127,568 research outputs found
Managing Uncertainty: A Case for Probabilistic Grid Scheduling
The Grid technology is evolving into a global, service-orientated
architecture, a universal platform for delivering future high demand
computational services. Strong adoption of the Grid and the utility computing
concept is leading to an increasing number of Grid installations running a wide
range of applications of different size and complexity. In this paper we
address the problem of elivering deadline/economy based scheduling in a
heterogeneous application environment using statistical properties of job
historical executions and its associated meta-data. This approach is motivated
by a study of six-month computational load generated by Grid applications in a
multi-purpose Grid cluster serving a community of twenty e-Science projects.
The observed job statistics, resource utilisation and user behaviour is
discussed in the context of management approaches and models most suitable for
supporting a probabilistic and autonomous scheduling architecture
Distributed computing in the LHC era
A large, worldwide distributed, scientific community is running intensively physics analyses on the first data collected at LHC. In order to prepare for this unprecedented computing challenge, the four LHC experiments have developed distributed computing models capable of serving, processing and archiving the large number of events produced by data taking, amounting to about 15 petabytes per year. The experiments workflows for event reconstruction from raw data, production of simulated events and physics analysis on skimmed data generate hundreds of thousands of jobs per day, running on a complex distributed computing fabric. All this is possible thanks to reliable Grid services, which have been developed, deployed at the needed scale and thouroughly tested by the WLCG Collaboration during the last ten years. In order to provide a concrete example, this paper concentrates on CMS computing model and CMS experience with the first data at LHC
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On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches
The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach
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A modular access control architecture for the Earth system grid federation
Cloud Services Brokerage: A Survey and Research Roadmap
A Cloud Services Brokerage (CSB) acts as an intermediary between cloud
service providers (e.g., Amazon and Google) and cloud service end users,
providing a number of value adding services. CSBs as a research topic are in
there infancy. The goal of this paper is to provide a concise survey of
existing CSB technologies in a variety of areas and highlight a roadmap, which
details five future opportunities for research.Comment: Paper published in the 8th IEEE International Conference on Cloud
Computing (CLOUD 2015
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