3 research outputs found
Budget-aware performance optimization of workows in multiple data center clouds
No full textInternational audienceUsers pay to use resources in cloud systems which makes them more demanding on performance and costs. Optimizing the response time of the applications and meeting user's budget needs are therefore critical requirements when scheduling applications. The approach presented in this work is a scheduling based-HEFT algorithm, which aims to optimize the makespan of tasks workow that is constrained by the budget. For this, we propose a new budget distribution strategy named Estimated task budget that we integrate in our budget-aware HEFT algorithm. We use a multiple datacenters cloud as a real platform model, where data transfer costs are considered. The results obtained by our algorithm relative to recent work, show an improvement of makespan in the case of a restricted budget, without exceeding the given budget
A Hybrid Algorithm for DAG Application Scheduling on Computational Grids
No full textInternational audienceIn the late three decades, grid computing has emerged as a new field providing a high computing performance to solve larger scale computational demands. Because Directed Acyclic Graph (DAG) application scheduling in a distributed environment is a NP-Complete problem, meta-heuristics are introduced to solve this issue. In this paper, we propose to hybridize two well-known heuristics. The first one is the Heterogeneous Earliest Finish Time (HEFT) heu-ristic which determines a static scheduling for a DAG in a heterogeneous environment. The second one is Particle Swarm Optimization (PSO) which is a sto-chastic meta-heuristic used to solve optimization problems. This hybridization aims to minimize the makespan (i.e., overall competition time) of all the tasks within the DAG. The experimental results that have been conducted under hy-bridization show that this approach improves the scheduling in terms of completion time compared to existing algorithms such as HEFT
