MOTORS: Multi-Objective Task Offloading and Resource Scheduling Algorithm for Heterogeneous Fog-Cloud Computing Scenario

Abstract

Along with the rising popularity of pay-as-you-go cloud services, many businesses and communities are deploying their business or scientific workflow applications on cloud-based computing platforms. The primary responsibility of cloud service providers is to reduce the monetary cost and execution time of Infrastructure as a Service (IaaS) cloud services. The majority of current solutions for cost and makespan reduction were developed for conventional cloud platforms and are incompatible with heterogeneous computing systems (HCS) having service-based resource management approaches and pricing models. Fog-cloud infrastructures (FCI) have emerged as desirable target areas for workflow automation across several fields of application. In heterogeneous FCI, the execution of workflows involving tasks having different properties, might influence the performance in terms of resource usage. The primary goal of this research is to efficiently offload the computational task and optimally schedule the workflow in such diverse computing environment. In this article, we present a novel strategy for building an environment that includes techniques for offloading and scheduling while balancing competing demands from the user and the resource providers. In order to address the issue of uncertainty, our approach incorporates a fuzzy dominance-based task clustering and offloading technique. To construct a suitable execution sequence of tasks that helps to limit the precedence relationship, by preserving dependency constraints amongst the tasks, a novel algorithm for tasks segmentation is employed. To simplify the problem of the complexity, a hybrid-heuristics based on Harmony Search Algorithm (HSA) and Genetic Algorithm (GA) for resource scheduling algorithm is used. The multi-objective optimization using three competing objectives are taken into consideration for investigation in heterogeneous FCI. The fitness function derived includes minimization of makespan and cost along with maximization of resource utilization. We performed experimental research using five workflow datasets in order to investigate and verify the efficacy of our proposed technique. We contrasted our proposed strategy with the primary, closely comparable strategies. Extensive testing using scientific workflows confirms the effectiveness of our offloading approach. Our solution provided a substantially better cost-makespan tradeoffs, while achieving significantly less energy consumption and can execute marginally quicker than the existing algorithms.

Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

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“…(11). (11) This section presents the mathematical formulation of the proposed meta-heuristic TES, which combines TD [30] with ES [31]. The Tasmanian devil feeds on live by attacking them or by scavenging carrion from dead animals.…”

“…Firstly, the task scheduling phase involves aligning user-submitted tasks concerning available VM resources. Secondly, a vital VM-tohost mapping process, which facilitates VM creation or migration [11,12,13]. Our main emphasis is on optimizing the former, as it directly influences the processing capabilities of a cloud computing system.…”