The goal of the SaaS provider is the most profitable; the user’s goal is to meet requirements as quickly as possible but still within budget and deadline. In this paper, a heuristic ACO (Ant Colony Optimization) is used to propose an algorithm to admission control, then building a scheduling algorithm based on the overlapping time between requests. | Journal of Computer Science and Cybernetics, , (2015), 231–243 DOI: SCHEDULING ALGORITHM FOR USER REQUIREMENTS ON CLOUD COMPUTING BASE ON DEADLINE AND BUDGET CONSTRAINTS NGUYEN HOANG HA† AND NGUYEN THANH BINH‡ Hue University of Sciences, Vietnam, † nhha76@, ‡ Abstract. The goal of the SaaS provider is the most profitable; the user’s goal is to meet requirements as quickly as possible but still within budget and deadline. In this paper, a heuristic ACO (Ant Colony Optimization) is used to propose an algorithm to admission control, then building a scheduling algorithm based on the overlapping time between requests. The goal of both algorithms is to minimize the total execution time of the system, satisfying QoS constraints for all requirements and provide the greatest returned profit for SaaS providers. These two algorithms are set up and run a complete test on CloudSim, the experimental results are compared with sequential and EDF (Earliest Deadline First) algorithms. Keywords. Admission control, scheduling algorithms, constraint QoS, resource allocation 1. INTRODUCTION Cloud computing is a distributed computing model for large scale; it provides services to users by employing resources (hardware, software, storage resources, etc.) via internet. Users may employ the various resources through their requirements and pay as they use. When users send requests together with the constraints as to arrival time, deadline, budget, workload, etc. to SaaS vendors, SaaS providers use PaaS to admission control, then conduct scheduling requirements as Figure 1. PaaS provider searches for suitable resources on IaaS to logical mapping to user requirements. Generally, the admission control and scheduling request with parameters such as arrival time, deadline, budget, workload, and penalty rate, etc. is an NP-complete problem [1]. Therefore, to give an optimal solution one must often do exhaustive search while .
