Edge Computing

Computing at the Edge with VIAVI

What is Edge Computing?

Edge computing is the process of retrieving, analyzing, processing, and storing data close to the use case, or the “edge” of the network. By definition, it includes several distributed data center models that move applications and computing functions away from centralized data centers and closer to the data consumers. 

With data much closer—distance-wise—to the processing elements, latency is significantly reduced. Ultra-reliable, low latency communication (URLLC) 5G uses cases and the internet of things (IoT) rely on edge computing to meet lofty customer expectations for speed, bandwidth, and real-time feedback. 

• Multi-access edge computing (MEC) is a broad term used to describe an edge data center capable of augmenting service for any available access technology. This includes wireless and wireline connections.
• Mobile edge computing is sometimes used interchangeably with Multi-access edge computing, although the former focuses specifically on wireless technology.
• Edge networking is the provisioning of network core elements to facilitate communication and application delivery at the edge. Edge network components perform protocol functions and handshakes to establish connections with external devices. 

What We Offer

The diverse VIAVI test product offerings cover all aspects of edge computing and edge network construction, deployment, maintenance and monitoring. Distributed edge computing reduces system latency by bringing user plane applications and network functionality closer to the use case, making test and monitoring a key component of delivering reliability at the edge and an overall edge deployment strategy.

• Certification: The widespread adoption of MPO makes the FiberChek Sidewinder an ideal solution for automated multi-fiber end face certification. Optical loss test sets (OLTS) designed specifically for the MPO interface, such as the SmartClass Fiber MPOLx, also make Tier 1 fiber certification easier and more reliable.
• High-Speed Test: Optical Transport Network (OTN) testing and Ethernet service activation must be performed quickly and accurately to support the high-speed requirements of edge computing. The versatile, cloud-enabled OneAdvisor 800 is designed to simplify the evolving network test needs of service providers, data centers, field technicians and contractors as they deploy, groom, and maintain a wide variety of wireline and wireless networks. The modular design of OneAdvisor 800 allows network technicians to easily switch between a multitude of test scenarios including transmission protocol, fiber, or over the air RF signals. 
• Multi-fiber, All-in-One: Edge architecture also creates an ideal setting for automated OTDR testing through MPO connections. The multi-fiber MPO switch module is an all-in-one solution for MPO dominated, high-density fiber environments. When used in conjunction with the T-BERD test platform, fibers can be characterized through OTDR without the need for time-consuming fan-out/break-out cables. Automated test workflows for certification can be performed for up to 12 fibers simultaneously.
• Automated Testing: Test process automation (TPA) reduces hyperscale construction times, manual test processes, and training hours. Automation enables efficient throughput and BER testing between hyperscale data centers and edge computing locations to support the end-to-end verification of complex 5G network slices. 

  • The SmartClass Fiber MPOLx optical loss test set brings TPA to Tier 1 fiber certification with native MPO connectivity, automated workflows, and full visibility of both ends of the link. Comprehensive 12-fiber test results are delivered in under 6 seconds.
  • The handheld Optimeter optical fiber meter makes the “no-test” option irrelevant by completing fully automated, one-touch fiber link certification in less than a minute.

  
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• Standalone, Remote Fiber Monitoring: Advanced, remote test solutions are ideal for scalable, unmanned Edge environments. With SmartOTU fiber monitoring, detected events including degradation, fiber-tapping, or intrusions are quickly converted to alerts, safeguarding SLA contracts and edge uptime. The ONMSi Remote Fiber Test System (RFTS) performs ongoing OTDR “sweeps” to accurately detect and predict fiber degradation throughout the network. OpEx, MTTR, and network downtime are dramatically reduced.
• Observability and Validation: The same machine learning (ML), artificial intelligence (AI), and network function virtualization (NFV) breakthroughs that enable 5G are also driving advanced edge computing test solutions 
• Fusion arms network operators and data centers with a virtual service activation and performance management tool set, designed on industry standards, that deliver accurate test results on which managers and engineers can depend. Fusion monitors and ensures network performance and verifies Service Level Agreements in both virtual and physical networks.
• The Observer platform goes beyond traditional monitoring by intelligently converting enriched flow data and traffic conversation details into real-time health assessments and valuable end-user experience scoring.
• The flexible TeraVM software appliance is ideal for validating virtualized network functions. Key network segments including access, mobile network backhaul, and security can be fully validated in the lab, data center, or cloud.

How Does Edge Computing Work?

As cloud computing evolved, a trend towards concentrated hyperscale data centers gradually increased the distances between data and users. Edge computing architecture has been developed to strategically distribute intelligence and untether applications and decisions from these centralized data centers. 

• The Internet of Things (IoT): The IoT is based on sensor technology within everyday objects, machines, and equipment continually streaming data to the cloud. Edge computing forms the lynchpin of three-tiered IoT architecture as the gateway for incoming data. Split-second decisions for low latency IoT applications such as advanced driver assistance systems (ADAS) and smart factories are also completed at the edge.
• Virtualized 5G RAN: Software defined networking (SDN) and network function virtualization (NFV) are central elements of 5G RAN architecture closely intertwined with edge computing. 5G baseband functionality is strategically split between a distributed unit (DU) at the edge and a centralized unit (CU) closer to the core.
• Public and private edge: New architectural models address a variety of cloud edge use cases and performance requirements. Hybrid (semi-private) edge deployments filter data at the point of use so that the public provider does not have access to confidential customer data packets.

Benefits of Edge Computing

Improvements in mobile communication have heightened customer expectations for performance and reliability. Unlike previous generations, 5G allows no wiggle room for slow or disrupted service. Edge computing technology is essential for meeting the requirements of the most demanding use cases. 

• Ultra-Low Latency: With processing completed closer to the data source, response times are trimmed significantly. Although some verticals can still tolerate latency in the 100ms range, advanced 5G applications like connected cars rely on latency of < 2ms, which is impossible to achieve without edge artificial intelligence.
• Privacy: Edge computing technology elevates enterprise and customer privacy by allowing sensitive data to remain onsite rather than transferring it to the cloud for processing. Since large-scale distributed denial of service (DDOS) attacks become more difficult with less concentrated data, network security also improves. 
• Quality of Service (QoS): The improved bandwidth, speed, and latency of edge computing are just some of the overall QoS benefits. With edge analytics and applications managed closer to the user, a more differentiated level of service than was previously possible can be provided.
• Near Real-time Optimization: The flexibility and local visibility of edge computing allows conditional changes to be implemented almost immediately. Feedback from the network and local devices can be incorporated to correct performance issues, increase available bandwidth, or adjust power consumption.

Why is Edge Computing Important?

Edge computing is a common enabler of cloud computing and telecommunications advancement as each enters a new era. The use cases made possible by the combination of 5G and edge computing open new revenue streams for operators and endless possibilities for developers.

• Applications are created more efficiently without the constraints of network infrastructure and legacy protocols. Opportunities abound for innovators and third parties to quickly develop and deploy new applications that leverage the versatility of edge computing platforms. 
• Autonomous systems are an important aspect of the ultra-low latency use cases projected for smart cities and factories. Edge artificial intelligence (AI) turns data collected from edge devices into actionable, real-time intelligence without leveraging the centralized cloud data center.
• Reliability of the network is improved by spreading compute functions geographically. Essential services can still be provided at the edge even when core servers experience interruptions. This reliability boost also contributes to a higher level of disaster recovery readiness. 

Challenges for Edge Computing Providers

As a new form of cloud architecture, edge computing resides at the leading edge of technology. The need for standardization is balanced by market demands for new services and innovation. As deployments multiply, the technical challenges become more evident.  

• Scalability and flexibility are hallmarks of the edge data center that must be continually tested and verified. Latency and security must also be monitored closely. This visibility is critical to avoid accidents or safety hazards in sensitive use cases such as smart factories and advanced driver-assistance systems (ADAS). 
• Edge data center propagation drives a need to continually optimize for space, energy, and IT resource consumption. This optimization is leading to more “lights-out” (unmanned) edge computing locations, which in turn require the adoption of advanced remote monitoring and self-healing capabilities. 
• Predictive and preventive analytics are necessary for applications to be managed reliably at the edge. Intelligence and machine learning capabilities proactively detect issues and automate responses without intervention from the hyperscale cloud data center. This raises the bar higher for edge analytics and computing power.