Performance-aware energy-efficient parallel job scheduling in HPC grid using nature-inspired hybrid meta-heuristics

Chhabra, Amit; Singh, Gurvinder; Kahlon, Karanjeet Singh

doi:10.1007/s12652-020-02255-w

Cited by 17 publications

(15 citation statements)

References 54 publications

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“…High-Performance Computing (HPC) systems offer massive computation strength to execute large-scale applications, hence consuming a lot of energy and increasing carbon emissions. To overcome these drawbacks and improve searching efficiency, a performance-aware, energy-efficient parallel job schedule in HPC grids using nature-inspired hybrid meta-heuristics was proposed [ 1 ].…”

Section: Related Workmentioning

confidence: 99%

“…The computational grid is a distributed technology consisting of multiple distributed heterogeneous resources in different network sites [ 1 , 2 , 3 ]. The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…The IoT grid benefits from deploying grid service architecture for IoT technology [ 10 , 12 ]. The grid sites are clusters, super computers, or multiprocessor computing networks [ 1 ]. Moreover, global resources and tasks send application jobs through the global grid job scheduler.…”

Section: Introductionmentioning

confidence: 99%

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Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The methods described in [28,40] was used to pick the parameters. To make an appropriate parameter selection, this methodology employs four metrics specified by the Equations ( 4)- (7). Values were generated using the instances 100_85_0.10_0.75, 100_100_0.15_0.85, and 85_100_0.10_0.75.…”

Section: Parameter Settingmentioning

confidence: 99%

“…The hybrid approaches were compared to the traditional approaches in this study, with the hybrid approaches outperforming the traditional approaches. In [7], the cuckoo search and firefly algorithm search methods are combined in order to avoid getting the procedure stuck in local optimum. The hybrid algorithm was applied to a job schedulers problem in high-performance computing systems.…”

Section: Introductionmentioning

confidence: 99%

A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem

et al. 2021

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Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

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GA-based QOS-aware workflow scheduling of deadline tasks in grid computing

Girgis,

Mahmoud,

Azzam

2024

Knowl Inf Syst

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Grid computing is the aggregation of the power of heterogeneous, geographically distributed computing resources to provide high-performance computing. To benefit from the grid computing capabilities, effectual scheduling algorithms are primarily essential. This paper presents a GA-based approach, called Grid Workflow Tasks Scheduling Algorithm (GWTSA), for scheduling workflow tasks on grid services based on users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time be within the time constraints (deadline) imposed by the user. In GWTSA, the workflow tasks are modeled as a DAG, which is divided, then the optimal sub-schedules of all task divisions are computed and used to obtain the execution schedule of the entire workflow. A GA-based technique is employed in GWTSA to compute the optimal execution sub-schedule for each branch division that consists of a set of sequential tasks. In this technique, the chromosome represents a branch division, where each gene holds the id of the service provider chosen to execute the corresponding task in the branch; and the fitness function is formulated as a multi-objective function of time and cost, this gives users the ability to determine their requirements if speed against cost or vice versa, by changing the weighting coefficients in the fitness function. The paper also exhibits the experimental results of assessing the performance of GWTSA with workflow samples of different sizes.

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Performance-aware energy-efficient parallel job scheduling in HPC grid using nature-inspired hybrid meta-heuristics

Cited by 17 publications

References 54 publications

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem

GA-based QOS-aware workflow scheduling of deadline tasks in grid computing

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