Efficient task allocation approach using genetic algorithm for cloud environment

Rekha, P; Dakshayini, M.

doi:10.1007/s10586-019-02909-1

Cited by 49 publications

(21 citation statements)

References 16 publications

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“…Genetic Algorithm based Efficient Task Allocation (ETA-GA) [18], a pure GA-based scheduler, was proposed to improve makespan and reduce failure in network delay. The parameters of GA are reflected to be tuned for improvement in objective function.…”

Section: Cloud Scheduling Meta-heuristicsmentioning

confidence: 99%

“…MGGS [13] is selected because it has a load balancing mechanism embedded in GA. BGA also has the same method of fusing the load balancing mechanism, but the working of GA and the mechanism of load balancing are unique. ETA-GA [18] is compared as it is a pure GA-based technique. DSOS [24] is a meta-heuristic, and RALBA [3] is a state-of-the-art heuristic technique.…”

Section: Benchmark Techniquesmentioning

confidence: 99%

“…Performance Metrics Demonstrated MGGS [13] Load balancing and makespan ETA-GA [18] Makespan and network failure RALBA [3] Load balancing, throughput and makespan DSOS [24] Load balancing, makespan, response time and convergence…”

Section: Techniquesmentioning

confidence: 99%

“…Some of the most popular choices of meta-heuristics for task scheduling are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Algorithm (ACO) and Symbiotic Organism Search (SOS). GA-based schedulers have been applied [13,[17][18][19] for task scheduling due to its strong search capabilities.…”

Section: Introductionmentioning

confidence: 99%

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Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing

et al. 2021

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Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.

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Section: Cloud Scheduling Meta-heuristicsmentioning

confidence: 99%

Section: Benchmark Techniquesmentioning

confidence: 99%

Section: Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing

et al. 2021

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“…Genetic algorithm (GA) has also been applied to solve the task scheduling problem. For example, Rekha and Dakshayini [39] introduced the GA to find a suitable solution for the task allocation which seeks to reduce task completion time.…”

Section: Related Workmentioning

confidence: 99%

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Attiya

Elaziz

Xiong

2020

Computational Intelligence and Neuroscience

103

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In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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Genetic System for Project Support with the Sequencing Problem

Viloria

Varela

Herazo-Beltran

et al. 2020

Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications

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Efficient task allocation approach using genetic algorithm for cloud environment

Cited by 49 publications

References 16 publications

Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing

Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Genetic System for Project Support with the Sequencing Problem

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