Enhancing Syndromic Surveillance through Autonomic Health Grids

The Centers for Disease Control defines syndromic surveillance as, “an investigational approach where health department staff, assisted by automated data acquisition and generation of statistical alerts, monitor disease indicators in real-time or near real-time to detect outbreaks of disease earlier than would otherwise be possible with traditional public health methods” (CDC, 2004). While syndromic surveillance has traditionally been used in the context of detecting natural outbreaks, it is increasingly being used to develop systems to detect bioterrorism outbreaks. Timely response to a bioterrorism event requires accurate information exchange between clinicians and public health officials. This entails building highly complex surveillance systems that provide access to heterogeneous/distributed medical data, computational resources and collaborative services, for real-time decision making in a highly reliable and secure environment. In this paper we propose enhancing syndromic surveillance through grid and autonomic computing augmentations, and present our approach to a proof of concept modeling and simulation environment

Autonomic Computing Correlation for Fault Management System Evolution

This paper discusses the emerging area of autonomic computing and its implications for the evolution of faultmanagement systems. Particular emphasis is placed on the concept of event correlation and its role in system self-management. A new correlation analysis tool to assist with the development, management and maintenance of correlation rules and beliefs is described

Pulse Monitoring: Extending the Health-check for the Autonomic GRID

This paper upon looking at the Autonomic Computing architecture and Grid Computing highlights the importance of health check mechanisms to achieve a reflex-healing duel strategy. This will provide new design options for the development of the Autonomic Grid. The resulting pulse monitor is based on extending the existing Grid heart-beat monitor with urgency or anxiety levels such as that used in the NASA beacon monitor. The paper concludes with a discussion that this health check mechanism may be utilized in the future to achieve the necessary sense of urgency within a system for affect and emotion intelligence

Adaptive Scheduling Across a Distributed Computation Platform

A programmable Java distributed system, which adapts to available resources, has been developed to minimise the overall processing time of computationally intensive problems. The system exploits the free resources of a heterogeneous set of computers linked together by a network, communicating using SUN Microsystems' Remote Method Invocation and Java sockets. It uses a multi-tiered distributed system model, which in principal allows for a system of unbounded size. The system consists of an n-ary tree of nodes where the internal nodes perform the scheduling and the leaves do the processing. The scheduler nodes communicate in a peer-to-peer manner and the processing nodes operate in a strictly client-server manner with their respective scheduler. The independent schedulers on each tier of the tree dynamically allocate resources between problems based on the constantly changing characteristics of the underlying network. The system has been evaluated over a network of 86 PCs with a bioinformatics application and the travelling salesman optimisation problem

Retrofitting Autonomic Capabilities onto Legacy Systems

Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is a promising solution to ever-increasing system complexity and the spiraling costs of human management as systems scale to global proportions. Most results to date, however, suggest ways to architect new software constructed from the ground up as autonomic systems, whereas in the real world organizations continue to use stovepipe legacy systems and/or build 'systems of systems' that draw from a gamut of disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand, modify or even recompile the target system's code. We present an autonomic infrastructure that operates similarly to active middleware, to explicitly add autonomic services to pre-existing systems via continual monitoring and a feedback loop that performs, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, independent of the rest of the full infrastructure, for use with a large variety of target systems. This work has been validated by several case studies spanning multiple application domains