A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems

Shirvani, Mirsaeid Hosseini

doi:10.1016/j.engappai.2020.103501

Cited by 71 publications

(41 citation statements)

References 23 publications

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“…[13][14][15][16][17] were presented in literature to solve single objective engineering problems with heuristic and exact approaches. Some meta-heuristics GA-based [18][19][20][21][22][23], PSO-based [3,24,25], SA-based [26][27][28] have been developed to solve optimization problems in engineering domain. In addition, multi-objective optimization algorithms such as NSGA-II [29], MOPSO [30], MOGA [18,31], MOBA [32], and MOGWO [33] among others have been extended in literature to solve multi-objective optimization problems which need to make a trade-off between conflicting objectives at the same time.…”

Section: Related Workmentioning

confidence: 99%

“…Eqs. (25,26) to uniformly permute (traverse) search space for generating new solutions. The second rule is an elitism based approach so that better solutions remain in next generation.…”

Section: Proposed Mocsa Algorithm For Component Deployment Problemmentioning

confidence: 99%

“…To produce next generation solutions, the elitism mechanism is applied so the better solutions are transferred to the next generation. The favorable trait of each meta-heuristic algorithm is how to make balance between exploration and exploitation in search space, but some of them fail to make a balance; for instance, PSO suffers from earlier convergence [24,25] or simulated annealing (SA) suffers from not to be strong in exploration phase [26][27][28]. Fortunately, our proposed MOCSA makes a good adjustment between exploitation and exploration.…”

Section: Superior Nests Possibly Updatesmentioning

confidence: 99%

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Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

Ramzanpoor

Shirvani

Golsorkhtabaramiri

2021

Complex Intell. Syst.

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Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.

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Section: Related Workmentioning

confidence: 99%

“…Eqs. (25,26) to uniformly permute (traverse) search space for generating new solutions. The second rule is an elitism based approach so that better solutions remain in next generation.…”

Section: Proposed Mocsa Algorithm For Component Deployment Problemmentioning

confidence: 99%

Section: Superior Nests Possibly Updatesmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

Ramzanpoor

Shirvani

Golsorkhtabaramiri

2021

Complex Intell. Syst.

Self Cite

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“…Pandey et al [12] used Particle Swarm Optimization (PSO) to schedule workflow applications in a cloud computing environment. PSO is a fast optimization algorithm but has a problem such as earlier convergence and trapping in local optimal solution [13]. Grey Wolf Optimization (GWO) is the recent proposed meta-heuristic algorithm that mimics grey wolves' leadership hierarchy [14].…”

Section: Introductionmentioning

confidence: 99%

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Arora¹,

Banyal²

2020

IJACSA

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Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user's tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user's tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data-and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

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“…Pegasus [7], and Galaxy [8]. Workflow systems have become an important facilitator for automating scientific experiments and business processes on distributed infrastructures [9].…”

Section: Introductionmentioning

confidence: 99%

Decentralized workflow management on software defined infrastructures

Wang

Zhao

2020

2020 IEEE World Congress on Services (SERVICES)

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Data-intensive workflow applications are characterized by their continuously growing volumes of data being processing, the complexity of tasks in the pipeline, and infrastructure capacity required for computation and storage. The infrastructure technologies of computing, storage and networking have made tremendous progress during the past yeas. We review the emerging trends in the data-intensive workflow applications, in particular the potential challenges and opportunities enabled by the decentralized application paradigm.

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A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems

Cited by 71 publications

References 23 publications

Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Decentralized workflow management on software defined infrastructures

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