Pub Date : 2018-10-01DOI: 10.1109/ISSREW.2018.00-12
Siqian Gong, Beibei Yin, K. Cai
With the growing demands of computing resources, cloud computing provides a cost-effective way to reshape the resources, such as CPU and memory for services. QoS (Quality of Service) is impacted by resource allocation. In order to fulfill the QoS requirements, it is necessary to provide sufficient resources for the services. Offering the high QoS with the low resource allocation becomes a key challenge of resource allocation in cloud computing. In this paper, we propose an adaptive PID (Proportional-Integral-Derivative) control based on FFRLS (Forgetting Factor Recursive Least Square) for QoS management that not only efficiently use resources but also ensures the QoS in real time.
{"title":"An Adaptive PID Control for QoS Management in Cloud Computing System","authors":"Siqian Gong, Beibei Yin, K. Cai","doi":"10.1109/ISSREW.2018.00-12","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-12","url":null,"abstract":"With the growing demands of computing resources, cloud computing provides a cost-effective way to reshape the resources, such as CPU and memory for services. QoS (Quality of Service) is impacted by resource allocation. In order to fulfill the QoS requirements, it is necessary to provide sufficient resources for the services. Offering the high QoS with the low resource allocation becomes a key challenge of resource allocation in cloud computing. In this paper, we propose an adaptive PID (Proportional-Integral-Derivative) control based on FFRLS (Forgetting Factor Recursive Least Square) for QoS management that not only efficiently use resources but also ensures the QoS in real time.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124755744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ISSREW.2018.00-22
Tong Jia, Ying Li, Chengbo Zhang, Wensheng Xia, Jie Jiang, Yuhong Liu
When systems fail, log data is often the most important information source for fault diagnosis. However, the performance of automatic fault diagnosis is limited by the ad-hoc nature of logs. The key problem is that existing developer-written logs are designed for humans rather than machines to automatically detect system anomalies. To improve the quality of logs for fault diagnosis, we propose a novel log enhancement approach which automatically identifies logging points that reflect anomalous behavior during system fault. We evaluate our approach on three popular software systems AcmeAir, HDFS and TensorFlow. Results show that it can significantly improve fault diagnosis accuracy by 50% on average compared to the developers' manually placed logging points.
{"title":"Machine Deserves Better Logging: A Log Enhancement Approach for Automatic Fault Diagnosis","authors":"Tong Jia, Ying Li, Chengbo Zhang, Wensheng Xia, Jie Jiang, Yuhong Liu","doi":"10.1109/ISSREW.2018.00-22","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-22","url":null,"abstract":"When systems fail, log data is often the most important information source for fault diagnosis. However, the performance of automatic fault diagnosis is limited by the ad-hoc nature of logs. The key problem is that existing developer-written logs are designed for humans rather than machines to automatically detect system anomalies. To improve the quality of logs for fault diagnosis, we propose a novel log enhancement approach which automatically identifies logging points that reflect anomalous behavior during system fault. We evaluate our approach on three popular software systems AcmeAir, HDFS and TensorFlow. Results show that it can significantly improve fault diagnosis accuracy by 50% on average compared to the developers' manually placed logging points.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127690512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ISSREW.2018.00-21
F. D. O. Neto, Michael Jones, R. Martins
This paper presents the design, development and evaluation of a software tool to assist the localisation of root causes of test case failures in distributed embedded systems, specifically vehicle systems controlled by a network of electronic control units (ECUs). We use data visualising to provide sensible information from a large number of test execution logs from large-scale software integration testing under a continuous integration process. Our goal is to allow more efficient root-cause identification of failures and foster a continuous feedback loop in the fault localisation process. We evaluate our solution in-situ at the Research and Development division of Volvo Car Corporation (VCC). Our prototype helps the failure debugging procedures by presenting clear and concise data and by allowing stakeholders to filter and control which information is displayed. Moreover, it encourages a systematic and continuous analysis of the current state of testing by aggregating and categorising historical data from test harnesses to identify patterns and trends in test results.
{"title":"Visualisation to Support Fault Localisation in Distributed Embedded Systems within the Automotive Industry","authors":"F. D. O. Neto, Michael Jones, R. Martins","doi":"10.1109/ISSREW.2018.00-21","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-21","url":null,"abstract":"This paper presents the design, development and evaluation of a software tool to assist the localisation of root causes of test case failures in distributed embedded systems, specifically vehicle systems controlled by a network of electronic control units (ECUs). We use data visualising to provide sensible information from a large number of test execution logs from large-scale software integration testing under a continuous integration process. Our goal is to allow more efficient root-cause identification of failures and foster a continuous feedback loop in the fault localisation process. We evaluate our solution in-situ at the Research and Development division of Volvo Car Corporation (VCC). Our prototype helps the failure debugging procedures by presenting clear and concise data and by allowing stakeholders to filter and control which information is displayed. Moreover, it encourages a systematic and continuous analysis of the current state of testing by aggregating and categorising historical data from test harnesses to identify patterns and trends in test results.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132628051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ISSREW.2018.00-28
R. Amarnath, S. Bhat, Peter Munk, E. Thaden
Central Processing Units (CPUs) that satisfy the throughput demands of highly automated driving trade reliability off for performance. Such CPUs often do not include extensive hardware-implemented reliability measures e. g., lockstep CPU cores. At the same time, POSIX-compliant (including Linux-like) operating systems (OSs) become increasingly popular for such complex automotive systems, e. g., the upcoming AUTOSAR Adaptive standard is based on POSIX [1]. In such systems, the fault analysis of critical software components such as the OS becomes an important dependability asset. We determine the robustness of a given OS by injecting random hardware faults into the CPU and measure the extent to which these faults propagate through the OS in order to manifest as application level side effects. In this paper, we present our QEMU-based fault injection framework that simulates bit flips in x86 registers during the execution of the system calls of Linux 4.10 and classifies their effects at the application level. Our results show that for the clone, futex, mmap, mprotect, and pipe syscalls in average 76.3% of the 4.48 million injected faults are benign.Our experiments also show that the program counter and stack pointer (in case of memory operations) are the most susceptible registers. Our measurements help to guide the appropriate deployment of software-implemented hardware fault-tolerance (SIHFT) measures. Re-evaluation of the implemented SIHFT measures can be potentially used as an argument for safety.
{"title":"A Fault Injection Approach to Evaluate Soft-Error Dependability of System Calls","authors":"R. Amarnath, S. Bhat, Peter Munk, E. Thaden","doi":"10.1109/ISSREW.2018.00-28","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-28","url":null,"abstract":"Central Processing Units (CPUs) that satisfy the throughput demands of highly automated driving trade reliability off for performance. Such CPUs often do not include extensive hardware-implemented reliability measures e. g., lockstep CPU cores. At the same time, POSIX-compliant (including Linux-like) operating systems (OSs) become increasingly popular for such complex automotive systems, e. g., the upcoming AUTOSAR Adaptive standard is based on POSIX [1]. In such systems, the fault analysis of critical software components such as the OS becomes an important dependability asset. We determine the robustness of a given OS by injecting random hardware faults into the CPU and measure the extent to which these faults propagate through the OS in order to manifest as application level side effects. In this paper, we present our QEMU-based fault injection framework that simulates bit flips in x86 registers during the execution of the system calls of Linux 4.10 and classifies their effects at the application level. Our results show that for the clone, futex, mmap, mprotect, and pipe syscalls in average 76.3% of the 4.48 million injected faults are benign.Our experiments also show that the program counter and stack pointer (in case of memory operations) are the most susceptible registers. Our measurements help to guide the appropriate deployment of software-implemented hardware fault-tolerance (SIHFT) measures. Re-evaluation of the implemented SIHFT measures can be potentially used as an argument for safety.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128615585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/issrew.2018.00-46
{"title":"Message from the WoSAR 2018 Workshop Chairs","authors":"","doi":"10.1109/issrew.2018.00-46","DOIUrl":"https://doi.org/10.1109/issrew.2018.00-46","url":null,"abstract":"","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121504788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ISSREW.2018.00018
Yu Qiao, Zheng Zheng, Yunyu Fang
The requirements for high reliability, availability, and performance of mobile devices have increased significantly. Android is the most widely used mobile operating system in the world, and it is affected by software aging, resulting in poor responsiveness. This paper investigates the software aging indicators prediction in Android, focusing on aging indicators such as system's free physical memory and application's heap memory. Due to the various user behavior sequences for Android applications and system, we utilize Long Short-Term Memory Neural Network (LSTM NN), which could capture the hidden long-term dependence in a time series to predict these aging indicators. We analyze the prediction results with traditional evaluation metrics like MAPE/MSE for evaluating the whole prediction performance, and with our proposed evaluation metrics TA, FA, SVA for evaluating the trend, fluctuation, and small variation of aging indicators respectively. The results show that LSTM NN has superior performance compared with other prediction methods in the history of software aging researches. Based on the results, proactive management techniques like software rejuvenation could be scheduled by predicting the proper moment to alleviate software aging effects and increase the availability of Android mobile.
{"title":"An Empirical Study on Software Aging Indicators Prediction in Android Mobile","authors":"Yu Qiao, Zheng Zheng, Yunyu Fang","doi":"10.1109/ISSREW.2018.00018","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00018","url":null,"abstract":"The requirements for high reliability, availability, and performance of mobile devices have increased significantly. Android is the most widely used mobile operating system in the world, and it is affected by software aging, resulting in poor responsiveness. This paper investigates the software aging indicators prediction in Android, focusing on aging indicators such as system's free physical memory and application's heap memory. Due to the various user behavior sequences for Android applications and system, we utilize Long Short-Term Memory Neural Network (LSTM NN), which could capture the hidden long-term dependence in a time series to predict these aging indicators. We analyze the prediction results with traditional evaluation metrics like MAPE/MSE for evaluating the whole prediction performance, and with our proposed evaluation metrics TA, FA, SVA for evaluating the trend, fluctuation, and small variation of aging indicators respectively. The results show that LSTM NN has superior performance compared with other prediction methods in the history of software aging researches. Based on the results, proactive management techniques like software rejuvenation could be scheduled by predicting the proper moment to alleviate software aging effects and increase the availability of Android mobile.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127744482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ISSREW.2018.00014
Maral Azizi, Hyunsook Do
To date, various test prioritization techniques have been developed, but the majority of these techniques consider a single objective that could limit the applicability of prioritization techniques by ignoring practical constraints imposed on regression testing. Multi-objective prioritization techniques try to reorder test cases so that they can optimize multiple goals that testers want to achieve. In this paper, we introduced a novel graph-based framework that maps the prioritization task to a graph traversal algorithm. To evaluate our approach, we performed an empirical study using 20 versions of four open source applications. Our results indicate that the use of the graph-based technique can improve the effectiveness and efficiency of test case prioritization technique.
{"title":"Graphite: A Greedy Graph-Based Technique for Regression Test Case Prioritization","authors":"Maral Azizi, Hyunsook Do","doi":"10.1109/ISSREW.2018.00014","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00014","url":null,"abstract":"To date, various test prioritization techniques have been developed, but the majority of these techniques consider a single objective that could limit the applicability of prioritization techniques by ignoring practical constraints imposed on regression testing. Multi-objective prioritization techniques try to reorder test cases so that they can optimize multiple goals that testers want to achieve. In this paper, we introduced a novel graph-based framework that maps the prioritization task to a graph traversal algorithm. To evaluate our approach, we performed an empirical study using 20 versions of four open source applications. Our results indicate that the use of the graph-based technique can improve the effectiveness and efficiency of test case prioritization technique.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124733676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/issrew.2018.00-58
{"title":"Message from the ISSRE 2018 Industry Track Chairs","authors":"","doi":"10.1109/issrew.2018.00-58","DOIUrl":"https://doi.org/10.1109/issrew.2018.00-58","url":null,"abstract":"","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126530146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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