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000-077 xSeries Technical elevated Performance Servers V2

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000-077 exam Dumps Source : xSeries Technical elevated Performance Servers V2

Test Code : 000-077
Test appellation : xSeries Technical elevated Performance Servers V2
Vendor appellation : IBM
: 147 actual Questions

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IBM xSeries Technical elevated Performance

IBM brings Watson to any cloud | killexams.com actual Questions and Pass4sure dumps

IBM these days introduced that it is freeing its Watson-branded AI services — infatuation the Watson Assistant for constructing conversational interfaces and Watson OpenScale for managing the AI lifestyles cycle — from its personal cloud and permitting enterprises to entangle its platform and working it of their own records facilities. In a way, that you can reckon of this as Watson as a managed provider.

“shoppers are basically combating infusing AI into their purposes since the information is disbursed in assorted places,” IBM Watson’s CTO and chief architects Ruchir Puri instructed me after I asked him for IBM’s reasoning behind this circulation. “It’s in these hybrid environments, they’ve obtained distinctive cloud implementations, they acquire got information in their inner most cloud as well. they acquire been struggling because the suppliers of AI had been attempting to lock them into a specific implementation that isn't proper to this hybrid cloud ambiance.”

So with this determination of bringing Watson to any cloud, IBM desires to supply these groups the alternative to bring AI to their facts, which is enormously tougher and costlier to move, in any case. Puri moreover wired that many organizations acquire lengthy wanted to employ AI to deserve their operations greater productive, but they vital to hasten their AI outfit in an atmosphere they manage and deem relaxed with.

at the core of the technical necessities for running Watson of their public or private cloud is IBM Cloud inner most, the business’s deepest cloud platform that uses open-source technologies for working outfit and capabilities infatuation Kubernetes and Cloud Foundry. That’s the platform that makes it possible for corporations to then hasten Watson, too (which itself runs on containers, too).

at this time, the heart of attention of this fireplace launch is on Watson coadjutant and Watson OpenScale. “The capabilities we're releasing at the minute are in accordance with their two flagship products. That addresses a really significant district of employ cases that they near throughout,” renowned Puri. “in the remaining section of the 12 months, they will carry the leisure of the capabilities [to the platform]. as an example, Watson skills Studio will near together with it as well, in addition to Watson’s herbal language understanding capabilities that they currently acquire obtainable in their public cloud atmosphere can breathe ported on to it as neatly.”

With that, Puri argues, IBM will present organisations a complete spectrum of tools for setting up and working AI models the usage of structured and unstructured records, in addition to a complete monitoring and existence cycle management suite.

moreover this, IBM moreover these days introduced that it's launching a brand unusual version of its Watson desktop researching Accelerator that brings excessive-performance GPU clustering to power methods and X86 methods and which guarantees to speed up AI performance as much as 10x.

The commerce additionally today announced IBM commerce Automation Intelligence with Watson, although it didn’t degree delve into the particulars. This unusual service, the enterprise says, will give enterprise leaders the skill “to practice AI at once to purposes, strengthening the group of workers, from clerical to knowledge laborers, to intelligently automate drudgery from the mundane to the complex.” I’m now not definitely unavoidable what that capability, but I’m unavoidable the enterprise leaders who buy this provider will figure it out.


Slidecast: IBM elevated performance functions for Technical Computing within the Cloud | killexams.com actual Questions and Pass4sure dumps

in this slidecast, Chris Porter and Jeff Kamiol from IBM portray how IBM exorbitant efficiency services bring versatile, software-capable clusters in the cloud for groups that exigency to promptly and economically add computing means for elevated efficiency application workloads.

IBM exorbitant efficiency capabilities enables quickly deployment of technical computing, analytics or Hadoop workloads in the cloud. groups using the carrier can conveniently meet further useful resource demands without the can suffuse of deciding to buy or managing in-apartment infrastructure, minimizing their administrative cross and promptly addressing evolving enterprise wants. The functions consist of market-leading IBM Platform LSF and IBM Platform Symphony workload management application, IBM Spectrum Scale application defined storage, IBM exorbitant efficiency functions for Hadoop and the brand unusual IBM exorbitant performance services for EDA. The application is built-in, provisioned and deployed as section of finished, integrated services which contains bare-metal IBM SoftLayer infrastructure, non-compulsory InfinfiBand interconnects and aid from an skilled and dedicated cloud operations group. a world presence with the option of facts core residence helps breathe sure that facts laws are met.

View the Slides * download the MP3 * Subscribe on iTunes * Subscribe to RSS 

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IBM Bets $2B looking for 1000X AI Hardware efficiency raise | killexams.com actual Questions and Pass4sure dumps

supply: shutterstock

For now, AI methods are ordinarily machine getting to know-based and “slim” – powerful as they're by latest requisites, they are constrained to performing a number of, narrowly-defined tasks. AI of the next decade will leverage the superior power of deep researching and develop into broader, fixing a better array of extra knotty issues.  moreover, the everyday-intention applied sciences used today for AI deployments will derive replaced by using a technology stack that’s AI-specific and exponentially sooner – and it’s going to entangle a lot of money.

in the hunt for to entangle heart stage in AI’s unfolding, IBM – in aggregate with broad apple state and several expertise heavies – is investing $2 billion within the IBM research AI Hardware core, focused on setting up subsequent era AI silicon, networking and manufacturing in an application to, IBM pointed out, bring 1,000x AI efficiency effectivity improvement over the next decade.

IBM's Mukesh Khare

“today, AI’s ever-increasing sophistication is pushing the boundaries of the trade’s existing hardware systems as clients discover more methods to embrace a considerable number of sources of facts from the aspect, web of things, and greater,” mentioned Mukesh Khare, VP, IBM research Semiconductor and AI Hardware community, in a blog announcing the challenge. “…today’s systems acquire achieved improved AI efficiency by means of infusing desktop-discovering capabilities with high-bandwidth CPUs and GPUs, specialized AI accelerators and high-performance networking equipment. To retain this trajectory, unusual considering is required to speed up AI efficiency scaling to healthy to ever-increasing AI workload complexities.”

IBM talked about the core will breathe the nucleus of a unusual ecosystem of research and industrial partners collaborating with IBM researchers. companions announced these days embrace Samsung for manufacturing and research, Mellanox applied sciences for top-performance interconnect machine, Synopsys for utility platforms, emulation and prototyping, and IP for establishing high-efficiency silicon chips, and semiconductor outfit businesses applied materials and Tokyo Electron.

Hosted at SUNY Polytechnic Institute, Albany, broad apple, in collaboration with neighboring Rensselaer Polytechnic Institute heart for Computational innovations, IBM mentioned the company and its companions will “enhance a number of technologies from chip smooth gadgets, substances, and architecture, to the software supporting AI workloads.”

IBM roadmap for 1,000x improvement in AI compute efficiency efficiency.

large Blue talked about analysis on the middle will heart of attention on overcoming “present machine-getting to know limitations via procedures that embrace approximate computing through Digital AI Cores and in-memory computing via Analog AI Cores. These technologies will provide the thousand-fold increases in efficiency effectivity required for complete cognizance of deep studying AI, the subsequent main milestone in AI evolution, based on IBM.

“A key district of analysis and building could breathe methods that meet the demands of deep learning inference and practising tactics,” Khare noted. “Such systems present huge accuracy improvements over extra regularly occurring computer studying for unstructured information. these extreme processing calls for will develop exponentially as algorithms develop into more knotty so as to convey AI programs with improved cognitive expertise.”

Khare referred to the research heart will host R&D, emulation, prototyping, testing and simulation actions for brand spanking unusual AI cores principally designed for working towards and deploying advanced AI models, together with a eye at various mattress by which members can demonstrate improvements in actual-world purposes. really respectable wafer processing for the heart will breathe completed in Albany with some usher at IBM’s Thomas J. Watson research core in Yorktown Heights, unusual york.

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xSeries Technical elevated Performance Servers V2

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Scale the Datacenter with Windows Server SMB Direct | killexams.com actual questions and Pass4sure dumps

In-Depth

Scale the Datacenter with Windows Server SMB Direct

RDMA networking has enabled high-performance computing for years, but Windows Server 2012 R2 with SMB Direct is bringing it to the mainstream.

File-based storage has grown tremendously over the final several years, far outpacing obscure storage, even as both grow at double-digit rates. Cloud datacenters are deploying file-based protocols at an accelerating pace for virtualized environments, as well as database infrastructure deployed for broad Data applications. The introduction of Server Message obscure (SMB) 3.0 with the efficiency and performance of the SMB Direct protocol, has opened unusual opportunities for file storage in Windows-based datacenters. SMB Direct, a key component of SMB 3.0, can utilize networking based on the Remote Direct memory Access (RDMA) protocol to deliver near-SAN-level performance and availability with integrated data protection and optimized data transfer between storage and server (see figure 1).

[Click on image for larger view.] figure 1. RDMA networking allows high-speed client to file service data transfers.

RDMA is a specification that has long-provided a means of reducing latency in the transmission of data from one point to another, by placing the data directly into final destination memory, thereby eliminating unnecessary CPU and memory bus utilization. Used primarily for high-performance computing (HPC) for more than a decade, RDMA is now on the cusp of becoming a mainstream means of providing a scalable and high-performance infrastructure. A key factor fueling its growing employ is Windows Server 2012 R2 offering several RDMA networking options. I'll review and compare those options within Windows Server 2012 R2 environments.

Windows Scale-Out File ServicesWindows Server 2012 R2 provides massive scale to transform datacenters into an elastic, always-on cloud-like operation designed to hasten the largest workloads. The server OS provides automated protection and aims to present cost-effective commerce continuity to ensure uptime. Windows Server 2012 R2 provides a moneyed set of storage features letting IT managers dash to lower-cost industry-standard hardware rather than purpose-built storage devices, without having to compromise on performance or availability. A vital storage capability in Windows Server 2012 R2 is the Scale-Out File Server (SOFS), which allows the storage of server application data, such as Hyper-V virtual machine (VM) files, on SMB file shares. utter files shares are online on utter nodes simultaneously. This configuration is commonly referred to as an active-active cluster configuration.

A SOFS allows for continuously available file shares. Continuous availability tracks file operations on a highly available file system so that clients can fail over to another node of the cluster without interruption. This is moreover known as Transparent Failover.

The Role of SMB DirectThe SMB 3.0 protocol in Windows Server 2012 R2 utilizes the Network Direct Kernel (NDK) layer within the Windows Sever OS to leverage RDMA network adapters (see figure 2). Using RDMA enables storage that rivals costly and infrastructure-intensive Fibre Channel SANs in efficiency, with lower latency, while operating over standard 10 Gbps and 40 Gbps Ethernet infrastructure. RDMA network adapters present this performance capability by operating at a line rate with very low latency thanks to CPU bypass and zero copy (the skill to write directly to the memory of the remote storage node using RPCs). In order to obtain these advantages, utter transport protocol processing must breathe performed in the adapter hardware, completely bypassing the host OS.

[Click on image for larger view.] figure 2. RDMA Networking Configuration on Windows Server 2012 R2.

With NDK, SMB can execute data transfers direct from memory, through the adapter, to the network, and over to the memory of the application requesting data from the file share. This capability is especially useful for I/O-intensive workloads such as Hyper-V or SQL Server, resulting in remote file server performance comparable to local storage.

In contrast, in traditional networking, a request from an application to a remote storage location must Go through numerous stages and buffers (involving data copies) on both the client and server side, such as the SMB client or server buffers, the transport protocol drivers in the networking stack, and the network card drivers.

With SMB Direct, the RDMA NIC transfers data straight from the SMB client buffer, through the client NIC to the server NIC, and up to the SMB server buffer, and vice versa. This direct transfer operation allows the application to access remote storage at the identical performance as local storage.

Windows Server 2012 provides built-in support for using SMB Direct with Ethernet RDMA NICs, including iWARP (RDMA/TCP), and RoCE NICs (RDMA/UDP), to support high-speed data transfers. These NICs implement RDMA in hardware so that they can transfer data between them without involving the host CPU. As a result, SMB Direct is extremely hastily with client-to-file server performance almost equaling that of using local storage.

RDMA NICs offload the server CPU, resulting in more efficient Microsoft virtualized datacenter installs. Windows Server 2012 SMB Direct 3.0 over RDMA provides higher performance by giving direct access to the data that resides on a remote file server, while the CPU reduction enables a larger number of VMs per Hyper-V server, resulting in CapEx and OpEx savings in power dissipation, system configuration and deployment scale throughout the life of the installation. native system software support for RDMA networking in Windows Server 2012 R2 simplifies storage and VM management for enterprise and cloud IT administrators, with no network reconfiguration required.

Live migration is an essential VM mobility feature and improving the performance of live migration has been a consistent focus for Windows Server. In Windows Server 2012 R2, Microsoft took these performance improvements to the next level. Live migration with RDMA is a unusual feature; it delivers the highest performance for migrations by offloading data transfers to RDMA NIC hardware.

iWARP: RDMA over TCP/IPiWARP is an implementation of RDMA using the ubiquitous Ethernet-TCP/IP networking as the network transport. iWARP NICs implement a hardware TCP/IP stack that eliminates utter inefficiencies associated with software TCP/IP processing, while preserving utter the benefits of the proven TCP/IP protocol. On the wire, iWARP traffic is thus identical to other TCP/IP applications and requires no special support from switches and routers, or changes to network devices. Thanks to the hardware offloaded TCP/IP, iWARP RDMA NICs present high-performance and low-latency RDMA operation that's comparable to the latest InfiniBand speeds, and native integration within today's great Ethernet-based networks and clouds.

iWARP is able to dramatically help upon the most common and widespread Ethernet communications in employ today and deliver on the swear of a single, converged Ethernet network for carrying LAN, SAN, and RDMA traffic with the unrestricted routability and scalability of TCP/IP. Today, 40 Gbps Ethernet (40 GbE) iWARP controllers and adapters are available from Chelsio Communications, while Intel Corp. has moreover announced plans for availability of iWARP Ethernet controllers integrated within upcoming Intel server chipsets.

The iWARP protocol is the open Internet Engineering assignment force (IETF) standard for RDMA over Ethernet. iWARP adapters are fully supported by the OpenFabrics Alliance Enterprise Software Distribution (OFED), with no changes needed for applications to migrate from specialized OFED-compliant RDMA fabrics such as InfiniBand to Ethernet.

Initially aimed at high-performance computing applications, iWARP is moreover now finding a home in datacenters thanks to its availability on high-performance 40 GbE NICs and increased datacenter claim for low latency, elevated bandwidth, and low server CPU utilization. It has moreover been integrated into server OSes such as Microsoft Windows Server 2012 with SMB Direct, which can seamlessly entangle advantage of iWARP RDMA without user intervention.

InfiniBandInfiniBand is an I/O architecture designed to increase the communication speed between CPUs, devices within servers and subsystems located throughout a network. InfiniBand is a point-to-point, switched I/O fabric architecture. Both devices at each abide of a link acquire complete access to the communication path. To Go beyond a point and traverse the network, switches near into play. By adding switches, multiple points can breathe interconnected to create a fabric. As more switches are added to a network, aggregated bandwidth of the fabric increases.

High-performance clustering architectures acquire provided the main break for InfiniBand deployment. Using the InfiniBand fabric as the cluster inter-process communications (IPC) interconnect may boost cluster performance and scalability while improving application response times. However, using InfiniBand requires deploying a divide infrastructure in addition to the requisite Ethernet network. The added costs in acquisition, maintenance and management acquire prompted interest in Ethernet-based RDMA alternatives such as iWARP.

Because it's layered on top of TCP, iWARP is fully compatible with existing Ethernet switching outfit that's able to process iWARP traffic out-of-the-box. In comparison, deploying InfiniBand requires environments where two divide network infrastructures are installed and managed, as well as specialized InfiniBand to Ethernet gateways for bridging between the two infrastructures.

RDMA over Converged Ethernet (RoCE)The third RDMA networking option is RDMA over Converged Ethernet (RoCE), which essentially implements InfiniBand over Ethernet. RoCE NICs are offered by Mellanox Technologies. Though it utilizes Ethernet cabling, this approach does suffer from deployment vicissitude and costs due to requiring support for knotty and expensive Ethernet "lossless" fabrics and Data heart Bridging (DCB) protocols. In addition, RoCE for the longest time lacked routability support, which limited its operation to a sole Ethernet subnet.

Instead of using pervasive TCP/IP networking, RoCE relies instead on InfiniBand protocols at Layer 3 (L3) and higher layers in combination with Ethernet at the Link Layer (L2) and Physical Layer (L1). RoCE leverages Converged Ethernet, moreover known as DCB or Converged Enhanced Ethernet as a lossless physical layer networking medium. RoCE is similar to the Fibre Channel over Ethernet (FCoE) protocols in relying on networking infrastructure with DCB protocols. However, such support has been viewed a significant impediment to FCoE deployment, which raises similar concerns for RoCE.

The just-released version 2 of the RoCE protocol will derive rid of the IB network layer, replacing it with the more commonly used UDP (connectionless) and IP layer to provide routability. However, RoCE v2 does not specify how lossless operation will breathe provided over an IP network, or how congestion control will breathe handled. RoCE v2 currently suffers from an uncongenial premise that continues to require DCB for Ethernet, while no longer operating within the confines of one Ethernet network.


Ambassador: building a Control Plane for an Envoy-Powered API Gateway on Kubernetes | killexams.com actual questions and Pass4sure dumps

Key Takeaways
  • Developed by Datawire, Ambassador is an open source API gateway designed specifically for employ with the Kubernetes container orchestration framework. 
  • At its core, Ambassador is a control plane tailored for edge/API configuration for managing the Envoy Proxy “data plane”. 
  • Envoy itself is a cloud native Layer 7 proxy and communication bus used for handling “edge” ingress and service-to-service networking communication.
  • This article provides an insight into the creation of Ambassador, and discusses the technical challenges and lessons erudite from building a developer-focused control plane for managing ingress traffic within microservice-based applications that are deployed into a Kubernetes cluster.
  • Migrating Ambassador to the Envoy v2 configuration and Aggregated Discovery Service (ADS) APIs was a long and difficult journey that required lots of architecture and design discussions, and plenty of coding, but early feedback from the community has been positive.  
  • Developed by Datawire, Ambassador is an open source API gateway designed specifically for employ with the Kubernetes container orchestration framework. At its core, Ambassador is a control plane tailored for edge/API configuration for managing the Envoy Proxy “data plane”. Envoy itself is a cloud native Layer 7 proxy and communication bus used for handling “edge” ingress and service-to-service networking communication. Although originating from Lyft, Envoy is rapidly becoming the de facto proxy for modern networking, and can breathe institute with practically utter of the public cloud vendors offerings, as well as bespoke usage by many great end-user organisations infatuation eBay, Pinterest and Groupon. 

    This article provides an insight into the creation of Ambassador, and discusses the technical challenges and lessons erudite from building a developer-focused control plane for managing ingress traffic within microservice-based applications that are deployed into a Kubernetes cluster.

    The Emerging “Cloud Native” Fabric: Kubernetes and Envoy

    Although the phrase “cloud native” is becoming as much of an overloaded term as “DevOps” and “microservices”, it is increasingly gaining traction throughout the IT industry. According to Gartner, the 2018 worldwide public cloud service revenue forecast was in the region of $175 Billion U.S. Dollars, and this could grow by over 15% next year. Although the current public cloud market is dominated by only a few key players that present mostly proprietary technologies (and increasingly, and sometimes controversially, open source-as-service), the Cloud native Computing Foundation (CNCF) was founded in 2015 by the Linux foundation to provide a residence for discussion and hosting of "open source components of a complete stack cloud native environment". 

    Possibly learning from the journey previously undertaken by the OpenStack community, the early projects supported by the CNCF were arguably less ambitious in scope, provided clearer (opinionated) abstractions, and were moreover proven in actual world usage (or inspired by real world usage in the case of Kubernetes). Two key platform components that acquire emerged from the CNCF are the Kubernetes container orchestration framework, originally contributed by Google, and the Envoy proxy for edge and service-to-service networking, originally donated by Lyft. Even when combined, the two specific technologies don’t provide a complete Platform-as-a-Service (PaaS) offering that many developers want. However, Kubernetes and Envoy are being included within many PaaS-like offerings.

    Many PaaS vendors, and also end-user engineering teams, are treating these technologies as the “data plane” for cloud native systems: i.e. the section of the system that does the “heavy-lifting”, such as orchestrating containers and routing traffic based on Layer 7 metadata (such as HTTP URIs and headers, or MongoDB protocol metadata). Accordingly, a lot of innovation and commercial opportunities are focused on creating an effectual “control plane”, which is where the end-user interacts with the technology, specifies configuration to breathe enacted by the data plane, and observes any metrics or logging.

    The Kubernetes control plane is largely focused around a string of well-specified REST-like APIs (known simply as “the Kubernetes API”), and the associated ‘kubectl’ CLI appliance provides a human-friendly abstraction over these APIs. The Envoy v1 control plane was initially based around JSON config loaded within files, with several loosely-defined APIs that allowed selective updating. These APIs acquire subsequently evolved into the Envoy v2 API, which provides a string of gRPC-based APIs that are strongly typed via the employ of Protocol Buffers. However, initially there wasn’t an Envoy analogy to the Kubernetes kubectl tool, and this led to challenges in adoption by some teams. Where there are challenges, though, there are moreover opportunities within the implementation of a human-friendly control plane.

    “Service Mesh-all-the-things”...Maybe?

    If they focus on the networking control plane, it would breathe difficult to miss the emergence of the concept of the “service mesh”. Technologies infatuation Istio, Linkerd and Consul Connect are aiming to manage cross-cutting service-to-service (“east-west”) traffic within a microservices systems. Indeed, Istio itself is effectively a control plane that enables a user to manage Envoy Proxy as the underlying data plane for managing Layer 7 networking traffic across the mesh. Linkerd offers its own (now Rust-based) proxy as the data plane, and Consul Connect offers both a bespoke proxy and, more recently, support for Envoy.

    Istio architecture, showing the Envoy Proxy data plane at the top half of the diagram, and the control plane below (image courtesy of Istio documentation)

    The essential thing to recollect with a service mesh is that the assumption is that you typically exert a high-degree of ownership and control on both parties that are communicating over the mesh. For example, two services may breathe built by divide engineering departments but they will typically drudgery for the identical organisation, or one service may breathe a third-party application but it is deployed within your trusted network boundary (which may span multiple data centers or Virtual Private Clouds). Here your operations team will typically coincide on sensible communication defaults, and service teams will independently configure inter-service routing. In these scenarios you may not fully reliance each service, and you most certainly will want to implement protections infatuation rate limiting and circuit breaking, but fundamentally you can investigate and change any unfavorable behaviour detected. This is not true, however, for managing edge or ingress (“north-south”) traffic that originates from outside your network boundary.

    Cluster “ingress” traffic generally originates from sources outside of your direct control

    Any communication originating from outside your trusted network can breathe from a unfavorable actor, with motivations that are intentional (e.g. cyber criminals) or otherwise (e.g. broken client library within a mobile app), and therefore you must establish preempt defenses in place. Here the operations team will specify sensible system defaults, and moreover accommodate these in real-time based on external events. In addition to rate limiting, you probably moreover want the skill to configure global and API-specific load shedding, for example, if the backend services or datastores become overwhelmed, and moreover implement DDoS protection (which may moreover breathe time- or geographically-specified). Service evolution teams moreover want access to the edge to configure routing for a unusual API, to test or release a unusual service via traffic shadowing or canary releasing, or other tasks.

    As a quick aside, for further discussion on the (sometimes confusing) role of API gateways, Christian Posta has recently published an keen blog post, “API Gateways Are Going Through an Identity Crisis”. I acquire moreover written articles about the role of an API gateway during a cloud/container migration or digital transformation, and how API gateways can breathe integrated with modern continuous delivery patterns.

    Although at first glance these service mesh and edge/API gateway employ cases may appear very similar, they believe there are subtle (and not so subtle) differences, and this impacts the design of the associated inter-service and edge control planes. 

    Designing a Edge Control Plane 

    The choice of control plane is influenced heavily by the scope of control required, and the persona(s) of the primary people using it. My colleague Rafael Schloming has talked about this before at QCon San Francisco, where he discussed how the requirements to centralise or decentralise control and moreover the development/operation lifecycle stage in which a service is currently at (prototype, mission censorious etc) impacts the implementation of the control plane. 

    As mentioned above, taking an edge proxy control plane as the example, a centralised operations or SRE team may want to specify globally sensible defaults and safeguards for utter ingress traffic. However, the (multiple) decentralised product evolution teams working at the front line and releasing functionality will want fine-grained control for their services in isolation, and potentially (if they are embracing the “freedom and responsibility” model) the skill to override global safeguards locally.

    A conscious choice that was made by the Ambassador community was that the primary persona targeted by the Ambassador control plane is the developeror application engineer, and therefore the focus on the control plane was on decentralised configuration. Ambassador was built to breathe Kubernetes-specific, and so a rational choice for specifying edge configuration was nigh to the Kubernetes Service specifications that were contained within YAML files and loaded into Kubernetes via kubectl. 

    Options for specifying Ambassador configuration included using the Kubernetes Ingress object, writing custom Kubernetes annotations or defining Custom Resource Definitions (CRDs). Ultimately the employ of annotations was chosen, as they were simple and presented a minimal learning curve for the end-user. Using Ingress may acquire appeared to breathe the most obvious first choice, but unfortunately the specification for Ingress has been stuck in perpetual beta, and other than the “lowest common denominator” functionality for managing ingress traffic, not much else has been agreed upon.

    An sample of an Ambassador annotation that demonstrates a simple endpoint-to-service routing on a Kubernetes Service can breathe seen here:

    kind: Service apiVersion: v1 metadata: name: my-service annotations: getambassador.io/config: | --- apiVersion: ambassador/v0 kind: Mapping name: my_service_mapping prefix: /my-service/ service: my-service spec: selector: app: MyApp ports: - protocol: TCP port: 80 targetPort: 9376

    The configuration within the getambassador.io/config should breathe relatively self-explanatory to anyone who has configured an edge proxy, reverse proxy or API gateway before. Traffic sent to the prefix endpoint will breathe “mapped” or routed to the “my-service” Kubernetes service. As this article is primarily focused on the designing and implementation of Ambassador, they won’t cover all of the functionality that can breathe configured, such as routing (including traffic shadowing), canarying (with integration with Prometheus for monitoring) and rate limiting. Although Ambassador is focused on the developer persona, there is moreover extensive support for operators, and centralised configuration can breathe specified for authentication, TLS/SNI, tracing and service mesh integration.

    Let’s now revolve their attention back onto the evolution of Ambassador over the past two years.

    Ambassador < v0.40: Envoy v1 APIs, Templating, and fiery Restarts

    Ambassador itself is deployed within a container as a Kubernetes service, and uses the annotations added to Kubernetes Services as its core configuration model. This approach enables application developers to manage routing as section of their Kubernetes service definition workflow process (perhaps as section of a “GitOps” approach). Translating the simple Ambassador annotation config into valid Envoy v1 config is not a paltry task. By design, Ambassador’s configuration isn’t based on the identical conceptual model as Envoy’s configuration -- they deliberately wanted to aggregate and simplify operations and config -- and herefore, a unbiased amount of logic within Ambassador translates between one set of concepts to the other. 

    Specifically when a user applies a Kubernetes manifest containing Ambassador annotations, the following steps occur:

  • Ambassador is asynchronously notified by the Kubernetes API of the change.
  • Ambassador translates the configuration into an abstract intermediate representation (IR).
  • An Envoy configuration file is generated from the IR.
  • The Envoy configuration file is validated by Ambassador (using Envoy in validation mode).
  • Assuming the file is valid configuration, Ambassador uses Envoy's hot restart mechanism to deploy the unusual configuration and properly drain connections.
  • Traffic flows through the restarted Envoy process.
  • There were many benefits with this initial implementation: the mechanics involved were fundamentally simple, the transformation of Ambassador config into Envoy config was reliable, and the file-based fiery restart integration with Envoy was dependable. 

    However, there were moreover notable challenges with this version of Ambassador. First, although the fiery restart was effectual for the majority of employ cases, it was not very fast, and some users (particularly those with great application deployments) institute it was limiting the frequency with which they could change their configuration. fiery restart can moreover inappropriately drop connections, especially long-lived connections infatuation WebSockets or gRPC streams.

    More crucially, though, the first implementation of the Ambassador-to-Envoy intermediate representation (IR) allowed rapid prototyping but was primitive enough that it proved very difficult to deserve substantial changes. While this was a stitch point from the beginning, it became a censorious issue as Envoy shifted to the Envoy v2 API. It was lucid that the v2 API would present Ambassador many benefits -- as Matt Klein outlined in his blog post, “The universal data plane API” -- including access to unusual features and a solution to the connection-drop problem renowned above, but it was moreover lucid that the existing IR implementation was not capable of making the leap.

    Ambassador Now: Envoy v2 APIs (with ADS), Intermediate Representations, and Testing with KAT

    In consultation with the Ambassador community, the Datawire team (stewarded by Flynn, lead engineer for Ambassador) undertook a redesign of the internals of Ambassador in 2018. This was driven by two key goals. First, they wanted to integrate Envoy’s v2 configuration format, which would enable the support of features such as Server appellation Indication (SNI), label-based rate limiting, and improved authentication. Second, they moreover wanted to achieve much more robust semantic validation of Envoy configuration, due to its increasing complexity (which was particularly when configuring Envoy for employ with large-scale application deployments).

    We started by restructuring the Ambassador internals more along the lines of a multipass compiler. The class hierarchy was made to more closely mirror the separation of concerns between the Ambassador configuration resources, the IR, and the Envoy configuration resources. Core parts of Ambassador were moreover redesigned to facilitate contributions from the community outside Datawire. They decided to entangle this approach for several reasons. First, Envoy Proxy is a very hastily stirring project, and they realised that they needed an approach where a seemingly minor Envoy configuration change didn’t result in days of reengineering within Ambassador. In addition, they wanted to breathe able to provide semantic verification of configuration. 

    As they started working more closely with Envoy v2, a testing challenge was quickly identified. As more and more features were being supported in Ambassador, more and more bugs appeared in Ambassador’s handling of less common but completely valid combinations of features. This drove to creation of a unusual testing requirement that meant Ambassador’s test suite needed to breathe reworked to automatically manage many combinations of features, rather than relying on humans to write each test individually. Moreover, they wanted the test suite to breathe hastily in order to maximise engineering productivity.

    This meant that as section of the Ambassador re-architecture, they moreover created the Kubernetes Acceptance Test (KAT) framework. KAT is an extensible test framework that:

  • Deploys a bunch of services (along with Ambassador) to a Kubernetes cluster
  • Run a string of verification queries against the spun up APIs
  • Perform a bunch of assertions on those query results
  • KAT is designed for performance -- it batches test setup upfront, and then runs utter the queries in step 3 asynchronously with a elevated performance HTTP client. The traffic driver in KAT runs locally using one of other open source tools, Telepresence, which makes it easier to debug issues.

    With the KAT test framework in place, they quickly ran into some issues with Envoy v2 configuration and fiery restart, which presented the break to switch to using Envoy’s Aggregated Discovery Service (ADS) APIs instead of fiery restart. This completely eliminated the requirement for a process restart upon configuration changes, which previously they had institute could lead to dropped connections under elevated loads or long-lived connections. They decided to employ the Envoy Go control plane to interface to the ADS. This did, however, introduce a Go-based dependency to the previously predominantly Python-based Ambassador codebase.

    With a unusual test framework, unusual IR generating valid Envoy v2 configuration, and the ADS, the major architectural changes in Ambassador 0.50 were complete. Now when a user applies a Kubernetes manifest containing Ambassador annotations, the following steps occur:

    Just before release they hit one more issue. On the Azure Kubernetes Service, Ambassador annotation changes were no longer being detected. Working with the highly-responsive AKS engineering team, they were able to identify the issue -- namely, the Kubernetes API server in AKS is exposed through a chain of proxies that was dropping some requests. The proper mitigation for this was to support calling the FQDN of the API server, which is provided through a mutating webhook in AKS. Unfortunately, support for this feature was not available in the official Kubernetes Python client. They therefore elected to switch to the Kubernetes Golang client -- introducing yet another Go-based dependency.

    Key Takeaways from building an Envoy Control Plane (Twice!)

    As Matt Klein mentioned at the inaugural EnvoyCon, with the current popularity of the Envoy Proxy in the cloud native technology domain, it’s often easier to inquire of who isn’t using Envoy. They know that Google’s Istio has helped raise the profile of Envoy with Kubernetes users, and utter of the other major cloud vendors are investing in Envoy, for example, within AWS App Mesh and Azure Service Fabric Mesh. At EnvoyCon they moreover heard how several broad players such as eBay, Pinterest and Groupon are migrating to using Envoy as their primary edge proxy. There are moreover several other open source Envoy-based edge proxy control planes emerging, such as Istio Gateway, Solo.io Gloo, and Heptio Contour. I would wrangle that Envoy is indeed becoming the universal data plane of cloud native communications, but there is much drudgery noiseless to breathe done within the domain of the control plane.

    In this article we’ve discussed how the Datawire team and Ambassador open source community acquire successfully migrated the Ambassador edge control plane to employ the Envoy v2 configuration and ADS APIs. We’ve erudite a lot in the process of building Ambassador 0.50, and they are keen to highlight their key takeaways as follows:

  • Kubernetes and Envoy are very powerful frameworks, but they are moreover extremely hastily stirring targets -- there is sometimes no substitute for reading the source code and talking to the maintainers (who are fortunately utter quite accessible!)
  • The best supported libraries in the Kubernetes / Envoy ecosystem are written in Go. While they cherish Python, but they acquire had to adopt Go so that we’re not forced to maintain too many components ourselves.
  • Redesigning a test harness is sometimes necessary to dash your software forward. Often the actual cost in redesigning a test harness is often in porting your customary tests to the unusual harness implementation.
  • Designing (and implementing) an effectual control plane for the edge proxy employ case has been challenging, and the feedback from the open source community around Kubernetes, Envoy and Ambassador has been extremely useful.
  • Migrating Ambassador to the Envoy v2 configuration and ADS APIs was a long and difficult journey that required lots of architecture and design discussions, and plenty of coding, but early feedback from results acquire been positive. Ambassador 0.50 is available now, so you can entangle it for a test hasten and participate your feedback with the community on our Slack channel or on Twitter. 

    About the Author

    Daniel Bryant is leading change within organisations and technology, and currently works as a freelance consultant, of which Datawire is a client. His current drudgery includes enabling agility within organisations by introducing better requirement gathering and planning techniques, focusing on the relevance of architecture within agile development, and facilitating continuous integration/delivery. Daniel’s current technical expertise focuses on ‘DevOps’ tooling, cloud/container platforms and microservice implementations. He is moreover a leader within the London Java Community (LJC), contributes to several open source projects, writes for well-known technical websites such as InfoQ, DZone and Voxxed, and regularly presents at international conferences such as QCon, JavaOne and Devoxx.


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