Communication service providers (CommSPs) are already saving money and generating revenue from network transformation investments. There is an expectation these benefits will continue to increase as NFV functions scale across the various elements of the infrastructure--enterprise, radio access network, wireless core, cable and cloud. New 5G and edge computing use cases promise to deliver new revenue along with even more data that must be moved, stored, processed and analyzed. The industry is looking to Artificial Intelligence (AI) and Machine Learning (ML) to enable CommSPs to solve problems and unlock value for their own business operations and their customers. As an example, distributed AI based on reinforcement learning will play a key role in building automated and self-managed networks.
Amid the imperative of digital transformation and the enterprise adoption of multicloud, network agility and flexibility are prized. The right network-automation tools can help, contributing to agile operations while enabling the portability of applications and data between on-premises datacenters and clouds. With the rise of cloud operating models and multicloud environments, there's no question that network professionals are compelled to forgo manual processes, inherently inefficient and error prone, in deference to automated processes that are more efficient and verifiable. IDC foresees network-automation tools evolving in lockstep with cloud-driven requirements to enable efficient and relevant connectivity of network service/functions within multicloud environments. Given the nature of multicloud, automation tools should work across a range of cloud services from different cloud service providers.
A network that can fix and optimize itself without human intervention could become a reality soon – but not without some training. With the help of machine learning and artificial intelligence, software-defined networks can learn to help with network management by using operational data. Initial application of AI to WAN operations includes security functions such as DDoS attack mitigation as well as near real-time, automated path selection, and eventually AI-defined network topologies and basic operations essentially running on'auto-pilot'. Enhancing IT operations with artificial intelligence (AI), including configuration management, patching, and debugging and root cause analysis (RCA) is an area of significant promise – enough so that Gartner has defined the emerging market as "AIOps". These platforms use big data and machine learning to enhance a broad range of IT operations processes, including availability and performance monitoring, event correlation and analysis, IT service management, and automation (Gartner "Market Guide for AIOps platforms," August 2017).
Juniper Networks announced Thursday it plans to acquire AppFormix, providers of cloud operations management and optimization technology. Financial terms of the deal were not disclosed. Founded in 2013 by former Microsoft and Cisco executive Sumeet Singh, AppFormix's software platform leverages streaming analytics and machine learning in a distributed architecture for use in cloud environments. The platform works with any OpenStack or Kubernetes distribution and includes services for real-time and historic monitoring, visibility, and dynamic performance optimization. Juniper said it plans to pair AppFormix's telemetry and operations management technology with its Contrail product line to improve cloud orchestration, security, accounting, and planning.
As cloud computing becomes the information technology mainstream, data center technology is accelerating at a breakneck speed. Concepts like software define infrastructure, data center analytics and Nonvolatile Memory Express (NVMe) over Fabrics are changing the very nature of data center management. According to industry research firm IDC, organizations will spend an additional $142.8 billion oninfrastructure for both public and private cloud environments in the next three years (2016-2018) to boost efficiency and business agility. To support this rapid evolving space, Intel announced a "Cloud for All" initiative last year in order to help businesses get the most out of their cloud infrastructure. Specific goals for this initiative include: Investing in the ecosystem to accelerate enterprise-ready, easy-to-deploy software defined infrastructure (SDI) solutions; Optimizing SDI solutions to deliver highly efficient clouds across a range of workloads by taking full advantage of Intel platform capabilities; and Aligning the industry and engaging the community through open industry standards, solutions and routes to market to accelerate cloud deployment.