Revenue Maximization in Cloud Federation Based on Multi-Choice Multidimensional Knapsack Problem

Abstract

Cloud federation is introduced to eliminate the resource limitation problem of individual Cloud Service Provider (CSP). In a federation, one CSP can outsource their overhead requests by hiring unused resources from other CSPs in the federation and CSPs cash greater revenue. In this paper, we propose a system that maximizes this revenue while putting some factor constraints in satisfactory standard. Factors include response time of instances that is analogous to Quality of Service (QoS), and profit that maintains a certain reputation. Response time is reduced by considering geographical location of the user and the data center of desired resources. In this system, CSPs choose suitable resources from resource pool of cloud federation that maximizes the profit while preserving Quality of Service (QoS) high and minimizing the rejection rate of user requests. We map factor constraints to MMKP (Multi-choice Multidimensional Knapsack Problem) algorithm and define objective function for revenue maximization. We calculate response time by Cloud Analyst to measure the QoS. Experimental results show that the proposed system can effectively increases QoS level by reducing response time and rejection requests in a cloud federation that eventually increases the revenue of the CSP.