Resource Allocation for Real-Time Tasks Using Cloud Computing

This paper presents a method to allocate resources for real-time tasks using the "Infrastructure as a Service" model offered by cloud computing. Real-time tasks have to be completed before deadlines, and cloud computing offers selection of resources with different speeds and costs. In cloud computing, resource allocations can be scaled up based on the requirements; this is called elasticity and is the key difference from existing multiprocessor task allocation. Scalable resources make economical allocation of resources an important problem. We analyze the problem of allocating resources for a set of realtime tasks such that the economic cost is minimized and all the deadlines are met. We formulate the problem as a constrained optimization problem and propose a polynomial-time solution to allocate resources efficiently. We compare the economic costs and performance provided by our solution with the optimal solution and an EDF (earliest deadline first) method. We show how the cost varies based on the distribution of the tasks.