QoS degradation based reimbursement for real-time cloud communication

Rapidly increasing digital media, specially multimedia content, has elicited the importance of cloud computing. Management and access of growing content becomes easy with cloud computing. Most of the cloud storage services now offer additional features related with multimedia management. For real-time communication (RTC), it is not always possible to maintain the quality of service (QoS), promised during service level agreement (SLA). When, for various reasons, a service is given up, the reimbursement is made on the standard mechanism, on the basis of utilized resources. This creates unfairness, since cloud consumer's satisfaction is very important to gain its loyalty. Service providers may lose their consumers if the resource management policy is not dynamic, according to the situations and scenarios. In this paper, we propose QoS degradation based reimbursement for given up services. We provide mathematical formulation to determine the effect of quality degradation, when SLA is violated, and calculate reimburse amount accordingly. Java/NetBeans was used to implement our work. The results show the effect of our model and its utility.

[1]  Baochun Li,et al.  Dynamic Cloud Pricing for Revenue Maximization , 2013, IEEE Transactions on Cloud Computing.

[2]  Ajaykumar T. Shah MEDIA CLOUD : WHEN MEDIA REVOLUTION MEETS RISE OF CLOUD COMPUTING , 2014 .

[3]  Cisco Visual Networking Index: Forecast and Methodology 2016-2021.(2017) http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual- networking-index-vni/complete-white-paper-c11-481360.html. High Efficiency Video Coding (HEVC) Algorithms and Architectures https://jvet.hhi.fraunhofer. , 2017 .

[4]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[5]  Achim Streit,et al.  SLA based Service Brokering in Intercloud Environments , 2012, CLOSER.

[6]  Dusit Niyato,et al.  A Framework for Cooperative Resource Management in Mobile Cloud Computing , 2013, IEEE Journal on Selected Areas in Communications.

[7]  Chong Luo,et al.  Multimedia Cloud Computing , 2011, IEEE Signal Processing Magazine.

[8]  Hai Jin,et al.  Towards Pay-As-You-Consume Cloud Computing , 2011, 2011 IEEE International Conference on Services Computing.

[9]  Bingsheng He,et al.  Transformation-Based Monetary CostOptimizations for Workflows in the Cloud , 2014, IEEE Transactions on Cloud Computing.

[10]  Eui-nam Huh,et al.  Advance resource reservation and QoS based refunding in cloud federation , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).

[11]  Rodney S. Tucker,et al.  Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport , 2011, Proceedings of the IEEE.

[12]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[13]  Xin Jin,et al.  Competitive Cloud Resource Procurements via Cloud Brokerage , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[14]  Eui-nam Huh,et al.  Redefining flow label in IPv6 and MPLS headers for end to end QoS in virtual networking for Thin client , 2013, 2013 19th Asia-Pacific Conference on Communications (APCC).