scholarly journals Fault Tolerant PLBGSA: Precedence Level Based Genetic Scheduling Algorithm for P2P Grid

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Piyush Chauhan ◽  
Nitin

Due to monetary limitation, small organizations cannot afford high end supercomputers to solve highly complex tasks. P2P (peer to peer) grid computing is being used nowadays to break complex task into subtasks in order to solve them on different grid resources. Workflows are used to represent these complex tasks. Finishing such complex task in a P2P grid requires scheduling subtasks of workflow in an optimized manner. Several factors play their part in scheduling decisions. The genetic algorithm is very useful in scheduling DAG (directed acyclic graph) based task. Benefit of a genetic algorithm is that it takes into consideration multiple criteria while scheduling. In this paper, we have proposed a precedence level based genetic algorithm (PLBGSA), which yields schedules for workflows in a decentralized fashion. PLBGSA is compared with existing genetic algorithm based scheduling techniques. Fault tolerance is a desirable trait of a P2P grid scheduling algorithm due to the untrustworthy nature of grid resources. PLBGSA handles faults efficiently.

Author(s):  
Rekha Kashyap ◽  
Deo P. Vidyarthi

Grid supports heterogeneities of resources in terms of security and computational power. Applications with stringent security requirement introduce challenging concerns when executed on the grid resources. Though grid scheduler considers the computational heterogeneity while making scheduling decisions, little is done to address their security heterogeneity. This work proposes a security aware computational grid scheduling model, which schedules the tasks taking into account both kinds of heterogeneities. The approach is known as Security Prioritized MinMin (SPMinMin). Comparing it with one of the widely used grid scheduling algorithm MinMin (secured) shows that SPMinMin performs better and sometimes behaves similar to MinMin under all possible situations in terms of makespan and system utilization.


Author(s):  
Ge Weiqing ◽  
Cui Yanru

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.


Author(s):  
Chafik Arar ◽  
Mohamed Salah Khireddine

The paper proposes a new reliable fault-tolerant scheduling algorithm for real-time embedded systems. The proposed algorithm is based on static scheduling that allows to include the dependencies and the execution cost of tasks and data dependencies in its scheduling decisions. Our scheduling algorithm is dedicated to multi-bus heterogeneous architectures with multiple processors linked by several shared buses. This scheduling algorithm is considering only one bus fault caused by hardware faults and compensated by software redundancy solutions. The proposed algorithm is based on both active and passive backup copies to minimize the scheduling length of data on buses. In the experiments, the proposed methods are evaluated in terms of data scheduling length for a set of DSP benchmarks. The experimental results show the effectiveness of our technique.


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