Efficient Multicast Delivery for Data Redundancy Minimization over Wireless Data Centres

Abstract

With the explosive growth of cloud-based services, large-scale data canters are widely built for housing critical computing resources to gain significant economic benefits .In data centres, the cloud services are generally accomplished by multicast –based group communications. Recently ,many well-known industries, such as Microsoft,google and IBM,adopt high speed wireless technologies to augment network capacity in data centers.However,those well-known multicast delivery schemes for traditional wired data centers.We prove the problem are NP-hard and propose efficient heuristic algorithms for the two problems. Based on real traces and practical settings obtained from commercial data centers,a series of experiment conducted and the experimental results show that our proposed algorithm are effective for reducing multicast data traffic. The results also provide useful insights into the design of multicast tree construction and maintenance for wireless data canter networks. Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable cipher text search techniques. In this paper, a hierarchical clustering method is proposed to support more semantistics and also meet the command for fast cipher text search with in a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold. The results show that with a sharp increase of documents in the data set. The search time of the proposed method increases exponentially.Furthermore, the proposed method has advantage over the traditional method in the rank privacy and relevance of retrieved documents. However, Bandwidth constraints may restrict the number of reference views sent to clients, limiting the quality of the synthesized viewpoints. In this work, we study the problem of in-network reference view synthesis aimed at improving the navigation quality at the clients. We consider a distributed cloud network architecture, where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. We argue that, in case of limited bandwidth from the cloudlet to the users, re-sampling at the could let the viewpoints of the 3D scene (i.e., synthesizing novel virtual views in the cloudlets to be used as new references to the decoder) is beneficial compared to mere sub sampling of the original set of camera views. We therefore cast a new reference view selection problem that seeks the subset of views minimizing the distortion over a view navigation window defined by the user under bandwidth constraints.