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Shared resource

About: Shared resource is a research topic. Over the lifetime, 7536 publications have been published within this topic receiving 123491 citations. The topic is also known as: network share.


Papers
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Proceedings ArticleDOI
29 Nov 2011
TL;DR: The Cloud Modeling Language (CloudML) is introduced, a vendor-neutral XML-based language intended to integrate the description of different cloud related aspects such as computational and network resources, services profiles, and developers' requests in an integrated way.
Abstract: Distributed Clouds, or just D-Clouds, can be seen as a paradigm that is able to exploit the potential of sharing resources across geographic boundaries and provide latency-bound allocation of resources to third-party developers. The representation of D-Cloud resources is a challenge that involves the careful choice of characteristics that drive the mapping of requests on the substrate resources. Regarding these problems, this paper introduces the Cloud Modeling Language (CloudML), a vendor-neutral XML-based language intended to integrate the description of different cloud related aspects such as computational and network resources, services profiles, and developers' requests in an integrated way. Furthermore, the CloudML provides a way to describe geographical location aware services, seen particularly indispensable in D-Cloud scenarios.

50 citations

Proceedings ArticleDOI
02 Dec 1990
TL;DR: It is shown that in case of the threshold-activated service scenario, it is possible to optimize the service performance by using the statistics of the service requests and customer patience.
Abstract: The key to an economical service is in the sharing of physical resources among the customers. This applies to the information storage and sourcing devices, as well as to the transmission bandwidth. One of the possible solutions to the resource sharing problem is the grouping of customers with similar service requests, and broadcasting the information rather than granting the service individually. The customers' behavior (in particular, the customers' patience) is modeled, and the service performance for different service scenarios is analyzed. It is shown that in case of the threshold-activated service scenario, it is possible to optimize the service performance by using the statistics of the service requests and customer patience. >

50 citations

Journal ArticleDOI
TL;DR: The proposed Bayesian RL-based coalition formation algorithms are implemented in a long-term evolution advanced network and evaluated using simulations, showing a superior performance when compared with other relevant resource allocation schemes and achieve near-optimal results after a relatively small number of RL iterations.
Abstract: This paper investigates the problem of distributed resource sharing in a device-to-device enabled heterogeneous network, where the various device pairs choose their transmission channels, modes, base stations (BSs), and power levels without any control by the BSs based only on the locally-observable information. This problem is represented as a Bayesian coalition formation game, where the players (device pairs) create coalitions to maximize their long-term rewards with no prior knowledge of the values of potential coalitions and the types of their members. To minimize these uncertainties, a novel Bayesian reinforcement learning (RL) model is derived. In this model, the players update (through repeated coalition formation) their beliefs about the types and coalitional values to reach a stable coalitional agreement. The proposed Bayesian RL-based coalition formation algorithms are implemented in a long-term evolution advanced network and evaluated using simulations. The algorithms show a superior performance when compared with other relevant resource allocation schemes and achieve near-optimal results after a relatively small number of RL iterations.

50 citations

Proceedings ArticleDOI
02 Jul 2002
TL;DR: A way to manage distributed file system caches based upon groups of files that are accessed together, using file access patterns to automatically construct dynamic groupings of files and then managing the cache by fetching groups, rather than single files.
Abstract: We describe a way to manage distributed file system caches based upon groups of files that are accessed together. We use file access patterns to automatically construct dynamic groupings of files and then manage our cache by fetching groups, rather than single files. We present experimental results, based on trace-driven workloads, demonstrating that grouping improves cache performance. At the file system client, grouping can reduce LRU demand fetches by 50 to 60%. At the server cache hit rate improvements are much more pronounced, but vary widely (20 to over 1200%) depending upon the capacity of intervening caches. Our treatment includes information theoretic results that justify our approach to file grouping.

50 citations

Proceedings ArticleDOI
10 Apr 2011
TL;DR: This research proposes and evaluates a general methodology for characterizing multi-threaded applications by determining the effect of shared-resource contention on performance and characterize the applications in the widely used PARSEC benchmark suite for shared-memory resource contention.
Abstract: For higher processing and computing power, chip multiprocessors (CMPs) have become the new mainstream architecture. This shift to CMPs has created many challenges for fully utilizing the power of multiple execution cores. One of these challenges is managing contention for shared resources. Most of the recent research address contention for shared resources by single-threaded applications. However, as CMPs scale up to many cores, the trend of application design has shifted towards multi-threaded programming and new parallel models to fully utilize the underlying hardware. There are differences between how single- and multi-threaded applications contend for shared resources. Therefore, to develop approaches to reduce shared resource contention for emerging multi-threaded applications, it is crucial to understand how their performances are affected by contention for a particular shared resource. In this research, we propose and evaluate a general methodology for characterizing multi-threaded applications by determining the effect of shared-resource contention on performance. To demonstrate the methodology, we characterize the applications in the widely used PARSEC benchmark suite for shared-memory resource contention. The characterization reveals several interesting aspects of the benchmark suite. Three of twelve PARSEC benchmarks exhibit no contention for cache resources. Nine of the benchmarks exhibit contention for the L2-cache. Of these nine, only three exhibit contention between their own threads-most contention is because of competition with a co-runner. Interestingly, contention for the Front Side Bus is a major factor with all but two of the benchmarks and degrades performance by more than 11%.

50 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202381
2022194
2021223
2020298
2019381
2018373