Fast multi-type resource allocation in local-edge-cloud computing for energy-efficient service provision

Abstract

Listen

Translate article

With the advancement of information technology, the concept of local-edge-cloud computing has gained prominence. Operating on a collaborative model, heterogeneous computing nodes converge to deliver a spectrum of multi-type services, including calculation-intensive, latency-sensitive, and privacy-requiring services. This collaborative approach fosters high-quality development in power economy. However, the proliferation of heterogeneous computing nodes, while beneficial, introduces challenges. The intricate connections and limited energy supply may lead to interruptions in the service processes of nodes. In this study, we present an energy-efficient resource allocation scheme designed for low-latency multi-type service provision within a local-edge-cloud collaboration. Our methodology focuses on optimizing the performance of multi-type service provision in a local-edge-cloud network, taking into account considerations such as latency, resource allocation, and energy consumption. To accomplish this, we employ the Alopex-based Differential Evolution algorithm. Initially, we construct three sub-models to analyze latency and energy aspects across various computing modes. Subsequently, we formulate a constrained optimization problem aimed at minimizing both latency and energy consumption in multi-type service provisioning. These models seek to derive optimal resource allocation decisions for the given scenario. To address this optimization problem, we introduce the hybrid differential evolution algorithm, Alopex-DE. A formal analysis is conducted to showcase its near-optimal performance in comparison to three state-of-the-art algorithms. Additionally, extensive simulations are carried out to validate the superior effectiveness of our proposed approach.