Research Of Cloud Task Scheduling Based On Artificial Colony Algorithm

Nowadays, enterprises are facing increasing data and the user, Concurrent access to flow, data storage and management pressure in various industries are more serious than the past. These "big user, big data and big system" problem become necessary in cloud computing development. With the development of information technology, cloud computing becomes important solution to the big data challenge in information society, and cloud computing applications requires more high quality of virtual service resources. Cloud task scheduling problem is one of the key issues affecting the quality of the virtual resource service, has become a hot research topic. The swarm intelligence algorithm is a kind of important analysis method in the optimal problem, has achieved good application effect in the scheduling problem in recent years.To meet high quality requirement of virtual resource service in cloud computing applications and solve the problem that cloud computing task scheduling only consider single objective currently, the multi-objective mathematical model of cloud task scheduling was established in theory that is more closed to the real by considering the users' shortest waiting time, resource load balancing and economic principle. Second, by combining with preference satisfaction policy, introducing the local search operator and changing the searching way of scout bee, an optimizing strategy based on the Multi-objective Discrete Artificial Bee Colony (MDABC) algorithm was proposed to solve the problem. Different cloud task scheduling simulation experimental results show that the proposed MDABC algorithm can obtain higher comprehensive satisfaction than the basic DABC algorithm, Genetic Algorithm (GA) and classical greedy algorithm. Thus, the proposed MDABC algorithm can better improve the performance of cloud task scheduling in virtual resource system, and its universality is better. Finally, the analysis result indicates the stability of the algorithm.