Performance problem localization in self-healing, service-oriented systems using Bayesian networks

Zhang, Rui; Moyle, Steve; McKeever, Steve; Bivens, Alan

doi:10.1145/1244002.1244027

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Bayesian networks have been used for developing self-aware services to detect any anomaly in their own behavior while functioning on the internet [8]. As performance problem localization is required to provide self-healing capabilities and deliver the desired quality of service (QoS) in distributed, service-oriented environments, an automated approach to identifying system elements causing performance problems was presented by building on a Bayesian network model that supports probabilistic inference among service elapsed times to end-to-end response time [9]. Bayesian reasoning techniques are applied to perform fault localization in complex communication systems while using dynamic, ambiguous, uncertain, or incorrect information about the system structure and state [10].…”

Section: Related Workmentioning

confidence: 99%

Fault Localization for Self-Managing Based on Bayesian Network

Piao¹,

Park²,

Lee³

2008

The KIPS Transactions:PartB

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Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

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Section: Related Workmentioning

confidence: 99%

Fault Localization for Self-Managing Based on Bayesian Network

Piao¹,

Park²,

Lee³

2008

The KIPS Transactions:PartB

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“…More recently, Global-view PL strategies that integrate the global QoS goals into the localization process [7,14] have emerged. They are designed and implemented with the clear aim of finding components that are most likely to improve end-to-end behaviors if fixed.…”

Section: Problem Localization Techniques and Expensesmentioning

confidence: 99%

“…They are designed and implemented with the clear aim of finding components that are most likely to improve end-to-end behaviors if fixed. This category is embodied by our previous work [14]:…”

Section: Problem Localization Techniques and Expensesmentioning

confidence: 99%

“…Most existing problem localization efforts [2,13,7,14], whilst effective in their own rights, make no attempt to address the issue of scalability. These works have been summarized in Section 2.…”

Section: Related Workmentioning

confidence: 99%

“…number of components considered) [11]. Existing PL solutions [7,14,2,9,13] often rely heavily on comparisons of component states. Their costs increase rapidly with the number of components, thus failing the large-scale challenge.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scalable Problem Localization for Distributed Systems: Principles and Practices

Zhang

Oliveira

Bivens

et al. 2007

Proceedings of the 2nd International ICST Conference on Scalable Information Systems

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Problem localization is a critical part of providing crucial system management capabilities to modern distributed environments. One key open challenge is for problem localization solutions to scale for systems containing hundreds or even thousands of nodes, whilst still remaining fast enough to respond to rapid environment changes and sufficiently cost-effective to avoid overloading any management or application component. This paper meets the challenge by introducing two scalable frameworks applicable to a wide range of existing problem localization solutions: one based on a summarydriven, narrow-down procedure, the other through decomposing and decentralizing the problem localization process. Both frameworks, at their best, are able to achieve O(logN ) problem localization time and O(1) per node communication load. The contrasting natures of both frameworks provide them with complimentary strengths that make them suitable for different scenarios in practice. We demonstrate our approaches in simulation settings and two real-world environments and show promising scalability benefits that can make a difference in system management operations.

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Problem localization using probabilistic dependency analysis for automated system management in ubiquitous computing

2009

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PurposeThis paper seeks to develop an approach to problem localization and an algorithm to address the issue of determining the dependencies among system metrics for automated system management in ubiquitous computing systems.Design/methodology/approachThis paper proposes an approach to problem localization for learning the knowledge of dynamic environment using probabilistic dependency analysis to automatically determine problems. This approach is based on Bayesian learning to describe a system as a hierarchical dependency network, determining root causes of problems via inductive and deductive inferences on the network. An algorithm of preprocessing is performed to create ordering parameters that have close relationships with problems.FindingsThe findings show that using ordering parameters as input of network learning, it reduces learning time and maintains accuracy in diverse domains especially in the case of including large number of parameters, hence improving efficiency and accuracy of problem localization.Practical implicationsAn evaluation of the work is presented through performance measurements. Various comparisons and evaluations prove that the proposed approach is effective on problem localization and it can achieve significant cost savings.Originality/valueThis study contributes to research into the application of probabilistic dependency analysis in localizing the root cause of problems and predicting potential problems at run time after probabilities propagation throughout a network, particularly in relation to fault management in self‐managing systems.

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Performance problem localization in self-healing, service-oriented systems using Bayesian networks

Cited by 7 publications

References 14 publications

Fault Localization for Self-Managing Based on Bayesian Network

Fault Localization for Self-Managing Based on Bayesian Network

Scalable Problem Localization for Distributed Systems: Principles and Practices

Problem localization using probabilistic dependency analysis for automated system management in ubiquitous computing

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