Edge computing and Function-as-a-Service are two emerging paradigms that enable a timed analysis of data directly in the proximity of cyber-physical systems and users. Function-as-a-service platforms deployed at the edge require mechanisms for resource management and allocation to schedule function execution and to scale the available resources in order to ensure the proper quality of service to applications. Large-scale deployments will also require mechanisms to control the energy consumption of the overall system, to ensure long-term sustainability. In this paper, we propose a technique to schedule function invocations on Edge resources by powering down idle edge nodes during period of low demands. In doing so, our technique aims at reducing the overall energy consumption without incurring in service level agreements violations. Experimental evaluations demonstrate that the proposed approach reduces service level agreement violations by at least 78.1% and energy consumption by at least 62.5% on average using synthetic and real-world datasets w.r.t. different baselines.

Energy-Efficient Resource Management for Real-Time Applications in FaaS Edge Computing Platforms

Shahrokh Vahabi;Francesca Righetti;Carlo Vallati;Nicola Tonellotto
2023-01-01

Abstract

Edge computing and Function-as-a-Service are two emerging paradigms that enable a timed analysis of data directly in the proximity of cyber-physical systems and users. Function-as-a-service platforms deployed at the edge require mechanisms for resource management and allocation to schedule function execution and to scale the available resources in order to ensure the proper quality of service to applications. Large-scale deployments will also require mechanisms to control the energy consumption of the overall system, to ensure long-term sustainability. In this paper, we propose a technique to schedule function invocations on Edge resources by powering down idle edge nodes during period of low demands. In doing so, our technique aims at reducing the overall energy consumption without incurring in service level agreements violations. Experimental evaluations demonstrate that the proposed approach reduces service level agreement violations by at least 78.1% and energy consumption by at least 62.5% on average using synthetic and real-world datasets w.r.t. different baselines.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1242268
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