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Data caching under number constraint
 In Proc. of ICC
, 2006
"... Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth usage. However, excessive caching can lead to prohibitive system cost and performance degradation. In this article, we consider the problem of caching a data item in a netw ..."
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Cited by 4 (2 self)
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Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth usage. However, excessive caching can lead to prohibitive system cost and performance degradation. In this article, we consider the problem of caching a data item in a network wherein the data item is read as well as updated by other nodes and there is a limit on the number of cache nodes allowed. More formally, given a network graph, the read/write frequencies to the data item by each node, and the cost of caching the data item at each node, the problem addressed in this article is to select a set of P nodes to cache the data item such that the sum of the reading, writing (using an optimal Steiner tree), and storage cost is minimized. For networks with a tree topology, we design an optimal dynamic programming algorithm that runs in O(n 2 P 2), where n is the size of the network and P is the allowed number of caches. For the general graph topology, where the problem is NPcomplete, we present a centralized heuristic and its distributed implementation. Through extensive simulations in general graphs, we show that the centralized heuristic performs very close to the exponential optimal algorithm for small networks, and for larger networks, the distributed implementation and the dynamic programming algorithm on an appropriately extracted tree perform quite close to the centralized heuristic. 1
Data Caching in Networks with Reading, Writing and Storage Costs
"... Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth/energy usage. However, caching in too many nodes can take up too much memory, incur extensive cachingrelated traffic, and hence, may even result in performance degradation ..."
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Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth/energy usage. However, caching in too many nodes can take up too much memory, incur extensive cachingrelated traffic, and hence, may even result in performance degradation. In this article, we address the problem of caching data items in networks with the objective of minimizing the overall cost under the constraint that the data item can be cached at only a limited number of network nodes. More formally, given a network, the access pattern of the data item to be shared (i.e., read and write frequencies to the data item by each node), and the storage cost (cost of caching the data item) at each node, our goal is to select at most P cache nodes so as to minimize the sum of reading, writing, and storage costs. We first consider networks with a tree topology and design an optimal dynamic programming algorithm which runs in O(n2P 2), where n is the size of the network and P is the allowed number of caches. For the general graph topology, where the problem is NPcomplete, we present a centralized heuristic which is amenable to an efficient distributed implementation. Through extensive simulations in general topology graphs, we show that the centralized heuristic performs very close to the exponential optimal algorithm for small networks. In larger networks, we observe that the distributed implementation as well as the dynamic programming algorithm on an appropriately extracted tree perform quite close to the centralized heuristic. Key words: Data caching, algorithm design and analysis, simulations. 1.
Data Caching under Number Constraint
"... Abstract — Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth usage. However, excessive caching can lead to prohibitive system cost and performance degradation. In this article, we consider the problem of caching a data ite ..."
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Abstract — Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth usage. However, excessive caching can lead to prohibitive system cost and performance degradation. In this article, we consider the problem of caching a data item in a network wherein the data item is read as well as updated by other nodes and there is a limit on the number of cache nodes allowed. More formally, given a network graph, the read/write frequencies to the data item by each node, and the cost of caching the data item at each node, the problem addressed in this article is to selected a set of P nodes to cache the data item such that the sum of the reading, writing (using an optimal Steiner tree), and storage cost is minimized. For networks with a tree topology, we design an optimal dynamic programming algorithm that runs in O(V  3 P 2), where V  is the size of the network and P is the allowed number of caches. For the general graph topology, where the problem is NPcomplete, we present a centralized heuristic and its distributed implementation. Through extensive simulations in general graphs, we show that the centralized heuristic performs very close to the exponential optimal algorithm for small networks, and for larger networks, the distributed implementation and the dynamic programming algorithm on an appropriately extracted tree perform quite close to the centralized heuristic. I.
Data Caching in Networks with Reading, Writing and Storage Costs
"... Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth/energy usage. However, caching in too many nodes can take up too much memory, incur extensive cachingrelated traffic, and hence, may even result in performance degradation ..."
Abstract
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Caching can significantly improve the efficiency of information access in networks by reducing the access latency and bandwidth/energy usage. However, caching in too many nodes can take up too much memory, incur extensive cachingrelated traffic, and hence, may even result in performance degradation. In this article, we address the problem of caching data items in networks with the objective of minimizing the overall cost under the constraint that the data item can be cached at only a limited number of network nodes. More formally, given a network, the access pattern of the data item to be shared (i.e., read and write frequencies to the data item by each node), and the storage cost (cost of caching the data item) at each node, our goal is to select at most P cache nodes so as to minimize the sum of reading, writing, and storage costs. We first consider networks with a tree topology and design an optimal dynamic programming algorithm which runs in O(n 2 P 2), where n is the size of the network and P is the allowed number of caches. For the general graph topology, where the problem is NPcomplete, we present a centralized heuristic which is amenable to an efficient distributed implementation. Through extensive simulations in general topology graphs, we show that the centralized heuristic performs very close to the exponential optimal algorithm for small networks. In larger networks, we observe that the distributed implementation as well as the dynamic programming algorithm on an appropriately extracted tree perform quite close to the centralized heuristic. I.
Certified by..........................................................
, 2009
"... This thesis studies the problem of determining achievable rates in heterogeneous wireless networks. We analyze the impact of location, traffic, and service heterogeneity. Consider a wireless network with n nodes located in a square area of size n communicating with each other over Gaussian fading ch ..."
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This thesis studies the problem of determining achievable rates in heterogeneous wireless networks. We analyze the impact of location, traffic, and service heterogeneity. Consider a wireless network with n nodes located in a square area of size n communicating with each other over Gaussian fading channels. Location heterogeneity is modeled by allowing the nodes in the wireless network to be deployed in an arbitrary manner on the square area instead of the usual random uniform node placement. For traffic heterogeneity, we analyze the n × n dimensional unicast capacity region. For service heterogeneity, we consider the impact of multicasting and caching. This gives rise to the n × 2 n dimensional multicast capacity region and the 2 n × n dimensional caching capacity region. In each of these cases, we obtain an explicit informationtheoretic characterization of the scaling of achievable rates by providing a converse and a matching (in the scaling sense) communication architecture.