An Improved Q-Learning-Based Scheduling Strategy with Load Balancing for Infrastructure-Based Cloud Services

Cloud computing provides computing resources on demand of users without their direct management. In this cloud paradigm, scheduling the tasks and allocating the resources become major aspect for cloud infrastructure as a service (IaaS). There are more existing algorithms and techniques suggested for task allocation problem. Still there is challenging research on efficient scheduling. To address this issue, many researches are in progress and all of them having their own drawbacks. In this paper, we are proposing a queue-based scheduling strategy with load balancing called as IQSLB and an extended IQSLB also proposed for dealing with critical situations. The proposed strategy calculates the placement value of the tasks in queue with the current status of the virtual machine (VM) in cluster and reshuffles the task accordingly. The extended IQSLB deals with handling deadlock situation where VM cannot adopt task for execution and task will be reshuffled with another task in another queue. The proposed strategy is compared with few existing systems, and the performance evaluation proves that IQSLB schedules tasks more efficiently than other systems. Our proposed IQSLB takes 75 s to allocate 1000 tasks by using 55 virtual machines which is much lesser than existing techniques.