The need of efficient provision resources in cloud computing is imperative in meeting the performance requirements. The design of any resource allocation algorithm is dependent on the type of workload. BoT (Bag-of-Tasks) which is made up of batches of independent tasks are predominant in large scale distributed systems such as the cloud and efficiently scheduling BoTs in heterogeneous resources is a known NP-Complete problem. In this work, the intelligent agent uses reinforcement learning to learn the best scheduling heuristic to use in a state. The primary objective of BISA (BoT Intelligent Scheduling Agent) is to minimize makespan. BISA is deployed as an agent in a cloud testbed and synthetic workload and different configurations of a private cloud are used to test the effectiveness of BISA. The normalized makespan is compared against 15 batch mode and immediate mode scheduling heuristics. At its best, BISA produces a 72% lower average normalized makespan than the traditional heuristics and in most cases comparable to the best traditional scheduling heuristic.
An intelligent system to efficiently provision resources in a hybrid cloud environment is necessary due to the high level of complexity. The semi-permeable agent for hybrid cloud scheduling (SPAH) is a bio-inspired agent that adapts the biological process of osmosis into cloud bursting. The primary objective of the agent is to minimize the makespan. The framework and algorithm for the two phases of SPAH, to recognize the state and decide on action are presented. A QoS (Quality of Service) deadline factor metric is proposed to study the indirect impact of SPAH in deadline satisfaction. SPAH shows significant improvement in deadline satisfaction of up to 85% as compared to other cloud bursting techniques. This is the result of a reduced makespan and a reduced cumulative waiting time. The analysis of SPAH shows that it works in quadratic time complexity.
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