A taxonomic framework for autonomous service management in Service-Oriented Architecture

Since Service-Oriented Architecture (SOA) reveals the black box nature of services,heterogeneity,service dynamism,and service evolvability,managing services is known to be a challenging problem.Autonomic computing (AC) is a way of designing systems that can manage themselves without direct human intervention.Hence,applying the key disciplines of AC to service management is appealing.A key task of service management is to identify probable causes for symptoms detected and to devise actuation methods that can remedy the causes.In SOA,there are a number of target elements for service remedies,and there can be a number of causes associated with each target element.However,there is not yet a comprehensive taxonomy of causes that is widely accepted.The lack of cause taxonomy results in the limited possibility of remedying the problems in an autonomic way.In this paper,we first present a meta-model,extract all target elements for service fault management,and present a computing model for autonomously managing service faults.Then we define fault taxonomy for each target element and inter-relationships among the elements.Finally,we show prototype implementation using cause taxonomy and conduct experiments with the prototype for validating its applicability and effectiveness.

Autonomic failure prediction based on manifold learning for large-scale distributed systems

This article investigates autonomic failure prediction in large-scale distributed systems with nonlinear dimensionality reduction to automatically extract failure features. Most existing methods for failure prediction focus on building prediction models or heuristic rules by discovering failure patterns, but the process of feature extraction before failure patterns recognition is rarely considered due to the increasing complexity of modern distributed systems. In this work, a novel performance-centric approach to automate failure prediction is proposed based on manifold learning （ML）. In addition, the ML algorithm named supervised locally linear embedding （SLLE） is applied to achieve feature extraction. To generalize the dimensionality reduction mapping, the nonlinear mapping approximation and optimization solution is also proposed. In experimental work a file transfer test bed with fault injection is developed which can gather multilevel performance metrics transparently. Based on the runtime monitoring of these metrics, the SLLE method can automatically predict more than 50% of the central processing unit （CPU） and memory failures, and around 70% of the network failure.

Autonomic Performance and Power Control on Virtualized Servers:Survey, Practices, and Trends

Modern datacenter servers hosting popular Internet services face significant and multi-facet challenges in performance and power control. The user-perceived performance is the result of a complex interaction of complex workloads in a very complex underlying system. Highly dynamic and bursty workloads of Internet services fluctuate over multiple time scales, which has a significant impact on processing and power demands of datacenter servers. High-density servers apply virtualization technology for capacity planning and system manageability. Such virtuMized computer systems are increasingly large and complex. This paper surveys representative approaches to autonomic performance and power control on virtualized servers, which control the quality of service provided by virtualized resources, improve the energy efficiency of the underlying system, and reduce the burden of complex system management from human operators. It then presents three designed self-adaptive resource management techniques based on machine learning and control for percentile-based response time assurance, non-intrusive energy-efficient performance isolation, and joint performance and power guarantee on virtualized servers. The techniques were implemented and evaluated in a testbed of virtualized servers hosting benchmark applications. Finally, two research trends are identified and discussed for sustainable cloud computing in green datacenters.