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 Improve 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.
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 Improve 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:
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.
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:
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:
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:
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).
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.
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Aug 01, 2022 (Heraldkeepers) -- Pune, India-Global Data Encryption Software Market Industry oversaw different associations of the business from various geologies or locales. The Report study comprises of subjective and quantitative data featuring key market improvements challenges that industry and rivalry are looking alongside hole investigation, new open doors accessible and pattern additionally incorporate COVID-19 effect Analysis in Global Data Encryption Software Market and effect different elements bringing about boosting Global Data Encryption Software Market at worldwide just as territorial level. There are colossal rivalries that happen worldwide and should require the investigation of MARKET Shares ANALYSIS quite a Top Competitors/Top Players are: Dell, Sophos, IBM, Eset, Pkware, Gemalto, Thales E-Security, Microsoft, Mcafee, Symantec, Trend Micro, Cryptomathic, Stormshield. Watchman’s Five Forces Analysis, sway examination of Coronavirus, and SWOT Analysis are additionally referenced to comprehend the elements affecting shopper and provider conduct.
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&#151; -- The Anti-Spyware Coalition (ASC), a group of IT companies and public interest groups, is hoping to succeed where a previous vendor organization failed in tackling the global problem of spyware. The ASC released an agreed-upon draft definition of spyware this week that it hopes will promote public comment and ultimately result in users becoming better educated about the dangers of spyware.
The Consortium of Anti-Spyware Technology Vendors (Coast), initially drawn from the security software vendor community, fell apart in February after a failed 16-month effort to coordinate its members' conflicting goals and an ongoing debate over admitting companies that created spyware. The ASC, convened by the Center for Democracy and Technology, has a much wider membership than Coast.
ASC member include the likes of America Online, Computer Associates International, Hewlett-Packard, Microsoft, and Yahoo, along with McAfee, Symantec, and Trend Micro, and anti-spyware specialist vendors Aluria Software and Webroot Software. The organization also numbers the Canadian Internet Policy and Public Interest Clinic, the Cyber Security Industry Alliance, and The University of California Berkeley's Samuelson Law, Technology, & Public Policy Clinic among its members.
The ASC was formed in early April, after a number of companies approached the Center for Democracy and Technology about forming a group to combat spyware. The organization's Web site went live this week.
Ari Schwartz, associate director of the Center for Democracy and Technology, has been heading up the ASC's work. He says that the new anti-spyware consortium had learned from Coast's experience. "The main difference between us and Coast is that we're trying to help anti-spyware companies communicate better together and with consumers," Schwartz says. "Coast was more about communication between anti-spyware companies and software publishers."
One fear the ASC has is the potential harm spyware could be having on consumers' Internet behavior, Schwartz says, as indicated by last week's Pew Internet & American Life Project survey. The study revealed that 91 percent of Internet users polled have changed their behavior online to try and avoid being attacked by spyware and other unwanted technologies.
Spyware isn't only plaguing consumers. "What we're hearing from companies is that spyware is starting to become a bigger enterprise problem," Schwartz says, pointing to the exact multimillion dollar contract for anti-spyware technology issued by the U.S. Department of Defense.
"We'd like to see more enforcement actions," Schwartz says, adding that the ASC will hope to Improve communications between anti-spyware vendors and law enforcement to track down spyware companies. A commissioner from the U.S. Federal Trade Commission (FTC) attended the ASC's Washington, D.C., meeting.
The ASC is inviting public comment for the next month on documents it released this week. "We're just trying to get a foundation down," Schwartz says. The documents include a list of spyware and other potentially harmful technologies aimed at users, a glossary defining commonly used terms relating to spyware, and safety tips about how to protect against spyware.
There's also a process laying out how to resolve disputes if a vendor believes its software has been wrongly tagged as spyware. Previously each anti-spyware company worked on developing its own process and spyware companies would try to play off one antispyware company against another using their various dispute processes, according to Schwartz. "We're leveling the playing field so that anti-spyware companies spend less time talking about the [vendor dispute] process and more time on how to tackle spyware," he says.
Spyware can be defined two ways, according to the ASC. "In its narrow sense, spyware is a term for tracking software deployed without adequate notice, consent or control for the user," the organization states in its glossary. However spyware is also used as an umbrella term encompassing not only its narrow definition, but also other "potentially unwanted technologies," the ASC adds, including harmful adware, unauthorized dialers, rootkits, and hacker tools.
In its anti-spyware safety tips document, the ASC has six major recommendations for users to defend themselves against spyware. The organization suggests that users keep the security on their computers up to date; only get programs from Web sites they trust; familiarize themselves with the fine print attached to any downloadable software; avoid being tricked into clicking dialog boxes; beware of so-called "free" programs; and use anti-spyware, antivirus, and firewall software.
Come August 12, ASC will review and respond to all the comments it has received, Schwartz says. The organization will then meet toward the end of August and produce a final document. "The next step is do risk modeling, help companies make decisions about what they flag as spyware, what's their objective criteria for flagging, and work on best practices," Schwartz says.
(MENAFN- EIN Presswire)
Storage Software Market Size 2022
Storage Software market size was estimated to reach over USD 17200 Million in 2020 and is projected to grow significantly with a CAGR of 8.3%
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Storage Software market Segmentation: Research Scope
Segmentation 1: Different types of Storage Software market
Segmentation 2: by Application - They are widely used in places including
Telecom and IT
Segmentation 3: Geographic regions
- North America (U.S. and Canada)
- Europe (Germany, United Kingdom, France, Italy, Spain, Russia, and Others)
- Asia Pacific (China, India, South Korea, Indonesia, Australia, and Others)
- Latin America (Brazil, Mexico)
- the Middle East and Africa
Highlights of the Report
#1. This report provides a comprehensive understanding of customer behavior and growth patterns in the Storage Software market.
#2. The report sheds light on the lucrative business prospects pertaining to the Storage Software market
#3. The readers will gain an insight into the upcoming products and related innovations in the Storage Software market
#4. The report provides details about the key strategic initiatives adopted by the key players functioning in the Storage Software market
#5. The authors of the Storage Software report have scrutinized the segments considering their profitability, market demand, sales revenue, production, and growth potential
#6. In the geographical analysis, the Storage Software report examines the current market developments in various regions and countries
Key questions answered in this report:
1. What Industry Is In High Demand?
2. What is Storage Software?
3. What is the expected market size of the Storage Software market in 2022?
4. What are the applications of Storage Software?
5. What is the share of the top 5 players in the Global Storage Software Market?
6. How much is the Global Storage Software Market worth?
7. What segments does the Storage Software Market cover?
Recent Trends in the Storage Software Market
• In exact years, the United States has seen a significant increase in demand for prototypes. Additive manufacturing has become more popular for high-volume production.
• Market participants participate actively in expanding the range and applications of Storage Software. Technology is rapidly improving. As such, Storage Software is focusing on streamlining pre and post-production.
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Sean Derrington is senior group manager of Product Management at Symantec Corporation. He is responsible for product marketing, product strategy and outbound marketing for VERITAS' Storage Foundation and Storage Management products in the Data Center Management Group at Symantec Corporation. He joined Symantec with the VERITAS acquisition. Prior to joining VERITAS, Derrington was senior program director of META Group's Server Infrastructure Strategies. During his eight years at META Group, he was the lead consultant and analyst covering storage and storage management market.
A promotional Asset Management In Chemical Industry Market research report has covered the significant aspects which are contributing to the growth of the global Asset Management In Chemical Industry market. The data and information about the industry are taken from reliable sources such as websites, annual reports of the companies, and journals, and then validated by the market experts. The primary objective of this business document is to highlight the various key market dynamics listed as drivers, trends, and restraints. The large scale Asset Management In Chemical Industry report on the global market is a valuable document for every market enthusiast, policymaker, investor, and market player.
For Better Understanding, Get PDF Broucher of Asset Management In Chemical Industry Market Research Report @ https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-asset-management-in-chemical-industry-market
Major Players in Asset Management In Chemical Industry markets are:
FIS, Broadridge Financial Solutions, Inc., SimCorp A/S, CreditPoint Software, Hewlett Packard Enterprise Development LP, softTarget, FundCount, eFront, Scalable, Micro Focus, Ivanti, Snow Software, Flexera, Certero, Broadcom, Symantec Corporation, Aspera Technologies Inc., IBM Corporation, Microsoft, ServiceNow and Cherwell Software, LLC among other domestic and global players.
Asset Management In Chemical Industry Market Insight:
The asset management in chemical industry market is expected to witness market growth at a rate of 10.10% in the forecast period of 2021 to 2028.
Asset management in chemical industry refers to a technological offering provided by information technology organizations and software providing companies. The technology assists in the monitoring of assets and stock of commodities. They are also utilized in the process of the production methods and manufacturing processes of the company, regardless of the fact that they are being produced with the proper compliances and regulations proposed by the authorities.
Global Asset Management In Chemical Industry Market Segmentation:
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Global Asset Management in Chemical Industry Market Country Level Analysis
The asset management in chemical industry market is analyzed and market size, volume information is provided by country, component, deployment type, organization size and application as referenced above.
The countries covered in the globally asset management in chemical industry market report are the U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, Israel, Egypt, South Africa, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.
North America dominates the asset management in chemical industry market because of the high adoption, infrastructure development, rapid digitalization, rising adoption of connected, smart and secure technologies for asset-centric applications and presence of large IT companies in the region. Asia-Pacific is expected to witness significant growth during the forecast period of 2021 to 2028 because of the growing adoption and deployment of advanced technologies such as cloud-based solutions, AI, IoT and big data analytics, economic growth and presence of a large number of SMEs in the region.
Some Points from Table of Content:
Market Overview: It includes six chapters, research scope, major manufacturers covered, market segments by type, Asset Management In Chemical Industry market segments by application, study objectives, and years considered.
Market Landscape: Here, the competition in the Worldwide Asset Management In Chemical Industry Market is analyzed, by price, revenue, sales, and market share by company, market rate, competitive situations Landscape, and latest trends, merger, expansion, acquisition, and market shares of top companies.
Profiles of Manufacturers: Here, leading players of the global Asset Management In Chemical Industry market are studied based on sales area, key products, gross margin, revenue, price, and production.
Market Status and Outlook by Region: In this section, the report discusses about gross margin, sales, revenue, production, market share, CAGR, and market size by region. Here, the global Asset Management In Chemical Industry Market is deeply analyzed on the basis of regions and countries such as North America, Europe, China, India, Japan, and the MEA.
Application or End User: This section of the research study shows how different end-user/application segments contribute to the global Asset Management In Chemical Industry Market.
Market Forecast: Production Side: In this part of the report, the authors have focused on production and production value forecast, key producers forecast, and production and production value forecast by type.
Research Findings and Conclusion: This is one of the last sections of the report where the findings of the analysts and the conclusion of the research study are provided.
TOC of This Report @https://www.databridgemarketresearch.com/toc/?dbmr=global-asset-management-in-chemical-industry-market
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While studying industrial engineering at Columbia University, Nelson Yuan “felt the gravitational pull of Wall Street.” For him, engineering wasn’t an end in itself, “but a framework for looking at problems. I like to apply an engineering perspective to operations challenges.” His budding interest in finance, combined with a desire to apply the Mandarin language skills he had learned at home, led to his first internship in Hong Kong. “Seeing the growth trajectory in China,” Nelson says, “made me want to study abroad.”
After graduation, Nelson continued to work in finance with three years of investment banking at Morgan Stanley and two at the Carlyle Group, where he worked in private equity and debt investment.
As Nelson’s career matured, he “didn’t find finance fulfilling.” But making the transition to operations meant addressing gaps in his education. “While finance is important, I needed an efficient way to acquire a larger understanding of business, including marketing, strategy and entrepreneurship. HBS’ general-management focus fits my ambitions. Here, I can develop a foundation for thinking about business problems.”
But when it came to business problems, the case study method offered an approach that had been unfamiliar to Nelson. “As an engineer, I saw things in black and white,” he says. “There’s either a right answer or a wrong one. But the cases teach you that there really isn’t a right or wrong answer – just lots of different shades of gray. You come up with what you think is the best answer and defend it with evidence. Then as a class, we can reach consensus based on what different people bring to their case arguments.”
HBS has changed Nelson’s understanding of leadership as well. “Working in the trenches,” he says, “it can be hard to see how business leaders affect the ethos of a company. One of the things they stress at HBS is responsibility, the strong sense of ethics that must inform the decisions we make, the way we influence our colleagues. Here, I’m internalizing what it means to be a significant figure as a business leader: a person who shapes a company’s culture.”
During the winter break, Nelson joined the Tech Media Club’s West Trek for a tour of Silicon Valley. “We met leaders of established tech companies, small startups, and emerging clean-tech firms,” says Nelson. “It was a great introduction to companies that might not otherwise recruit at HBS. And a convenient way to weigh the advantages of working at an established company versus working at a start-up. We got a sense of the various lifestyle choices from the people we met.”
Nelson has accepted a summer internship with Symantec in the Bay Area, where he will help manage a new enterprise product focused on storage software. “In the future,” he says, “I’m leaning more toward established companies right after graduation, though I may ultimately join a startup as a founder or early employee with an eye on becoming a CEO.”
New Jersey, United States- Cloud Security Platform Market 2022 – 2028, Size, Share, and Trends Analysis Research Report Segmented with Type, Component, Application, Growth Rate, Region, and Forecast | key companies profiled – Cisco, Akamai, Google, Microsoft, IBM, Palo Alto, Broadcom, Okta and others.
Cloud security platform is an expansive scope of innovations and systems that assist to safeguard different uses of information and PCs from cloud-assault and unscrupulous access by giving secure organizations and data. Reasonable security and protection challenges for cloud computing are likewise introduced through cloud security platform administrations and arrangements. The construction of cloud security platforms is compelling for giving avoidance, hooligan skin and adjustment control. This further develops the security capacities of cloud suppliers and their own gamble evaluation. The central point powering the cloud security platform market incorporates developing refinement of cybercrimes, digital surveillance missions, and age of new cyberattacks, upthrust in the utilization of cloud-based arrangements and upsurge in BYOD and CYOD patterns to support the interest for cloud security platform. Additionally, expanding government drives to help savvy foundation projects, and getting on the web installment applications, virtual entertainment, and OS would deliver worthwhile chances to cloud security platform merchants.
Top Key Players of Cloud Security Platform Market Report are Cisco Systems, Inc., CA Technologies, Inc., Fortinet, Inc., Intel Corporation (McAfee, Inc.), IBM Corporation, Panda Security, Symantec Corporation, Sophos, Ltd., Trend Micro, Inc., CloudPassage Inc.
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The cloud security platform market is seeing critical development in the previous years. This development is credited to the expansion in reception of cloud-based security arrangements, rising interest for cloud-based administrations among a larger number of cloud-based organization models in industry verticals, quickly expanding pattern of CYOD and BYOD, and a developing number of digital assaults because of an upsurge in digitalization. Further, people are more drawn to embrace advanced innovations like cloud arrangements because of the inescapable use of BYOD instruments, the work from home pattern, and web infiltration around the world, growing the interest for cloud security platform measures to make preparations for digital assaults. Besides, many undertakings are putting resources into innovative work to foster in light of cloud examination programming to survey the spread of COVID-19.
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Cloud security platform is called cloud computing insurance, including a bunch of strategies, controls, innovations, and approaches that cooperate to safeguard cloud-based foundation, frameworks, and information. They accept their information is more solid on their neighborhood servers, where they believe they ought to have more power across the information. It gives all the usefulness of ordinary IT wellbeing. It is of indispensable importance for industry endeavors that are making a framework to cloud innovation. The worldwide cloud security platform market is supposed to observe critical development, attributable to rising interests in cloud foundation and a rising number of designated digital assaults, the requirement for assurance administrations and strategy execution, expanding interest for cloud computing through SMBs. Further, the ideal unofficial laws and drives for implementing and guaranteeing the determination of digital security measures are likewise expected to drive market development during the figure time frame. The lack of mindfulness with organizations and clients in regards to the interest for assurance administrations will confine the market’s development.
Cloud Security Platform Market Segmentation:
Based on the organization model, the worldwide cloud security platform market is fragmented into private cloud, public cloud, and mixture cloud. The Mixture cloud portion overwhelmed the market and held the biggest piece of the pie of 39.21% in the year 2020. This development is credited to the rising reception of crossover cloud security platform with the populace, a multi-level technique for security in light of plaited wellbeing data, and occasion organization (SIEM) items inside the help web.
Based on association, the worldwide cloud security platform market is fragmented into SMEs and enormous undertakings. The SMEs section overwhelmed the market and held the biggest piece of the pie of 57.34% in the year 2020. This development is credited to the moving from traditional computing administrations to cloud security platform administrations.
Based on application, the worldwide cloud security platform market is fragmented into character and access to the board (IAM), interruption discovery framework (IDS)/interruption avoidance framework (IPS), security data and occasion the executives (SIEM), and information misfortune anticipation (DLP).
North America locale holds the biggest piece of the pie of 38.32% in the year 2020. This development is ascribed to expanding mindfulness about the danger of cyberattacks and corporate reconnaissance. Moreover, the high reception of trend setting innovation and the presence of conspicuous merchants are likewise driving this district’s development. The market in the Asia Pacific is supposed to observe critical development, attributable to the rising cloud administrations in creating economies. Further, the developing mindfulness and acknowledgment of practical cloud-based arrangements through arising new businesses and SMEs working in the locale are likewise pushing the market development during the projection time frame.
The following are some of the reasons why you should Buy a Cloud Security Platform market report:
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Table of Contents:
1 Cloud Security Platform Market Overview
2 Company Profiles
3 Cloud Security Platform Market Competition, by Players
4 Cloud Security Platform Market Size Segment by Type
5 Cloud Security Platform Market Size Segment by Application
6 North America by Country, by Type, and by Application
7 Europe by Country, by Type, and by Application
8 Asia-Pacific by Region, by Type, and by Application
9 South America by Country, by Type, and by Application
10 Middle East & Africa by Country, by Type, and by Application
11 Research Findings and Conclusion
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Story updated at 5:53 p.m. Wednesday:
Haskell Indian Nations University has received the largest National Science Foundation award ever granted to a tribal college or university, a $20 million award to fund an Indigenous science hub project.
The five-year award is funded under the American Rescue Plan Act and was announced Wednesday by Bryan Newland, the U.S. Department of the Interior’s assistant secretary for Indian affairs. The project will create a hub called “Rising Voices, Changing Coasts: The National Indigenous and Earth Sciences Convergence Hub.”
“The ‘Rising Voices, Changing Coasts’ hub to be located at Haskell Indian Nations University is a tremendous step forward in supporting tribal communities as they address challenges from a rapidly changing climate,” Newland said in a press release. “This is an exciting and much-needed opportunity for scientists and Indigenous knowledge keepers to collaborate on how Indigenous people in coastal areas can build resiliency to the dynamic forces resulting from climate change.”
The space is aimed at addressing coastal hazards resulting from climate change through the combined efforts of Indigenous scientists, students and scholars. The hub will focus on place-based research in four regions: the Alaskan Arctic, the Gulf of Mexico in Louisiana, the Pacific Islands in Hawaii and Puerto Rico’s Caribbean Islands.
Longtime Haskell professor Daniel Wildcat is set to serve as the hub’s lead investigator. In addition to Haskell as the lead institution, the hub will also include the National Center for Atmospheric Research, the Scripps Institution of Oceanography, and the Indigenous Peoples Climate Change Working Group as partners, along with community partners in the four targeted regions.
In a phone call early Wednesday evening, Wildcat told the Journal-World he isn’t taking his new role lightly.
“It’s clearly the biggest challenge I’ve ever faced in my career, but I’m approaching this as when you’ve been involved in education, teaching, research, writing as long as I have, I guess you consider yourself lucky when you get a chance like this to play such an important role in a large project like this,” Wildcat said.
Wildcat said that the hope is that the hub will eventually be located in a renovated Hiawatha Hall, but that the hub would probably be mostly virtual for the first year. He said some of the foundation staff would be housed in Parker Hall to start, once that hall is open following some ongoing renovations.
Wildcat said hiring staff like a grant coordinator and others who will help manage the grant is built into the funding. The first meetings with principal players and partners for the hub will take place at Haskell Sept. 12 and 13, Wildcat said, during which they’ll lay out their plans for the first year.
Wildcat said there are also plans to offer research internships during the academic year and undergraduate research experiences during summers at one of the four principal research sites.
“I kind of see it, in the big picture, as we’re trying to train the next generation of leaders, policymakers and scientists to help us deal with, in my mind, the most pressing problem we’re facing on the planet today — climate change,” Wildcat said.