A Research of Resource Scheduling Strategy for Cloud Computing Based on Pareto Optimality M×N Production Model

As a new computing pattern for commercial application, Cloud computing makes the aggregation, selection, and sharing of geographically distributed heterogeneous resources possible to solve various of tasks including finance, engineering, science, and no matter how big the scale of those tasks are. However, it is problematic that the geographic distributed resources owned by different institutions with their different price models, usage policies and changing load. The resource providers and resource consumers have different objects, strategies, and requirement. Meanwhile, the availability of resources and the load on them dynamically varies with time. Hence, resource management in Clouds is a complicated task. An economic-based method is presented to allocate Cloud resources, which is based on Pareto optimality theory and realizes the optimal allocation of Cloud resources. And this method can largely avoid a waste of resources and achieve equilibrium between maximizing resource providers´ incomes and minimizing consumers´ paying. This paper describes a Cloud bank model that depends on market mechanism to understand deeply Pareto optimality.