FRINK Linked Data Fragments server

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• W2004261580 abstract "Parallel database systems and MapReduce systems (most notably Hadoop) are essential components of today’s infrastructure for Big Data analytics. These systems process multiple concurrent workloads consisting of complex user requests, where each request is associated with an (explicit or implicit) service level objective. For example, the workload of a particular user or application may have a higher priority than other workloads. Or a particular workload may have strict deadlines for the completion of its requests. The research area of Workload Management focuses on ensuring that the system meets the service level objectives of various requests while at the same time minimizing the resources required to achieve this goal. At a high level, workload management can be viewed as looking beyond the performance of an individual request to the performance of an entire workload consisting of multiple requests. Questions addressed by workload management research and technologies include: How to implement different priorities for different workloads? How to isolate the performance of one workload from the effect of other workloads? What is the best way to do request scheduling and admission control? What are good mechanisms and policies to control the allocation of resources to workloads statically and dynamically? How to define a workload and associated requests within that workload? How to monitor request performance, resource consumption, and data access patterns to ensure that workload management is effectively achieving its goals? How to ensure that workload management goals are met even in the presence of failures? This tutorial will discuss the fundamentals of workload management, and present tools and techniques for workload management in parallel databases and MapReduce. Workload management for parallel databases is an established topic, and most parallel database systems have sophisticated workload management tools. The tutorial will present some of these tools as case studies and discuss the underlying techniques that they use. Workload management for MapReduce is still a fledgling research area, and the tutorial will discuss recent advances in this area and future research directions. II. TUTORIAL OUTLINE" @default.
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• W2004261580 date "2013-06-22" @default.
• W2004261580 modified "2023-09-29" @default.
• W2004261580 title "Workload management for big data analytics" @default.
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• W2004261580 doi "https://doi.org/10.1145/2463676.2467801" @default.
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