A Joint Study On Collaborative Caching,Processing And Resource Provisioning Based On Adaptive Bitrate

With the rapid growth of adoption of smart mobile devices,the internet traffic is now increasing at an amazingly fast speed,among which video data has gradually become the mainstream data.The massive data volume brings great pressure to cloud vendors as well as video content providers.Mobile-Edge Computing(MEC)shows up as a cost-effective paradigm which provides storage,computation and network resources in proximity to users residing at the network edge.Video content providers are able to deploy low-latency and computation-intensive video services thanks to the resources that MEC provides.Thus how to save MEC resource leasing cost as much as possible while still satisfying the Quality of Service(QoS)becomes an important topic to video content providers.In this paper,a joint collaborative caching,processing and resource leasing model is proposed to help video content providers make better decisions on the video variants placement,video request scheduling and resource leasing strategy so as to minimize the leasing cost of MEC resources.The model takes Adaptive Bitrate video streaming into consideration to better meets the diversity of user's requests,which allows users to adjust their needs for some specific bitrate versions of videos according to their network conditions,mobile device capacity and their preferences.Bandwidth constraints are also considered to provide a simulative scenario more close to the real world scenario,which indeed could be the bottleneck of video transferring because of the limited network resource of backhaul link.The NP-complete property of the model sets great barriers to solve the primal problem as no non-trivial algorithms for this model existed.Inspired by the thoughts of ‘divide and conquer',this paper decomposes the primal problem into two subproblems:(i)maximizing the number of the users' requests covered using the unexpired sources remained in the current MEC system and(ii)minimizing the resource leasing cost while meets all the unhandled requests in the current MEC system.For subproblem(i),this paper transforms the subproblem into a monotone submodular maximization problem and gives an ABR-aware proactive caching algorithm and an approximately optimal online request routing algorithm based on the given caching strategy.For subproblem(ii),this paper incures the Lagrangian Relaxation method to relax the complex constraints in the subproblem and decomposes it into two less complex problems.Subgradient algorithm is used to adjust the solutions given by solving the above-mentioned two less complex problems to provide an approximately optimal solution to subproblem(ii).Massive simulations was taken to prove the merits of our algorithms in increasing cache hit ratio,reducing the backhaul traffic load between root servers and MEC systems and minimizing resource leasing cost compared to conventional approaches.