecar) by executing the command kubectl get pods and ensuring that under the column ready, we see the value “2/2” indicating that both containers are running. As seen below:

$ kubectl get podsNAME READY STATUS RESTARTS AGEsa-feedback-55f5dc4d9c-c9wfv 2/2 Running 0 12msa-frontend-558f8986-hhkj9 2/2 Running 0 12msa-logic-568498cb4d-2sjwj 2/2 Running 0 12msa-logic-568498cb4d-p4f8c 2/2 Running 0 12msa-web-app-599cf47c7c-s7cvd 2/2 Running 0 12m

Visually presented in figure 7.

Figure 7. Envoy proxy in one of the Pods

With the application up and running now we need to allow incoming traffic to reach our application.

Ingress Gateway

A best practice for allowing traffic into the cluster is through Istio’s Ingress Gateway which positions itself at the edge of the cluster and on incoming traffic enables Istio’s features like routing, load balancing, security, and monitoring.

During Istio’s installation, the Ingress Gateway component and a service that exposes it externally were installed into the cluster. To get the services External IP execute the command below:

$ kubectl get svc -n istio-system -l istio=ingressgatewayNAME TYPE CLUSTER-IP EXTERNAL-IPistio-ingressgateway LoadBalancer 10.0.132.127 13.93.30.120

In the continuation of this article we will access the application on this IP (referred to as the EXTERNAL-IP), for convenience, save it in a variable by executing the command below:

$ EXTERNAL_IP=$(kubectl get svc -n istio-system \-l app=istio-ingressgateway \-o jsonpath='{.items[0].status.loadBalancer.ingress[0].ip}')

If you reach this IP in your browser and you will get a Service Unavailable error, as by default Istio blocks any incoming traffic until we define a Gateway.

The Gateway Resource

A Gateway is a Kubernetes Custom Resource Definition defined upon Istio’s installation in our cluster that enables us to specify the Ports, Protocol and Hosts for which we want to allow incoming traffic.

In our scenario, we want to allow HTTP traffic on port 80, for all hosts. Achieved with the following definition:

All the configuration is self-explanatory besides the selector istio: ingressgateway. Using this selector, we can specify to which Ingress Gateway to apply the configuration, and in our case, it’s the default ingress gateway controller installed on Istio setup.

Apply the configuration by executing the command below:

$ kubectl apply -f resource-manifests/istio/http-gateway.yaml gateway.networking.istio.io/http-gateway created

The gateway now allows access in port 80 but it has no concept where to route the requests. That is achieved using Virtual Services.

The VirtualService resource

The VirtualService instructs the Ingress Gateway how to route the requests that were allowed into the cluster.

For our application requests coming through the http-gateway must be routed to the sa-frontend, sa-web-app and sa-feedback services (show in figure 8).

Figure 8. Routes to be configured with VirtualServices

Let’s break down the requests that should be routed to SA-Frontend:

Exact path /should be routed to SA-Frontend to get the Index.html

Prefix path /static/* should be routed to SA-Frontend to get any static files needed by the frontend, like Cascading Style Sheets and JavaScript files.

Paths matching the regex '^.*\.(ico|png|jpg)$' should be routed to SA-Frontend as it is an image, that the page needs to show.

This is achieved with the following configuration:

The important points here are:

This VirtualService applies to requests coming through the http-gateway.

Destination defines the service where the requests are routed to.

Note: The configuration above is in the file sa-virtualservice-external.yaml, it also contains the configuration to route to SA-WebApp and SA-Feedback but was shortened for brevity.

Apply the VirtualService by executing:

$ kubectl apply -f resource-manifests/istio/sa-virtualservice-external.yamlvirtualservice.networking.istio.io/sa-external-services created

Note: When we apply Istio resources the Kubernetes API Server creates an event received by Istio’s Control Plane which then applies the new configuration to the envoy proxies of every pod. And the Ingress Gateway controller is another Envoy which is configured by the Control Plane, visually presented in figure 9.

Figure 9. Configuring Istio-IngressGateway to route requests

The Sentiment Analysis app is now accessible on http://{EXTERNAL-IP}/. If you get a Not Found status, do not worry sometimes it takes a bit for the configuration to go in effect and update the envoy caches.

Before moving to the next section use the app to generate some traffic.

Kiali — Observability

To access Kiali’s Admin UI execute the command below:

$ kubectl port-forward \$(kubectl get pod -n istio-system -l app=kiali \-o jsonpath='{.items[0].metadata.name}') \-n istio-system 20001

And open http://localhost:20001/ login using “admin” (without quotes) for user and password. There is a ton of useful features, for example checking the configurations of Istio Components, visualizing services according to the information collected by intercepting network requests and answering, “who is calling who?”, “which version of a service has failures?” etc. Take some time to checkout Kiali before moving on to the next goodie, visualizing metrics with Grafana!

Figure 10. Kiali — Service Observability Grafana — Metrics Visualization

The metrics collected by Istio are scraped into Prometheus and Visualized using Grafana. To access the Admin UI of Grafana execute the command below and open http://localhost:3000.

$ kubectl -n istio-system port-forward \$(kubectl -n istio-system get pod -l app=grafana \-o jsonpath={.items[0].metadata.name}) 3000

On the top left click the menu Home and select Istio Service Dashboard and on the top left corner select the service starting with sa-web-app, you will be presented with the collected metrics, as seen on the image below:

Holly molly that’s an empty and totally non-exciting view, management would never approve of this. Let’s cause some load by executing the command below:

$ while true; do \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I love yogobella”}’; \sleep .8; done

Now we have prettier graphs 😊, and additionally, we have the amazing tools of Prometheus for monitoring and Grafana for visualizing the metrics that enable us to know the performance, health, improvement or degradation of our services throughout time!

Lastly, we will investigate Tracing requests throughout services.

Jaeger — Tracing

We need tracing because the more services we have the harder it gets to pinpoint the cause of failure. Let’s take the simple case in the image below:

Figure 12. A commonly random failed request

The request goes in, failure goes out, what was the cause? The first service? Or the second? Exceptions are in both, Let’s get down to the logs of each. How many times do you find yourself doing this? Our job feels more like Software Detectives than Developers.

This is a prevalent problem in Microservices and it’s solved using Distributed Tracing Systems where the services pass a unique header to each other and then this information is forwarded to the Distributed Tracing System where the request trace is put together. An example is shown in figure 13.

Figure 13. TraceId used to identify the span of a request

Istio uses Jaeger Tracer that implements the OpenTracing API, a vendor-neutral framework. To get access the Jaegers UI execute the command below:

$ kubectl port-forward -n istio-system \$(kubectl get pod -n istio-system -l app=jaeger \-o jsonpath='{.items[0].metadata.name}') 16686

Then open the UI in http://localhost:16686, select the sa-web-app service, if the service is not shown on the dropdown generate some activity on the page and hit refresh. Afterward click the button Find Traces, which displays the most recent traces, select any and a detailed breakdown of all the traces will be shown, as seen in figure 14.

Figure 14. Jaeger — a request trace

The trace shows:

The request comes to the istio-ingressgateway (it’s the first contact with one of the services so for the request the Trace ID is generated) then the gateway forwards the request to the sa-web-app service.

In the sa-web-app service, the request is picked up by the Envoy sidecar and a span child is created (that’s why we see it in the traces) and forwarded to the sa-web-app container instance.

Here the method sentimentAnalysis handles the request. These traces are generated by the application, meaning that code changes were required).

From where a POST request is started to sa-logic. Trace ID needs to be propagated by sa-web-app.

5. …

Note: At the 4th point our application needs to pick up the headers generated by Istio and pass them down on the next requests, as shown in the image below.

Figure 15. (A) Istio propagating headers, (B) Services propagating headers

Istio does the main heavy lifting as it generates the headers on incoming requests, creates new spans on every sidecar, propagates them, but without our services propagating the headers as well, we will lose the full trace of the request.

The headers to propagate are:

x-request-idx-b3-traceidx-b3-spanidx-b3-parentspanidx-b3-sampledx-b3-flagsx-ot-span-context

Despite it being a simple task, there are already many libraries that simplify the process, for example in the sa-web-app service, the RestTemplate client is instrumented to propagate the headers by simply adding the Jaeger and OpenTracing libraries in the dependencies.

Note: The Sentiment Analysis app showcases implementations for Flask, Spring and ASP.NET Core.

Now after investigating what we get out of the box (or partially out of the box 😜) let’s get to the main topic here, fine-grained routing, managing network traffic, security and more!

Traffic Management

Using the Envoy’s Istio provides a host of new capabilities to your cluster enabling:

Dynamic request routing: Canary deployments, A/B testing,

Load balancing: Simple and Consistent Hash balancing,

Failure Recovery: Timeouts, retries, circuit breakers,

Fault Injection: Delays, abort requests etc.

In the sequence of this article, we’ll showcase these capabilities in our application and get introduced to new concepts along the way. The first concept we will delve into is DestinationRules and using those we’ll enable A/B Testing.

A/B Testing — Destination Rules in Practice

A/B Testing is used when we have two versions of an application (usually the versions differ visually) and we aren’t 100% sure which will increase user interaction, so we try both versions at the same time and collect metrics.

Execute the command below to deploy a second version of the frontend, needed for demonstrating A/B Testing:

$ kubectl apply -f resource-manifests/kube/ab-testing/sa-frontend-green-deployment.yamldeployment.extensions/sa-frontend-green created

The deployment manifest for the green version differs in two points:

The image is based on a different tag: istio-green and

Pods are labeled with version: green.

And as both deployments have the label app: sa-frontend requests routed by the virtual service sa-external-services to the service sa-frontend will be forwarded to all of its instances and will be load balanced using the round robin algorithm, which causes the issue presented in figure 16.

Figure 16. Requested files are not found

The files are not found because they are named differently in the different versions of the app. Let’s verify that:

$ curl --silent http://$EXTERNAL_IP/ | tr '"' '\n' | grep main/static/css/main.c7071b22.css/static/js/main.059f8e9c.js$ curl --silent http://$EXTERNAL_IP/ | tr '"' '\n' | grep main/static/css/main.f87cd8c9.css/static/js/main.f7659dbb.js

This means that the index.html that requests one version of the static files might be load balanced to the pods delivering the other version, where understandably the other files do not exist.

This means that for our app to work we need to introduce the restriction that “the version of the app that served the index.html, must serve subsequent requests”.

We’ll achieve this using Consistent Hash Loadbalancing, which is the process that forwards requests from the same client to the same backend instance, using a predefined property, like for example an HTTP header. Made possible by DestionatioRules.

DestinationRules

After a request gets routed by the VirtualService to the correct service, then using DestinationRules we can specify policies that apply to the traffic intended for the instances of this Service, as seen in figure 17.

Figure 17. Traffic Management with Istio Resources

Note: Figure 17, visualizes how Istio resources are affecting the network traffic, in an easily understandable way. But, to be precise the decision to which instance to forward the request to is made in the Ingress Gateway’s Envoy, configured by the CRDs.

Using Destination Rules we can configure load balancing to have consistent hashing and ensure that the same user is responded by the same instance of the service. Achieved with the following configuration:

Generate a consistent hash according to the contents of the “version” header.

Apply the configuration by executing the command below and give it a try!

$ kubectl apply -f resource-manifests/istio/ab-testing/destinationrule-sa-frontend.yamldestinationrule.networking.istio.io/sa-frontend created

Execute the command below and verify that you get the same files when specifying the version header:

$ curl --silent -H "version: yogo" http://$EXTERNAL_IP/ | tr '"' '\n' | grep main

Note: You can use this chrome extension to add different values to the version header, to test in your browser.

DestinationRules have more LoadBalancing capabilities, for all the details check out the official docs.

Before moving on to explore VirtualService in more detail, remove the green version of the app and the destination rule by executing the commands below:

$ kubectl delete -f resource-manifests/kube/ab-testing/sa-frontend-green-deployment.yamldeployment.extensions “sa-frontend-green” deleted$ kubectl delete -f resource-manifests/istio/ab-testing/destinationrule-sa-frontend.yamldestinationrule.networking.istio.io “sa-frontend” deleted Shadowing — Virtual Services in Practice

Shadowing or Mirroring is used when we want to test a change in production but not affect end-users, so we mirror the requests into a second instance that has the change and evaluate it. To phrase it simpler it’s when one of your colleagues picks the most critical issue and makes a Big ball of mud Pull Request, that nobody can really review.

To test out this feature lets create a second instance of SA-Logic (which is buggy) by executing the command below:

$ kubectl apply -f resource-manifests/kube/shadowing/sa-logic-service-buggy.yamldeployment.extensions/sa-logic-buggy created

Execute the following command and verify that all instances are labeled with the respective versions and additionally with app=sa-logic:

$ kubectl get pods -l app=sa-logic --show-labelsNAME READY LABELSsa-logic-568498cb4d-2sjwj 2/2 app=sa-logic,version=v1sa-logic-568498cb4d-p4f8c 2/2 app=sa-logic,version=v1sa-logic-buggy-76dff55847-2fl66 2/2 app=sa-logic,version=v2sa-logic-buggy-76dff55847-kx8zz 2/2 app=sa-logic,version=v2

As the service sa-logic targets pods labeled with app=sa-logic, any incoming requests will be load balanced between all instances, as shown in figure 18.

Figure 18. Round Robin load balancing

But we want requests to be routed to the instances with version v1 and mirrored to the instances with version v2, as shown in figure 19.

Figure 19. Routing to v1 and Mirroring to v2

This is achieved using a VirtualService in combination with a DestinationRule, where the destination rule specifies the subsets and VirtualService routes to the specific subset.

Specifying Subsets with Destination Rules

We define the subsets with the following configuration:

Host defines that this rule applies only when routing has occurred towards sa-logic service

Subset name used when routing to instances of a subset.

Label defines the key-value pairs that need to match for an instance to be part of the subset.

Apply the configuration executing the command below:

$ kubectl apply -f resource-manifests/istio/shadowing/sa-logic-subsets-destinationrule.yamldestinationrule.networking.istio.io/sa-logic created

With the subsets defined we can move on and configure a VirtualService to apply to requests towards sa-logic where the requests are:

Routed to the subset named v1 and,

Mirrored to the subset named v2.

And this is achieved with the manifest below:

As everything is self-explanatory let’s just see it in action:

$ kubectl apply -f resource-manifests/istio/shadowing/sa-logic-subsets-shadowing-vs.yamlvirtualservice.networking.istio.io/sa-logic created

Add some load by executing the following command:

$ while true; do curl -v http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I love yogobella”}’; \sleep .8; done

Check the results in Grafana, where we can see that the buggy version is failing about 60% of the requests, but none of the failures affected the end-users as they were responded by the currently active service.

Figure 20. The success rate of sa-logic service versions

In this section, we saw for the first time a VirtualService that was applied to the envoys of our services, when the sa-web-app makes a request towards sa-logic this goes through the sidecar envoy, which via the VirtualService is configured to route to the subset v1 and mirror to the subset v2 of the sa-logic service.

I can see you thinking “Darn man Virtual Services are simple!”, in the next section, we’ll extend the sentence to “Simply Amazing!”.

Canary Deployments

Canary Deployment is the process of rolling out a new version of an application to a small set of users, as a step to verify the absence of issues, and then with a higher assurance of quality release to the wider audience.

We will continue with the same buggy subset of sa-logic to demonstrate canary deployments.

Let’s start boldly and send 20% of the users to the buggy version (this represents the canary deployment) and 80% to the healthy service by applying the VirtualService below:

Weight specifies the percentage of requests to be forwarded to the destination or subset of the destination.

Update the previous sa-logic virtual service configuration using the following commands:

$ kubectl apply -f resource-manifests/istio/canary/sa-logic-subsets-canary-vs.yamlvirtualservice.networking.istio.io/sa-logic configured

We immediately see that some of our requests are failing:

$ while true; do \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I love yogobella”}’ \--silent -w “Time: %{time_total}s \t Status: %{http_code}\n” \-o /dev/null; sleep .1; doneTime: 0.153075s Status: 200Time: 0.137581s Status: 200Time: 0.139345s Status: 200Time: 30.291806s Status: 500

VirtualServices enable Canary Deployments and with this method, we reduced potential damages to 20% of our user base. Beautiful! Now we can use Shadowing and Canary Deployments every time we are insecure about our code, in other words always. 😜

Timeouts & Retries

It’s not always that the code is buggy. In the list of “The 8 fallacies of distributed computing” the first fallacy is that “The network is reliable”. The network is NOT reliable, and that’s why we need Timeouts and Retries.

For demonstration purposes, we will continue to use the buggy version of sa-logic, where the random failures simulate the unreliability of the network.

The buggy service has a one-third chance of taking too long to respond, one-third chance of ending in an Internal Server Error and the rest complete successfully.

To alleviate these issues and improve the user experience we can:

Timeout if the service takes longer than 8 seconds and

Retry on failed requests.

This is achieved with the following resource definition:

The request has a timeout of 8 seconds,

It attempts 3 times,

An attempt is marked as failed if it takes longer than 3 seconds.

This is an optimization, as the user won’t be waiting for longer than 8 seconds and we retry three times in case of failures, increasing the chance of resulting in a successful response.

Apply the updated configuration with the command below:

$ kubectl apply -f resource-manifests/istio/retries/sa-logic-retries-timeouts-vs.yamlvirtualservice.networking.istio.io/sa-logic configured

And check out the Grafana graphs for the improvement in success rate (shown in figure 21).

Figure 21. Improvement after using Timeouts & Retries

Before moving into the next section delete sa-logic-buggy and the VirtualService by executing the command below:

$ kubectl delete deployment sa-logic-buggydeployment.extensions “sa-logic-buggy” deleted$ kubectl delete virtualservice sa-logicvirtualservice.networking.istio.io “sa-logic” deleted Circuit Breaker and Bulkhead patterns

Two important patterns in Microservice Architectures that enable self-healing of the services.

The Circuit Breaker is used to stop requests going to an instance of a service deemed as unhealthy and enable it to recover, and in the meantime client’s requests are forwarded to the healthy instances of that service (increasing success rate).

The Bulkhead pattern isolates failures from taking the whole system down, to take an example, Service B is in a corrupt state and another service (a client of Service B) makes requests to Service B this will result that the client will use up its own thread pool and won’t be able to serve other requests (even if those are not related to Service B).

I will skip implementations of these patterns because you can check out implementations in the official docs and I’m way too excited to showcase Authentication and Authorization, which will be the subject of the next article.

Part I — Summary

In this article, we deployed Istio in a Kubernetes cluster and using its Custom Resource Definitions like Gateways, VirtualServices, DestinationRules and it’s components it enabled the following features:

Observability over our services by answering what services are running, how are they performing and how are they related, using Kiali.

Metric collection and visualization, with Prometheus and Grafana.

Request tracing with Jaeger (german for Hunter).

Full and fine-grained control over the network traffic, enabling Canary Deployments, A/B Testing, and Shadowing.

Easy implementation of Retries, Timeouts, and CircuitBreakers.

And all were possible without code changes or any additional dependencies, keeping your services small, easy to operate and maintain.

For your development team removing these cross-cutting concerns and centralizing them into Istio’s Control plane, means that new services are easy to be introduced, they aren’t resource-heavy as developers can focus in solving business problems. And up to now, no developer complained about “having to solve interesting business problems!”.

I would love to hear your thoughts in the comments below and feel free to reach out to me on Twitter or on my page rinormaloku.com, and stay tuned for the next article where we tackle the final layers of Authentication and Authorization!

Week In Review: Design, Low Power | killexams.com real questions and Pass4sure dumps

Royalty-free I3C; CFET parasitic variation modeling; Intel funds analog IP generation.

The MIPI Alliance released MIPI I3C Basic v1.0, a subset of the MIPI I3C sensor interface specification that bundles 20 of the most commonly needed I3C features for developers and other standards organizations. The royalty-free specification includes backward compatibility with I2C, 12.5 MHz multi-drop bus that is over 12 times faster than I2C supports, in-band interrupts to allow slaves to notify masters of interrupts, dynamic address assignment, and standardized discovery.

Efinix will expand its product offering, adding a 200K logic element FPGA to its lineup with the Triton T200. The T200 targets AI-driven products, and its architecture has enough LEs, DSP blocks, and on-chip RAM to deliver 1 TOPS for CNN at INT8 precision and 5 TOPS for BNN, according to Efinix CEO Sammy Cheung. The company also released samples of its Trion T20 FPGA.

Faraday Technology released multi-protocol video interface IP on UMC 28nm HPC. The Multi-Protocol Video Interface IP solution supports both transmitter (TX) and receiver (RX). The transmitter allows for MIPI and CMOS-IO combo solutions for package cost reduction and flexibility, while the receiver combo PHY includes MIPI, LVDS, subLVDS, HiSPi, and CMOS-I/O to support a diversified range of interfaces to CMOS image sensors. Target applications include panel and sensor interfaces, projectors, MFP, DSC, surveillance, AR and VR, and AI.

Analog tool and IP maker Movellus closed a second round of funding from Intel Capital. Movellus’ technology automatically generates analog IPs using digital implementation tools and standard cells. The company will use the funds to expand its customer base and to increase its portfolio of PLLs, DLLs and LDOs for use in semiconductor and system designs at advanced process nodes.

Imec and Synopsys completed a comprehensive sub-3nm parasitic variation modeling and delay sensitivity study of complementary FET (CFET) architectures. The QuickCap NX 3D field solver was used by Synopsys R&D and imec research teams to model the parasitics for a variety of device architectures and to identify the most critical device dimensions and properties, which allowed for optimization of CFET devices for better power/performance trade-offs.

Credo utilized Moortec’s Temperature Sensor and Voltage Monitor IP to optimize performance and increase reliability in its latest generation of SerDes chips. Moortec’s PVT sensors are utilized in all Credo standard products which are being deployed on system OEM linecards and 100G per lambda optical modules. Credo cited ease of integration and reduced time-to-market and project risk.

Wave Computing selected Mentor’s Veloce Strato emulation platform for functional verification and validation of its latest Dataflow Processor Unit chip designs, which will be used in the company’s next-generation AI system. Wave cited capacity and scaling advantages, breadth of virtual use models, reliability, and determinism as behind the choice.

MaxLinear adopted Cadence’s Quantus and Tempus timing signoff tools in developing the MxL935xx Telluride device, a 400Gbps PAM4 SoC using 16FF process technology. MaxLinear estimated they got 2X faster multi-corner extraction runtimes versus single-corner runs and 3X faster timing signoff flow.

The European Processor Initiative selected Menta as its provider of eFPGA IP. The EPI, a collaboration of 23 partners including Atos, BMW, CEA, Infineon and ST, has the objective of co-designing, manufacturing and bringing to market a system that supports the high-performance computing requirements of exascale machines.

Jesse Allen   (all posts)Jesse Allen is the Knowledge Center administrator and a senior editor at Semiconductor Engineering.

Cisco refused to participate in NSS Labs report on SD-WAN | killexams.com real questions and Pass4sure dumps

Cisco refused to activate the Viptela software-defined WAN product NSS Labs bought for testing, leaving the research firm with a noticeable hole in its recent comparative report on SD-WAN vendors.

Cisco did not provide a reason for refusing to activate the product NSS Labs had purchased for between $30,000 and $40,000, NSS Labs CEO Vikram Phatak said this week. "There was no reason given other than, effectively, they didn't want to be tested (for the NSS Labs report)."

Cisco's action marked the first time a vendor had refused to turn on a product NSS Labs had bought for evaluation, Phatak said. Cisco's Viptela team had initially told NSS Labs it would support the test, which led the firm to buy the product.

"That's a first for us, candidly," Phatak said. "And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock because normally, they're pretty straightforward to work with."

Cisco's response

After initially refusing to discuss the matter, Cisco released a statement that said NSS Labs's proposed testing methodology "fell well short" of reflecting "the enterprise-grade nature of customer SD-WAN requirements."

"We shared real customer deployment requirements with them, but our feedback wasn't incorporated," Cisco said. "At that point, we decided not to participate in the test."

Not participating, however, usually doesn't mean the vendor will prevent the test from going forward. Typically, when a company refuses to join a test, NSS Labs will conduct it alone.

"If someone says they don't want to be tested, we say, 'That's great, but if a product is good enough to be sold to the public, it's good enough to be tested,'" Phatak said. "We're going to buy it, and we'll report to the public."

NSS Labs wants a refund

NSS Labs wants Cisco to refund the money spent on Viptela. It is hoping it can get the money back without going to court.

"I hope it doesn't come to that," Phatak said. "We haven't talked to any lawyers. I'm assuming that we'll be able to have the conversation and get our money back."

Typically, NSS Labs buys products, and the vendors turn them on like they would for any other customer.

That's a first for us, candidly. And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock. Vikram PhatakCEO, NSS Labs

NSS Labs noted Cisco's refusal to activate the Viptela purchase in its SD-WAN Comparative Report, which was the company's first SD-WAN test. Not having Cisco in the evaluation left out one of the largest SD-WAN vendors and a major tech company.

In the first quarter, London-based IHS Markit listed Cisco as No. 4 in the SD-WAN market, just behind Silver Peak. VMware was first with a 19% share, followed by Aryaka with 18%.

The NSS Labs report, released this month, compared the products of nine vendors, including VMware's NSX SD-WAN, formerly VeloCloud. VMware is Cisco's largest competitor.

NSS Labs had also planned to include Silver Peak in the comparison but noted it was unable to obtain the product in time for testing.

Tech companies often cite recommended ratings in NSS Labs reports in marketing materials. In April, Cisco highlighted in a blog post the organization's "recommended" rating for the Cisco Advanced Malware Protection for Endpoints product.

Based on its recent SD-WAN tests, NSS Labs recommended products from VMware, Talari Networks and Fortinet and listed products from Citrix Systems, FatPipe Networks, Forcepoint and Versa Networks as "verified." Tech buyers should consider recommended and verified products as candidates for purchase, according to NSS Labs.

The company issued "caution" ratings for Barracuda Networks and Cradlepoint, which means companies should not deploy their products without a comprehensive evaluation, NSS Labs said.

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