CMIP: Data Transmission Latency Optimization for Cooperative Group in Multi-cloud by Adaptive Routing

The proliferation of real-time applications, such as online gaming, video chatting, brings unprecedented pressure for multi-cloud application providers to secure the good quality of experience. These applications are designed for cooperative group users/members/clients and are expected to have low data transmission latency due to frequent interactions. We use total data transmission latency (TDTL) to represent the group latency in this paper. However, existing approaches perform poorly since they often ignore the feature of the cooperative group scenario and cannot provide a flexible rental cost model for application providers. Minimizing TDTL is challenging due to the difficulties in 1) Finding an effective method to transmit data through multi-cloud datacenters, 2) Taking the cooperative group scenario into consideration, 3) Providing a flexible transmission rental cost model. To this end, we propose CMIP, an innovative approach that aims to minimize the TDTL by renting virtual machines of well-connected datacenters as the proxy for the cooperative group scenario in multi-cloud providers. First, a weighted graph is constructed to illustrate the multi-cloud network. Second, we define a path latency model and a rental cost model for the cooperative group. After that, Yen's algorithm and mixed integer programming are adopted to optimize the TDTL with rental cost constraint. To study the performance of CMIP, we conduct extensive evaluations using multiple real data traces and compare it with related approaches. The comprehensive evaluation analysis shows that the CMIP achieves much lower total latency and is more flexible than other approaches.