![]() It applies a semi-Markov Process and is based on a novel resource availability model, combining generic hardware-software failures with domain-specific resource behavior in FGCS. ![]() This paper presents a method for resource availability prediction in FGCS systems. To provide fault tolerance to guest jobs without adding significant computational overhead, it requires to predict future resource availability. Guest jobs may fail because of unexpected resource unavailability. A characteristic of such resources is that they are generally provided voluntarily and their availability fluctuates highly. In FGCS, host computers allow guest jobs to utilize the CPU cycles if the jobs do not significantly impact the local users of a host. KNOX COLLEGE ISHARE OFFLINEExperimental results show the system achieves more than 76% accuracy in offline prediction and more than 70% accuracy in online prediction during the time from May 2006 to April 2007.įine-grained cycle sharing (FGCS) systems aim at utilizing the large amount of computational resources available on the Internet. We evaluate the performance of hPREFECTs in both offline prediction of failure by using the Los Alamos HPC traces and online prediction in an institute-wide clusters coalition environment. We implemented a failure prediction framework, called PREdictor of Failure Events Correlated Temporal-Spatially (hPREFECTs), which explores correlations among failures and forecasts the time-between-failure of future instances. We cluster failure events based on their correlations and predict their future occurrences. We further utilize the information of application allocation to discover more correlations among failure instances. In this paper, we develop a spherical covariance model with an adjustable timescale parameter to quantify the temporal correlation and a stochastic model to describe spatial correlation. Failure events in coalition systems exhibit strong correlations in time and space domain. Failure prediction is a crucial technique for self-managing resource burdens. In large-scale networked computing systems, component failures become norms instead of exceptions. From these results, we analyze the effectiveness of several algorithms at accurately detecting a variety of performance anomalies. ![]() We present results gathered using this infrastructure from instrumented Grid middleware and applications running on the Emulab testbed. We present an infrastructure for troubleshooting complex middleware, a general purpose technique for configurable log summarization, and an anomaly detection technique that works in near-real time on running Grid middleware. Any given action, for example, reliably transferring a directory of files, can involve a wide range of complex and interrelated actions across multiple pieces of software: checking user certificates and permissions, getting details for all files, performing third-party transfers, understanding re-try policy decisions, etc. KNOX COLLEGE ISHARE SOFTWAREUnfortunately, the same cannot yet be said of the end-to-end distributed software stack. Today's system monitoring tools are capable of detecting system failures such as host failures, OS errors, and network partitions in near-real time. Simultaneous monitoring in both user space and kernel space is also demonstrated. Hidden Markov models working in user space and neural network models working in kernel space are shown to be effective. The results provided demonstrate that the integrated intelligent agents can detect the execution of unauthorized applications and network faults that are not obvious in the standard output of traditional monitoring systems. ![]() Mechanisms provided by Ganglia make it relatively easy to integrate anomaly detection systems and to visualize the output of the agents. The Ganglia monitoring system was used as a test bed for inte-gration case studies. The intelligent agents pre-sented in this study employ machine learning techniques to develop profiles of normal behavior as seen in sequences of operating system calls (kernel-level monitoring) and function calls (user-level monitoring) generated by an application. This paper describes the integration of intelligent anomaly agents and traditional monitoring systems for high-performance distributed systems. Effective detection of these anomalies has become a high pri-ority because of the need to guarantee security, privacy and reliability. Anomalies in such systems can be caused by activities such as user misbehavior, intrusions, corrupted data, deadlocks, and failure of cluster components. High-performance computing clusters have be-come critical computing resources in many sensitive and/or economically important areas. ![]()
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