Analysis of Software Aging in a Web Server

A number of recent studies have reported the phenomenon of “software aging”, characterized by progressive performance degradation and/or an increased occurrence rate of hang/crash failures of a software system due to the exhaustion of operating system resources or the accumulation of errors. To counteract this phenomenon, a proactive technique called 'software rejuvenation' has been proposed. It essentially involves stopping the running software, cleaning its internal state and/or its environment and then restarting it. Software rejuvenation, being preventive in nature, begs the question as to when to schedule it. Periodic rejuvenation, while straightforward to implement, may not yield the best results, because the rate at which software ages is not constant, but it depends on the time-varying system workload. Software rejuvenation should therefore be planned and 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 and activity parameters. Non-parametric statistical methods are then applied for detecting and 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. --Software aging,software rejuvenation,Linux,Apache,web server,performance monitoring,prediction of resource utilization,non-parametric trend analysis,time series analysis

Modeling and Analysis of Software Aging and Rejuvenation

Software systems are known to suffer from outages due to transient errors. Recently, the phenomenon of “software aging”, one in which the state of the software system degrades with time, has been reported. To counteract this phenomenon,a proactive approach of fault management, called “software rejuvenation”, has been proposed. This essentially involves gracefully terminating an application or a system and restarting it in a clean internal state. In this paper, we discuss stochastic models to evaluate the effectiveness of proactive fault management in operational software systems and determine optimal times to perform rejuvenation, for different scenarios. The latter part of the paper deals with measurement-based methodologies to detect software aging and estimate its effect on various system resources. Models are constructed using workload and resource usage data collected from the UNIX operating system over a period of time. The measurement-based models are intended to help development of strategies for software rejuvenation triggered by actual measurements. 1

Analysis and implementation of software rejuvenation in cluster systems

Several recent studies have reported the phenomenon of \software aging", one in which the state of a software system degrades with time. This may eventually lead to performance degradation of the software or crash/hang failure or both. \Software rejuvenation " is a pro-active technique aimed to prevent unexpected or unplanned outages due to aging. The basic idea is to stop the running software, clean its internal state and restart it. In this paper, we discuss software rejuvenation as applied to cluster systems. This is both an innovative and an e cient way to improve cluster system availability and productivity. Using Stochastic Reward Nets (SRNs), we model and analyze cluster systems which employ software rejuvenation. For our proposed time-based rejuvenation policy, we determine the optimal rejuvenation interval based on system availability and cost. We also introduce a new rejuvenation policy based on prediction and show that it can dramatically 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. We then brie y describe an implementation of a software rejuvenation system that performs periodic and predictive rejuvenation, and show some empirical data from systems that exhibit aging 1

Phosphoinositide signaling and mechanotransduction in cardiovascular biology and disease

14 p.-3 fig.-1 tab.Phosphoinositides, which are membrane-bound phospholipids, are critical signaling molecules located at the interface between the extracellular matrix, cell membrane,and cytoskeleton. Phosphoinositides are essential regulators of many biological and cellular processes, including but not limited to cell migration, proliferation, survival, and differentiation, as well as cytoskeletal rearrangements and actin dynamics. Over theyears, a multitude of studies have uniquely implicated phosphoinositide signaling as being crucial in cardiovascular biology and a dominant force in the development of cardiovascular disease and its progression. Independently, the cellular transduction of mechanical forces or mechanotransduction in cardiovascular cells is widely accepted to be critical to their homeostasis and can drive aberrant cellular phenotypes and resultant cardiovascular disease. Given the versatility and diversity of phosphoinositide signaling in the cardiovascular system and the dominant regulation of cardiovascular cell functions by mechanotransduction, the molecular mechanistic overlap and extent to which these two major signaling modalities converge in cardiovascular cells remain unclear. In this review, we discuss and synthesize recent findings that rightfully connect phosphoinositide signaling to cellular mechanotransduction in the context of cardiovascular biology and disease, and we specifically focus on phosphatidylinositol-4,5-phosphate, phosphatidylinositol-4-phosphate 5-kinase, phosphatidylinositol-3,4,5-phosphate, and phosphatidylinositol 3-kinase. Throughout the review, we discuss how specific phosphoinositide subspecies have been shown to mediate biomechanically sensitive cytoskeletal remodeling in cardiovascular cells. Additionally, we discuss the direct interaction of phosphoinositides with mechanically sensitive membrane-bound ion channels in response to mechanical stimuli. Furthermore, we explore the role of phosphoinositide subspecies in association with critical downstream effectors of mechanical signaling in cardiovascular biology and disease.This work was supported by the American Heart Association Career Development Award (18CDA34080415) to YB.Peer reviewe

Characterizing Intrusion Tolerant Systems using a State Transition Model

Intrusion detection and response research has so far mostly concentrated on known and well-defined attacks. We believe that this narrow focus of attacks accounts for both the successes and limitation of commercial intrusion detection systems (IDS). Intrusion tolerance, on the other hand, is inherently tied to functions and services that require protection. This paper presents a state transition model to describe the dynamic behavior of intrusion tolerant systems. This model provides a framework from which we can define the vulnerability and the threat set to be addressed. We also show how this model helps us to describe both known and unknown security exploits by focusing on impacts rather than specific attack procedures. By going through the exercise of mapping known vulnerabilities to this transition model, we identify a reasonably complete fault space that should be considered in a general intrusion tolerant system

Synthetic biology for the directed evolution of protein biocatalysts:navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the ‘search space’ of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (K (d)) and catalytic (k (cat)) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving k (cat) (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the ‘best’ amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust