Cloud computing has become another buzzword after Web 2.0. Many companies, such as Amazon, Google, Microsoft and so on, accelerate their paces in developing Cloud computingsystems. However, there is no study that foc...
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Cloud computing has become another buzzword after Web 2.0. Many companies, such as Amazon, Google, Microsoft and so on, accelerate their paces in developing Cloud computingsystems. However, there is no study that focuses on search engine and web portal for Cloud computing system. Hence, this paper presents Cloud portal with various service categories and Cloud service search engine for Cloud computing system. In Cloud service search engine, we use Cloud ontology to semantically define the relationship among Cloud services. It contains a set of Cloud concepts, individuals of those concepts, and the relationship among those individuals. It is used for determining the similarity among Cloud services with three kinds of reasoning methods (1) concept similarity reasoning, (2) object property similarity reasoning, and (3) datatype property similarity reasoning. The interface and features of the Cloud portal are briefly presented. Finally a proof-of-concept-example demonstrates the features and functionalities of our Cloud portal and Cloud search engine.
MapReduce programming model is emerging as an efficient tool for data-intensive applications. Hadoop, an open-source implementation of MapReduce, has been widely adopted and experienced by both academia and enterprise...
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ISBN:
(纸本)9781605589428
MapReduce programming model is emerging as an efficient tool for data-intensive applications. Hadoop, an open-source implementation of MapReduce, has been widely adopted and experienced by both academia and enterprise. Recently, lots of efforts have been done on improving the performance of MapReduce system and on analyzing the MapReduce process based on the log files generated during the Hadoop execution. Visualizing log files seems to be a very useful tool to understand the behavior of the Hadoop process. In this paper, we present MRScope, a real-time MapReduce tracing tool. MR-Scope provides a real-time insight of the MapReduce process, including the ongoing progress of every task hosted in Task Tracker. In addition, it displays the health of the Hadoop cluster data nodes, the distribution of the file system's blocks and their replicas and the content of the different block splits of the file system. We implement MR-Scope in native Hadoop 0.1. Experimental results demonstrat that MR-Scope's overhead is less than 4% when running wordcount benchmark. Copyright 2010 ACM.
As the degree of virtualization is growing considerably, improving performance of virtual machine environments motivates deeper investigation of the internal processes and performance implications of virtualization. S...
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VM (virtual machine) scheduling is a fundamental topic of virtualization, and fairness is its important design goal. Most VMMs (virtual machine monitor) provide PS (proportional share) schedulers. A PS scheduler assig...
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The most cost effective approach to study the performance of virtualization technology is to use the benchmark. In this paper, we present a new evaluation methodology to deploy a benchmark evaluation with a Benchmark ...
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In this work, we propose computing resource management system based on Xen VMM and multi-core system, called XenMVM. It adjusts the computing resource dynamically according to the actual workload generated by the appl...
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Different virtual educational experiments (VEEs) have different frameworks and implementations in different disciplines. How to integrate different VEEs into one unified supporting platform is a challenging problem. T...
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With the development of client virtualization technology, it has become an important tendency to apply soft real-time applications in virtual environment. Currently, most schedulers in VMM (i.e., virtual machine monit...
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ISBN:
(纸本)9781424477159
With the development of client virtualization technology, it has become an important tendency to apply soft real-time applications in virtual environment. Currently, most schedulers in VMM (i.e., virtual machine monitor) take the fairly sharing of processor resources and load balancing as a main concern, while show less regard to application diversity and I/O responsiveness. This would be unable to meet the requirement for latency-sensitive tasks, such as audio application. Audio stream may suffer from severe input buffer overrun or output buffer underrun, especially in the case that there is no real-time guarantee on virtualized clients under heavy load. In this paper, we introduce experiments to illustrate that current scheduler in Xen does a poor job in guaranteeing fluent audio playing, and then formulate the fluent playing conditions a scheduler should satisfy. A scheduling strategy with soft real-time support is proposed to improve the responsiveness of latency-sensitive guests. To implement our proposition, we extend the Credit scheduler by using flexible time slice and real-time priority. Our solution is audio-aware and capable of adjusting the real-time priority of guest domains adaptively, achieving a better experience for end-users. The experimental results show that audio glitches can be completely eliminated via our extended scheduler even when the system load is very high.
The largest difference between a distributed and a non-distributed system is that the former introduces network messages to the system. Network messages bring the scalab.lity to a distributed system as well as complex...
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