When the workload of a service increases quickly, obtainable approaches cannot respond to the growing performance necessity. To efficiently because of either inaccuracy of adaptation decisions or the slow process of adjustments, both of which may result insufficient resource provisioning. The main concept of this paper is ability to add or remove the cloud resource provisioning. To improve the Quality of Service in the resource management, Resource management policies and objective separately in each jobs. Large scale problems are handled In online scheduling the decisions regarding how to schedule tasks are done during the runtime of the system. The scheduling decisions are based on the tasks priorities which are either assigned dynamically or statically. Static priority driven algorithms assign fixed priorities to the tasks before the start of the system. Dynamic priority driven algorithms assign the priorities to tasks during runtime. An online algorithm is forced to make decisions that may later turn out not to be optimal, and the study of online algorithms has focused on the quality of decision-making that is possible in this setting. Online resource placement develops systems to predict the dynamic resource demand of resources and guide the placement process considers minimizing the long-term routing cost between resources.

Data mining, frequent sequence mining, parallel algorithms, static load-balancing, probabilistic algorithms

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