Optimizing load scheduling and data distribution in heterogeneous cloud environments using fuzzy-logic based two-level framework

Bei Cheng, Dongmei Li and Xiaojun Zhu

PLOS ONE, 2024, vol. 19, issue 12, 1-24

Abstract: Cloud environment handles heterogeneous services, data, and users collaborating on different technologies and resource scheduling strategies. Despite its heterogeneity, the optimality in load scheduling and data distribution is paused due to unattended requests for a prolonged time. This article addresses the aforementioned issue using a Two-level Scheduling and Distribution Framework (TSDF) using Fuzzy Logic (FL). This framework houses different fuzzification processes for load balancing and data distribution across different resource providers. First, the fuzzification between regular and paused requests is performed that prevents prolonged delays. In this process, a temporary resource allocation for such requests is performed at the end of fuzzification resulting in maximum waiting time. This is the first level optimality determining feature from which the second level’s scheduling occurs. In this level, the maximum low and high delay exhibiting distributions are combined for joint resource allocations. The scheduling is completely time-based for which the cumulative response delay is the optimal factor. Therefore, the minimum time-varying requests observed in the second level are fuzzified for further resource allocations. Such allocations follow the distribution completed intervals improving its distribution (13.07%) and reducing the wait time (7.8%).

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0310726

DOI: 10.1371/journal.pone.0310726

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