Unifying data and replica placement for data-intensive services in geographically distributed clouds

The increased reliance of data management applications on cloud computing technologies has rendered research in identifying solutions to the data placement problem to be of paramount importance. The objective of the classical data placement problem is to optimally partition, while also allowing for replication, the set of data-items into distributed data centers to minimize the overall network communication cost. Despite significant advancement in data placement research, replica placement has seldom been studied in unison with data placement. More specifically, most of the existing solutions employ a two-phase approach: 1) data placement, followed by 2) replication. Replication should however be seen as an integral part of data placement, and should be studied as a joint optimization problem with the latter. In this paper, we propose a unified paradigm of data placement, called CPR, which combines data placement and replication of data-intensive services into geographically distributed clouds as a joint optimization problem. Underneath CPR, lies an overlapping correlation clustering algorithm capable of assigning a data-item to multiple data centers, thereby enabling us to jointly solve data placement and replication. Experiments on a real-world trace-based online social network dataset show that CPR is effective and scalable. Empirically, it is approximate to 35% better in efficacy on the evaluated metrics, while being up to 8 times faster in execution time when compared to state-of-the-art techniques

A cost-effective cloud computing framework for accelerating multimedia communication simulations

Multimedia communication research and development often requires computationally intensive simulations in order to develop and investigate the performance of new optimization algorithms. Depending on the simulations, they may require even a few days to test an adequate set of conditions due to the complexity of the algorithms. The traditional approach to speed up this type of relatively small simulations, which require several develop-simulate-reconfigure cycles, is indeed to run them in parallel on a few computers and leaving them idle when developing the technique for the next simulation cycle. This work proposes a new cost-effective framework based on cloud computing for accelerating the development process, in which resources are obtained on demand and paid only for their actual usage. Issues are addressed both analytically and practically running actual test cases, i.e., simulations of video communications on a packet lossy network, using a commercial cloud computing service. A software framework has also been developed to simplify the management of the virtual machines in the cloud. Results show that it is economically convenient to use the considered cloud computing service, especially in terms of reduced development time and costs, with respect to a solution using dedicated computers, when the development time is longer than one hour. If more development time is needed between simulations, the economic advantage progressively reduces as the computational complexity of the simulation increases