Task allocation for maximizing reliability of distributed computing systems using honeybee mating optimization

Kang, Qi; He, Hong; Song, Huimin; Deng, Rong

doi:10.1016/j.jss.2010.06.024

Cited by 60 publications

(31 citation statements)

References 20 publications

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“…kang at el. 2010 [9] have discussed in maximizing reliability of distributed computing systems with genetic algorithm based task allocation and the task have represented in task graph. This comparison of different heuristic through simulations proves the effectiveness of genetic algorithms on HDCS.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithmic Approach for Energy Efficient Task Consolidation in Cloud Computing

Kumar¹,

Sahoo²,

Mondal³

et al. 2015

IJCA

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In cloud, processing loads arrive from many users at random time instants in the form of task. A proper resource allocation policy attempts to assign this task to available VMs on different host so to complete the execution of the tasks in the shortest possible time with minimum power consumption. The complexity of the resource allocation problem with cloud increases with the number of hosts and becomes difficult to solve effectively. The resource allocation problem is a combinatorial problem and known to be NP-complete. The exponential solution space of the load balancing problem can be searched using heuristic techniques based on Genetic algorithms to obtain a sub -optimal solution in acceptable time. The novel genetic algorithm framework has been proposed for task scheduling to minimize the energy consumption in cloud computing infrastructure. The performance of the proposed GA resource allocation strategy has been compared Random and Round Robin scheduling using in house simulator. The experimental results show that the GA based scheduling model outperforms the existing Random and Round Robin scheduling models.

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Section: Related Workmentioning

confidence: 99%

A Genetic Algorithmic Approach for Energy Efficient Task Consolidation in Cloud Computing

Kumar¹,

Sahoo²,

Mondal³

et al. 2015

IJCA

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“…In software fault tolerance tasks, to deal with faults messages are added into the system. Distributed computing is different from traditionally distributed system [7]. Fault Tolerance is important method in distributed computing because nodes are distributed geographically in this system under different geographically domains throughout the web.…”

Section: Fault Tolerance In Distributed Systemsmentioning

confidence: 99%

“…Fault Tolerance is important method in distributed computing because nodes are distributed geographically in this system under different geographically domains throughout the web. The most difficult task in distributed computing is design of fault tolerant task assignment model that meet all the reliability requirements [7]. The most commonly used techniques for fault-tolerance are replication and check pointing [15].…”

Section: Fault Tolerance In Distributed Systemsmentioning

confidence: 99%

Weight based Task Assignment Model to Tolerate Faults in Heterogeneous Distributed Systems

Kaur¹,

Kaur²

2015

IJCA

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Distributed frameworks play a critical part on accomplishing superior performance and better system utilization. The objective of a task allocation framework is to productively deal with the disseminated computing power of workstations, servers, and supercomputers keeping in mind the end goal to expand work throughput and system utilization. There are many issues of distributed computing system which are discussed in this paper in brief. This paper focuses on task assignment which in turn emphasizes on fault tolerance and recovery from fault with less processing time. The proposed algorithm assigns tasks to other nodes only when candidate node moves from its original position. The major area of concern in this architecture is task scheduling, if one slave node fails the task allocated by master node will not be completed and this situation is considered as fault. In this paper, we have exchanged views about a method which serves to lessen faults of the system and increase performance of the system.

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“…Step-1: first of all, we have to calculate the communication link sum (CLS) of each task using Thus, we get CLS ( i t ) = 29, 18,18,19,18,15,17,31,27 corresponding to i= 1, 2, 3,…, 9.…”

Section: Example-1mentioning

confidence: 99%

“…Much research efforts on the task allocation problem have been identified in the past with the main concern on the performance measures such as minimizing the total sum of execution and communication costs [1][2][3][4]6,7,11 ] or minimizing the program turnaround time [8,10,22], the maximization of the system reliability [12][13][14][15][16][17][18][19] and safety [16].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Task Allocation Model for System Cost Analysis in Heterogeneous Distributed Computing Systems: A Heuristic Approach

Yadav¹,

Singh²,

Sharma³

2011

IJCA

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In Distributed computing systems (DCSs), task allocation strategy is an essential phase to minimize the system cost (i.e. the sum of execution and communication costs). To utilize the capabilities of distributed computing system (DCS) for an effective parallelism, the tasks of a parallel program must be properly allocated to the available processors in the system. Inherently, task allocation problem is NP-hard in complexity. To overcome this problem, it is necessary to introduce heuristics for generating near optimal solution to the given problem. This paper deals with the problem of task allocation in DCS such that the system cost is minimized. This can be done by minimizing the inter-processor communication cost (IPCC). Therefore, in this paper we have proposed an algorithm that tries to allocate the tasks to the processors, one by one on the basis of communication link sum (CLS). This type of allocation policy will reduce the inter-processor communication (IPC) and thus minimize the system cost. For an allocation purposes, execution cost of the tasks on each processor and communication cost between the tasks has been taken in the form of matrices.

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Task allocation for maximizing reliability of distributed computing systems using honeybee mating optimization

Cited by 60 publications

References 20 publications

A Genetic Algorithmic Approach for Energy Efficient Task Consolidation in Cloud Computing

A Genetic Algorithmic Approach for Energy Efficient Task Consolidation in Cloud Computing

Weight based Task Assignment Model to Tolerate Faults in Heterogeneous Distributed Systems

An Optimal Task Allocation Model for System Cost Analysis in Heterogeneous Distributed Computing Systems: A Heuristic Approach

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