Software Aging Research Articles

An Efficient Strategy for Online Performance Monitoring of Datacenters via Adaptive Sampling

Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, and capacity planning decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, this paper develops and validates a strategy based on adaptive-rate compressive sampling. It exploits the fact that the signals of interest often can be sparsified under an appropriate representation basis and that the sampling rate can be tuned as a function of sparsity. We use the Trade6 application as our experimental platform and measure the signals of interest—in our case, signals pertaining to memory and disk I/O activity—using adaptive sampling. We then evaluate whether the reconstructed signals can be used for trend detection to track the gradual deterioration of system performance associated with software aging. Our experiments show that the signals recovered by our methods can be used to detect, with high confidence, the existence of trends within the original signal. We also evaluate the reconstructed signals for threshold-violation detection wherein the magnitude of the signal exceeds a preset value. Our experiments show that performance bottlenecks and anomalies that manifest themselves in portions of the signal where its magnitude exceeds a threshold value can also be detected using the reconstructed signals. Most importantly, detection of these anomalies is achieved using a substantially reduced sample size—a reduction of more than 70 percent when compared to the standard fixed-rate sampling method.

Model Predictive Control for Energy-Efficient, Quality-Aware, and Secure Virtual Machine Placement

Modern datacenters rely on virtualization to deliver complex and scalable cloud services. To avoid inflating costs or reducing the perceived service level, suitable resource optimization techniques are needed. Placement can be used to prevent inefficient maps between virtual and physical machines. In this perspective, we propose a holistic placement framework considering conflicting performance metrics, such as the service level delivered by the cloud, the energetic footprint, hardware or software outages, and security policies. Unfortunately, computing the best placement strategies is nontrivial, as it requires the ability to trade among several goals, possibly in a real-time manner. Therefore, we approach the problem via model predictive control to devise optimal maps between virtual and physical machines. Results show the effectiveness of our technique in comparison with classical heuristics. Note to Practitioners —This paper is motivated by the success of cloud-based services. Specifically, we consider the case where the resources of datacenters are accessed through the infrastructure-as-a-service paradigm. To efficiently handle the load of requests and to tame operational costs, a proper optimization is needed. To this end, we focus on the selection of the best maps between virtual and physical machines. We propose a holistic placement framework for the deployment of user-requested virtual machines by pursuing different performance goals, such as counteract to hardware outages or reboots due to software aging issues, ensure proper security policies, maintain a suitable service level perceived by users, and reduce power requirements. The optimal strategies are computed with model predictive control, which allows to consider complex constraints and take advantage of future information. The proposed framework is tested in different scenarios characterized by a variety of workloads and traces gathered in a real cloud datacenter. Results indicate that our approach outperforms bin-packing techniques.

Performability modelling and analysis of server virtualised systems subject to workload-dependent software aging

This paper tackles performability modelling and analysis of versatile server virtualised systems subject to workload-dependent software aging, failures and rejuvenation. We develop a modular modell...

Performability modelling and analysis of server virtualised systems subject to workload-dependent software aging

Performability modelling and analysis of server virtualised systems subject to workload-dependent software aging

Joint optimal checkpointing and rejuvenation policy for real-time computing tasks

Performance of a software system can deteriorate from higher to lower levels due to software aging. To counteract the aging effect, software rejuvenation is widely implemented to restore the performance of a degraded system before the system crash actually takes place. To facilitate an effective system function restoration after each rejuvenation action, it is desirable to apply checkpointing to occasionally save the system state on a reliable storage so that the mission task can be resumed from the last saved checkpoint (instead of being restarted from the very beginning). As both rejuvenation and checkpointing procedures incur system overhead while bringing these benefits, it is significant to determine the optimal rejuvenation and checkpointing scheduling policy optimizing the system performance measures of interest. This paper makes new contributions by modeling and optimizing the joint maintenance policy involving state-based rejuvenation and periodic checkpointing schedule for software systems performing real-time computing tasks. The system can undergo multiple performance degradation levels or states, and transition time between different states can assume arbitrary types of distributions. The proposed solution methodology encompasses an efficient numerical algorithm for evaluating the probability of task completion (PTC) by a pre-specified deadline. The joint optimal rejuvenation and checkpointing policy is further determined to maximize the PTC of the considered real-time task. Examples are provided to illustrate applications of the proposed methodology as well as effects of system parameters on the optimization solution.

Optimal periodic software rejuvenation policies based on interval reliability criteria

Software aging often affects the performance of software systems and may eventually cause them to fail. A complementary approach to handle transient software failures due to the software aging is called software rejuvenation. It is a preventive and proactive solution that is particularly useful for counteracting the phenomenon of software aging. In this paper, we consider the optimal software rejuvenation policies maximizing the interval reliability using the Markov regenerative process formalism. We derive analytically the optimal software rejuvenation timing that maximizes the limiting interval reliability or the interval reliability with exponentially distributed operation time. Further, we examine numerically the transient behavior of the interval reliability at an arbitrary operation time. Our results under the interval reliability criteria are extensions of some earlier papers, since the interval reliability is a comprehensive measure that specializes to pointwise system availability and to system reliability.

Models for availability and power consumption evaluation of a private cloud with VMM rejuvenation enabled by VM Live Migration

Software aging affects the availability of Virtual Machine Monitor (VMM), one of the main components of virtualized environments. Software aging causes internal software degradation due to bugs activation and accumulation during its execution. Software rejuvenation implemented through Virtual Machine (VM) Live Migration may be applied to cope with software aging effects on cloud computing system. Another relevant problem is the power consumption of using software rejuvenation techniques in the virtualized environments. This paper presents availability models based on Stochastic Petri Nets to evaluate two VM Live Migration approaches. These approaches are based on the redundancy schemes: Warm-Standby and Cold-Standby. In the Cold-Standby migration, the migration target machine is started only before the VM Live Migration. In the Warm-Standby Migration, the migration target machine runs along with the source migration machine. Results show that VM Live Migration causes a significant improvement in system availability. Scenarios with a heavy workload present an annual downtime reduction of 164 h. The availability comparison between two approaches reveals that the Cold-Standby approach has a slightly better result due to the decrease in the total number of VM Live Migrations. The power consumption results show that Cold-Standby approach is more efficient in power consumption. In all the observed scenarios, the costs savings by using Cold-Standby approach exceed 40%. The highlights of this paper are: i) a comprehensive model for availability evaluation of cloud computing with VMM rejuvenation through VM migration scheduling; ii) sensitivity analysis to define proper rejuvenation scheduling to maximize system availability and iii) power consumption comparison of the two adopted migration approaches.

Software Group Rejuvenation Based on Matrix Completion and Cerebellar Model Articulation Controller

This paper aims to accurately evaluate the aging state of nodes in largescale networks and identify the optimal rejuvenation plan for these nodes. To this end, the aging phenomenon in distributed systems was described as a random low-rank matrix. The CMAC network was introduced to collect the data of network nodes and evaluate their aging state and rejuvenation plan. Based on the aging state and plan applicability, the node relationship was integrated with matrix completion, aiming to improve the efficiency of aging evaluation. Compared to the traditional methods, our method significantly improved aging evaluation and reduced hardware cost, and offered suitable rejuvenation plans for aging nodes. The improvement is partially attributable to the incorporation of node relationship. The research findings shed new light on software aging and group rejuvenation.

Optimizing software rejuvenation policy for real time tasks

Software rejuvenation is a proactive maintenance technique adopted in diverse and wide applications for mitigating performance deterioration effects of software aging and further preventing the system crash from happening. As the software rejuvenation procedure incurs system overhead and downtime, it is relevant and crucial to optimize the software rejuvenation policy to maximize its benefit and effectiveness. This work considers the optimal rejuvenation policy problem for systems subject to multiple performance degradation levels and performing real-time tasks. The solution encompasses a new iterative method for time-dependent evaluation of the task successful completion probability for the considered real-time systems, enriching and expanding the existing evaluation methodologies for software aging and rejuvenation systems. Based on event transitions, the proposed evaluation method is efficient and applicable to arbitrary types of state transition time distributions. The state-based rejuvenation policy is further optimized to maximize the probability of the task completion by the predetermined deadline. Examples are provided to illustrate applications of the proposed methodology as well as effects of different system parameters on the optimization solution.

SWARE: A Methodology for Software Aging and Rejuvenation Experiments

Reliability and availability are mandatory requirements for numerous applications. Technical apparatus to study system dependability is essential to support software deployment and maintenance. Software aging is a related issue in this context. Software aging is a cumulative process which leads systems with long-running execution to hangs or failures. Software rejuvenation is used to prevent software aging problems. Software rejuvenation actions comprise system reboot or application restart to bringing software to a stable fresh state. This paper proposes a methodology to conduct software aging and software rejuvenation experiments. The approach has three phases: (i) Stress Phase - stress environment with the accelerated workload to induce bugs activation; (ii) Wait Phase - stop workload submission to observe the system state after workload submission; (iii) Rejuvenation Phase - find the impacts caused by the software rejuvenation. We named our methodology as SWARE (Stress-Wait-Rejuvenation). To validate the SWARE methodology, we present a case study. This case study consists of an experiment of VM Live Migration as rejuvenation mechanism for VMM software aging. The considered testbed is a Private Cloud with OpenNebula and KVM 1.0. The obtained results show that VM live migration is useful as rejuvenation for VMM software aging.

Variance analysis of software ageing problems

Software ageing problems are mainly caused by resource consumption exhaustion, so many researchers focused on predicting software resource consumption. However, the loss analysis using variance has not been done. In this study, the authors propose a framework to analyse variance change in the resource consumption prediction problems. This framework is made up of three steps. First, an original variance decomposition is proposed in view of data sampling and partitioning process. Second, in order to study the influence of data sampling and partitioning process to the variance, the enhanced Friedman test plus Nemenyi post-hoc test is introduced. Lastly, they propose a corrected t-test to analyse the performances of two regression algorithms: auto-regressive integrated moving average and artificial neuron network. In the experiments, they analyse the variance in two levels: operating system level and application level. They find the result that k is equal to ten for k-fold cross-validation is proper for resource consumption prediction, although the contribution to variance is almost same for the sensitivity of forecasted estimation loss in consideration of data partitioning process and the sensitivity of forecasted estimation loss in consideration of data sampling procedure.

Performability evaluation of server virtualized systems under bursty workload

This paper proposes a performability analysis based on stochastic reward nets (SRNs) of an energy-efficient server virtualized system (SVS) model handling data-intensive applications and implementing a workload-aware power management (PM) mechanism. The SVS model accounts for workload-aware dependent software aging, unplanned failures and Migrate-VM rejuvenation. We show through numerical analysis of perfomability metrics how workload burstiness and software aging impact SVS behavior in terms of availability and performance.

Continuous Reproducibility in GNSS Signal Processing

This paper discusses the reproducibility of scientific experiments in which global navigation satellite system (GNSS) signals play a role. After analyzing the factors that impact the reproducibility of an experiment in the given context, this paper proposes a methodology that, leveraging on software containerization technologies and the best practices from professional software development, ensures the automated reproduction of scientific experiments involving GNSS data and software-defined GNSS receivers, including the generation of figures or tables of a research publication, while fostering scientific collaboration and contributing to mitigate the effects of software aging in mutating computer environments. In order to show a practical implementation of the proposed work flow, the authors propose a simple experiment based on a freely available open source software-defined receiver and the automation of its execution in a popular online platform.

Software Health Measurement Method Based on Aging-related Bugs

Aging-related bugs can lead to software aging, whose performance is the slow response on software system though the software does not crash. This paper aims to propose the measurement method of software health to quantitatively show the impact of aging-related bugs on software. The effect of the bugs and the elements were analyzed. The Gaussian mixture model was developed to measure the degree of software health, which provide the basis for the software failure alarming and failure prediction. Finally, the details of the method were shown by a case study on Hadoop Distributed File System.

Software Aging Forecasting Using Time Series Model

<p class="Normal1">With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also housed on cloud platform, it is necessary to ensure continuous availability by implementing all necessary measures. Windows Active Directory is one such service that Microsoft developed for Windows domain networks. It is included in Windows Server operating systems as a set of processes and services for authentication and <a href="https://en.wikipedia.org/wiki/Authorization">authorization </a>of users and computers in a Windows domain type network. The service is required to run continuously without downtime. As a result, there are chances of accumulation of errors or garbage leading to software aging which in turn may lead to system failure and associated consequences. This results in software aging. In this work, software aging patterns of Windows active directory service is studied. Software aging of active directory needs to be predicted properly so that rejuvenation can be triggered to ensure continuous service delivery. In order to predict the accurate time, a model that uses time series forecasting technique is built.</p>

A Comprehensive Evaluation of Software Rejuvenation Policies for Transaction Systems With Markovian Arrivals

Software rejuvenation is one of the proactive fault management techniques to prevent system performance degradation, which may lead to the system failure caused by software aging. In the design of software rejuvenation, it is important to determine the optimal timing of triggering the rejuvenation in terms of the system overhead. In this paper, we consider six software rejuvenation policies, which are categorized into time-based and workload-based policies, under the environment where the arrival stream of system follows a Markovian arrival process (MAP). After building the stochastic models with respective rejuvenation policies, we formulate the loss probability of transaction and the upper bound of mean response time as the system performance indices. In the numerical illustrations, we exhibit a comprehensive study to compare six software rejuvenation policies numerically and show that the proposed rejuvenation policies called wait-time policies are superior to the others under the MAP arrival stream.

Initial Review on ICTS Governance for Software Anti-Aging

For the past 20 years various researches regarding software aging have been conducted. Software aging is the situation in which the accumulation of errors occurring in operational software system that has run for a long time that may lead to performance degradation, resource depletion and eventually causing the software to crash or hang [1]. David Parnas divided software aging into two categories: 1) the failure of the software to adapt with environment that is dynamic and 2) the result of the changes itself [2]. Factors that can affects software aging can be classified into several categories: 1) functional, 2) human, 3) product and 4) environment [3]. In general, the factors that affect software aging can be divided into internal and external factors. The main objectives of this paper are to briefly describe the definition of software aging and also ICTS governance. In addition  to that, this paper also compiles the software aging factors that are being investigated by previous researchers. The need for future research regarding ICTS governance and Software aging also determined at the end of this paper.Â

Software Aging Forecasting Using Time Series Model

<p class="Normal1">With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also housed on cloud platform, it is necessary to ensure continuous availability by implementing all necessary measures. Windows Active Directory is one such service that Microsoft developed for Windows domain networks. It is included in Windows Server operating systems as a set of processes and services for authentication and authorization of users and computers in a Windows domain type network. The service is required to run continuously without downtime. As a result, there are chances of accumulation of errors or garbage leading to software aging which in turn may lead to system failure and associated consequences. This results in software aging. In this work, software aging patterns of Windows active directory service is studied. Software aging of active directory needs to be predicted properly so that rejuvenation can be triggered to ensure continuous service delivery. In order to predict the accurate time, a model that uses time series forecasting technique is built.</p>

Aging-related performance anomalies in the apache storm stream processing system

Event stream processing has recently emerged as a popular paradigm for implementing high-volume distributed (near-)real time data processing applications. Several open source systems are today available, supporting the development of such applications, many of which developed with the technologies of the Apache Software Foundation. These so called stream processors are long-running complex software systems which may be affected by software aging, a well-known phenomenon among operation engineers, consisting of a progressive increase in the failure rate or in performance degradation of a software system over time.We address the problem of identifying symptoms and sources of software aging in the Apache Storm event stream processing system; this helps to identify proper strategies to prevent or mitigate anomalous behaviors in production environments. To this aim, we present an experimental study investigating aging manifestations in a popular system, namely Apache Storm. Results show that Storm presents anomalous behaviors in long runs, which prevent some topologies from working continuously. These can be attributed to software aging, due to Storm internal resource management mechanisms influenced by the garbage collector and the memory assigned to worker processes. We discuss the aging-related Apache Storm behaviors, and we experiment rejuvenation actions, showing that they are actually able to remove them.

SWARE: An approach to support software aging and rejuvenation experiments

The need for uninterrupted computing services demands for high system availability and reliability. Techniques and methods to estimate and analyze system dependability are essential to support software deployment and maintenance. Software aging appears as a relevant issue in this context. Software aging is a cumulative process which leads long-running systems to hangs or failures. Software rejuvenation is used to counteract software aging. Software rejuvenation usually comprises system reboot or application restart to bringing software to a stable fresh state. This paper proposes an approach to investigate software aging effects and software rejuvenation effectiveness on a single experiment. The approach has three phases: (i) Stress Phase - stress environment with the accelerated workload to induce bugs activation; (ii) Wait Phase - stop workload submission to observe the system state after workload submission; (iii) Rejuvenation Phase - find the impacts caused by the software rejuvenation. We named our approach as SWARE (Stress-Wait-Rejuvenation). To validate the SWARE approach, we present a case study. This case study consists of an experiment of VM Live Migration as rejuvenation mechanism for VMM software aging. The considered testbed is a Private Cloud with OpenNebula and KVM 1.0. The obtained results show that VM live migration is useful as rejuvenation for VMM software aging.