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IEEE International Conference on Cluster computing

作者： Song Wu Huahua Sun Like Zhou Qingtian Gan Hai Jin Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509036547

With the development of multi-core platforms andcloud computing, Non-Uniform Memory Access (NUMA) architecturehas been dominant in cloud data centers in recentyears. However, NUMA architecture is not well supported invirtualized environments. Because of the semantic gap introducedby the virtualization layer, hypervisors know little aboutthe characteristics of applications running in virtual machines (VMs). More importantly, in order to guarantee hypervisors' applicability, load balance strategies of virtual CPU (VCPU) schedulers do not consider the memory access characteristicsof applications running in VMs, which probably introducessignificant shared resource contention and unnecessary remotememory accesses. In this paper, we propose a NUMA-aware VCPU schedulerbased on Xen, named vProbe, to improve the performanceof memory-intensive applications while maintaining the transparencyof the virtualization layer in NUMA-based servers. It collects performance monitoring units (PMU) data for eachVCPU and analyzes their memory access characteristics. Then, according to the memory access characteristics of each VCPU, it periodically reassigns all memory-intensive VCPUs to eachNUMA node evenly while preferentially allocating them to theirlocal nodes, which aims to alleviate shared resource contentionand reduce unnecessary remote memory accesses. Moreover, when a physical CPU (PCPU) becomes idle, it preferentiallysteals a VCPU from the run queues of PCPUs in the local nodeto this PCPU, which helps to maintain balanced last-level cache (LLC) contention and reduce extra remote memory accesses. Our evaluation shows that vProbe can significantly improve theperformance of memory-intensive applications (e.g., up to 45.2% performance improvement compared with the Credit scheduler) while introducing negligible overheads.

关键词： Servers Phasor measurement units Virtualization Virtual machine monitors Resource management Bandwidth Cloud computing

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Cost-Effective Resource Allocation in C-RAN with Mobile Cloud

Cost-Effective Resource Allocation in C-RAN with Mobile Clou...

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Ad-Hoc and Sensor Networks Symposium

作者： Kezhi Wang Kun Yang Xinhou Wang Chathura Sarathchandra Magurawalage School of Computer Sciences and Electrical Engineering University of Essex Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781479966653

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operator's cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms non-optimal solutions.

关键词： C-RAN Cost-Effective Resource Allocation MCC

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SymS: A symmetrical scheduler to improve multi-threaded program performance on NUMA systems

SymS: A symmetrical scheduler to improve multi-threaded prog...

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

Summary The nonuniform memory access (NUMA) architecture has been used extensively in data centers. Most of the previous works used single-threaded multiprogrammed workloads to study the performance of NUMA systems, which mainly focus on two classes of problems: resource contention and data locality. However, when running multi-threaded programs on NUMA systems, the critical thread of these programs significantly influences the system performance and brings new challenges that are different from those in a single-threaded situation. In particular, an additional scheduling scheme is desired to avoid the performance degradation caused by the critical thread of multi-threaded programs running on NUMA systems. This work presents a scheduler, Symmetrical Scheduler, which successfully solves the lagging problem by balancing the number of the costly remote shared data accesses for threads on NUMA systems. To the best of our knowledge, little work has been conducted to examine the performance impacted by the critical thread of multi-threaded programs on NUMA systems. By running the PARSEC benchmark on such systems, our methodology can improve the program performance by a factor of 6% on average and achieve maximally 25.3% improvement compared with Linux kernel scheduling mechanism. © 2015 John Wiley & Sons, Ltd.

关键词： Scheduling

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PSG： a two-layer graph model for document summarization

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Frontiers of Computer Science 2014年第1期8卷 119-130页

作者： Heng CHEN Hai JIN Feng ZHAO Service Computing Technology and System Lab & Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is presented, that not only sentences relevance but also phrases relevance information included in sentences are utilized. In a word, we construct a phrase-sentence two-layer graph structure model （PSG） to summarize document（s） . We use this model for generic document summarization and query-focused sum- marization. The experimental results show that our model greatly outperforms existing work.

关键词： relationship graph Markov random walk doc-ument summarization

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An adaptive switching scheme for iterative computing in the cloud

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Frontiers of Computer Science 2014年第6期8卷 872-884页

作者： Yu ZHANG Xiaofei LIAO Hai JIN Li LIN Feng LU Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Delta-based accumulative iterative computation （DAIC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers from the serious network jitter and load jitter caused by multi- tenancy in the cloud. In this paper, we develop a system, namely Hyblter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.

关键词： iterative algorithm computational skew communication skew cloud delta-based accumulative iterative computation

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Accelerating GPGPU architecture simulation

Accelerating GPGPU architecture simulation

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2013 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2013

作者： Yu, Zhibin Eeckhout, Lieven Goswami, Nilanjan Li, Tao John, Lizy K. Jin, Hai Xu, Chengzhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China ELIS Department Ghent University Belgium Intelligent Design of Efficient Architectures Lab. University of Florida Gainesville FL United States Department of Electrical and Computer Engineering University of Texas Austin TX United States Service Computing Technologies and System Lab./Cluster and Grid Computing Lab. HUST Wuhan China Department of Electrical and Computer Engineering Wayne State University MI United States

ISBN: (纸本)9781450319003

Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.

关键词： Graphics processing unit

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System level power characterization of multi-core computers with dynamic frequency scaling support

System level power characterization of multi-core computers ...

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2012 IEEE International Conference on Cluster computing Workshops, Cluster Workshops 2012

作者： Jiang, Congfeng Grid and Service Computing Technology Lab Hangzhou Dianzi University Hangzhou 310037 China

ISBN: (纸本)9780768548449

Multi-core architecture has become prominent in modern processors including personal computers, large scale server systems and embedded systems. While multi core processors provide higher computing performance, they have higher power density and also consume more energy than traditional single core processors. Therefore, Dynamic Frequency Scaling (DFS) is mostly implemented in multi-core processors in order to reduce power and energy consumptions. However, the built-in dynamic frequency scaling ability in processors is not enough for highly dynamic and mixed workloads including computing intensive and I/O intensive computing workloads. In this paper we conducted extensive experiments to characterize the processor utilization, power consumption, and application performance. We provide the experiment results and observations. We found that: (1) There is no power model that is capable for different computing systems, even for the same workloads. (2) Processor utilization is not always the indicator for power consumption. For example, in some scenarios such as mixed type and I/O intensive applications, the power consumption is not proportional to the processor utilization and even keeps approximately unchanged. (3) Different computing systems with different subsystems configurations, i.e., processors, memories, and disks, have different power curves due to their proportion in total system-level power consumption because these different subsystems consume different basic power and dynamic power when running different workloads. © 2012 IEEE.

关键词： Electric power utilization

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Accelerating GPGPU architecture simulation 13

Accelerating GPGPU architecture simulation

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Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems

作者： Zhibin Yu Lieven Eeckhout Nilanjan Goswami Tao Li Lizy John Hai Jin Chengzhong Xu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China ELIS Department Ghent University Ghent Belgium Intelligent Design of Efficient Architectures Lab University of Florida Gainesville USA Department of Electrical and Computer Engineering University of Texas at Austin Austin USA Service Computing Technologies and System Lab/Cluster and Grid Computing Lab HUST Wuhan China Department of Electrical and Computer Engineering Wayne State University Detroit USA

ISBN: (纸本)9781450319003

关键词： micro-architecture simulation performance general purpose graphics processing unit (GPGPU)

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A novel memory-based storage optimization approach for RDF data supporting SPARQL query 1

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4th International Conference on Human-Centric computing, HumanCom'11 and the 6th International Conference on Embedded and Multimedia computing, EMC'11

作者： Zhao, Feng Wu, Delong Yuan, Pingpeng Jin, Hai Service Computing Technology and System Lab. Cluster and Grid Computing Lab. Huazhong University of Science and Technology 430074 Wuhan China

ISBN: (数字)9789400721050

ISBN: (纸本)9789400721043

Due to sparse of RDF data, RDF storage approaches using triple table or binary file rarely show high storage usage and high query performance. To achieve the goal of decreasing storage space and improving the efficiency and generality of query on RDF data, a memory-based storage optimization approach supporting SPARQL query is proposed in this paper. For storage efficiency, strings are transferred to 32-bit integer identifiers, RDFS/OWL is used to organize the RDF data storage model, vertical partition method and other ways are utilized to split and organize the RDF triples. For query generality, the storage model also partially supports SPARQL query. Furthermore, by getting the statistics of the underlying storage data, we construct a SPARQL query optimized module. Experiments show that our query optimization module greatly improves the performance of the SPARQL query. Compared with Jena Memory and RDF-3X, our storage method has higher performance. © 2011 Springer Science+Business Media B.V.

关键词： Digital storage

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Resources Allocation in Virtualized Systems Based on Try- Before-Buy Approach

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Procedia Environmental Sciences 2011年 11卷 193-199页

作者： Ritai Yu Congfeng Jiang Xianghua Xu Hui Cheng Jian Wan Grid and Service Computing Technology Lab Hangzhou Dianzi University Hangzhou 310037 China

In virtualization environments, resources are shared across multiple virtual machines(VMs), which results in contentions and even conflicting under heavily loaded or consolidated situations. In order to accommodate as many as service instances while still delivering performance guarantees, resource allocation should be optimized in a just adequate manner such that less resources will be utilized for a specific performance requirement. To achieve this goal, in this paper we propose a try-before-buy approach for allocating contending resources. This approach firstly does a try of minor resource allocation to find the performance feedback and search for the optimal amount of resource that should be allocated to the target virtual machine. This approach does not require a highly accurate performance model in a virtualized system where workloads usually change frequently with time in some intervals. Experiments on a Xen based virtualized environment are conducted and evaluated for its effectiveness. The results show that the proposed approach utilizes less CPU and memory resources to achieve the same performance goals compared to default Xen configuration with over-provisioning.

关键词： Virtualization Provisioning Resource allocation Performance feedback

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