Abstract-Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both the execution time and execution costs. In solving the problem of optimizing the execution costs while meeting deadline constraints, we developed an efficient approach based on ant colony system (ACS). For scheduling T tasks on R resources, an ant in ACS represents a solution with T dimensions, with each dimension being a task and the value of each dimension being an integer ranges in [1, R] to indicate scheduling the task on which resource. With such solution encoding, the ant in ACS constructs a solution in T steps, with each step optimally selecting one resource from the R resources, according to both the pheromone and heuristic information. Therefore, the solution encoding is very simple and straight to reflect the mapping relation of tasks and resources. Moreover, the solution construct process is very natural to find optimal solution based on the encoding scheme. We have conducted extensive experiments based on workflows with various scales and various cloud resources. We compare the results with those of particle swarm optimization (PSO) and dynamic objective genetic algorithm (DOGA) approaches. The experimental results show that ACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions.

No abstract

Resources scheduling is a significant research topic in cloud computing, which is often modeled as a cost-minimization and deadline-constrained workflow scheduling model. This is a constrained single objective problem that to minimize the overall workflow execution cost while meeting deadline constraints. In this paper, we offer a new horizon to convert this single-objective problem to a multi-objective problem and present coevolutionary multiswarm particle swarm optimization (CMPSO) to find the non-dominated solutions with different execute cost and time. Meanwhile, the renumber strategy is adopted in CMPSO to make the learning efficient. CMPSO is compared with a renumber PSO (RNPSO) by setting the execute time in the CMPSO's nondominated solutions as the deadline constraint of RNPSO. Results show that CMPSO not only offers many non-dominated solutions with different prices and execute time, but also obtains better solution than RNPSO under a same deadline.