NARA:网络感知资源分配机制,用于在处理志愿网络能耗的同时尽量减少对服务质量的影响

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Sergio Gonzalo San José , Joan Manuel Marquès , Javier Panadero , Laura Calvet
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引用次数: 0

摘要

大规模志愿计算系统是一种分布式系统,贡献者自愿提供个人电脑或移动设备等计算资源,为更大规模的计算工作做出贡献。志愿者资源通过互联网连接起来,共同组成一个强大的计算系统,能够在不依赖服务提供商的情况下提供服务。志愿者网络资源分配是将计算任务或服务分配给志愿者资源网络的过程。分配过程包括确定所需资源、选择合适的志愿者,以及根据志愿者的能力分配任务或服务。志愿计算系统由大量异构资源组成,这些资源在处理能力、存储和可用性方面都属于不同的机构(用户或组织),并在连接、断开、容量和故障方面表现出不确定性。所有这些都使得资源分配成为一项具有挑战性的任务,需要复杂的算法和优化技术来确保在尊重可用资源限制的同时有效分配服务,从而确保最低服务质量。本文介绍了网络感知资源分配机制,该机制利用志愿节点的位置、连通性和网络延迟,最大限度地减少服务运行时服务质量下降的时间,并旨在处理数据复制要求产生的能耗。这种资源分配机制既适用于服务在网络中的初始部署,也适用于在所分配的节点之一出现故障或不可用时的节点重新分配。我们的方法已在现实志愿者系统的模拟环境中得到验证。结果分析表明,我们的机制既能满足用户的质量要求,又能最大限度地减少服务数据副本的同步和复制时间,以及服务质量下降的服务运行时间,在服务部署阶段减少了 70% 以上的时间,在服务执行阶段减少了 60% 以上的时间。它还有助于降低总体能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks
A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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