Xiaozhi Du

Software aging estimation and prediction of a real VOD system based on PCA and neural networks

The phenomenon of software aging refers to the exhaustion of operating system resource, fragmentation and accumulation of errors, which results in progressive performance degradation or transient failures or even crashes of applications. In this paper, we investigate the software aging patterns of a real VOD system. First, we collect data on several system resource usage and application server. Then, non-parametric statistical methods and linear regression models are adopted to detect aging and estimate trends in the data sets. Finally, artificial neural network (ANN) models are constructed to model the extracted data series of systematic parameters and to predict software aging of the VOD system. In order to reduce the complexity of ANN and to improve its efficiency, principal component analysis (PCA) is used to reduce the dimensionality of input variables of ANN. The experimental results show that the software aging prediction model based on ANN is superior to the time series models in the aspects of prediction precision. Based on the models employed here, software rejuvenation policies can be triggered by actual measurements.

Modeling and Performance Analysis of Software Rejuvenation Policies for Multiple Degradation Systems

Software rejuvenation is a preventive and proactive technology to counteract the phenomenon of software aging and system failures and to improve the system reliability. In this paper we present and analyze three software rejuvenation policies for an operational software system with multiple degradations, called preemptive rejuvenation, delayed rejuvenation and mixed rejuvenation. These policies consider both history data and current running state, and the rejuvenation action is triggered on the basis of predetermined performance threshold and rejuvenation interval respectively. Continuous-time Markov chains are used to describe the analytic models. To evaluate these polices expediently, we utilize deterministic and stochastic Petri nets to solve the models. Numerical results show that the deployment of software rejuvenation in the system leads to significant improvement in availability and throughput. These three rejuvenation policies are better than the standard rejuvenation policy, and the mixed policy is the best one.

A Mixed Software Rejuvenation Policy for Multiple Degradations Software System

Software rejuvenation is a preventive and proactive technology to counteract the phenomenon of software aging and system failures, and to improve the system reliability. In this paper we present a mixed software rejuvenation policy for an operational software system with multiple degradation states, which considers both the history information and the current running state. By this policy, the system is rejuvenated when it achieves to a degradation threshold or it comes to the pre-determined rejuvenation interval. For comparison, standard rejuvenation policy is also discussed. Continuous-time Markov chains are used to describe the multiple degradation states model. To evaluate these polices expediently, we utilize deterministic and stochastic Petri nets (DSPN) to solve the models. Numerical results show that the deployment of software rejuvenation in the system leads to significant improvement in availability and throughput. And the mixed rejuvenation policy is better than the standard rejuvenation policy.

FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC

Recently, system-on-chips (SoCs) are increasingly employed in reliable applications for their high-performance and high-densities. Moreover, the structure shrinking of SoC leads to its proneness to radiation-induced soft errors. This paper presents a fine-grained fault injection framework for SoC (FFI4SoC) to assess the reliability of SoC against soft errors. FFI4SoC facilitates fault injection for SoC by defining the primary components and rules that are required by fine-grained fault injection. Furthermore, based on FFI4SoC, we develop a fine-grained fault injection tool named SSIFFI for bare-metal MicroZed. The design of SSIFFI is presented in order to illustrate the application of FFI4SoC. Finally, SSIFFI is engaged in simulated fault injection experiments to explore the cause of single event functional interrupts (SEFIs) and to validate functional properties of FFI4SoC. The experimental results disclose detailed reasons for SEFI and prove that FFI4SoC can be employed to assess reliability of SoC well with the merit of fine-grained injection.

Modeling and Analyses of Operational Software System with Rejuvenation and Reconfiguration

In this paper, a software rejuvenation model with reconfiguration is proposed to improve the software performance. Firstly, continuous-time Markov chain is adopted to describe the system model. Then, the formal definitions and analyses of system availability and throughput are given. Finally, some numeric experiments are done. The results show that the presented method is effective and adopting reconfiguration can improve the system throughput though the availability has a trivial reduction.

A Fine-Grained Software-Implemented DMA Fault Tolerance for SoC Against Soft Error

In system-on-chips (SoCs), DMA, as a peripheral module, plays an important role in data transmission. However, the structure shrinking of SoC leads to its proneness to radiation-induced soft errors, especially for DMA. This paper presents a fine-grained software-implemented fault tolerance for SoC, named DCRH, to enhance the reliability of DMA against soft errors. DCRH achieves fine-grained selective fault tolerance, protecting DMA without interfering other modules of SoC. Furthermore, it is transparent to the user application because it performs on driver layer. In this paper, we present our fault source analysis for DMA based on Xilinx Zynq-7010 SoC and the detailed design of DCRH. The method is then applied to bare-metal MicroZed so that a DCRH-enhanced DMA driver is developed. Finally, SSIFFI is engaged in the simulated DMA fault injection experiments to validate DCRH. The experimental results prove that DCRH can achieve high fault coverage for DMA, above 97%, with stable performance.