Energy Efficient Resource Scheduling Based On Polymorphic Ant Colony

Cloud computing is a hot topic in recent years. It arises as a new IT service model, which can provide people credible, cheap computing resource. In cloud environment, different type of resource are virtualized to conduct unified management and presented as various kinds of resource pool. In this model, the cloud Datacenter can deploy tasks submitted automatically, cloud users can use the computing resource without purchasing extra physical devices and withdraw from maintaining. To build such a platform, there must be an excellent algorithm to solve the problem that how to schedule the resource for tasks. Actually, resource scheduling strategy is to build the mapping relationships between resource and tasks. There are two level in the scheduling model: one is to schedule virtual machines for cloud tasks and another is to find suitable hosts to load virtual machines. A well performed strategy mainly decide the performance of the platform and it should reach some goals, such as user QoS, minimum execution time, load balance, and economical efficiency.Ant Colony Optimization(ACO) is an algorithm that imitates the behavior of ants when they are searching foods. It is a swarm intelligence system that ants work together to find the best solutions for the same goal. When an ant is walking, it will detect the pheromone on the path. Pheromone is a kind of volatile secretion which is released by the ants pass by. Pheromone can provide some guides for ants and ants will select an appropriate searching direction according to the roulette rule based on the pheromone concentration. So pheromone acts as a medium between ants in fact. As time goes by, the pheromone concentration on the shorter path will increase gradually, and more and more ants will tend to choose this path. Conversely, larger numbers of ants means that it will release more and more pheromone here. At last, most of them will reach the best situation. So the pheromone of ants is a positive feedback mechanism. ACO is a good solution for combinatorial optimization problem and the work to build the mapping relationship between resources and tasks is also a combinatorial optimization problem indeed. So in this paper, it will use the ACO algorithm to explore the problem of resource scheduling.In this paper, it explores the architecture and business model of cloud computing to make the resource scheduling problem clear first. Then, it introduces Subspace Searching & Polymorphic Ant Colony Mechanism into the basic ACO to speed up algorithm convergence and adapt the modified ACO algorithm to solving the second level of scheduling problem which is to place virtual machines on the physical host. The goal is to reach load balance and energy conservation. Finally, the algorithm is simulated on the CloudSim platform and the result shows that the energy in datacenter is under control and the load balance is at a good level.