As an effective and efficient way to provide computing resources and services to customers on demand, cloud computing has become more and more popular. From cloud service providers’ perspective, profit is one of the most important considerations, and it is mainly determined by the configuration of a cloud service platform under given market demand. However, a single long-term renting scheme is usually adopted to configure a cloud platform, which cannot guarantee the service quality but leads to serious resource waste.
In this paper, a double resource renting scheme is designed firstly in this double renting scheme can effectively guarantee the quality of service of all requests and reduce the resource waste greatly.
Secondly, a service system is considered as an M/M/m+D queuing model and the performance indicators that affect the profit of our double renting scheme are analyzed, e.g., the average charge, the ratio of requests that need temporary servers, and so forth.
Thirdly, a profit maximization problem is formulated for the double renting scheme and the optimized configuration of a cloud platform is obtained by solving the profit maximization problem.
Finally, a series of calculations are conducted to compare the profit of our proposed scheme with that of the single renting scheme. The results show that our scheme can not only guarantee the service quality of all requests, but also obtain more profit than the latter.
We aim at researching the multiserver configuration of a service provider such that its profit is maximized. Like all business, the profit of a service provider in cloud computing is related to two parts, which are the cost and the revenue. For a service provider, the cost is the renting cost paid to the infrastructure providers plus the electricity cost caused by energy consumption, and the revenue is the service charge to customers. In general, a service provider rents a certain number of servers from the infrastructure providers and builds different multiserver systems for different application domains. Each multiserver system is to execute a special type of service requests and applications. Hence, the renting cost is proportional to the number of servers in a multiserver system. The power consumption of a multiserver system is linearly proportional to the number of servers and the server utilization, and to the square of execution speed. The revenue of a service provider is related to the amount of service and the quality of service. To summarize, the profit of a service provider is mainly determined by the configuration of its service platform. To configure a cloud service platform, a service provider usually adopts a single renting scheme.
However, the waiting time of the service requests cannot be too long. In order to satisfy quality-of-service requirements, the waiting time of each incoming service request should be limited within a certain range, which is determined by a service-level agreement (SLA). If the quality of service is guaranteed, the service is fully charged, otherwise, the service provider serves the request for free as a penalty of low quality. To obtain higher revenue, a service provider should rent more servers from the infrastructure providers or scale up the server execution speed to ensure that more service requests are processed with high service quality. However, doing this would lead to sharp increase of the renting cost or the electricity cost. Such increased cost may counterweight the gain from penalty reduction. In conclusion, the single renting scheme is not a good scheme for service providers. In this paper, we propose a novel renting scheme for service providers, which not only can satisfy quality-of-service requirements, but also can obtain more profit.
OPTIMAL MULTISERVER CONFIGURATION FOR PROFIT MAXIMIZATION IN CLOUD COMPUTING
AUTHOR: J. Cao, K. Hwang, K. Li, and A. Y. Zomaya,
PUBLICATION: IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 6, pp. 1087–1096, 2013.
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 queuing 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 constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.
PROFITDRIVEN SCHEDULING FOR CLOUD SERVICES WITH DATA ACCESS AWARENESS
AUTHOR: Y. C. Lee, C. Wang, A. Y. Zomaya, and B. B. Zhou
PUBLICATION: J. Parallel Distr. Com., vol. 72, no. 4, pp. 591– 602, 2012
Resource sharing between multiple tenants is a key rationale behind the cost effectiveness in the cloud. While this resource sharing greatly helps service providers improve resource utilization and increase profit, it impacts on the service quality (e.g., the performance of consumer applications). In this paper, we address the reconciliation of these conflicting objectives by scheduling service requests with the dynamic creation of service instances. Specifically, our scheduling algorithms attempt to maximize profit within the satisfactory level of service quality specified by the service consumer. Our contributions include (1) the development of a pricing model using processor-sharing for clouds (i.e., queuing delay is embedded in processing time), (2) the application of this pricing model to composite services with dependency consideration, (3) the development of two sets of service request scheduling algorithms, and (4) the development of a prioritization policy for data service aiming to maximize the profit of data service.
ENERGY AND PERFORMANCE MANAGEMENT OF GREEN DATA CENTERS: A PROFIT MAXIMIZATION APPROACH
AUTHOR: M. Ghamkhari and H. Mohsenian-Rad
PUBLICATION: IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 1017–1025, 2013.
While a large body of work has recently focused on reducing data center’s energy expenses, there exists no prior work on investigating the trade-off between minimizing data center’s energy expenditure and maximizing their revenue for various Internet and cloud computing services that they may offer. In this paper, we seek to tackle this shortcoming by proposing a systematic approach to maximize green data center’s profit, i.e., revenue minus cost. In this regard, we explicitly take into account practical service-level agreements (SLAs) that currently exist between data centers and their customers. Our model also incorporates various other factors such as availability of local renewable power generation at data centers and the stochastic nature of data centers’ workload. Furthermore, we propose a novel optimization-based profit maximization strategy for data centers for two different cases, without and with behind-the-meter renewable generators. We show that the formulated optimization problems in both cases are convex programs; therefore, they are tractable and appropriate for practical implementation. Using various experimental data and via computer simulations, we assess the performance of the proposed optimization-based profit maximization strategy and show that it significantly outperforms two comparable energy and performance management algorithms that are recently proposed in the literature.
Existing works relevant to the profit of service providers is related with many factors such as the price, the market demand, the system configuration, the customer satisfaction and so forth. Service providers naturally wish to set a higher price to get a higher profit margin; but doing so would decrease the customer satisfaction, which leads to a risk of discouraging demand in the future. Hence, selecting a reasonable pricing strategy is important for service providers. The pricing strategies are divided into two categories, i.e., static pricing and dynamic pricing. Static pricing means that the price of a service request is fixed and known in advance, and it does not change with the conditions.
Previous statically pricing a service provider delays the pricing decision until after the customer demand is revealed, so that the service provider can adjust prices accordingly. Static pricing is the dominant strategy which is widely used in real world and in research. Ghamkhari et al. Adopted a flat-rate pricing strategy and set a fixed price for all requests, but Odlyzko argued that the predominant flat-rate pricing encourages waste and is incompatible with service differentiation of static pricing strategies are usage-based pricing. For example, the price of a service request is proportional to the service time and task execution requirement.
- In Many existing research they only consider the power consumption cost. As a major difference between their models and ours, the resource rental cost is considered in this paper as well, since it is a major part which affects the profit of service providers.
- The traditional single resource renting scheme cannot guarantee the quality of all requests but wastes a great amount of resources due to the uncertainty of system workload. To overcome the weakness, we propose a double renting scheme as follows, which not only can guarantee the quality of service completely but also can reduce the resource waste greatly.
In this paper, we propose a novel renting scheme for service providers, which not only can satisfy quality-of-service requirements, but also can obtain more profit. Our contributions in this paper can be summarized as follows.
A novel double renting scheme is proposed for service providers. It combines long-term renting with short-term renting, which can not only satisfy quality-of-service requirements under the varying system workload, but also reduce the resource waste greatly.
A multiserver system adopted in our paper is modeled as an M/M/m+D queuing model and the performance indicators are analyzed such as the average service charge, the ratio of requests that need shortterm servers, and so forth.
The optimal configuration problem of service providers for profit maximization is formulated and two kinds of optimal solutions, i.e., the ideal solutions and the actual solutions, are obtained respectively.
A series of comparisons are given to verify the performance of our scheme. The results show that the proposed Double-Quality-Guaranteed (DQG) renting scheme can achieve more profit than the compared Single-Quality-Unguaranteed (SQU) renting scheme in the premise of guaranteeing the service quality completely.
In this paper, to overcome the shortcomings mentioned above, a double renting scheme is designed to configure a cloud service platform, which can guarantee the service quality of all requests and reduce the resource waste greatly. Moreover, a profit maximization problem is formulated and solved to get the optimal multiserver configuration which can product more profit than the optimal configuration.
- We first propose the Double-Quality- Guaranteed (DQG) resource renting scheme which combines long-term renting with short-term renting. The main computing capacity is provided by the long-term rented servers due to their low price. The short-term rented servers provide the extra capacity in peak period.
- In proposed system we are using the Double-Quality-Guaranteed (DQG) renting scheme can achieve more profit than the compared Single-Quality-Unguaranteed (SQU) renting scheme in the premise of guaranteeing the service quality completely.
HARDWARE & SOFTWARE REQUIREMENTS:
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