A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Wen, Yun; Xu, Hua; Yang, Jinn‐Shyong

doi:10.1016/j.ins.2010.10.001

Cited by 103 publications

(65 citation statements)

References 45 publications

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“…Task scheduling problems are related to the efficiency of all computing facilities and are of paramount importance [16]. Cloud computing task scheduling is a NP-complete problem; it can be solved in different methods: traditional deterministic algorithms and heuristic intelligent algorithms [17][18][19][20][21][22][23][24][25]. However, those methods don not take energy consumption into account, and, to overcome this limitation, researchers have proposed some approaches.…”

Section: Task Scheduling Optimizationmentioning

confidence: 99%

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

Pang

Zhang

et al. 2017

Mathematical Problems in Engineering

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With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy-efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task-oriented resource allocation method (LET-ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min-Min, and RR scheduling strategy, the proposed LET-ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.

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Section: Task Scheduling Optimizationmentioning

confidence: 99%

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

Pang

Zhang

et al. 2017

Mathematical Problems in Engineering

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“…Experiments were carried on a set of benchmark problems and it was concluded that the proposed HEAprovide better results. Wen et al [22] incorporated GA with both Variable Neighborhood Search (VNS) and a heuristic extracted from traditional list scheduling algorithms for the minimization of makespan in the heterogeneous multiprocessor scheduling problem resulting into a heuristic based hybrid genetic variable neighborhood search algorithm. The performance of proposed approach was compared with four related algorithms, HEFT, AIS, VNS and IGA on standard benchmarks problems and it was concluded that proposed algorithm constantly outperforms the other four algorithms in terms of schedule quality.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

Dhingra¹,

Dhingra²,

Gupta³

et al. 2016

ACII

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Present work considers the minimization of the bi-criteria function including weighted sum of makespan and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most Keywords:Multiprocessor task scheduling, heuristics, Hybrid Genetic algorithm, makespan, Total Completion time. 1.INTRODUCTIONParallel processing is the most promising approach for meeting the increased computational requirements thathas introduced a number of problems including multiprocessor task scheduling. Usually a large program is divided into smaller tasks havingsome dependencies representing the precedence constraints such that a task cannot be started until all its predecessors have finished. The goal of a task scheduling algorithm is to schedule all the tasks on the given number of available processors without violating precedence constraints so as to minimize the different performance measures in order to maximize the throughput and utilization of the system. The multiprocessor task scheduling problem with precedence constraints belongs to the class of NP hard problems and its importance led to several comparative studies. The conventional research on scheduling considers heuristic based algorithms which are divided into three categories: list scheduling, clustering based and duplication based. Among heuristic algorithms, List scheduling algorithms are commonly used and work by assigning priority to each task according to attributes such as t-level (top level), b-level (bottom level), static level (sl) and ALAP (As Late As Possible)

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“…A taxonomy of scheduling algorithms in parallel and distributed systems was presented by [8]. Algorithms have been developed to solve this problem [1,9].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

Nourjou¹,

Smith²,

Hatayama³

et al. 2014

IJMLC

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Abstract-Problem: This paper addresses a centralized scheduling problem in multi-agent systems in which the incident commander (IC) of a disaster-response team aims to coordinate the actions of the field units (rational agents) to minimize the total operation time in uncertain, dynamic, and spatial environments.Objective: The purpose of this paper is to propose an intelligent software system that assists the IC in the dynamic assignment of geospatial-temporal macro tasks to agents under human strategic decisions. This system autonomously executes a heuristic algorithm to minimize the maximum total dependent duration according to human high-level strategies.Results: The result is a schedule composed of macro decisions, each comprised of seven types of information: 1) what task type is going to be accomplished, 2) who (a subset of agents) are assigned to this assignment, 3) where this task is to be performed (a road segment or zone as a macro geospatial object) containing a subset of tasks, 4) when operations start, 5) when operations finish, 6) how many tasks are estimated to be completed, 7) what task types and the estimated number to be revealed (identified" or "enabled) in this location to complete this job.Conclusion: This result, which is a feasible solution for the addressed problem, permits the IC to coordinate agents, partially specify the activities of the agents in time and space, minimize the overall execution time for all the tasks, calculate the correct time to revise the strategic decisions, evaluate the efficiency of the high-level strategy, and estimate the makespan.

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A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Cited by 103 publications

References 45 publications

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

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