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UiLog： Improving Log-Based Fault Diagnosis by Log Analysis

Journal of Computer Science & technology 2016年第5期31卷 1038-1052页

作者： De-Qing Zou Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan ~3007~ China Big Data Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074 China Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan 430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.

关键词： fault diagnosis system event log log classification fault correlation analysis

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HCOpt: An automatic optimizer for configuration parameters of Hadoop 2nd

HCOpt: An automatic optimizer for configuration parameters o...

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2nd International Conference on Human Centered computing, HCC 2016

作者： Zhang, Xiong Zeng, Linxi Shi, Xuanhua Wu, Song Xie, Xia Jin, Hai Services Computing Technology and System Laboratory Big Data Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319318530

MapReduce is an efficient tool for data-intensive applications. Hadoop, an open-source implementation of MapReduce, has been widely adopted and experienced by some enterprises and scientific computing communities. However, when users intend to run a MapReduce program in Hadoop, they have to set a number of configuration parameters to make sure the program runs efficiently. Users often run into performance problems because they are unaware of how to set these parameters. To address these performance problems, we focus on the optimization opportunities presented by the high configurability of Hadoop, and propose an automation tool named HCOpt for performance optimization of Hadoop configuration parameters. HCOpt uses a Profile Engine to collect monitoring information from running MapReduce programs, a Prediction Engine to estimate the performance of a given Hadoop configuration and a genetic-based search algorithm to find an optimized configuration in the large search space. Our evaluation shows that HCOpt reduces the job completion time of Hadoop applications by up to 20 % when compared to applications run with configuration parameters that suggested by the rule-based optimization. © Springer International Publishing Switzerland 2016.

关键词： MapReduce

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Sponge:An Oversubscription Strategy Supporting Performance Interference Management in Cloud

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China Communications 2015年第11期12卷 196-209页

作者： LIU Ying Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Resource oversubscription optimizes the utilization of the computing resources. Many well-known virtual machine monitors(VMMs)such as Xen and KVM,adopt this approach to help maximize the yield of the cloud datacenters That is,with proper resource oversubscription strategies,more virtual machines(VMs) can be supported by limited resources. However performance interference among VMs hosting in the same physical machines(PMs) exists in cloud environment,and probably aggravated by resource oversubscription strategies,which aims to put more VMs into the same PM. In this paper,we present a resource oversubscription strategy called Sponge targeting cloud platforms Sponge mitigates the issue of performance interference among the oversubscribed co-hosting VMs. Sponge also provides a VM association strategy for each PM to handle with its besteffort. We performed our evaluation on a virtua datacenter simulated by Xen. Our evaluation results show that Sponge improves the resources utilization and manages to make each VM mee its performance requirement even hosting with other VMs in the same PM.

关键词： resources oversubscription performance interference cloud computing resource management

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A Novel Metadata Extraction Method for Surveillance Video

A Novel Metadata Extraction Method for Surveillance Video

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International Conference on computing, Networking and Communications

作者： Ran Zheng Long Chen Hai Jin Lei Zhu Qin Zhang Services 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)9781467385800

Surveillance videos are increasing massively with monitoring cameras widely deployed in cities. Metadata of moving objects in videos can reduce video storage and be utilized in many video analysis applications. The whole metadata extraction is too time-consuming to be fulfilled quickly. Therefore, it is urgent to parallelize and accelerate the extraction process. However, iterative execution of traditional metadata extraction methods makes the parallelization quite challenging. In this paper, we propose a novel Parallel Metadata Extraction (PME) method for surveillance video. A novel video segmentation algorithm is designed to segment whole video into independent video segments. The metadata of each video segment is extracted independently and simultaneously on computer nodes, which will be integrated later to guarantee the completeness of the metadata. Performance evaluations demonstrate that PME can accelerate extraction process greatly almost without metadata loss.

关键词： Surveillance Videos Video Segmentation Metadata Extraction Iterative Execution MapReduce

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Conch: A Cyclic MapReduce Model for Iterative Applications

Conch: A Cyclic MapReduce Model for Iterative Applications

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Ran Zheng Genmao Yu Hai Jin Xuanhua Shi Qin Zhang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

MapReduce programming model is a popular model to simplify but speed up data parallel applications. However, it is not efficient for iterative applications because of its repeated data transmission with HDFS (Hadoop Distributed File System). Conch, a cyclic MapReduce model, is designed for efficient processing of iterative applications. In order to minimize network overhead, shared data is cached locally and a "map-shuffle" phase is presented with a combined transmission mechanism. Meanwhile, a prediction scheduler for iterative applications is brought out to achieve better data locality in terms of runtime information. The experiments show that Conch can support iterative applications transparently and efficiently. Compared with Hadoop and HaLoop in single-job environment, Conch can achieve 13%-17% improvements on K-Means and fuzzy C-Means. Especially in multi-job environment, 63.6% and 28.6% improvements can be obtained compared with Hadoop and HaLoop.

关键词： Delays Iterative methods Computational modeling Prediction algorithms Heart beat Data transfer Data models

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Piggyback game: Efficient event stream dissemination in Online Social Network systems

Piggyback game: Efficient event stream dissemination in Onli...

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International Conference on Network Protocols

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

ISBN: (纸本)9781509032822

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffics due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server messages for event stream dissemination. Different sub-graph structures are exploited for achieving the approximated optimal assignment. However, such schemes incur high costs of computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the high complex graph computation problem into individuals' rational strategy selection of each node. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existing of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimal. We conduct comprehensive experiments to evaluate the performance of this design using large-scale real-world traces from popular OSN systems. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.

关键词： Games Schedules Heuristic algorithms Algorithm design and analysis Nash equilibrium Social network services Servers

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Understanding and identifying latent data races cross-thread interleaving

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Frontiers of Computer Science 2015年第4期9卷 524-539页

作者： Long ZHEN~ Xiaofei LIAO Song WU Xuepeng FAN Hai JIN Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Data races are ubiquitous in multi-threaded ap- plications, but they are by no means easy to detect. One of the most important reasons is the complexity of thread in- terleavings. A volume of research has been devoted to the interleaving-insensitive detection. However, all the previous work focuses on the uniform detection （unknown to the char- acteristics of thread interleavings）, thereby making the detec- tion defective in either reporting false positives or suffering from prohibitive overhead. To cope with the problem above, we propose an efficient, precise, and sound step-by-step res- olution based on the characteristics of thread interleavings. We first try to tease apart the categories of thread interleav- ings from the several typical sources arising from the lock synchronizations. We then conduct a brief study and find a new and complex pattern the previous work cannot detect. It is also revealed that the simple pattern with the majority of thread interleavings can be resolved by a simple processing to achieve a big profit for the previous reordering-based design. Our final experimental results demonstrate the effectiveness of our empiricism-based approach, and show that 51.0% of execution time and 52.3 % of trace size arising from the state- of-the-art reordering technique can be saved through a quick filtering of the simple pattern with a negligible （4.45%） per- formance overhead introduced on-the-fly.

关键词： data race happens-before thread interleaving

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A Fine-Grained Parallel Intra Prediction for HEVC Based on GPU

A Fine-Grained Parallel Intra Prediction for HEVC Based on G...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Wenbin Jiang Ye Chi Hai Jin Xiaofei Liao Yangsong Zhang Geyan Ye Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509053827

Intra prediction in HEVC is much more complex compared to the one in H.264 because of the more diversifications of the block sizes and prediction modes. The state-of-the-art researches for its parallelization only focus on block-level methods, which only take very limited advantage of GPUs. It is still a big challenge to implement fine-grained parallelism on GPU in consideration of the HEVC branch instructions and the different prediction formulae. We present a novel pixel-level parallelism method for the intra prediction of HEVC based on GPU combined with mode-level parallelism. By unifying not only the prediction formulae between angular mode and planar mode but also a predictor array, an algorithm based on look-up table is proposed to greatly reduce branches and improve prediction efficiency. With the help of look-up table algorithm, each pixel in a block can obtain the offset of corresponding reference pixels and find the value in the unifying predictor array at the same time which makes it possible to predict all pixels in parallel regardless of their relative positions in the block. The experimental results show that the proposed algorithm outperforms previous work and can reduce encoding time effectively.

关键词： Parallel processing Graphics processing units Encoding Prediction algorithms Instruction sets Algorithm design and analysis Standards

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vProbe: Scheduling Virtual Machines on NUMA Systems

vProbe: Scheduling Virtual Machines on NUMA Systems

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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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Robust Scheme to Protect Authentication Code of Message/Image Documents in Cloud computing

Robust Scheme to Protect Authentication Code of Message/Imag...

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International Conference on computing, Networking and Communications

作者： Zaid Ameen Abduljabbar Hai Jin Ali A. Yassin Zaid Alaa Hussien Mohammed Abdulridha Hussain Salah H. Abbdal Deqing Zou Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology University of Basrah

ISBN: (纸本)9781467385800

A number of image/message document authentication and integrity schemes have been conducted to recognize any modification in the exchange of documents between two entities (sender and receiver) within a cloud environment. Existing solutions are based on combining key-based hash function with traditional factors (steganography, smart-card, timestamp). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent common forms of attack such as replay, forgery, stolen verifier, brute force, and insider attacks. In this paper, we propose a scheme to ensure message/image document integrity for each user's login by providing one-time biometric message/image authentication code called MACLESS, which is a summation of combining the key-based hash function (MAC-SHA-1) of a message/image document and the one-time bio-key. Thereafter, MACLESS is hidden in a cover image based steganography anonymity. The proposed scheme has several important security attributes, such as phase key agreement, users' one-time bio-key, and one-time authentication code is valid only for one user's login session. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme.

关键词： Cloud computing MACLESS One-Time Bio-key One-Time Message/Image Document Authentication Code

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