The minimum connected dominating set problem (MCDSP) has become increasingly important in recent years due to its applicability to mobile ad hoc networks and sensor grids. This paper presents a restricted swap-based neighborhood (RSN) tailored for solving MCDSP. This novel neighborhood structure is embedded into tabu Search (TS) and a perturbation mechanism is employed to enhance diversification. The proposed RSN-TS algorithm is tested on four sets of public benchmark instances widely used in the literature. The results demonstrate the efficacy of the proposed algorithm in terms of both solution quality and computational efficiency. In particular, the RSN-TS algorithm was able to improve the best known results on 41 out of the 97 problem instances while matching the best known results on all the remaining 56 instances. Furthermore, the article analyzes some key features of the proposed approach in order to identify its critical success factors.

Delta compression, which is efficient in removing repeated string among similar chunks, can be used as a complement to data deduplication in backup storage for extra space savings. The process of detecting similar candidates to use as the base for delta compression is called resemblance detection. Several indexes are required for resemblance detection. Maintaining them in RAM would limit the system scalability and increase system cost. Storing them on the disk suffers from low throughput due to poor random I/O performance of the disk. In this article, we present the history-aware resemblance detection (HARD), a cost-efficient resemblance detection approach that captures most of the similar chunks with a limited memory footprint. HARD is based on the observation that, for chunks in a backup, most of their similar chunks can be found in the most recent backups. HARD thus only indexes super-features in the most recent backups for resemblance detection to reduce the memory footprint of resemblance indexes while captures most of the potential similar chunks for delta compression.Experimental results based on three real-world datasets show that HARD achieves higher compression than the state-of-the-art approach.

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