CRII: CNS: Towards an Efficient Serverless Mobile Edge Computing Network

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Serverless computing has emerged as an event-driven cloud computing paradigm that enjoys many advantages over traditional cloud computing. This includes automated server management, pay-as-you-use pricing, and massive parallelism, all of which make it an attractive approach for mobile edge computing (MEC). However, both the resource-constrained nature of edge nodes and the latency-sensitive nature of bursty workloads in MEC result in fundamental challenges to fulfilling the promise of serverless computing in MEC networks from the perspectives of both service providers and application developers. To unleash the full potential of serverless mobile edge computing, this project is organized around three interrelated tasks: (i) the development of locality-aware load balancing schemes for serverless requests that guarantee an optimal response time, (ii) the development of cost-effective, latency-driven resource configuration schemes, and (iii) the development of principles and techniques that allow application developers to utilize the great potential of parallelism offered by serverless computing. This project will lead to important societal and educational impacts. MEC has enabled a range of Internet of Things (IoT) applications across many areas, including security and surveillance, healthcare, industrial manufacturing, agriculture, and others. An efficient serverless mobile edge computing network will directly impact these applications, and ultimately, society at large. Moreover, the developed algorithms in this project will be a direct add-on for existing serverless computing platforms, and our proposed parallelism-assisted online learning approach will be a benchmark for the community when dealing with parallel computing-based machine learning edge applications. From an education perspective, this project will develop a curriculum that will be broadly shared, as well as train undergraduate and graduate students, while broadening participation in computing and engineering.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.