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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.
Publication Tags: 5G, network slice
Roadside LiDAR (Light Detection and Ranging) sensors are recently being explored for intelligent transportation systems aiming at safer and faster traffic management and vehicular operations. A key challenge in such systems is to efficiently transfer massive point-cloud data from the roadside LiDAR devices
5G services and applications explicitly reserve compute and network resources in today’s complex and dynamic infrastructure of multi-tiered computing and cellular networking to ensure application-specific service quality metrics, and the infrastructure providers charge the 5G services for the resources
With the growth of 5G, Internet of Things (IoT), edge computing and cloud computing technologies, the infrastructure (compute and network) available to emerging applications (AR/VR, autonomous driving, industry 4.0, etc.) has become quite complex. There are multiple tiers of computing (IoT devices, near
Edge computing and 5G have made it possible to perform analytics closer to the source of data and achieve super-low latency response times, which isn’t possible with centralized cloud deployment. In this paper, we present a novel fever screening system, which uses edge machine learning techniques and
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
Applications that use edge computing and 5G to improve response times consume both compute and network resources. However, 5G networks manage only network resources without considering the application’s compute requirements, and container orchestration frameworks manage only compute resources without
We design and build SkyHaul, the first large-scale, self-organizing network of Unmanned Aerial Vehicles (UAVs) that are connected using a mm Wave wireless mesh backhaul. While the use of a mmWave backhaul paves the way for a new class of bandwidth-intensive, latency-sensitive cooperative applications
We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key
The advances in unmanned aerial vehicle (UAV) technology have empowered mobile operators to deploy LTE base stations (BSs) on UAVs, and provide on-demand, adaptive connectivity to hotspot venues as well as emergency scenarios. However, today’s evolved packet core (EPC) that orchestrates the LTE RAN faces
