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AIOps and Beyond: Promoting Rapid Development of Enterprise ICT

Peter Ye, Data Communication Product Line, Huawei Technologies Co., Ltd.

“Strategic technology trends have the potential to both create opportunity and drive significant disruption,” said Andrew Lerner, Vice President of Gartner Research. “Enterprise architecture and technology innovation leaders must evaluate these top trends and find the best combination for powering their innovation strategy.” As one of Gartner’s top analysts in enterprise networking, Andrew Lerner has successfully predicted the application trends of SDN and intent-driven networks. This has led to him garnering immense popularity in the industry, winning recognition from customers and vendors alike.

Recently, Andrew Lerner led his team to propose key technologies that will promote enterprise Infrastructure and Operations (I&O) in the future. Specifically, he believes that these technologies will have a transformative impact on enterprise I&O, presenting significant disruptive potential over the next five years. The following sections will provide more details on the technologies, before analyzing their impact on enterprise network development.

Technology No. 1: Artificial Intelligence for IT Operations (AIOps) Platforms

AIOps is the application of AI in ICT operations. It is the future of ITOps. To be more specific, it uses AI to provide full visibility into the status and performance of IT systems on which enterprises depend. The AIOps platform combines big data and machine learning to analyze a large amount of data from multiple data sources and provide multiple analytical and presentation technologies.

Gartner predicts that, by 2022, at least 25 percent of large enterprises will be using AIOps platforms, up from 2 percent in 2018.

We believe that there is no time to waste when it comes to building AIOps platforms for enterprise network O&M. AIOps can go deeper than automatic O&M, solving more problems and providing a more intelligent basis for ICT O&M decision-making. It can even predict trends that will occur, thereby providing better assurance for stable operation and growth of enterprise services. Such capabilities mean that, sooner or later, all businesses will have to adopt AIOps.

Technology No. 2: Compute Accelerators

Compute accelerators offer outstanding performance and power efficiency. The mainstream compute accelerators are:

Graphics Processing Unit (GPU) accelerators accelerate highly parallel compute-intensive portions of workloads. They are usually used for High Performance Computing (HPC), and Deep Neural Network (DNN) training and inferencing. GPU computing is also available as a cloud service and may be economical for applications where utilization is low but time to market is a priority.

DNN Application-Specific Integrated Circuits (ASICs) accelerate DNN computations. Use cases that can benefit from DNNs include speech-to-text, image recognition, and natural-language processing.

Field-Programmable Gate Array (FPGA) accelerators deliver outstanding performance by enabling programmable hardware-level application acceleration. They are well suited to AI inference workloads due to their excellent low-precision processing capabilities in energy-efficient footprints.

Gartner predicts that, by 2022, computational resources used in AI will be four times more than in 2018, making AI the number one workload factor driving infrastructure decisions.

We believe that compute accelerators are able to greatly improve the computing performance. However, if there is packet loss on a traditional computing network, it will not be able to reach its full potential in terms of computing power. As such, it has become the bottleneck for improving computing power in the AI era. To fully unleash the computing power of data centers, enterprises urgently need to introduce the zero-packet-loss network.

Technology No. 3: Edge Computing

Edge computing is a distributed computing paradigm that places information processing close to the things or people that produce or consume that information. It keeps traffic and processing local to reduce latency and unnecessary traffic. Additionally, it establishes a hub for the data thinning of complex media types or computationally heavy loads. Edge computing solves many pressing issues, such as excessive latency, insufficient bandwidth, and high costs, helping to cope with the massive increase in edge-located data as positioning applications become increasingly popular.

Gartner predicts that, by 2022, more than 50 percent of enterprise-generated data will be created and processed at the network edge, up from 10 percent in 2018.

We believe that edge computing poses new requirements on network intelligence, latency, bandwidth, and access, which will transform edge computing network technologies from lossy to lossless, from “best-effort” to “deterministic”, from dumb traffic pipes to intelligent computing networks, and from limited access to access anytime, anywhere.

Technology No. 4: Intent-Based Networking

An intent-based networking system (IBNS) provides:

Translation and validation: It can take a higher-level business policy as input from end users and convert it to the required network configuration.

Automation: It can configure appropriate network changes across existing network infrastructure.

State awareness: The system ingests real-time network status for systems that it controls.

Assurance and dynamic optimization: The system continuously validates that business intent is being met and can take corrective action when it isn’t.

Intent-based networking can transform network operations. IBNSs improve network agility and availability and support unified intent and policy across heterogeneous infrastructures. When the technology matures, a full IBNS implementation will reduce the time to deliver network infrastructure services to business leaders by 50 percent to 90 percent. It will also reduce the number and duration of outages by at least 50 percent. In addition, IBNSs reduce operating expenditure, optimize performance, cut dedicated tooling costs, enhance documentation, and improve compliance.

Gartner predicts that, by 2022, more than 1500 large enterprises will use intent-based networking systems in production, up from less than 15 today.

We believe that networks will inevitably evolve to be intent-based and finally to autonomous driving. To help realize this, Huawei is exploring this aspect and actively carrying out joint innovation practices with customers. In 2018, Huawei released the Intent-Driven Network (IDN) solution with the aim of building a digital world that connects physical networks with business intents. Such a digital world is driven by users’ business logic and business strategy intents, enabling customers to evolve from the live network to the target network architecture centered on user experience.

Summary

Various innovative technologies are emerging, and digital transformation requirements of various industries impose increasingly strict requirements on networks. Users need more secure, intelligent, and agile networks. Huawei’s IDN solution helps customers to build ubiquitous ultimate connection experience and accelerates enterprise digital transformation. Driven by technology and customer requirements, Huawei continuously invests in and innovates to build better ICT solutions, maximize customers’ business value, and lead the intelligent IP era.

(The Gartner report cited in this article is excerpted from the ICT-related content of Andrew Lerner’s Top 10 Technologies That Will Drive the Future of Infrastructure and Operations. For the original report, visit https://www.gartner.com/document/97084?ref=solrResearch&refval=24926222.)

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