Efficient application scheduling in mobile cloud computing based on MAX–MIN ant system

Edge cloud is a cloud computing system built on edge infrastructure. Task scheduling optimization is the key technology to ensure the quality of service in edge cloud. However, the openness of the edge cloud environment challenges the load balancing and profit optimization of task scheduling. In this paper, we analyze the business process and optimization factors of task scheduling in edge cloud. First, we propose a resource constrained task scheduling profit optimization algorithm (RCTSPO), which consists of clustering preprocessing, classification, profit matrix construction and optimal scheduling strategy calculation. Clustering preprocessing gathers similar tasks into one class and perform a classification on the clustered tasks. Then construct the profit matrix for resource constrained task scheduling, and the optimal task scheduling strategy is obtained based on the constructed profit matrix. Second, Petri nets are used to construct the different components of edge cloud, such as resource, task, user request and virtual machine, thus forming the task scheduling model of edge cloud. Third, the properties of task scheduling model are verified by using the related theory and tools of Petri nets. Finally, several experiments are done to evaluate the proposed method, the simulation results show that the algorithm not only achieves the maximum profit, but also performs well in terms of time, reliability and load balancing of task scheduling.

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“…In Ref. [20], ant colony algorithm is proposed to achieve load balancing and profit in cloud task scheduling. Aziza.…”

Section: Related Workmentioning

confidence: 99%

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

et al. 2020

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“…In this section, we study the comparative performance of our proposed QCASH (with static SI) and QCASH (with dynamic SI) with some of the existing schedulers in the literature. Like in , the benchmark algorithm adopted for this comparison is the FCFS algorithm. Furthermore, we compare the performances of QCASH (with static and dynamic SIs) with those of PTMM , PAS , and CAAJS algorithms.…”

Section: Performance Evaluationmentioning

confidence: 99%

Maximizing quality of experience through context‐aware mobile application scheduling in cloudlet infrastructure

et al. 2016

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Summary Application software execution requests, from mobile devices to cloud service providers, are often heterogeneous in terms of device, network, and application runtime contexts. These heterogeneous contexts include the remaining battery level of a mobile device, network signal strength it receives and quality‐of‐service (QoS) requirement of an application software submitted from that device. Scheduling such application software execution requests (from many mobile devices) on competent virtual machines to enhance user quality of experience (QoE) is a multi‐constrained optimization problem. However, existing solutions in the literature either address utility maximization problem for service providers or optimize the application QoS levels, bypassing device‐level and network‐level contextual information. In this paper, a multi‐objective nonlinear programming solution to the context‐aware application software scheduling problem has been developed, namely, QoE and context‐aware scheduling (QCASH) method, which minimizes the application execution times (i.e., maximizes the QoE) and maximizes the application execution success rate. To the best of our knowledge, QCASH is the first work in this domain that inscribes the optimal scheduling problem for mobile application software execution requests with three‐dimensional context parameters. In QCASH, the context priority of each application is measured by applying min–max normalization and multiple linear regression models on three context parameters—battery level, network signal strength, and application QoS. Experimental results, found from simulation runs on CloudSim toolkit, demonstrate that the QCASH outperforms the state‐of‐the‐art works well across the success rate, waiting time, and QoE. Copyright © 2016 John Wiley & Sons, Ltd.

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MFHS: A modular scheduling framework for heterogeneous system

2020

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SummaryCurrent innovative distributed architectures, proposing on‐line services, involve more and more computing resources. From a provider point of view, the platform management leads to challenging problematic relating to resource allocation, which involve different kind of quality of service parameters, the provider has to focus on to keep his platform reliable and efficient. MFHS is a modular generic framework, which can be adapted to any distributed computing environment. Structured in modules, MFHS allows to discover the existing computing resources in terms of computing performance, network throughput and disk I/O speeds (Resources Discovery module) and to predict how the experiment should behave (Pi value). As the setting up of real experiments is often complex, MFHS allows: to make theoretical experimentation (based on models), to use any kind of distributed emulators, or to deploy experiments on real‐experimental platforms. In this article, these three environments are used to highlight the reliability of MFHS (measured Pi=90% against 94% for the predicted Pi). Deployment and scheduling studies have also been achieved using an experimental Cloud based on OpenStack while Emulab test‐bed has been used as emulator. During experiments, four QoS parameters are taken into account (Resources Monitoring module): energy consumption, cost, resource utilization, and makespan. These studies also includes a new heuristic called MMin, based on Max‐Min and Min‐Min algorithms. Experimentation section, proposes a detailed comparative analysis of these algorithms in terms of QoS results, while the abilities of the proposed heuristic MMin regarding the makespan metric is shown.

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Efficient application scheduling in mobile cloud computing based on MAX–MIN ant system

Cited by 16 publications

References 27 publications

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

Maximizing quality of experience through context‐aware mobile application scheduling in cloudlet infrastructure

MFHS: A modular scheduling framework for heterogeneous system

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