A Utility Game Driven QoS Optimization for Cloud Services
ABSTARCT :
Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode.In the cloud services market, service providers attempt to make more profits from their services,while users hope to choose low-cost services with high-quality.The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This paper characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties.By analyzing the behaviors of users and service providers,we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions.In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method.The results are applicable to win-win service applications between service providers and users.
EXISTING SYSTEM :
? The first experiment aims at proving that federated infrastructure of clouds has potential to deliver better performance and service quality as compared to existing nonfederated approaches.
? However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels.
? Although the components that contribute to a federated system may be distributed, existing techniques usually employ centralized approaches to overall system monitoring and management.
? Cloud Brokers query the registry to learn information about existing SLA offers and resource availability of member Clouds in the federation.
DISADVANTAGE :
? The problem is modeled as a mixed integer linear programming (MILP). A two-stage heuristic algorithm based on linear programming (LP) is proposed to solve the problem.
? The method can not only maximize the amount of resources allocated to users but also consider the dynamic QoS level of users. So, edge user allocation problem can be made more general and improving the quality of experience.
? It solves the problem of data loss under the condition of transmission delay, which is caused by the uneven requirements of user equipments on resources.
? Both of these heuristic algorithms can effectively solve the task assignment problem.
PROPOSED SYSTEM :
• The proposed InterCloud environment supports scaling of applications across multiple vendor clouds.
• This is followed by some initial experiments and results, which quantifies the performance gains delivered by the proposed approach.
• Mapping functions will be implemented by leveraging various economic models such as Continuous Double Auction (CDA) as proposed in earlier works.
• We present our experiments and evaluation that we undertook using CloudSim framework for studying the feasibility of the proposed research vision.
ADVANTAGE :
? All these methods can reasonably configure resources in the cloud data center, improve system performance, reduce additional costs of using resources, and meet the required QoS.
? And control-theoretic technologies realize the automatic management of computer performance and energy consumption.
? VM management refers to reasonable scheduling or integration of VMs to achieve better performance.
? Compared with other schemes, this scheme not only sacrifices the performance of execution operations but also saves more energy.
? The framework can achieve the best allocation and effectively improve the networks’ performance.
? And then it balances QoS performance and privacy protection to achieve joint optimization.
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