Resource Scheduling Catering to Real-Time IoT Services in a Serverless-Enabled Edge Network is Particularly Challenging Owing to the Workload Variability, Strict Constraints on Tolerable Latency, and Unpredictability in the Energy Sources Powering the Edge Devices. This Paper Proposes a Framework LEASE that Dynamically Schedules Resources in Serverless Functions Catering to Different Microservices and Adhering to their Deadline Constraint. to Assist the Scheduler in Making Effective Scheduling Decisions, We Introduce a Priority-Based Approach that Offloads Functions from over-Provisioned Edge Nodes to Under-Provisioned Peer Nodes, Considering the Expended Energy in the Process Without Compromising the Completion Time of Microservices. for Real-World Implementations, We Consider a Testbed Comprising a Raspberry Pi Cluster Serving as Edge Nodes, Equipped with Container Orchestrator Tools Such as Kubernetes and Powered by OpenFaaS, an Open-Source Serverless Platform. Experimental Results Demonstrate that Compared to the Benchmarking Algorithm, LEASE Achieves a 23.34% Reduction in the overall Completion Time, with 97.64% of Microservices Meeting their Deadline. LEASE Also Attains a 30.10% Reduction in Failure Rates.


Computer Science

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Article - Conference proceedings

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Publication Date

01 Jan 2024