Cloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.
Cloud computing a big pool of resources dynamically reconfigures its resources as per user requirement in real time. Cloud environment mostly works with virtualized environment, which is a key consideration for providing virtual machines as a service to the users. It is difficult to manage virtual machine, and to deploy on any data centers. In cloud environment users expects better services from the vendors, in order to improve resource utilization. The factors in cloud environment as if scalability and availability are consider for better outcomes for users. This not only saves time, but also improves cost utilization. This paper proposes architecture, based on clustering virtual machines in datacenters for higher availability of resources with improved scalability. Clustering helps virtual machines to reconfigure and easy scheduling. The resource sharing in cloud will optimize and users get maximized result. Further, the paper directs a mathematical model for explaining the concepts of the proposed system. The existing system is being modulated using simulation tools and further elaborated in the paper.
Cloud has grown significantly and has become a popular serviceoriented paradigm offering users a variety of services. The end-user submits requests to the cloud in the form of tasks with the expectation that they will be executed at the best possible lowest time, cost and without any errors. On the other hand, the cloud executes these tasks on the Virtual Machines (VM) by using resource scheduling algorithms. The cloud performance is directly dependent on how the resources are managed and allocated for executing the tasks. The main aim of this research paper is to compare the behaviour of cloud resource scheduling algorithms: First Come First Serve (FCFS) and Shortest Job First (SJF) by processing high-sized tasks. This research paper is broadly divided into four phases: the first phase includes an experiment conducted by processing approximately 80 thousand tasks from the Alibaba task event dataset using the resource scheduling algorithms: FCFS and SJF on the cloud VMs under different circumstances; the second phase includes the experimental results; the third phase includes a empirical analysis of the behaviour of resource scheduling algorithms; the last phase includes the proposed need of Reinforcement Learning (RL) to improve cloud resource scheduling and its overall performance.
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