2,671 research outputs found

    Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems

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    Heterogeneity has grown in popularity both at the core and server level as a way to improve both performance and energy efficiency. However, despite these benefits, scheduling applications in heterogeneous machines remains challenging. Additionally, when these heterogeneous resources accommodate multiple applications to increase utilization, resources are prone to contention, destructive interference, and unpredictable performance. Existing solutions examine heterogeneity either across or within a server, leading to missed performance and efficiency opportunities. We present Mage, a practical interference-aware runtime that optimizes performance and efficiency in systems with intra- and inter-server heterogeneity. Mage leverages fast and online data mining to quickly explore the space of application placements, and determine the one that minimizes destructive interference between co-resident applications. Mage continuously monitors the performance of active applications, and, upon detecting QoS violations, it determines whether alternative placements would prove more beneficial, taking into account any overheads from migration. Across 350 application mixes on a heterogeneous CMP, Mage improves performance by 38% and up to 2x compared to a greedy scheduler. Across 160 mixes on a heterogeneous cluster, Mage improves performance by 30% on average and up to 52% over the greedy scheduler, and by 11% over the combination of Paragon [15] for inter- and intra-server heterogeneity

    Toward sustainable data centers: a comprehensive energy management strategy

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    Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers. In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft

    D-SPACE4Cloud: A Design Tool for Big Data Applications

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    The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method
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