Particle Swarm Optimization Scheduling for Energy Saving in Cluster Computing Heterogeneous Environments

Gabaldon, Eloi; Guirado, Fernando; Lérida, Josep L.; Planes, Jordi

doi:10.1109/w-ficloud.2016.71

Cited by 9 publications

(10 citation statements)

References 25 publications

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“…Gabaldon, Guirado [99] have proposed a PSO-AE (Energy-Aware) technique that uses computational resources of a real workload traces to determine the task allocation to available resources with minimum energy consumption. In PSO-AE, each particle consists of two parts.…”

Section: Marotta and Avallonementioning

confidence: 99%

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

et al. 2019

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Cloud computing connotes the systematic delivery of computing resources as services to a wide range of users via the Internet. One form of Cloud computing, Infrastructure as a Service (IaaS), ensures the availability of the resources in the form of Virtual Machines (VMs). Such services are leased to users based on demand and are paid for on a pay-per-use basis. This helps to reduce the cost of running the computing needs of the users. Usually, the VMs are ran on datacenters comprise several computing resources that consume lots of energy, and causing hazardous levels of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods of reducing the energy consumption of the datacenters. Nature has been a cause of inspiration and had a solution to all problem. Therefore, this paper presents a comprehensive review of the state-of-the-art, Nature-Inspired algorithms that have been used in solving the energy issues in the Cloud datacenters. We have categorized all the methods considered into three main techniques; virtualization, consolidation, and energy-awareness. Moreover, we reviewed the different methods in terms their goals, methods, advantages, and limitations. We then compared the nature-inspired algorithms based on their features to indicate their utilization of resources and their levels of energy-efficiency. Finally, we have suggested the potential research directions in this research field. We believe that this review work will be of interest to researchers and professional in Cloud computing datacenters in their quest to providing better energy-efficient methods to address the energy consumption issues of the Cloud datacenters..

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Section: Marotta and Avallonementioning

confidence: 99%

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

et al. 2019

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“…Such heuristics produced good results for small problem and system sizes. However, to effectively deal with the NP-hard JSP problem in complex HCS platforms, several meta-heuristic based offline solutions are developed over the years [14][15][16][17] [30]. The major focus of the research was on scheduling independent jobs i.e.…”

Section: Related Workmentioning

confidence: 99%

“…For offline scheduling of parallel jobs with co-operating tasks on federated clusters systems, Authors in [15] has proposed a PSO based scheduling approach for minimization of energy consumption. The proposed PSO approach was used to deal with job ordering and mapping of resources to jobs.…”

Section: A Scheduling On Parallel Jobsmentioning

confidence: 99%

“…Most of the research works listed in this section focused on scheduling either independent jobs or parallel jobs consisting of independent tasks in HCS platforms without considering bandwidth penalties. We found very few research works [15][16], [18][19][20] which provide offline solution for scheduling collaborative parallel jobs by considering computation heterogeneity and inter-cluster communications. Such parallel job's tasks have computation and communication phases and tasks can collaborative with each other during communication phase.…”

Section: B Scheduling On Independent Sequential Jobsmentioning

confidence: 99%

“…avail_clusters_list1← avail_cluster_list1∪ cluster 7.mark the job as "non-coallocated" and update non-coallocatedJobs 8. else 9.avail_clusters_lsit2 ← avail_clusters_list1∪avail_clusters_list2∪ cluster 10. mark the job as "co-allocated" and update co-allocatedJobs 11. end if 12 end for 13. avail_clusters_list1 = ascendingSort(avail_clusters_list1, MIPS) 14. avail_clusters_list2 = ascendingSort(avail_clusters_list2, MIPS) 15. if (job∈non-coallocatedJobs) 16.…”

Section: Solution Representationmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Chhabra*,

Singh,

Kahlon

2019

IJRTE

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Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.

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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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Particle Swarm Optimization Scheduling for Energy Saving in Cluster Computing Heterogeneous Environments

Cited by 9 publications

References 25 publications

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

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

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