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A decade ago, large-scale services like enterprise resource planning (ERP) were a corner-case scenario, often designed as one-off systems. Today, applications like social networks, automated trading and video streaming have made large-scale services the norm rather than the exception. In the future, advancement in 5G networks and an explosion in the number of smart devices, microservices, databases, computing-tiers and end-points in a service will make services so complex that they cannot be tuned or managed by humans. The sheer scale, dynamic nature and concurrency in these services will require them to be autonomic. They will need to continuously self-assess, learn and automatically adjust for resource needs, data quality and service reliability.
Our focus is on systematic models for designing, implementing and managing large-scale services that will evolve to support autonomic behavior. Large services will be partitioned to a greater degree with a greater number of loosely coupled microservices. AI techniques will monitor, analyze and automatically optimize the large ensemble of microservices based on service-specific knowledge, experience and dynamic environment to eliminate barriers like high service-response latencies, poor service quality, reliability and scalability.
Applications can tailor a network slice by specifying a variety of QoS attributes related to application-specific performance, function or operation. However, some QoS attributes like guaranteed bandwidth required by the application do vary over time. For example, network bandwidth needs of video streams
