Energy-Efficient Independent Task Scheduling in Cloud Computing

Zhu, Xiaodong; Hussain, Mehboob; Li, Xiaoping

doi:10.1007/978-3-030-15127-0_43

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Sobhanayak et al [20] presented a combined method using GA and the Bacterial Foraging (BF) algorithms in the computing cloud. This algorithm has two main objectives.…”

Section: Related Workmentioning

confidence: 99%

“…These links are demonstrated as a Directed Acyclic Graph (DAG), and it is comprised of vertices that represent computations and directed edges that represent the dependencies between those vertices. The DAG graph is a directional graph and does not have a path whose beginning and end are the same and one of its important applications is in routing algorithms [20].…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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“…Sobhanayak et al [20] presented a combined method using GA and the Bacterial Foraging (BF) algorithms in the computing cloud. This algorithm has two main objectives.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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show abstract

“…A natural resource allocation technique assigns the work to available VMs across hosts. When working with the load balancing issue, we can use heuristic techniques, which rely on genetic algorithms to explore the exponential solution space [41]. It uses an objective function (genetic) to choose a single solution from the population [42].…”

Section: Genetic Algorithm For Task Schedulingmentioning

confidence: 99%

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

Ilankumaran,

Narayanan

2023

Cybernetics and Information Technologies

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In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance.

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“…The meta-heuristic algorithm belongs to the dynamic task scheduling method, which combines the random algorithm and the local search algorithm, and can dynamically search for the optimal solution in the problem solution space. For example, the particle swarm optimization algorithm can use a group of particles to quickly search for the optimal solution in the solution space of the problem, and in the search process, evaluate the quality of the solution through the fitness function, and finally converge to a stable value [11].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

Liu

Zeng

Bilal

et al. 2023

IEEE Trans. on Green Commun. Netw.

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With the development of cloud computing and data intelligence, datacenters have become an important part of ensuring service quality and production efficiency in intelligent applications. However, datacenters are also facing increasingly complex and heavy task processing requirements currently, and more efficient scheduling methods are urgently needed. Therefore, this paper proposes a multi-swarm particle swarm optimization task scheduling method based on load balancing, aiming at reducing the maximum completion time (makespan) and response time in task scheduling. The proposed method designs a new fitness function for particles, and promotes the load balance of the cluster during the scheduling process by optimizing the combination of makespan and machine completion time variance. And a novel inertia weight is designed to dynamically adjust the particle search performance. The new initialization method and multi-swarm search design are used to improve the quality and diversity of solutions and avoid particles falling into local optimum. Finally, the proposed algorithm is verified experimentally using the task dataset released by Alibaba datacenter, and compared with other benchmark algorithms. The results show that the algorithm can improve the task scheduling performance of datacenters in supply chain management when dealing with different workloads and changes in the number of machines.

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Energy-Efficient Independent Task Scheduling in Cloud Computing

Cited by 5 publications

References 17 publications

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

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