A scheduling algorithm for revenue maximisation for cluster-based Internet services
Quality of service of e-commerce sites has been usually managed by the allocation of resources such as processors, disks, and network bandwidth, and by tracking conventional performance metrics such as response time, throughput, and availability. However, the metrics that are of utmost importance to the management of a Web store are revenue and profits. Thus, resource management schemes for e-commerce servers should be geared towards optimizing business metrics as opposed to conventional performance metrics. This paper introduces a state transition graph called Customer Behavior Model Graph (CBMG) to describe a customer session. It then presents a family of priority-based resource management policies for e-commerce servers. Priorities change dynamically as a function of the state a customer is in and as a function of the amount of money the customer has accumulated in his/her shopping cart. A detailed simulation model was developed to assess the gain of adaptive policies with respect to policies that are oblivious to economic considerations. Simulation results show that the adaptive priority scheme suggested here can increase, during peak periods, business-oriented metrics such as revenue/sec by as much as 43% over the non priority case.