Communities are important attributes of today's networking, since people who join networks tend to join communities. Thus, community detection is an important issue in designing algorithms for delay-tolerant networks (DTNs). Multicasting is an appropriate method to share information within a community or between communities, because it delivers messages from a source to a group of targets using limited resources. This paper addresses the problem of detecting communities in weighted networks with irregular topologies. Based on communities, we propose an efficient data distribution algorithm for DTNs. The distribution strategy is based on the construction of a number of multicast trees, where each tree can be used to select the best relay node for each target to improve multicast efficiency. The proposed strategy provides better delivery ratio compared with other strategies and it reduces latencies when multiple nodes from a community need to multicast to other community members.

Data replication is used to track the most popular files (i.e., the ones with most requests) and replicate them in selected nodes. In this way, more requests for such popular files can be completed over a period of time and bandwidth consumption is reduced, since these files do not need to be transferred from remote nodes. In this article, we extend our previous work [1] to make it more efficient in terms of memory and total computation cost, so that it becomes more efficient and suitable for larger grids. To reduce the memory costs, we present a centralized strategy which estimates the potential for selected batches of files. The computations required for these estimations are executed in a pipelined way, so their cost is also reduced.

Recently, data replication has received considerable attention in the field of grid computing. The main goal of data replication algorithms is to optimize data access performance by replicating the most popular files. When a file does not exist in the node where it was requested, it necessarily has to be transferred from another node, causing delays in the completion the file requests. The general idea behind data replication is to keep track of the most popular files requested in the grid and create copies of them in selected nodes. In this way, more file requests can be completed over a period of time and average job execution time is reduced. In this paper, we introduce an algorithm that estimates the potential of the files located in each node of the grid, using a binary tree structure. Also, the file scope and the file type are taken into account. By potential of a file, we mean its increasing or decreasing demand over a period of time. The file scope generally refers to the extent of the group of users which are interested or potentially interested in a file. The file types are divided into read and write intensive. Our scheme mainly promotes the high-potential files for replication, based on the temporal locality principle. The simulation results indicate that the proposed scheme can offer better data access performance in terms of the hit ratio and the average job execution time, compared to other state-of-the-art strategies.