GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center

Natesha, B V; Sharma, Neeraj Kumar; Domanal, Shridhar G.; Guddeti, Ram Mohana Reddy

doi:10.1109/skg.2018.00034

Cited by 18 publications

(12 citation statements)

References 11 publications

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“…We compared our proposed DE-GWO algorithm, with the PSO [46], ACO [47], GWO [48], and HPSOGWO [49] algorithms in this section in terms of performance. Under five well-known workflows-Montage, CyberShake, Epigenomics, LIGO (Inspiral), and Sipht, the performance is assessed in terms of Average Makespan (AM) and Average Energy Consumption (AEC) and Average Cost (AC), with task counts rising from 25 to 1000.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…FogWorkflowSim [40] is an extension of iFogsim, used for testing resource management techniques in FC through simulation of user-defined workflows of tasks. Particle swarm optimization (PSO) [46] , Ant colony optimization (ACO) [47], Grey wolf optimization (GWO) [48] and HPSOGWO [49] techniques are compared with the proposed algorithm. All algorithms have a total of 100 iterations.…”

Section: B Simulation Settingsmentioning

confidence: 99%

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DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

Shukla

Pandey

2022

Preprint

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The demand for a quick response from cloud services is rapidly increasing day-by-day. Fog computing is a trending solution to fulfil the demands. When integrated with the cloud, this technology can tremendously improve the performance. Like any other technology, Fog also has the shortcoming of limited resources. Efficient scheduling of tasks among limited resources is one of the significant issues for research. This paper proposes a multi-objective hybrid task scheduling algorithm named Differential evolution-Grey wolf optimization (DE-GWO), which combines Differential evolution (DE) and Grey wolf optimization (GWO) approach to address the workflow scheduling issue. The proposed algorithm is applied on five different scientific workflows (Montage, Epigenomics, SIPHT, LIGO and Cybershake) and evaluated on three performance indicators (execution time, energy consumption and cost). The DE method is chosen as the evolutionary pattern of wolves to speed up convergence and enhance GWO's accuracy. Simulation results show that the DE-GWO performs better than the other traditional and recently proposed optimization algorithms, since DE incorporates evolution and elimination mechanisms in GWO and GWO retains a good balance between exploration and exploitation.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: B Simulation Settingsmentioning

confidence: 99%

DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

Shukla

Pandey

2022

Preprint

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“…In this section, simulation results are provided to evaluate the performance of the HWGWOA. Specifically, we compare the HWGWOA with the genetic algorithm (GA) [17], GWO [37], and WOA [38]. As a classical algorithm to solve TSP and MTSP, the GA has good stability.…”

Section: Simulation and Results Analysismentioning

confidence: 99%

Multi-UAV Cooperative Assisted RSU Data Acquisition Strategy considering Coverage Quality

Guo

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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The multiple Unmanned Aerial Vehicle (multi-UAV) assisted roadside unit (RSU) data acquisition problem considering the coverage quality is a multiobjective optimization problem, which is a NP-hard problem. Heuristic and hyperheuristic algorithms are effective to solve problems of this type. These algorithms can find the optimal or suboptimal solution in a reasonable time. However, such algorithms still have the problems of low convergence accuracy, slow convergence speed, and being easy to fall into the local optimal solution. In this paper, firstly, according to the specific problem scenarios of roadside unit data collection, minimum cost and maximum coverage models based on task cost and coverage quality are established. Then, to solve the optimization model, combined with the update characteristics of the gray wolf optimization algorithm (GWO) and the whale optimization algorithm (WOA), a hybrid weighted gray wolf and whale optimization algorithm (HWGWOA) is proposed. Finally, to verify the effectiveness of the proposed algorithm, extensive simulation experiments are conducted under four different task acquisition scenarios, and the results are compared with those of genetic algorithm (GA), GWO, and WOA. Simulation results show that the algorithm proposed in this paper not only can get lower task cost and higher coverage quality but also has faster convergence speed and better robustness. Specifically, in terms of task cost, the HWGWOA is about 9.54% lower than the GA, about 7.31% lower than the GWO, and about 5.8% lower than the WOA. In terms of coverage, the HWGWOA is up to about 27.87% higher than the GA, about 15.19% higher than the GWO, and about 9.86% higher than the WOA. Therefore, the algorithm is more suitable for large-scale optimization problems.

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“…The proposed algorithm adopted the backward learning method to increase the initial population's exploration, avoid falling into the local optimum solution, and increase diversity. Also, employ the nonlinear adjustment strategy to manage the parameters and improve the global exploration of the algorithm [38] reporting the ideal utilization of cloud resources by the proposed MGWO technique for task scheduling. The study aims to reduce the energy consumption of cloud data centers for the scheduler makespan for users' requests.…”

Section: B Gwo Techniques In Task Schedulingmentioning

confidence: 99%

Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing

et al. 2023

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The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users' energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloudfog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption.INDEX TERMS Cloud-fog computing, delay, energy consumption, grey wolf optimizer, Internet of Things, meta-heuristic, task scheduling.

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GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center

Cited by 18 publications

References 11 publications

DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

Multi-UAV Cooperative Assisted RSU Data Acquisition Strategy considering Coverage Quality

Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing

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