Machine Learning and Meta-Clouds Next for Cloud Services (Part 2)
INNOVATION IN CLOUD SERVICES USHERS IN A NEW ERA FOR DATA CENTER IT, RE-DEFINING MARKET LANDSCAPES
by Dr. Cliff Grossner
Part 2 of a 2-part series. Don’t miss Part 1.
Machine Learning and AI Transform Business
Machine-learning, artificial intelligence, and analytics capabilities integrated with enterprise and mobile applications are set to bring more innovation, changing how enterprises will serve their customers. The following three trends have come together, to make it possible for enterprises of all sizes to apply analytic techniques to business processes, changing how they will serve their customers:
- 1. The availability of more data from an expanded ecosystem of diverse sources — such as social media, location tracking, monitoring of individual behaviors and news feeds — allowing for accurate prediction of individual needs that vary from moment to moment
- 2. CSPs moving to package analytic technology in an easy-to-consume format, allowing enterprises to apply the technology without requiring highly skilled data scientists
- 3. Off-premises cloud services make it affordable for enterprises to use large-scale compute environments needed to provide the required high-volume computation – examples include the following: IaaS, such as AWS; CaaS, such as Google Container Engine; and PaaS, such as IBM’s Blue Mix.
In addition, Google recently released a machine-learning service for developers. While IBM released three new Watson APIs, allowing developers access to technology for sensing emotional and visual cues, delivering insight on tone, emotional context, or sentiments of voice, text, or images. Microsoft launched a new machine-learning PaaS service, supporting Hadoop and Spark, with languages Python and R, providing developers a high-performance, real-time application-execution platform with pre-built components designed for predictive analytics solutions.
The resulting mobile apps and on-line websites consolidate decision making by people who need to act in real time, where insight from nontraditional data sources is infused in business processes. Several examples include the following:
- – Using IBM Watson content analytics, Wimbledon gained new insight into trending topics and was able to provide compelling digital content to mobile and on-line users, enriching the fan experience and boosting site visits
- – Combining WSI’s weather alert service, together with IBM analytics, enables insurance providers to send policyholders text messages that alert them to impending hailstorms, saving insurers millions of dollars annually
- – Use of collaboration and content management services — with analytics tools processing large volumes of on-line information — to provide insights to geographically distributed medical teams collaborating to solve complex cases, or to help engineers and researchers identify patterns in patents, reports, and academic journals
Off-premises Cloud Service Adoption Drives Data Center IT Innovation
According to the latest information from IHS, 22 percent of physical servers, 17 percent of virtual machines, and 12 percent of Linux containers, on average, are expected to be used in off-premises data centers by 2018. When this happens, there will be opportunity for hardware and software innovation that drives the use of bare-metal servers and switches, combined with open-source software.
Microsoft Azure has already indicated 90 percent of its new servers are running open source software. Goldman Sachs has stated that it will continue to migrate certain applications to the cloud, while increasing its purchase of bare-metal servers running open-source software for its data centers. Goldman Sachs has stated that more than 80 percent of the servers it has acquired since last summer are based on Open Compute Project (OCP) standards, and its OCP server count has increased from 2,500 to 4,300 since last year. This number still represents a small fraction of the 125,000 servers in Goldman Sachs data centers, but the trend towards use of open equipment is clear.
On the network side, Facebook revealed it has significantly stepped up its use of Wedge and 6 Pack (its OCP chassis switch) from last year. The majority of new rack and data center sites being commissioned are using Wedge, which already provides connectivity for thousands of servers. These “light-house deployments are being watched very closely by data-center operators, as the trend towards open hardware with open source software continues to accelerate.
We can expect continued innovation that brings more diversity in compute and switching hardware and provides a choice of silicon. Strong open-source switch OS projects will foster additional innovation. The variety of bare-metal hardware and open-source software in the market continues to multiply and become more mature, providing alternate avenues for enterprises and CSPs to purchase and deploy data center infrastructure.
Cliff Grossner, Ph.D
Dr. Cliff Grossner is a Senior Research Director responsible for data center compute and networking, cloud services and SDN research at IHS Markit technology . He has more than 25 years of telecommunications industry experience covering market analysis, corporate and product strategy, product management and marketing, and scientific research. A recognized thought leader, he is also a frequent expert judge for industry and technology innovation awards and an invited speaker at industry conferences and is frequently quoted in technical publications such as Light Reading, Fierce Telecom, eWeek, Network Computing and Lightwave Online.
Cliff joined IHS (now IHS Markit) in December 2014, when IHS acquired Infonetics Research where he led the firm’s data center networking, cloud and SDN research coverage. During his tenure at Infonetics, Cliff launched the SDN practice and grew revenue 100%.
Prior to Infonetics, Cliff held a range of senior positions in the information and communications technology industries including heading strategic marketing for Alcatel-Lucent’s enterprise network business unit, tenures at Bell Labs, Sesame Networks, NewStep Networks, and Nortel. He has also worked as a research scientist and faculty lecturer at McGill and Concordia universities, specializing in artificial intelligence, distributed computing, and computer architecture.
He earned his Doctor of Philosophy at McGill University, and his Master of Science in Computer Science at Concordia University, both in Montreal, Quebec, Canada He holds multiple patents in computer networking, networking embedded security and telecommunications applications.