Dynamic Adaptation of Temporal Event Correlation Rules

Temporal event correlation is essential to realizing self-managing distributed systems. Autonomic controllers often require that events be correlated across multiple components using rule patterns with timer-based transitions, e.g., to detect denial of service attacks and to warn of staging problems with business critical applications. This short paper discusses automatic adjustment of timer values for event correlation rules, in particular compensating for the variability of event propagation delays due to factors such as contention for network and server resources. We describe a corresponding Management Station architecture and present experimental studies on a testbed system that suggest that this approach can produce results at least as good as an optimal fixed setting of timer values

Dynamic Adaptation of Rules for Temporal Event Correlation in Distributed Systems

Event correlation is essential to realizing self-managing distributed systems. For example, distributed systems often require that events be correlated from multiple systems using temporal patterns to detect denial of service attacks and to warn of problems with business critical applications that run on multiple servers. This paper addresses how to specify timer values for temporal patterns so as to manage the trade-off between false alarms and undetected alarms. A central concern is addressing the variability of event propagation delays due to factors such as contention for network and server resources. To this end, we develop an architecture and an adaptive control algorithm that dynamically compensate for variations in propagation delays. Our approach makes Management Stations more autonomic by avoiding the need for manual adjustments of timer values in temporal rules. Further, studies we conducted of a testbed system suggest that our approach produces results that are at least as good as an optimal fixed setting of timer values

A Control Theory Foundation for Self-Managing Computing Systems

The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems. Control theory provides a rich set of methodologies for building automated self-diagnosis and self-repairing systems with properties such as stability, short settling times, and accurate regulation. However, there are challenges in applying control theory to computing systems, such as developing effective resource models, handling sensor delays, and addressing lead times in effector actions. We propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing research problems in applying control theory to computing systems. The initial DTAC architecture is described along with several problems that it can be used to investigate

SLA Impact Modeling for Service Engagement

Abstract-During the customer engagement phase it is critical for the service providers to estimate the impact of service level constraints on service personnel needs. However, it is often difficult due to the implication from customer workload. In this paper we propose an SLA impact evaluation methodology that uses queueing models to quantitatively evaluate the impact of SLAs to the engagement cost model

IT service management automation -An automation centric approach leveraging configuration control, audit verification and process analytics

Abstract. People, processes, technology and information are the service provider's resources for delivering IT services. Process automation is one way in which service providers can reduce cost and improve quality by automating routine tasks thereby reducing human error and reserving people resources for those tasks which require human skill and complex decision making. In this paper we propose a conceptual methodology for IT service management process automation in the area of configuration control, audit verification, and process analytics. We employ a complexity model to assist in identifying the opportunities for process automation. We recommend and outline an automated approach to the complex task of variance detection of the hierarchically defined Configuration Items in a Configuration Management Database (CMDB) against the Configuration Items in the IT environment. We also recommend the integration of this automated detection with human centric remediation for resolving the variances detected and outline an automated approach to the variance detection