Reputation-guided Evolutionary Scheduling Algorithm for Independent Tasks in inter-Clouds Environments

Self-Adaptation provides software with flexibility in terms of the different behaviours (configurations) it incorporates and the autonomous or semi-autonomous ability to switch between these behaviours to maintain and maximize its quality in response to changes. For Clouds it becomes important to accommodate uncertainty about clients and the evolving nature of their business and IT worlds: their profiles and skills, competitive technology and business, the devices and network accesses they use, etc. To empower Clouds with ability to capture and respond to the quality feedback, provided by users at runtime, we propose a reputation guided genetic scheduling algorithm for independent tasks. Current resource management services consider evolutionary strategies in order to improve the performance on resource allocation procedures or tasks scheduling algorithms - but they fail to consider the user as part of the scheduling process. Evolutionary computing offers different methods to solve NP-hard problems, finding a near-optimal solution. In this paper we extended our previous work with new optimization heuristics for the problem of scheduling. We show how reputation is considered as an optimization metric analyze how our considered metrics can be considered as upper bounds for others in the optimization algorithm. By experimental comparison, we show that our optimization techniques can be hybridized for optimized results.