Collaborative Research: CNS Core: Small: Robust Resource Planning and Orchestration to Satisfy End-to-End SLA Requirements in Mobile Edge Networks

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Mobile edge computing has emerged to address the long end-to-end latency, low throughput, and unpredictability of cloud computing as an Internet-based service when supporting modern mobile applications. Nevertheless, the lack of performance guarantees in the form of service-level agreements (SLAs) can lead to performance degradation of critical applications, rendering them incompetent or unsafe to use. Due to the high dynamics in the mobile environment, the edge provider can incur substantial financial risks for providing SLA guarantees that could be violated. This discourages edge providers from providing SLA guarantees without first understanding and being able to control the associated risks. This project seeks to develop tools that help the edge provider to quantify and minimize the risks associated with providing edge SLA guarantees on key performance metrics through resource planning and orchestration. This project will significantly advance our knowledge on the risk factors in edge computing and give rise to new frontiers in edge computing research. Also, this project will enable and enhance life-changing edge applications such as mobile vision and autonomous driving, promote investment and expedite development in the edge computing industry, train highly qualified personnel for the future computing workforce, and broaden awareness and interest in edge computing through curriculum development and research dissemination.This project will lay the theoretical and algorithmic foundation of comprehensive risk modeling and optimization for providing edge SLA guarantees. This project combines a realistic performance model of mobile edge applications with the established theory of risk management in portfolio management and develops efficient algorithms for risk assessment and optimization through stochastic optimization, convex optimization, sampling techniques, approximations, and learning-based methods. Specifically, this project makes the following technical contributions: 1) development and validation of a realistic performance and SLA model for mobile edge computing applications, 2) modeling and optimization of three risk measures (risk probability, value-at-risk, and conditional value-at-risk) for single-user risk-aware edge resource orchestration, 3) modeling and optimization for multi-user single-node risk-aware edge resource orchestration, and 4) modeling and optimization for multi-user multi-node risk-aware edge resource provisioning. All research outcomes will be evaluated with testbed and/or large-scale simulations with public traces, and will be made publicly available on the PIs' websites to promote result reproduction and future research advancements along the line.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.