With the rise of the cloud computing, saving energy consumed by cloud systems has become a tricky issue nowadays. How to place data efficiently and schedule the nodes effectively in a cloud platform are very important...
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With the rise of the cloud computing, saving energy consumed by cloud systems has become a tricky issue nowadays. How to place data efficiently and schedule the nodes effectively in a cloud platform are very important issues from the view of the energy-saving. However, the state-of-the-art node-scheduling strategies can't save large amount of energy for the cloud computing platforms significantly. This paper proposes a heuristic data placement algorithm and two node scheduling strategies for cloud platforms to save energy with tasks guaranteed. The Cloudsim is employed to simulate a private cloud system. Energy-saving is achieved by turning on minimum nodes to cover maximum data blocks. The problem of covering data block with computing nodes is abstracted as a set cover problem, and a greedy algorithm is utilized to solve this problem. This approach is practical to any cloud computing infrastructure. The designed experiment verifies the efficiency of the data placement algorithm and node scheduling strategies proposed in this paper.
Distributed storage allocation problems are an important optimization problem in reliable distributed storage, which aims to minimize storage cost while maximizing error recovery probability by optimal storage of data...
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Distributed storage allocation problems are an important optimization problem in reliable distributed storage, which aims to minimize storage cost while maximizing error recovery probability by optimal storage of data in distributed storage nodes. A key characteristic of distributed storage is that data is stored in remote servers across a network. Thus, network resources especially communication links are an expensive and non-trivial resource which should be optimized as well. In this article, the authors present a simulation-based study of the network characteristics of a distributed storage network in the light of several allocation patterns. By varying the allocation patterns, the authors have demonstrated the interdependence between network bandwidth, defined in terms of link capacity and allocation pattern using network throughput as a metric. Motivated by observing the importance of network resource as an important cost metric, the authors have formalized an optimization problem that jointly minimizes both the storage cost and the cost of network resources. A hybrid meta heuristic algorithm is employed that solves this optimization problem by allocating data in a distributed storage system. Experimental results validate the efficacy of the algorithm.
Wireless data broadcast is an efficient way of delivering data of common interest to a large population of mobile devices within a proximate area, such as smart cities, battle fields, etc. In this work, we focus ourse...
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Wireless data broadcast is an efficient way of delivering data of common interest to a large population of mobile devices within a proximate area, such as smart cities, battle fields, etc. In this work, we focus ourselves on studying the dataplacement problem of periodic XML data broadcast in mobile and wireless environments. This is an important issue, particularly when XML becomes prevalent in today's ubiquitous and mobile computing devices and applications. Taking advantage of the structured characteristics of XML data, effective broadcast programs can be generated based on the XML data on the server only. An XML data broadcast system is developed and a theoretical analysis on the XML dataplacement on a wireless channel is also presented, which forms the basis of the novel data placement algorithm in this work. The proposed algorithm is validated through a set of experiments. The results show that the proposed algorithm can effectively place XML data on air and significantly improve the overall access efficiency.
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