Robust Optimization Model for Primary and Backup Resource Allocation in Cloud Providers

Abstract : This paper proposes a backup resource allocation model that provides a probabilistic protection for primary physical machines in a cloud provider to minimize the required total capacity. When any random failure occurs, workloads are transferred to preplanned and dedicated backup physical machines for prompt recovery. In the proposed model, a probabilistic protection guarantee is introduced to prevent the cloud provider from capacity overbooking. We apply robust optimization in our model to formulate the backup resource allocation problem as an integer linear programming problem. A simulated annealing heuristic is adopted to solve the same optimization problem when the cloud provider is large. Finally, the results reveal that the required backup capacity depends on the reliability of primary physical machines. Specifically, the more the resources in primary physical machines share backup capacity when the failure probabilities of primary physical machines are sufficiently small, the less capacity is required for backup resource allocation.

 EXISTING SYSTEM :

 ? Cloud computing provides configurable computing resources such as networks, servers, storage, applications, and services, to users through the Internet. ? Robust optimization deals with the problems in which some data are uncertain and the data belong to some uncertainty set. ? We set three survivability parameters for CPU, memory, and the entire cloud provider, whereas the previous works in used only one survivability parameter. ? Pareto optimality is the predominant concept, which defines an optimal solution for a multi-objective optimization problem. ? The global criterion method transforms the multi-objective optimization problem into a single-objective optimization problem with a global criterion.

 DISADVANTAGE :

 ? We consider large-scale, network-based, resource allocation problems under uncertainty, with specific focus on the class of problems referred to as multi-commodity flow problems with time-windows. ? These problems are at the core of many network-based resource allocation problems. ? The master problem has the structure of a multi-commodity flow problem and the sub-problem is a set of network flow problems. ? Uncertainty is captured in part by the master problem and in part by the sub-problem. ? As proof-of-concept, we apply our approach to a vehicle routing and scheduling problem and compare its solutions to those of other robust optimization approaches.

 PROPOSED SYSTEM :

 • The proposed algorithm is robust against uncertainties such as performance variation and scheduling time and offers multi-objective resource allocation policies to add a task based on deadline and budget constraints. • However, in practice these assumptions are likely to be not accurate that may turn a proposed optimal solution to a highly infeasible or sub-optimal one. • The ability of estimating the risk taken by a given migration plan will provide the datacenter operators with a deeper insight into the performance of the proposed plan. • This risk measure provides a deeper insight on the power consumption for the proposed VM consolidation plan.

 ADVANTAGE :

 ? We compare its performance with conventional models that are built using the time-space network approach . ? We need not identify all cliques in the graph to improve the algorithmic efficiency; adding even a few clique constraints can improve algorithmic performance. ? While providing enhanced modeling capabilities, the performance of the solutions are best evaluated not by the objective function values but rather by other means such as simulation. ? In this work, he developed a UAV Mission Planner that couples the scheduling of tasks with the assignment of these tasks to UAVs, while maintaining the characteristics of longevity and efficiency in the plans. ? The potential impact of providing robust, efficient resource allocations over networks can be enormous.