Virus tests to maximize availability of software systems

AbstractSoftware systems in which many user's or programmers intervene may easily contain software items — such as viruses — which will endanger the integrity of the system. This paper proposes that in addition to the conventional recovery techniques, such as dumps and roll-back recovery, system availability be enhanced by the introduction of virus tests or other types of “failure tests”. We present a model to analyze the effect of the failure rate, the frequency of virus and failure testing, and the frequency of periodic dumps, on global system availability. We assume that the “failure” rate of the system increases as time elapses beyond any individual instant at which a virus test or failure test has been carried out. Thus, we are dealing with a system in which failures will be naturally time-dependent. We compute the optimum value of the interval between dumps, and also the best time interval between virus or failure tests for this system. In order to illustrate the methodology of this work, numerical examples are presented for various time-dependent failure statistics

Checkpoint placement algorithms for mobile agent system

2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Integrated analysis of error detection and recovery

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms

On the engineering of crucial software

The various aspects of the conventional software development cycle are examined. This cycle was the basis of the augmented approach contained in the original grant proposal. This cycle was found inadequate for crucial software development, and the justification for this opinion is presented. Several possible enhancements to the conventional software cycle are discussed. Software fault tolerance, a possible enhancement of major importance, is discussed separately. Formal verification using mathematical proof is considered. Automatic programming is a radical alternative to the conventional cycle and is discussed. Recommendations for a comprehensive approach are presented, and various experiments which could be conducted in AIRLAB are described

Resource management for extreme scale high performance computing systems in the presence of failures

2018 Summer.Includes bibliographical references.High performance computing (HPC) systems, such as data centers and supercomputers, coordinate the execution of large-scale computation of applications over tens or hundreds of thousands of multicore processors. Unfortunately, as the size of HPC systems continues to grow towards exascale complexities, these systems experience an exponential growth in the number of failures occurring in the system. These failures reduce performance and increase energy use, reducing the efficiency and effectiveness of emerging extreme-scale HPC systems. Applications executing in parallel on individual multicore processors also suffer from decreased performance and increased energy use as a result of applications being forced to share resources, in particular, the contention from multiple application threads sharing the last-level cache causes performance degradation. These challenges make it increasingly important to characterize and optimize the performance and behavior of applications that execute in these systems. To address these challenges, in this dissertation we propose a framework for intelligently characterizing and managing extreme-scale HPC system resources. We devise various techniques to mitigate the negative effects of failures and resource contention in HPC systems. In particular, we develop new HPC resource management techniques for intelligently utilizing system resources through the (a) optimal scheduling of applications to HPC nodes and (b) the optimal configuration of fault resilience protocols. These resource management techniques employ information obtained from historical analysis as well as theoretical and machine learning methods for predictions. We use these data to characterize system performance, energy use, and application behavior when operating under the uncertainty of performance degradation from both system failures and resource contention. We investigate how to better characterize and model the negative effects from system failures as well as application co-location on large-scale HPC computing systems. Our analysis of application and system behavior also investigates: the interrelated effects of network usage of applications and fault resilience protocols; checkpoint interval selection and its sensitivity to system parameters for various checkpoint-based fault resilience protocols; and performance comparisons of various promising strategies for fault resilience in exascale-sized systems