Abstract
Although workflow scheduling problem has been discussed by many researchers, a few efficient solutions have been introduced for Cloud computing. In this article, we present LPSO, a novel algorithm for workflow scheduling. Based on the Particle Swarm Optimization method, our proposed algorithm not only ensures the fast convergence but also avoid being trapped on local extrema. Our simulation experiments using CloudSim testing real scenarios reveal that LPSO is superior to formerly proposed algorithms. Moreover, the deviation between the solution found by LPSO and the optimal solution is negligible.
Highlights
Cloud computing emerged with the promise of securing on-demand and convenient access to shared computing resources such as storage, servers and networks
We conducted some experiments in order to compare the performance of the Local-search Particle Swarm Optimization (PSO) (LPSO) algorithm with others, namely the PSO_H (Pandey et al, 2010) and Random (Mitzenmacher and Upfal, 2005)
If xik[t] + vik[t] = 3.8 Tt gets assigned to server S4. This introduces some sort of randomness in the assignment of servers in the PSO_Halgorithm (Pandey et al, 2010) and it cannot maintain the diversification of swarm
Summary
Cloud computing emerged with the promise of securing on-demand and convenient access to shared computing resources such as storage, servers and networks. Scheduling is one of the challenges that are encountered when processing workflow tasks over geographically distributed servers. An effective solution for that problem requires a reasonably efficient scheduling algorithm in order to minimize the completion time (called makespan) of tasks.
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