Energy efficient scheduling on heterogeneous federated clusters using a fuzzy multi-objective meta-heuristic

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

doi:10.1109/fuzz-ieee.2017.8015589

Cited by 9 publications

(14 citation statements)

References 19 publications

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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%

“…The PSO approach was found to be least computationally expensive approach than other metaheuristic technique. Authors in [16] proposed MOPSO-FGA approach for scheduling batch of parallel jobs of collaborative tasks in heterogeneous multi-cluster systems. During PSO iteration, a fuzzy genetic crossover operator was applied over PSO particles with the objective to minimize makespan and energy consumption simultaneously.…”

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%

“…We follow the execution time model of [38] which is also adapted by many related research works [16] [18][19], to model the execution time slowdown due to heterogeneity of computational resources and communication contention present on shared communication link in multi-cluster HCS platform. For a parallel job j, Cumulative slowdown in the presence of processing slowdown ( ) and communication slowdown ( ), is calculated using (1).…”

Section: B Job Execution Time Modelmentioning

confidence: 99%

“…represents the relevance of processing time with respect to communication time. For more details on calculating SP and SC, readers can refer to [16][19] [38].…”

Section: B Job Execution Time Modelmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: A Scheduling On Parallel Jobsmentioning

confidence: 99%

Section: B Scheduling On Independent Sequential Jobsmentioning

confidence: 99%

Section: B Job Execution Time Modelmentioning

confidence: 99%

“…represents the relevance of processing time with respect to communication time. For more details on calculating SP and SC, readers can refer to [16][19] [38].…”

Section: B Job Execution Time Modelmentioning

confidence: 99%

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Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Chhabra*,

Singh,

Kahlon

2019

IJRTE

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Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization

Nayak,

Singh,

Kushwaha

et al. 2023

Concurrency and Computation

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SummaryA difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%).

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Design of interactive enterprise financial system based on Multi-user scheduling and cellular network

2023

Soft Comput

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Enterprise nancial system is an activity to establish common and reusable rules for actual or potential problems in order to achieve the best order of enterprise nance. The expansion of each theory is based on the collection of practical experience and the development of science and technology, which also brings demand and space for the research of interactive enterprise nancial system. This paper uses heterogeneous cellular network to provide technical support for the construction of interactive nancial system, proposes an algorithm to use small base stations in traditional macro cellular network, and plans the status of base stations according to various tra c patterns. Under the premise of ensuring the quality of service, the number of small base stations can be effectively reduced, and the status of base stations can dynamically adapt to the changes of spatial tra c. With the support of heterogeneous cellular network, interactive enterprise nancial system can be built on the basis of more complete and comprehensive information. The nal simulation results show that the method can effectively reduce the cost of providing heterogeneous cellular network while maintaining the quality of service, and improve the user and energy e ciency of interactive enterprise nancial system. Heterogeneous cellular network provides a new insight for the design of interactive enterprise nancial system, which helps enterprises improve the e ciency of processing nancial transactions and maintain the nancial security of enterprises.

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Energy efficient scheduling on heterogeneous federated clusters using a fuzzy multi-objective meta-heuristic

Cited by 9 publications

References 19 publications

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization

Design of interactive enterprise financial system based on Multi-user scheduling and cellular network

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