Distributed Serverless Computing in Cloud-Edge Environments

Considered as the next evolution in Cloud computing, the 'Serverless' computing paradigm facilitates on-demand computation without an end-user provisioning a server. The 'Serverless' paradigm is realised using a Functions-as-a-Service (FaaS) model in which Cloud providers monetise usage by charging per function invocation or function duration. FaaS differs from conventional Cloud models as end-users only pay for the resources they consume as opposed to paying for the resources they allocate (even when these resources are idle). Although 'Serverless' is known for its Cloud impact, there are numerous benefits of FaaS that could benefit the Edge. Edge Internet of Things (IoT) applications are often distributed and resource-constrained, as such, 'Serverless' function initialisation and execution of code provide a model of computation that minimises resource and energy consumption.

This project has three aims and objectives that map to one year of study each with the intent to publish findings. The three objectives will form the basis for the final thesis in the third year. The first objective is to develop a 'Serverless' Edge platform that is reactive to Edge IoT application demand. In particular, it will investigate methods of providing real-time responsiveness to the varied workloads that Edge IoT deployments present. Main areas of research include trend analysis of Edge workloads, intelligent provisioning and scheduling of Edge node resources, and networking/routing capabilities of a 'Serverless' Edge platform. The second is investigating reducing latency for Edge Machine Learning inference on heterogeneous Edge nodes. This objective involves mapping 'Serverless' principles to an Edge context where inference latency should be consistent for all Edge nodes in the network. The third is developing sufficient and performative security for 'Serverless' execution on the Edge where hardware is shared and resources are constrained.

The project research methodology involves continued study and review of related literature in Edge and 'Serverless' computing. Profiling and benchmarking of 'Serverless' methods including gathering quantitative data such as latency, memory and energy consumption, and volume throughput under various workloads. Analysis and identification of current challenges in existing research and use cases. Creation of computation testbeds for synthetic data generation and development of 'Serverless' Edge systems. Development of innovative systems and services for real-world domain applications.

This project directly aligns with numerous EPSRC digital economy strategies such as the 'Sustainable Digital Society' research theme. 'Serverless' principles synergise with IoT device requirements by only consuming limited device resources when computation is necessary (e.g. when a sensor takes periodic readings), therefore, battery operated and energy-conscious IoT deployments benefit from lower electricity costs and extended device longevity. Another EPSRC research theme this project aligns with is enhancing individual privacy as part of the 'Beyond a Data-Driven Economy'. Edge computing brings computational ability closer to the data source, as such, sensitive information is not transported to Cloud servers. Less data is stored in centralised data centres that are more likely to be leaked from security vulnerabilities or exploited for commercial gain.

This project will be carried out in collaboration with an industry partner, namely Rakuten Mobile, Japan. They will provide advice on technical aspects and on applying the proposed research to real-world use-cases. An internship with one of their labs in Tokyo is expected either remotely or in person, which is subject to when international travel is more seamless.