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2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Ma, Zhiyi Bai, Yanwei Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Peking Univ Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China

ISBN: (纸本)9781450365123

With(1) the scale of distributed system increasing fast, it becomes more and more difficult to monitor the whole system. The industry and academia have already provided some tools to solve this problem. However, they all compress the three-dimensional world into two-dimensional web pages. This makes monitoring tools inconvenient and not intuitive. It is widely known that the system monitoring and maintenance work is highly repetitive, and may lead to stress and health risks. Therefore, it is important to improve the experience of user interaction and make the monitoring tool more user-friendly. In order to achieve these goals, this paper proposes a novel distributed system monitoring tool that can restore the real world, using VR technology. This tool provides a more user-friendly interface and adequate monitoring functions.

关键词： distributed system monitoring Virtual Reality

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MiCA: Real-time Mixed Compression Scheme for Large-Scale distributed monitoring 43

MiCA: Real-time Mixed Compression Scheme for Large-Scale Dis...

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43rd Annual International Conference on Parallel Processing (ICPP)

作者： Wang, Bo Song, Ying Sun, Yuzhong Liu, Jun Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing 100190 Peoples R China

ISBN: (纸本)9781479956180

Real-time monitoring, providing the real-time status information of servers, is indispensable for the management of distributed systems, e.g. failure detection and resource scheduling. The scalability of fine-grained monitoring faces more and more severe challenges with scaling up distributed systems. The real-time compression which suppresses remote information update to reduce continuous monitoring cost is a promising approach to address the scalability problem. In this paper, we present the Linear Compression Algorithm (LCA) which is the application of the linear filter to real-time monitoring. To our best knowledge, existing work and LCA only explores the correlations of values of each single metric at various times. We present a novel lightweight REal-time Compression Algorithm (ReCA) which employs discovery methods of the correlation among metrics to suppress remote information update in distributed monitoring. The compression algorithms mentioned above have limited compression power because they only explore either the correlations of values of each single metric at various times or that among metrics. Therefore, we propose the Mixed Compression Algorithm (MiCA) which explores both of the correlations to achieve higher compression ratio. We implement our algorithms and an existing compression algorithm denoted by CCA in a distributed monitoring system Ganglia and conduct extensive experiments. The experimental results show that LCA and ReCA have comparable compression ratios with CCA, that MiCA achieves up to 38.2%, 27% and 44.5% higher compression ratios than CCA, LCA and ReCA with negligible overhead, respectively, and that LCA, and ReCA can both increase the scalability of Ganglia about 1.5 times and MiCA can increase about 2.33 times under a mixed-load circumstance.

关键词： distributed system monitoring real-time data compression

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Resilient Self-Compressive monitoring for Large-Scale Hosting Infrastructures

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IEEE TRANSACTIONS ON PARALLEL AND distributed systemS 2013年第3期24卷 576-586页

作者： Tan, Yongmin Venkatesh, Vinay Gu, Xiaohui MathWorks Inc Natick MA 01760 USA N Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA IBM Corp Res Triangle Pk NC 27709 USA

Large-scale hosting infrastructures have become the fundamental platforms for many real-world systems such as cloud computing infrastructures, enterprise data centers, and massive data processing systems. However, it is a challenging task to achieve both scalability and high precision while monitoring a large number of intranode and internode attributes (e. g., CPU usage, free memory, free disk, internode network delay). In this paper, we present the design and implementation of a Resilient self-Compressive monitoring (RCM) system for large-scale hosting infrastructures. RCM achieves scalable distributed monitoring by performing online data compression to reduce remote data collection cost. RCM provides failure resilience to achieve robust monitoring for dynamic distributed systems where host and network failures are common. We have conducted extensive experiments using a set of real monitoring data from NCSU's virtual computing lab (VCL), PlanetLab, a Google cluster, and real Internet traffic matrices. The experimental results show that RCM can achieve up to 200 percent higher compression ratio and several orders of magnitude less overhead than the existing approaches.

关键词： Online data compression distributed system monitoring

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Towards an Efficient Online Causal-Event-Pattern-Matching Framework

Towards an Efficient Online Causal-Event-Pattern-Matching Fr...

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33rd IEEE International Conference on distributed Computing systems (ICDCS)

作者： Pramanik, Sukanta Taylor, David Wong, Bernard Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780769550008

Event monitoring and logging, that is, recording the communication events between processes, is a critical component in many highly reliable distributed systems. The event logs enable the identification of certain safety-condition violations, such as race conditions and mutual-exclusion violations, as safety is generally contingent on processes communicating in a specific causally ordered pattern. Previous efforts at finding such patterns have often focused on offline techniques, which are unable to identify operational problems as they occur. Online monitoring tools exist but they are often restricted to identifying a specific violation condition, such as a deadlock or a race condition, using dedicated data structures. We address the more general problem of detecting causally related event patterns that can be used to identify various undesired behaviours in the system. The main challenge for online pattern matching is the need to store the partial matches to the pattern, as they may combine with future events to form a complete match. Unlike pattern matching in most other domains, causally ordered patterns can span a potentially unbounded number of events and efficiently searching through this large collection poses a significant challenge. In this paper, we introduce OCEP, an efficient online causalevent- pattern-matching framework that bounds the number of partial matches it stores by reporting only a representative subset of pattern matches. We define a subset of matches as representative if it has at least one occurrence of each event in the pattern on each process, which is applicable for a large class of distributed applications. With this definition, OCEP introduces a backtracking algorithm to efficiently find a representative subset from the history of events. An evaluation of the framework shows that OCEP is capable of handling several frequently occurring violation patterns at the event rates of some representative distributed applications.

关键词： Causal Ordering Event-Based system distributed system distributed system monitoring

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