Software aging

Abstract: A frequent characterization of open source software is the somewhat outdated, mythical one of a collective of supremely talented software hackers freely volunteering their services to produce uniformly high-quality software. I contend that the open source software phenomenon has metamorphosed into a more mainstream and commercially viable form, which I label as OSS 2.0. I illustrate this transformation using a framework of process and product factors, and discuss how the bazaar metaphor, which up to now has been associated with the open source development process, has actually shifted to become a metaphor better suited to the OSS 2.0 product delivery and support process. Overall the OSS 2.0 phenomenon is significantly different from its free software antecedent. Its emergence accentuates the fundamental alteration of the basic ground rules in the software landscape, signifying the end of the proprietary-driven model that has prevailed for the past 20 years or so. Thus, a clear understanding of the characteristics of the emergent OSS 2.0 phenomenon is required to address key challenges for research and practice.

Abstract: Software failures are now known to be a dominant source of system outages. Several studies and much anecdotal evidence point to "software aging" as a common phenomenon, in which the state of a software system degrades with time. Exhaustion of system resources, data corruption, and numerical error accumulation are the primary symptoms of this degradation, which may eventually lead to performance degradation of the software, crash/hang failure, or other undesirable effects. "Software rejuvenation" is a proactive technique intended to reduce the probability of future unplanned outages due to aging. The basic idea is to pause or halt the running software, refresh its internal state, and resume or restart it. Software rejuvenation can be performed by relying on a variety of indicators of aging, or on the time elapsed since the last rejuvenation. In response to the strong desire of customers to be provided with advance notice of unplanned outages, our group has developed techniques that detect the occurrence of software aging due to resource exhaustion, estimate the time remaining until the exhaustion reaches a critical level, and automatically perform proactive software rejuvenation of an application, process group, or entire operating system, depending on the pervasiveness of the resource exhaustion and our ability to pinpoint the source. This technology has been incorporated into the IBM Director for xSeries servers. To quantitatively evaluate the impact of different rejuvenation policies on the availability of cluster systems, we have developed analytical models based on stochastic reward nets (SRNs). For timebased rejuvenation policies, we determined the optimal rejuvenation interval based on system availability and cost. We also analyzed a rejuvenation policy based on prediction, and showed that it can further increase system availability and reduce downtime cost. These models are very general and can capture a multitude of cluster system characteristics, failure behavior, and performability measures, which we are just beginning to explore.

Abstract: The phenomenon of software aging refers to the accumulation of errors during the execution of the software which eventually results in it's crash/hang failure. A gradual performance degradation may also accompany software aging. Pro-active fault management techniques such as "software rejuvenation" (Y. Huang et al., 1995) may be used to counteract aging if it exists. We propose a methodology for detection and estimation of aging in the UNIX operating system. First, we present the design and implementation of an SNMP based, distributed monitoring tool used to collect operating system resource usage and system activity data at regular intervals, from networked UNIX workstations. Statistical trend detection techniques are applied to this data to detect/validate the existence of aging. For quantifying the effect of aging in operating system resources, we propose a metric: "estimated time to exhaustion", which is calculated using well known slope estimation techniques. Although the distributed data collection tool is specific to UNIX, the statistical techniques can be used for detection and estimation of aging in other software as well.

Abstract: Several recent studies have reported & examined the phenomenon that long-running software systems show an increasing failure rate and/or a progressive degradation of their performance. Causes of this phenomenon, which has been referred to as "software aging", are the accumulation of internal error conditions, and the depletion of operating system resources. A proactive technique called "software rejuvenation" has been proposed as a way to counteract software aging. It involves occasionally terminating the software application, cleaning its internal state and/or its environment, and then restarting it. Due to the costs incurred by software rejuvenation, an important question is when to schedule this action. While periodic rejuvenation at constant time intervals is straightforward to implement, it may not yield the best results. The rate at which software ages is usually not constant, but it depends on the time-varying system workload. Software rejuvenation should therefore be planned & initiated in the face of the actual system behavior. This requires the measurement, analysis, and prediction of system resource usage. In this paper, we study the development of resource usage in a web server while subjecting it to an artificial workload. We first collect data on several system resource usage & activity parameters. Non-parametric statistical methods are then applied toward detecting & estimating trends in the data sets. Finally, we fit time series models to the data collected. Unlike the models used previously in the research on software aging, these time series models allow for seasonal patterns, and we show how the exploitation of the seasonal variation can help in adequately predicting the future resource usage. Based on the models employed here, proactive management techniques like software rejuvenation triggered by actual measurements can be built