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Many of the applications running in cloud data center are data intensive, processing large amount of data inside the data center. File replication, which brings data files closer to the computing virtual machines (VMs), is an effective strategy that reduces data access latencies and bandwidth consumption, thus saving energy in data centers. In this paper, we formulate and study the file replication...
We study overall storage overflow problem in sensor networks, wherein data-collecting base station is not available while more data is generated than available storage spaces in the entire network. Existing research designs a two-stage solution to solve this problem. It first aggregates overflow data to the size that can be accommodated by the available storage capacity in the network, and then offloads...
We study an energy-efficient routing problem in static ad hoc networks. The problem, referred to as maxR, is to maximize the number of routing requests that can be satisfied in the network, under the constraint that each node has finite battery power. The online version of the problem, where the sequence of messages that has to be routed over the network is not known ahead of time, has been studied...
We study data preservation in intermittently connected sensor networks, wherein the sensor nodes do not always have connected paths to the base station. In such networks, the generated data is first stored inside the network before being uploaded to the base station when uploading opportunity arises. How to preserve the data inside the network is therefore an important problem. The problem becomes...
We address the energy-efficient data redistribution problem in data intensive sensor networks (DISNs). The key question in sensor networks with large volumes of sensory data is how to redistribute the data efficiently under limited storage and energy constraints at the sensor nodes. The goal of the redistribution scheme is to minimize the energy consumption during the process, while guaranteeing full...
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