HERP: Hierarchical and distributed clouds oriented efficient resource scheduling algorithm with stochastic requirements

• Published in: Expert systems with applications Vol. 258; p. 125177
• Main Authors: Wei, Wei, Li, Haoyi, Zhang, Qinghui
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2024
• Subjects: Distributed computing; Edge computing; Hierarchical and distributed clouds; Resource scheduling; Stochasticity; Resource scheduling; Hierarchical and distributed clouds; Stochasticity; Distributed computing; Edge computing
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125177

With the development of cloud technology, hierarchical and distributed cross-cloud architecture is gradually replacing traditional centralized architecture, for example, used in edge (or fog) computing. Due to the fluctuation of resource requirements, if a node does not have sufficient resources to process requests, the same or higher-level nodes can share their resources by offloading or redirecting requests to themselves, at the possible cost of reduced service quality. However, it is difficult to effectively optimize the sharing effect based on mean requirements. We formulate the multilevel problem with horizontal and vertical resource sharing using stochastic models, identify the optimal structures with embedded subproblems, and obtain the approximation solution in an efficient dynamic programming manner. In the problem setting with a wide range of different parameters, the proposed algorithm can outperform existing mean and heuristic algorithms in all scenarios to improve the total satisfied requirements by up to 26%, and can be hundreds of times faster than these heuristic algorithms. •The demand fluctuation in edge clouds is a new challenge for resource scheduling.•We formulate the general problem in hierarchical and distributed edge clouds.•The problem has non-linear object with non-linear constraints.•We identify the nested subproblems with their optimal condition.•The algorithm outperforms the existing mean and heuristic based algorithms.