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IBM Decision Optimization Technical Mastery Test v2
IBM Optimization resources
Killexams : IBM Optimization resources - BingNews https://killexams.com/pass4sure/exam-detail/P2020-795 Search results Killexams : IBM Optimization resources - BingNews https://killexams.com/pass4sure/exam-detail/P2020-795 https://killexams.com/exam_list/IBM Killexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Use Cases And Partnerships

I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Boost future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Boost quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Boost the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html https://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/
Killexams : IBM Fine-Tunes Disaster Management Technology

— -- IBM's research laboratories in the U.S. and India have fine-tuned technology to help model and manage natural disasters such as wildfires, floods, and diseases.

The new enhancements are to a budgeting system being developed by IBM, starting from 2003, for managing natural disaster events, with a focus on better preparedness for future uncertain disaster scenarios. The optimization models and algorithms were initially prototyped on a large U.S. government program, to deploy a large number of critical resources to a range of disaster event scenarios, said Gyana Parija, lead researcher in the Analytics and Optimization Research team at IBM India Research Laboratory in Delhi, India.

That system however only generated a single solution for each disaster scenario. The current enhancements to the budgeting system include the development of a decision support system to allow decision makers to consider multiple solutions to each disaster scenario, so that a range of alternatives can be generated by the system, IBM said Tuesday.

A model that supports multiple criteria can be used effectively in situations where there is a contention for resources, as for example when then are more than one disasters demanding resources, according to Parija. "Typically what happens in a particular disaster scenario is that you would have different budget alternatives, and at different budget alternatives, you would like to explore what kind of resource organization you can have," he added.

IBM's stochastic optimization model is designed to deal with uncertainties in data, and models with probability distributions based on historic trends, Parija said. The model can also be used to work in applications other than natural disaster management, such as asset liabilities management problems in the financial services and other business sectors, he added.

Wed, 02 Apr 2008 00:00:00 -0500 en text/html https://abcnews.go.com/Technology/PCWorld/story?id=4565775
Killexams : 7 Quantum Computing Stocks to Buy for the Next 10 Years No result found, try new keyword!Recent breakthroughs in this emerging field — such as IBM’s (IBM ... In finance, it will speed up and augment portfolio optimization, risk modeling and derivatives creation. Fri, 08 Jul 2022 00:45:00 -0500 text/html https://www.nasdaq.com/articles/7-quantum-computing-stocks-to-buy-for-the-next-10-years Killexams : Global Mobile Edge Computing Market Report 2022: Featuring Key Players IBM, Ericsson, Samsung Electronics & Others

DUBLIN, July 8, 2022 /PRNewswire/ -- The "Global Mobile Edge Computing Market by Infrastructure, Deployment Model, Computing as a Service, Network Connectivity, Applications, Analytics Types, Market Segments, and Industry Verticals 2022-2027" report has been added to ResearchAndMarkets.com's offering.

Select Report Findings

  • Mobile edge computing will be a key enabler of immersive technologies deployed with 5G
  • Greatest opportunities will be in teleoperation/cloud robotics, telepresence, and virtual reality
  • The global mobile edge computing market for software and APIs will reach $2.32 billion by 2027
  • The market for MEC software in support of IoT applications will reach $637 million globally by 2027
  • The largest industry vertical opportunities for MEC will be manufacturing, healthcare and automobile

In cellular networks, edge computing via MEC is beneficial for LTE but virtually essential for 5G. This is because Mobile Edge Computing facilitates optimization of fifth generation network resources including focusing communications and computational capacity where it is needed the most. The author's research findings indicate a strong relationship between edge computing and 5G. In fact, if it were not for MEC, 5G would continue to rely upon back-haul to centralized cloud resources for storage and computing, diminishing much of the otherwise positive impact of latency reduction enabled by 5G.

Another driver for the multi-access edge computing market is that MEC will facilitate an entirely new class of low-power devices for IoT networks and systems. These devices will rely upon MEC equipment for processing. Stated differently, some IoT devices will be very lightweight computationally speaking, relying upon edge computing nodes for most of their computation needs.

Mobile Edge Computing Market Drivers

  • Improved Overall Throughput
  • Core Congestion Reduction
  • Application Latency Reduction
  • Backhaul Reduction
  • Network Awareness and Context
  • Streaming Data and Real-time Analytics
  • Network and Application Resiliency

Mobile Edge Computing Market Deployment Alternatives

As the author has stated in the past, the primary standards body for MEC standardization is the European Telecommunications Standards Institute (ETSI), which has done much to move edge computing in mobile/wireless networks forward.

ETSI identifies four physical areas for MEC deployment as follows:

  • Co-location at Base Station
  • Co-location at Transmission Node
  • Co-location at Network Aggregation Point
  • Co-location with Core Network Functions

Carrier Mobile Edge Computing Market Deployment Considerations

It is important to understand that multi-access edge computing servers and platforms can be deployed in many locations including, but not limited to, an LTE and/or 5G macro base station site, the 3G Radio Network Controller site, a multi-RAT cell aggregation site, or at an aggregation point. Communication Service Providers (CSP) are not accustomed to planning for remote servers.

However, MEC essentially needs many remote data centers. The author predicts that CSPs will need to partner with network integration companies to realize the full vision of MEC. CSPs cannot be bogged down in negotiations, planning, engineering, and deployment of MEC communications/computing platforms every time a new site is acquired.

With the multi-access edge computing market, there is clearly a new computational-communications paradigm in which communications and computing are no longer thought of as separate things. Furthermore, they are planned, engineered, deployed, and operated together. In parallel with this new paradigm, mobile networks are becoming video networks.

Key subjects Covered:

1. Executive Summary

2. Introduction

3. MEC Technology, Platforms, and Architecture
3.1 MEC Platform Architecture Building Blocks
3.2 Edge Cloud Computing Value Chain
3.3 MEC Technology Building Block
3.4 MEC Technology Enablers
3.5 MEC Deployment Considerations

4. MEC Market Drivers and Opportunities

5. MEC Ecosystem

6. MEC Application and Service Strategies
6.1 Optimizing the Mobile Cloud
6.2 Context Aware Services
6.3 Data Services and Analytics

7. Multi-Access Edge Computing Deployment

8. Multi-Access Edge Computing Market Analysis and Forecasts

9. Conclusions and Recommendations

 Companies Mentioned

  • ADLINK Technology Inc.
  • Advanced Micro Devices
  • Advantech
  • Affirmed Networks
  • Akamai Technologies
  • Allot Communications
  • AT&T
  • Brocade Communications Systems
  • Cavium Networks
  • Ceragon Networks
  • China Mobile
  • China Unicom
  • Cisco Systems
  • Cloudify
  • Cradlepoint
  • Deutsche Telekom
  • EdgeConneX
  • Edgeworx
  • Ericsson
  • ETRI
  • Fujitsu Technology Solutions
  • Hewlett Packard Enterprise (HPE)
  • Huawei Technologies Co. Ltd.
  • IBM Corporation
  • Integrated Device Technology
  • Intel Corporation
  • InterDigital Inc.
  • ITRI
  • Juniper Networks
  • Mimic Technology
  • MobiledgeX (Google)
  • NEC Corporation
  • Nokia Corporation
  • NTT Communications
  • NTT DoCoMo
  • Orange
  • Ori
  • PeerApp Ltd.
  • Pixeom
  • Pluribus Networks
  • Quortus
  • Redhat, Inc.
  • Saguna Networks
  • Samsung Electronics Co. Ltd.
  • SK Telecom
  • Sony Corporation
  • SpiderCloud Wireless
  • Telefonica
  • TIM
  • Vapor IO
  • Vasona Networks (ZephyrTel)
  • Verizon
  • Viavi Solutions
  • Vodafone
  • Xilinx, Inc.
  • Yaana Ltd.
  • ZTE Corporation

For more information about this report visit https://www.researchandmarkets.com/r/5ko1v8

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Killexams : Accenture, Deloitte, TCS top Gartner's cloud IT transformation providers list

Channel partner giants like Accenture, Capgemini, Deloitte and Tata Consultancy Services (TCS) are some of the biggest and best public cloud IT transformation providers across the globe, according to a new report by Gartner.

However, depending on the cloud transformation use case—such as modernising legacy applications and the ability to share knowledge to customers during IT transformation—the worldwide market leaders vary.

IT research firm Gartner recently scored and ranked the top 20 cloud transformation service providers that includes some of the largest MSPs, system integrators and channel partners with its new 2022 Critical Capabilities for Public Cloud IT Transformation Services report.

Cloud transformation key findings

One of the key findings in Gartner’s new report is that cloud transformation services offered by MSPs, system integrators and cloud service providers are more readily available than ever.

This is a reaction to the significant 41 percent annual growth in infrastructure as-a-service (IaaS) and platform as-a-service (PaaS) cloud services in 2021.

Another finding from Gartner is that business outcome models are increasingly being sought by clients and can be now delivered by many providers. Shorter payment transactions, time to market and increased customer engagement are just a few of the outcomes now being offered, Gartner said.

Gartner’s scoring system

Gartner ranks each company on a scoring basis from 1.0 to 5.0.

Companies are scored in several areas including their cloud-native and DevSecOps capabilities, as well as their professional and managed services abilities.

Gartner’s Critical Capabilities for Public Cloud IT Transformation Services report offers insight specifically focusing on public cloud transformation, with partners needing to hold specializations or certifications from the likes Amazon Web Services (AWS), Google Cloud and/or Microsoft Azure.

The leading companies that made Gartner’s deliver positive business impacts born from IT transformation with customer engagements being application-led compared to infrastructure-led.

Other key aspects Gartner took into consideration when scoring these companies were their cloud-native engineering, application development, multi-cloud and security compliance capabilities. Companies also needed to show large professional and managed services revenues, skilled employees, and customer-base.

CRN US breaks down the top 10 companies that captured Gartner’s highest scores in each of the five use case categories.

Scores for agile migration use case

  • Deloitte: 3.65
  • Accenture: 3.63
  • Capgemini: 3.34
  • TCS: 3.34
  • HCL Technologies: 3.29
  • Hexaware: 3.16
  • SMX: 3.05
  • NTT Data: 3.02
  • Wipro: 2.98
  • Hitachi Vantara: 2.84

Deloitte edged out Accenture to rank No. 1 for Gartner’s agile migration use case with a score of 3.65.

For this use case, Gartner said an agile, cloud-native application life cycle is of the highest priority, especially for high-usage applications.

Deloitte is a global company with 80 percent transformative cloud services clients. Deloitte’s services around agile migration are rapidly deployable, with an evolving breadth of insight-generating functionality. Gartner said it achieves for its clients, on average, a six-month migration timeline for estates over 500 workloads.

The No. 2 score at 3.63 was Accenture, who has global support for all major hyperscale clouds. It has conducted more large and complex workload migrations than almost any other provider using an automated factory-based approach supported by its proprietary tools.

Other notable companies outside of Gartner’s top 10 rankings for agile migration includes Atos, IBM, Tech Mahindra and Cognizant.

Scores for modernisation legacy applications use case

  • TCS: 3.44
  • Capgemini: 3.33
  • Hexaware: 3.32
  • Accenture: 3.28
  • Deloitte: 3.26
  • HCL Technologies: 3.16
  • Infosys: 3.10
  • NTT Data: 3.03
  • SMX: 3.01
  • Atos: 2.98

TCS received the highest score of 3.44 for modernisation of legacy applications use case, followed by Capgemini at 3.33 and Hexaware at 3.32.

For this use case, Gartner said customers have legacy applications that are not cloud-native and need the improved scalability, elasticity, flexibility and reliability public cloud offers.

TCS has a track record of application modernization with specific proof points in banking and retail. The company has mainframe modernization experience with over 300 million lines of code ported. TCS has 45,000 individual certifications as well as 70 percent cloud developers driving transformation, according to Gartner.

For No. 2 Capgemini, Gartner said the company has a long history of support for legacy applications and has translated that into a strong capability for application modernization. It leads with analytics and accelerators, but has relatively low certifications on cloud and cloud-native client engagements.

Other notable companies outside of Gartner’s top 10 rankings for modernisation of legacy applications include Wipro, IBM, Cloud4C and Cognizant.

 

Score for enabling, monitoring and optimisation use case

  • Deloitte: 3.61
  • Accenture: 3.33
  • Hexaware: 3.19
  • HCL Technologies: 3.18
  • Capgemini: 3.13
  • TCS: 3.12
  • SMX: 2.88
  • NTT Data: 2.87
  • Infosys: 2.86
  • Atos: 2.81

Deloitte stands above the rest of the pack with a score of 3.61 for Gartner’s enabling, monitoring and optimisation use case.

For this use case, Gartner said MSPs must have considerable experience in migrating, monitoring and optimizing their client’s environments by using multiple frameworks, tools and standard ITSM practices.

Deloitte has developed its own comprehensive integrated automation tools as part of its cloud management platform. The company has over 10,000 prebuilt automation artifacts, 20,000 global automation resources, and over 55 percent of incidents resolved by automation.

Accenture had the second-highest score of 3.33. The company’s managed services capability uses over one-third of staff, owning a strong cloud management platform and automation in many areas.

Other notable companies outside of Gartner’s top 10 rankings for enabling, monitoring and optimization include Wipro, Tech Mahindra, Despin Global and Cognizant.

Scores for strategic cloud transformation use case

  • Accenture: 3.37
  • Hexaware: 3.34
  • TCS: 3.34
  • Capgemini: 3.23
  • Infosys: 3.12
  • Deloitte: 3.08
  • HCL Technologies: 3.04
  • IBM: 2.99
  • Atos: 2.85
  • NTT Data: 2.82

Accenture held a slightly higher score of 3.37 for Gartner’s strategic cloud transformation category versus TCS and Hexaware who both scored 3.34.

For this use case, Gartner said customers have ambitions to transform its entire IT organization, application portfolio and infrastructure to be more agile in order to achieve strategic business outcomes.

Channel partner consolidator Accenture has strong professional services, generating an estimated three-quarters of its revenue from professional services engagements in the last year. The company supports clients in an agile and iterative manner via capabilities in big data and cloud analytics, and actively moves clients to PaaS and SaaS services.

TCS uses a three-horizon “crawl, walk, run” model to structure cloud transformation strategies: visualize the journey, validate with proof of concepts, then move into a co-innovate or co-create execution phase.

Hexaware provides strategic advice on cloud adoption working back from the client’s transformational aims. It then designs a cloud adoption approach that supports the required change, a different approach from its competitors.

Other notable companies outside of Gartner’s top 10 for strategic cloud transformation include SMX, Hitachi Vantara, Cognizant and Hanu.

 

Scores for knowledge share use case

  • Deloitte: 3.39
  • TCS: 3.30
  • Capgemini: 3.28
  • Hexaware: 3.20
  • HCL Technologies: 3.18
  • Accenture: 3.16
  • SMX: 3.08
  • NTT Data: 3.01
  • Infosys: 2.92
  • Atos: 2.90

Deloitte claimed the top score from Gartner at 3.39 for knowledge-sharing, followed by TCS at 3.30 and Capgemini at 3.28.

For this use case, Gartner said MSPs are being asked to not only stand up agile transformative environments for their clients, but also share their knowledge along the way.

Deloitte has a mature approach to enabling DevOps with clients, and had 7,500 deals in the last year where it handled DevOps enablement for clients—the largest figure of any provider in Gartner’s report. The company also has a progressive talent management vision that combines professional development programs and well-defined diversity and inclusion programs.

No. 2 TCS is starting to focus heavily on knowledge-sharing in response to clients, but has little experience to date. It has implemented DevOps enablement for 455 clients, according to Gartner.

Other notable companies outside of Gartner’s top 10 rankings for knowledge sharing include Tech Mahindra, Cloud4C, Wipro and Cognizant.

This article originally appeared at crn.com

Sun, 07 Aug 2022 08:54:00 -0500 text/html https://www.crn.com.au/news/accenture-deloitte-tcs-top-gartners-cloud-it-transformation-providers-list-583676
Killexams : Blockchain can change healthcare for the better. Here's how

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Killexams : Network Management System Market Statistics, Opportunities, Assessment, Demand, Scope and Forecast 2027

"Cisco (US), IBM (US), Broadcom CA Technologies (US), Micro Focus (UK), Juniper Networks (US), Nokia (Finland), Ericsson (Sweden), ManageEngine, a Division of Zoho Corporation (US), Huawei (China), LiveAction (US), NETSCOUT (US), Progress (Ipswitch) (US), Paessler (Germany), Cubro Network Visibility (Austria), Kentik (US), VIAVI Solutions (US), Kaseya (US)."

Network Management Systems Market by Component, Enterprise Size, Deployment Mode, Business Function (Accounting & Legal, Sales & Marketing, and Procurement & Supply Chain) Vertical and Region - Global Forecast to 2027

The Network Management System Market size to grow from USD 9.3 billion in 2022 to USD 14.6 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 9.4% during the forecast period. Various factors such as growing demand for better optimization of business operations, rise in the need for in-depth visibility into network security, and maintaining QoE and QoS are expected to drive the adoption of network management system.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=1041

As per verticals, the government segment to grow at highest CAGR during the forecast period

The network management system market is segmented on verticals into BFSI, IT & telecom, government, manufacturing, healthcare, retail, transportation & logistics, and others (education and hospitality). As per verticals, the government vertical is expected to grow at the highest CAGR during the forecast period. This vertical is one of the major revenue contributors to the NMS market and is expected to be a promising vertical in the future as well. Since businesses are changing, the network infrastructure continues to be a significant backbone, linking users to the necessary IT resources and enabling immediate distribution of information. The change in the government network infrastructure is driving the growth of the NMS market.

On-premises Segment to grow at the highest CAGR during the forecast period

As per deployment mode, On-premises Segemnt to grow at the highest CAGR for the network management system market during the forecast period. The network management system market by deployment mode is segmented into cloud and on-premises. The on-premises solutions are seen to be in greater demand, due to their wide range of functionalities, such as high-end security, easy deployment, and complete access to network solutions. With advancements in technology, enterprises and service providers are seen to prefer cloud-based network management solutions, as they offer various benefits, such as a pay-per-use model, flexibility, speed inaccessibility, and low installation and maintenance costs.

Request sample Pages: https://www.marketsandmarkets.com/requestsampleNew.asp?id=1041

Some major players in the network management system market include Cisco (US), IBM (US), Broadcom CA Technologies (US), Micro Focus (UK), Juniper Networks (US), Nokia (Finland), Ericsson (Sweden), ManageEngine, a Division of Zoho Corporation (US), Huawei (China), LiveAction (US), NETSCOUT (US), Progress (Ipswitch) (US), Paessler (Germany), Cubro Network Visibility (Austria), Kentik (US), VIAVI Solutions (US), Kaseya (US), Extreme Networks (US), eG Innovations (US), Colasoft (China), SolarWinds (US), ExtraHop Networks (US), Riverbed (US), Accedian (Canada), BMC Software (US), HelpSystems (US), and AppNeta (US). These players have adopted various organic and inorganic growth strategies, such as new product launches, partnerships and collaborations, and mergers and acquisitions, to expand their presence in the global network management system market.

Broadcom is a publicly held global infrastructure technology provider that helps its clients in innovation, collaboration, and engineering excellence. The company focuses on technologies that connect the world in collaboration with industry leaders such as Avago Technologies, LSI, Broadcom Corporation, Brocade, and CA Technologies. The company is a global provider in numerous product segments, serving the world’s most successful companies. The company has a wide product and solution portfolio for segments such as storage and systems, wireless, wired connectivity, optical products, Broadcom software, mainframe software, enterprise software, and security. It caters its products to several companies and has a presence in more than 15 countries across North America, Europe, the Middle East & Africa, and Asia Pacific. Broadcom’s NMS offering includes the DX NetOps Platform. The platform converts inventory, topology, device metrics, faults, and flow and packet analysis into actionable intelligence for network operations teams. It is complemented by its AIOps solution that enables IT teams to establish proactive, autonomous remediation capabilities across applications, infrastructure, and networks for providing superior user experiences.

Cisco gear, software, and service options are implemented to build Internet solutions that allow users, corporations, and governments to access information anytime. Cisco has also developed the use of the Internet in its commercial operations and provides consulting solutions depending on its expertise to assist other firms worldwide. Cisco researchers have been at the forefront of IP-based networking technology development from the firms inception. The development of industry-leading solutions in core routing and switching technologies, as well as advanced technologies in areas such as home networking, IP telephony, optical networking, security, storage area networking, and wireless technology, continues this IP innovative history. The company sells its products and solutions, both directly through its own sales force as well as through its channel partners, to large enterprises, commercial businesses, service providers, and consumers. Cisco provides SD-WAN, a secure, cloud-scale architecture that is accessible, programmable, and scalable. Likewise, Cisco Application Centric Infrastructure (Cisco ACI) is an industry-leading secure, accessible, and broad SDN solution. This solution delivers an intent-based networking structure to accelerate responsiveness in data centers. The company caters to clientele all over the world and generates its revenue majorly from the Americas, Europe, the Middle East & Africa, and Asia Pacific.

Juniper Networks is a leading provider of networking and cybersecurity solutions for service providers, enterprises, and public sectors. The company designs, develops, and sells network products and solutions worldwide. Juniper Networks provides network automation solutions that transform the network experience of users with resilient, closed-loop, and intent-driven automation powered by AI and ML. NorthStar Controller provides flexible traffic engineering solutions, which simplify and automate the provisioning, management, and monitoring of segment routing and IP/MPLS flows across large networks. NorthStar Planner is a network planning and simulation tool that provides in-depth network views, health audits, and scenario planning without impacting the live network of users.

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To view the original version on ABNewswire visit: Network Management System Market Statistics, Opportunities, Assessment, Demand, Scope and Forecast 2027

© 2022 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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Killexams : Global Mobile Edge Computing Market Report 2022: Featuring Key Players IBM, Ericsson, Samsung Electronics & Others

DUBLIN, July 8, 2022 /PRNewswire/ -- The "Global Mobile Edge Computing Market by Infrastructure, Deployment Model, Computing as a Service, Network Connectivity, Applications, Analytics Types, Market Segments, and Industry Verticals 2022-2027" report has been added to ResearchAndMarkets.com's offering.

Research and Markets Logo

Select Report Findings

  • Mobile edge computing will be a key enabler of immersive technologies deployed with 5G

  • Greatest opportunities will be in teleoperation/cloud robotics, telepresence, and virtual reality

  • The global mobile edge computing market for software and APIs will reach $2.32 billion by 2027

  • The market for MEC software in support of IoT applications will reach $637 million globally by 2027

  • The largest industry vertical opportunities for MEC will be manufacturing, healthcare and automobile

In cellular networks, edge computing via MEC is beneficial for LTE but virtually essential for 5G. This is because Mobile Edge Computing facilitates optimization of fifth generation network resources including focusing communications and computational capacity where it is needed the most. The author's research findings indicate a strong relationship between edge computing and 5G. In fact, if it were not for MEC, 5G would continue to rely upon back-haul to centralized cloud resources for storage and computing, diminishing much of the otherwise positive impact of latency reduction enabled by 5G.

Another driver for the multi-access edge computing market is that MEC will facilitate an entirely new class of low-power devices for IoT networks and systems. These devices will rely upon MEC equipment for processing. Stated differently, some IoT devices will be very lightweight computationally speaking, relying upon edge computing nodes for most of their computation needs.

Mobile Edge Computing Market Drivers

  • Improved Overall Throughput

  • Core Congestion Reduction

  • Application Latency Reduction

  • Backhaul Reduction

  • Network Awareness and Context

  • Streaming Data and Real-time Analytics

  • Network and Application Resiliency

Mobile Edge Computing Market Deployment Alternatives

As the author has stated in the past, the primary standards body for MEC standardization is the European Telecommunications Standards Institute (ETSI), which has done much to move edge computing in mobile/wireless networks forward.

ETSI identifies four physical areas for MEC deployment as follows:

  • Co-location at Base Station

  • Co-location at Transmission Node

  • Co-location at Network Aggregation Point

  • Co-location with Core Network Functions

Carrier Mobile Edge Computing Market Deployment Considerations

It is important to understand that multi-access edge computing servers and platforms can be deployed in many locations including, but not limited to, an LTE and/or 5G macro base station site, the 3G Radio Network Controller site, a multi-RAT cell aggregation site, or at an aggregation point. Communication Service Providers (CSP) are not accustomed to planning for remote servers.

However, MEC essentially needs many remote data centers. The author predicts that CSPs will need to partner with network integration companies to realize the full vision of MEC. CSPs cannot be bogged down in negotiations, planning, engineering, and deployment of MEC communications/computing platforms every time a new site is acquired.

With the multi-access edge computing market, there is clearly a new computational-communications paradigm in which communications and computing are no longer thought of as separate things. Furthermore, they are planned, engineered, deployed, and operated together. In parallel with this new paradigm, mobile networks are becoming video networks.

Key subjects Covered:

1. Executive Summary

2. Introduction

3. MEC Technology, Platforms, and Architecture
3.1 MEC Platform Architecture Building Blocks
3.2 Edge Cloud Computing Value Chain
3.3 MEC Technology Building Block
3.4 MEC Technology Enablers
3.5 MEC Deployment Considerations

4. MEC Market Drivers and Opportunities

5. MEC Ecosystem

6. MEC Application and Service Strategies
6.1 Optimizing the Mobile Cloud
6.2 Context Aware Services
6.3 Data Services and Analytics

7. Multi-Access Edge Computing Deployment

8. Multi-Access Edge Computing Market Analysis and Forecasts

9. Conclusions and Recommendations

 Companies Mentioned

  • ADLINK Technology Inc.

  • Advanced Micro Devices

  • Advantech

  • Affirmed Networks

  • Akamai Technologies

  • Allot Communications

  • AT&T

  • Brocade Communications Systems

  • Cavium Networks

  • Ceragon Networks

  • China Mobile

  • China Unicom

  • Cisco Systems

  • Cloudify

  • Cradlepoint

  • Deutsche Telekom

  • EdgeConneX

  • Edgeworx

  • Ericsson

  • ETRI

  • Fujitsu Technology Solutions

  • Hewlett Packard Enterprise (HPE)

  • Huawei Technologies Co. Ltd.

  • IBM Corporation

  • Integrated Device Technology

  • Intel Corporation

  • InterDigital Inc.

  • ITRI

  • Juniper Networks

  • Mimic Technology

  • MobiledgeX (Google)

  • NEC Corporation

  • Nokia Corporation

  • NTT Communications

  • NTT DoCoMo

  • Orange

  • Ori

  • PeerApp Ltd.

  • Pixeom

  • Pluribus Networks

  • Quortus

  • Redhat, Inc.

  • Saguna Networks

  • Samsung Electronics Co. Ltd.

  • SK Telecom

  • Sony Corporation

  • SpiderCloud Wireless

  • Telefonica

  • TIM

  • Vapor IO

  • Vasona Networks (ZephyrTel)

  • Verizon

  • Viavi Solutions

  • Vodafone

  • Xilinx, Inc.

  • Yaana Ltd.

  • ZTE Corporation

For more information about this report visit https://www.researchandmarkets.com/r/5ko1v8

Media Contact:

Research and Markets 
Laura Wood, Senior Manager 
press@researchandmarkets.com

For E.S.T Office Hours Call +1-917-300-0470 
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SOURCE Research and Markets

Tue, 12 Jul 2022 06:55:00 -0500 en-AU text/html https://au.finance.yahoo.com/news/global-mobile-edge-computing-market-094500412.html
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