Modified genetic algorithm for multiobjective task scheduling on heterogeneous computing system

Sathappan, O. L.; Chitra, P.; Venkatesh, P.; Prabhu, M. Santhosh

doi:10.1504/ijitcc.2011.039282

Cited by 42 publications

(19 citation statements)

References 15 publications

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“…Moreno et al presents a new algorithm called Max-Min [7], and it prefers scheduling big tasks. In paper [8,9], authors use General Algorithm to solve the problem of task scheduling. In addition, there are some improved algorithms based on QoS such as [10,11].…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

Zhou¹,

Hu²

2015

TOCSJ

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Abstract:In view of Min-Min algorithm prefers scheduling small tasks and Max-Min algorithm prefers scheduling big tasks led to the problem of load imbalance in cloud computing, a new algorithm named Min-Max is proposed. Min-Max makes good use of time of greedy strategy, small tasks and big tasks are put together for scheduling in order to solve the problem of load imbalance. Experimental results show that the Min-Max improves the utilization rate of the entire system and saves 9% of the overall execution time compared with Min-Min. As compared to Max-Min, Min-Max improves the utilization rate of the entire system and the total completion time and average response time are saved by 7% and 9%, respectively.

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Section: Introductionmentioning

confidence: 99%

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

Zhou¹,

Hu²

2015

TOCSJ

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“…Zhao et al [21] proposed a novel gateway load balancing aware multicasting mesh network. They took the load balancing with respect to the multicast traffic flow on the gateways.…”

Section: Rela Ted Workmentioning

confidence: 99%

LBASMPL: Load balancing adaptive scheduling with minimum packet loss in Wireless Mesh Network

Goyal

Kumar

2014

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

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With the enhancement in the wireless technology, the accessing quantity of mobile Internet has increased exponentially. According to the usage quantity of the mobile Internet, the demand of Quality of Service (QoS) is also increased. These type of applications requires high data rate. Wireless Mesh Networks (WMNs) is one of the best example for these type of services. For QoS in the WMNs we proposed a LBASMPL algorithm. It minimize the loss of the packets and send them in the priority manners. The Variable Load Index (VLI), Total Capacity (TC), Estimation of Delay (ED) are also calculated. The traffic is schedule according to Time Division Multiple Access (TDMA). Lower and Upper bound of the link on TC is also calculated. The traffic is load on the link according to priority. For scheduling this traffic matrix is generated. This proposed algorithm is used to minimize the packet loss at the routers and gateway.

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“…In [7], a modified algorithm which combines bacteriological algorithm and genetic algorithm is proposed to maximize the system reliability and reduce makespan. A two-phase cellular genetic scheduling algorithm is proposed in [8] to reduce both energy consumption and makespan.…”

Section: Related Workmentioning

confidence: 99%

A DEA-GA multi-objective scheduling algorithm for Chip-Multiprocessor

Chai¹,

Li²,

Wu³

et al. 2013

Proceedings of the 2nd International Conference on Science and Social Research

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Abstract. In this paper, a Data Envelopment analysis based Genetic Algorithm (DEA-GA) is proposed for multi-objective scheduling on Chip-Multiprocessor. The proposal adopts modified GA as the searching heuristic to explore the solution space, and the fitness of each individual (schedule) is evaluated using the DEA approach. Three of the schedule metrics, namely makespan, energy and load balance are used to construct the multi-input multi-output Decision Making Units in the DEA, and the BCC super efficiency of each schedule is calculated. In the modified genetic algorithm, the metapopulation is divided into three subpopulations each optimizing a single metric. The top performance individuals in each subpopulation are then regrouped and applied DEA evaluation. Comparing to other multi-objective scheduling algorithm in simulations, our proposal always produces more efficient schedule solutions.

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Modified genetic algorithm for multiobjective task scheduling on heterogeneous computing system

Cited by 42 publications

References 15 publications

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

LBASMPL: Load balancing adaptive scheduling with minimum packet loss in Wireless Mesh Network

A DEA-GA multi-objective scheduling algorithm for Chip-Multiprocessor

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