2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems最新文献_第4页

Taxonomy-Driven Adaptation of Multi-layer Applications Using Templates 使用模板的多层应用程序的分类驱动适应

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2010-09-27 DOI: 10.1109/SASO.2010.23

R. Popescu, A. Staikopoulos, Peng Liu, Antonio Brogi, S. Clarke

引用次数: 33

Extracting Overlay Invariants of Distributed Systems for Autonomic System Management 基于自治系统管理的分布式系统覆盖不变量提取

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2010-09-27 DOI: 10.1109/SASO.2010.17

Hanhuai Shan, Guofei Jiang, K. Yoshihira

{"title":"Extracting Overlay Invariants of Distributed Systems for Autonomic System Management","authors":"Hanhuai Shan, Guofei Jiang, K. Yoshihira","doi":"10.1109/SASO.2010.17","DOIUrl":"https://doi.org/10.1109/SASO.2010.17","url":null,"abstract":"Many large-scale distributed systems have been built with great complexity to run Internet services. Due to the heterogeneity and dynamics of complex systems, it is very difficult to characterize their behavior precisely for system management. While we collect large amount of monitoring data from distributed systems as system observables, it is hard for us to interpret the data without constructing reasonable system models. Our previous work proposed algorithms to extract invariants from monitoring data to profile complex systems. However, such invariants are extracted between pair wise system measurements but not among multiple measurements. Based on minimal redundancy maximal relevance subset selection and least angle regression, this paper proposes an efficient algorithm to automatically extract overlay invariants from the layer of pair wise invariant networks. The overlay invariants link separated pair wise invariant subnets and enable us to support many system management tasks such as fault detection and capacity planning. Experimental results from synthetic data and real commercial systems are also included to demonstrate the effectiveness and efficiency of our algorithm.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122874841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A Machine Learning Approach to Performance Prediction of Total Order Broadcast Protocols 全序广播协议性能预测的机器学习方法

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2010-09-27 DOI: 10.1109/SASO.2010.41

Maria Couceiro, P. Romano, L. Rodrigues

引用次数: 29

Statistical Approaches to Predicting and Diagnosing Performance Problems in Component-Based Distributed Systems: An Experimental Evaluation 基于组件的分布式系统性能问题预测和诊断的统计方法:实验评估

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2010-09-01 DOI: 10.1109/SASO.2010.32

S. Correa, Renato Cerqueira

{"title":"Statistical Approaches to Predicting and Diagnosing Performance Problems in Component-Based Distributed Systems: An Experimental Evaluation","authors":"S. Correa, Renato Cerqueira","doi":"10.1109/SASO.2010.32","DOIUrl":"https://doi.org/10.1109/SASO.2010.32","url":null,"abstract":"One of the major problems in managing large-scale distributed systems is the prediction of the application performance. The complexity of the systems and the availability of monitored data have motivated the applicability of machine learning and other statistical techniques to induce performance models and forecast performance degradation problems. However, there is a stringent need for additional experimental and comparative studies, since there is no optimal method for all cases. In addition to a deeper comparison of different statistical techniques, studies lack on two important dimensions: resilience to transient failures of the statistical techniques, and diagnostic abilities. In this work, we address these issues, presenting three main contributions: first, we establish the capability of different statistical learning techniques for forecasting the resource needs of component-based distributed systems, second, we investigate an analysis engine that is more robust to false alarms, introducing a novel algorithm that augments the predictive power of statistical learning methods by combining them with a statistical test to identify trends in resources usage, third, we investigate the applicability of statistical tests for identifying the nature and cause of performance problems in component-based distributed systems.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130130580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Distributed Creation and Adaptation of Random Scale-Free Overlay Networks 随机无标度覆盖网络的分布式创建与自适应

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Pub Date : 2010-05-31 DOI: 10.1109/SASO.2010.45

Ingo Scholtes

{"title":"Distributed Creation and Adaptation of Random Scale-Free Overlay Networks","authors":"Ingo Scholtes","doi":"10.1109/SASO.2010.45","DOIUrl":"https://doi.org/10.1109/SASO.2010.45","url":null,"abstract":"Random scale-free overlay topologies provide a number of properties like for example high resilience against failures of random nodes, small (average) diameter as well as good expansion and congestion characteristics that make them interesting for the use in large-scale distributed systems. A number of these properties have been shown to be influenced by the exponent of their power law degree distribution. In this article, we present a distributed rewiring scheme that is suitable to effectuate random scale-free overlay topologies with an adjustable degree distribution exponent. The scheme uses a biased random walk strategy to sample new endpoints of edges being rewired and relies on an equilibrium model for scale-free networks. The bias of the random walk strategy can be tuned to produce random scale-free networks with arbitrary degree distribution exponents greater than two. We argue that the rewiring strategy can be implemented in a distributed fashion based on a node’s information about its immediate neighbors. We present both analytical arguments as well as results that have been obtained in simulations of the proposed protocol.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115298699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11