Optimal multiserver configuration for profit
- 1. Optimal Multiserver Configuration for Profit Maximization in Cloud Computing Abstract— As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low quality service, the cost of renting, the cost of energy consumption, and a service provider’s margin and profit. Our approach is to treat a multiserver system as an M/M/m queueing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constantspeed model. Reasons for the proposal : To increase the revenue of business, a service provider can construct and configure a multiserver system with many servers of high speed. Since the actual service time (i.e., the task response time) contains task waiting time and task execution time, more servers reduce the waiting time and faster servers reduce both waiting time and execution time. Hence, a powerful multiserver system reduces the penalty of breaking a service level agreement and increases the revenue. However, more
- 2. servers (i.e., a larger multiserver system) increase the cost of facility renting from the infrastructure vendors and the cost of base power consumption. Furthermore, faster servers increase the cost of energy consumption. Such increased cost may counterweight the gain from penalty reduction. Therefore, for an application environment with specific workload which includes the task arrival rate and the average task execution requirement, a service provider needs to decide an optimal multiserver configuration (i.e, the size and the speed of a multiserver system), such that the expected profit is maximized. Existing system & demerits : One related research is user-centric and marketbased and utility-driven resource management and task scheduling, which have been considered for cluster computing systems and grid computing systems. To compete and bid for shared computing resources through the use of economic mechanisms such as auctions, a user can specify the value (utility, yield) of a task, i.e., the reward (price, profit) of completing the task. A utility function, which measures the value and importance of a task as well as a user’s tolerance to delay and sensitivity to quality of service, supports marketbased bidding, negotiation, and admission control. By taking an economic approach to providing serviceoriented and utility computing, a service provider allocates resources and schedules tasks in such a way that the total profit earned is maximized. Instead of traditional system-centric performance optimization such as minimizing the average task response time, the main concern in such computational economy is user-centric performance optimization, i.e., maximizing the total utility delivered to the users (i.e., the total user-perceived value). Proposed system:
- 3. In this paper, we study the problem of optimal multiserver configuration for profit maximization in a cloud computing environment. Our approach is to treat a multiserver system as an M/M/m queueing model, such that our optimization problem can be formulated and solved analytically. We consider two server speed and power consumption models, namely, the idle-speed model and the constant- speed model. Our main contributions are as follows. We derive the probability density function of the waiting time of a newly arrived service request. This result is significant in its own right and is the base of our discussion. We calculate the expected service charge to a service request. Based on these results, we get the expected net business gain in one unit of time, and obtain the optimal server size and the optimal server speed numerically.