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4th International Conference on Cloud computing, CloudComp 2013

作者： Xu, Fei Liu, Fangming Zhu, Dekang Jin, Hai Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319055053

Running MapReduce in a shared cluster has become a recent trend to process large-scale data analytical applications while improving the cluster utilization. However, the network sharing among various applications can make the network bandwidth for MapReduce applications constrained and heterogeneous. This further increases the severity of network hotspots in racks, and makes existing task assignment policies which focus on the data locality no longer effective. To deal with this issue, this paper develops a model to analyze the relationship between job completion time and the assignment of both map and reduce tasks across racks. We further design a network-aware task assignment strategy to shorten the completion time of MapReduce jobs in shared clusters. It integrates two simple yet effective greedy heuristics that minimize the completion time of map phase and reduce phase, respectively. With large-scale simulations driven by Facebook job traces, we demonstrate that the network-aware strategy can shorten the average completion time of MapReduce jobs, as compared to the state-of-the-art task assignment strategies, yet with an acceptable computational overhead. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014.

关键词： MapReduce

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HRF: A resource allocation scheme for moldable jobs 15

HRF: A resource allocation scheme for moldable jobs

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12th ACM International Conference on computing Frontiers, CF 2015

作者： Wu, Song Tuo, Qiong Jin, Hai Yan, Chuxiong Weng, Qizheng Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450333580

Moldable jobs, which allow the number of allocated processors to be adjusted before running in clusters, have attracted increasing concern in parallel job scheduling research. Compared with traditional rigid jobs where the number of allocated processors is fixed, moldable jobs are more flexible and therefore have more potential for improving their average turnaround time (a crucial metric to describe performance of jobs in a cluster). Average turnaround time of moldable jobs depends greatly on resource allocation schemes. Unfortunately, existing schemes do not perform well in reducing average turnaround time, either because they only consider a single job's turnaround time instead of the average turnaround time of all jobs, or because they just aim at fairness between short and long jobs instead of their average turnaround time. In this paper, we investigate how resource allocation affects the average turnaround time of moldable jobs in clusters, and propose a scheme named HRF (highest revenue first), which allocates processors according to the highest revenue of shortening runtime. In our simulations, experimental results show that HRF can reduce average turnaround time up to 71% when compared with state-of-the-art schemes. © Copyright 2015 ACM.

关键词： Resource allocation

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A data-centric tool to improve the performance of multithreaded program on NUMA 15th

A data-centric tool to improve the performance of multithrea...

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Zeng, Dan Zhu, Liang Liao, Xiaofei Jin, Hai Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319271392

Non-uniform memory access (NUMA) is one of the main architectures of today’s high-performance server. The key feature of NUMA is the non-uniformity of access latency. Access from a processor to attached memory is faster, and it also reduces the possibility of causing contention on interconnect links and memory controller. Multithreaded programs may experience high memory latency without careful placement of data and thread. Thus, it is necessary to develop a tool to identify and help ameliorate NUMA problems. In this paper, we present a data-centric tool to analyze the performance of multithreaded programs on NUMA architectures and provide advices on how to improve the performance. This paper describes the design and implementation of the tool. The tool is evaluated on Linux using three benchmark applications, and the evaluation shows how this tool helps to identify costly variables and choose optimization methods. The result shows performance improvement of up to 51.92%. © 2015 Springer International Publishing Switzerland.

关键词： Benchmarking

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Dependency-aware maintenance for dynamic grid services

Dependency-aware maintenance for dynamic grid services

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36th International Conference on Parallel Processing in Xi'an, ICPP

作者： Jin, Hai Qi, Li Wu, Song Luo, Yaqin Dai, Jie Cluster and Grid Computing Lab. Service Computing Technology and System Huazhong University of Science and Technology

ISBN: (纸本)076952933X

Any mistaken maintenance for the complicated and distributed grid can bring unpredictable disaster. Here we focus on the system availability issues caused by service dependencies during the maintenance in grid. A novel mechanism, called Cobweb Guardian, is proposed in this paper. It provides multiple granularities (service-, container-, and node-level) maintenance for service components in grid. By using the Cobweb Guardian, grid administrators can execute the maintaining task safely in runtime with high availability. The evaluation results show that our proposed dependency-aware maintenance can make the grid management more automatic and available. © 2007 IEEE.

关键词： Maintenance

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Automatic detection and recognition of silicosis in chest radiograph

Automatic detection and recognition of silicosis in chest ra...

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作者： Zhu, Lei Zheng, Ran Jin, Hai Zhang, Qin Zhang, Wei Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Silicosis remains one of the most harmful occupational respiratory diseases. It threatens the workers exposed to dust environment. Chest radiograph is the main available image source for silicosis diagnosis according to the diagnostic criteria of pneumoconiosis (DCP). Automatic detection and recognition of silicosis in chest radiograph has great importance on aiding the process of silicosis diagnosis. This paper proposes a multi-scale opacity detection approach to detect all suspected opacities from the chest radiograph. A support vector machine (SVM) based computer-aided silicosis diagnosis is proposed to recognize silicosis opacity from a large amount of candidate regions, and gives processing result for radiologist reference. Comprehensive experiments conducted on real world chest radiographs demonstrate that the proposed approach can reveal changes of silicosis pathology well, and it can be adopted as an effective tool for automatic silicosis diagnosis. © 2014 - IOS Press and the authors.

关键词： Support vector machines

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Minimizing Overheads of Checkpoints in Distributed Stream Processing systems 7

Minimizing Overheads of Checkpoints in Distributed Stream Pr...

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7th IEEE International Conference on Cloud Networking, CloudNet 2018

作者： Muhammad Abrar Akber, Syed Chen, Hanhua Wang, Yonghui Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab Wuhan430074 China

ISBN: (纸本)9781538668313

Failure occurrence in large-scale systems is inevitable, which makes the resilience a key challenge for modern systems. Checkpoints with rollback recovery is a well-known approach to provide fault tolerance in distributed systems. The checkpoint based fault tolerance approach periodically persists the application state to reliable storage, which serves as a recovery point in case of failure. These periodic checkpoints are not inline with the failure rate of the systems as many studies conclude that failure occurrence is not periodic. The optimal size of checkpoint interval is a crucial decision, which directly determines the checkpoint overheads. To minimize the checkpoint overheads, we propose to reduce the number of checkpoints during the application execution. We suggest reducing the number of checkpoints by successively increasing the checkpoint intervals. We consider the failure probability of the underlying infrastructure and iteratively increase the checkpoint intervals. The proposed checkpoint approach tailors the checkpoint initializing based on the failure probability. If failure probability is low, it increases the checkpoint interval, and eventually reduces the total number of checkpoints triggered during application timespan. Reducing the total number of checkpoints during application execution results in decreasing the checkpoint overheads. The experiment results show that the proposed checkpoint policy considerably reduces the checkpoint overheads as compared to periodic checkpoints. © 2018 IEEE.

关键词： Fault tolerance

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25th IEEE International Conference on Network Protocols, ICNP 2017

作者： Chen, Hanhua Zhang, Fan Jin, Hai Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781509065011

Real-world stream data with skewed distribution raises unique challenges to distributed stream processing systems. Existing stream workload partitioning schemes usually use a 'one size fits all' design, which leverage either a shuffle grouping or a key grouping strategy for partitioning the stream workloads among multiple processing units, leading to notable problems of unsatisfied system throughput and processing latency. In this paper, we show that the key grouping based schemes result in serious load imbalance and low computation efficiency in the presence of data skewness while the shuffle grouping schemes are not scalable in terms of memory space. We argue that the key to efficient stream scheduling is the popularity of the stream data. We propose and implement a differentiated distributed stream processing system, call DStream, which assigns the popular keys using shuffle grouping while assigns unpopular ones using key grouping. We design a novel efficient and light-weighted probabilistic counting scheme for identifying the current hot keys in dynamic real-time streams. Two factors contribute to the power of this design: 1) the probabilistic counting scheme is extremely computation and memory efficient, so that it can be well integrated in processing instances in the system;2) the scheme can adapt to the popularity changes in the dynamic stream processing environment. We implement the DStream system on top of Apache Storm. Experiment results using large-scale traces from real-world systems show that DStream achieves a 2.3× improvement in terms of processing throughput and reduces the processing latency by 64% compared to state-of-the-art designs. © 2017 IEEE.

关键词： Higher order statistics

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A survey of cloud resource management for complex engineering applications

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Frontiers of Computer Science 2016年第3期10卷 447-461页

作者： Haibao CHEN Song WU Hai JIN Wenguang CHEN Jidong ZHAI Yingwei LUO Xiaolin WANG Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Institute of High Performance Computing Tsinghua University Beijing 100084 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China School of Computer and Information Engineering Chuzhou University Chuzhou 239000 China

Traditionally, complex engineering applications （CEAs）, which consist of numerous components （software） and require a large amount of computing resources, usu- ally run in dedicated clusters or high performance computing （HPC） centers. Nowadays, Cloud computing system with the ability of providing massive computing resources and cus- tomizable execution environment is becoming an attractive option for CEAs. As a new type on Cloud applications, CEA also brings the challenges of dealing with Cloud resources. In this paper, we provide a comprehensive survey of Cloud resource management research for CEAs. The survey puts forward two important questions： 1） what are the main chal- lenges for CEAs to run in Clouds？ and 2） what are the prior research topics addressing these challenges？ We summarize and highlight the main challenges and prior research topics. Our work can be probably helpful to those scientists and en- gineers who are interested in running CEAs in Cloud envi- ronment.

关键词： Cloud computing complex engineering application resource management virtualization

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High performance RDF updates with triplebit + 13

High performance RDF updates with triplebit +

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13th International Conference on Digital Information Management, ICDIM 2018

作者： Yuan, Pingpeng Fan, Lijian Jin, Hai Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781538652435

The volume of RDF data continues to grow over the past decade and many known RDF datasets have billions of triples. A grant challenge of managing this huge RDF data is how to access this big RDF data efficiently. A popular approach to addressing the problem is to build a full set of permutations of (S, P, O) indexes. Although this approach has shown to accelerate joins by orders of magnitude, the large space overhead limits the scalability of this approach and makes it heavyweight. In this paper, we present TripleBit +, a fast and compact system for updating RDF data. The design of TripleBit + has two salient features. First, the efficient maintenance strategies of TripleBit + reduces both the overhead to update data and indexes. Second, effective maintenance technologies to handle online updates over RDF repositories are proposed. Our experiments show that TripleBit + outperforms RDF-3X, MonetDB, BitMat on LUBM, UniProt, and BTC 2012 benchmark queries and it offers orders of mangnitude performance improvement for some complex join queries. Our design also yields high task rates as high as 660,000 per second and fast average response time of task which is faster than x-RDF-3X and PostgreSQL. © 2018 IEEE.

关键词： Benchmarking

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RB-GACA: An RBAC based grid access control architecture

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International Journal of grid and Utility computing 2005年第1期1卷 61-70页

作者： Jin, Hai Qiang, Weizhong Shi, Xuanhua Zou, Deqing Cluster and Grid Computing Lab. Huazhong University of Science and Technology Wuhan 430074 China

grid computing is emerging as a new format of wide area distributed computing. Because the distribution of services and resources in wide-area networks are heterogeneous, dynamic, and multi-domain, security is a critical concern in grid computing. Authorisation and access control, which are important aspects of security, have obtained more and more attention. This paper proposes a universal, scalable authorisation and access control architecture, RB-GACA, for grid computing. It is based on classical access control mechanism in distributed applications, Role Based Access Control (RBAC). The paper provides a flexible policy management approach for various grid environments. We also use a standard policy language for the presentation of access control policies to provide a general and standard support for different services and resources. Copyright © 2005, Inderscience Publishers.

关键词： Network architecture

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