A fair resource allocation algorithm for peer-to-peer overlays

Drougas, Yannis; Kalogeraki, Vana

doi:10.1109/infcom.2005.1498575

Cited by 13 publications

(17 citation statements)

References 24 publications

(21 reference statements)

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“…However, even with such an opportunistic scheduling policy, resource over-utilization and load unbalancing phenomena cannot be totally eliminated if the number of tasks to be executed at a node cannot be controlled. We investigate three different policies to control imported tasks or disperse the load distribution, namely double-check policy [21], queueassistant policy, and extra-virtual-dimension policy [19]. For the double-check policy, each requesting task will recheck the current resource availability of the selected node before the task is actually migrated.…”

Section: Algorithm 5 Pointer-relay (Hopping Manner)mentioning

confidence: 99%

“…The fairness index is given in Equation (21), where ϵ ij refers to task t ij 's execution efficiency. Higher φ implies more steady execution efficiency.…”

Section: Fig 5 Presents the Throughput Ratio Between Soc And P2pmentioning

confidence: 99%

“…To mitigate the contention problem due to analogous queries in CAN, our range query protocol proactively diffuses resource indexes over the network and randomly route query messages among nodes to locate qualified ones that satisfy tasks' minimal demands. To avoid possibly uneven load distribution and abrupt resource over-utilization caused by un-coordinated node selection process from autonomous participants, we investigate three node selection policies, namely double-check policy [21], queuing policy [22], and extra-virtual-dimension policy [19].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing Clouds

Wang

2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-By leveraging virtual machine (VM) technology which provides performance and fault isolation, Cloud resources can be provisioned on demand in a fine-grained, multiplexed manner rather than in monolithic pieces. By integrating volunteer computing into Cloud architectures, we envision a gigantic Self-Organizing Cloud (SOC) being formed to reap the huge potential of untapped commodity computing power over the Internet. Towards this new architecture where each participant may autonomously act as both resource consumer and provider, we propose a fully distributed, VM-multiplexing resource allocation scheme to manage decentralized resources. Our approach not only achieves maximized resource utilization using the proportional share model (PSM), but also delivers provably and adaptively optimal execution efficiency. We also design a novel multi-attribute range query protocol for locating qualified nodes. Contrary to existing solutions which often generate bulky messages per request, our protocol produces only one lightweight query message per task on the Content Addressable Network (CAN). It works effectively to find for each task its qualified resources under a randomized policy that mitigates the contention among requesters. We show the SOC with our optimized algorithms can make an improvement by 15%-60% in system throughput than a P2P Grid model. Our solution also exhibits fairly high adaptability in a dynamic node-churning environment.

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Section: Algorithm 5 Pointer-relay (Hopping Manner)mentioning

confidence: 99%

“…The fairness index is given in Equation (21), where ϵ ij refers to task t ij 's execution efficiency. Higher φ implies more steady execution efficiency.…”

Section: Fig 5 Presents the Throughput Ratio Between Soc And P2pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing Clouds

Wang

2013

IEEE Trans. Parallel Distrib. Syst.

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“…We focus only on control of the number of replicas in this paper, meanwhile many other researchers focus on replica allocation [8], [9]. Our algorithms can be combined to these allocation protocols since the algorithms can control the number of replicas independently from replicas' allocation.…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Self-Adaptation of Replica Density Control

Izumi

Ooshita

Kakugawa

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYBiologically-inspired approaches are one of the most promising approaches to realize highly-adaptive distributed systems. Biological systems inherently have self- * properties, such as self-stabilization, self-adaptation, self-configuration, self-optimization and self-healing. Thus, the application of biological systems into distributed systems has attracted a lot of attention recently. In this paper, we present one successful result of bio-inspired approach: we propose distributed algorithms for resource replication inspired by the single species population model. Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. In systems using resource replication, generally, a larger number of replicas lead to shorter time to reach a replica of a requested resource but consume more storage of the hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for the resource sharing application. This paper considers the problem for controlling the densities of replicas adaptively in dynamic networks and proposes two bio-inspired distributed algorithms for the problem. In the first algorithm, we try to control the replica density for a single resource. However, in a system where multiple resources coexist, the algorithm needs high network cost and the exact knowledge at each node about all resources in the network. In the second algorithm, the densities of all resources are controlled by the single algorithm without high network cost and the exact knowledge about all resources. This paper shows by simulations that these two algorithms realize self-adaptation of the replica density in dynamic networks.

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“…In our previous work we have investigated fair resource allocation for data-sharing applications [7], as well as different aspects of overlays for distributed applications In particular, in [4] we have focused on the task scheduling algorithm, while in [5] we have described a totally decentralized media streaming and transcoding architecture. Our current work builds upon [17] and provides a detailed experimental evaluation.…”

Section: Related Workmentioning

confidence: 99%