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Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we…
Observing, understanding, and mitigating the effects of failure in embedded systems is essential for building dependable control systems. We develop a software-based monitoring methodology to further this goal. This methodology can be…
Autonomous vehicle safety and reliability are the paramount requirements when developing autonomous vehicles. These requirements are guaranteed by massive functional and performance tests. Conducting these tests on real vehicles is…
Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level,…
Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, the performance of these workloads is influenced by numerous factors, such as job inputs…
To understand and explain process behaviour we need to be able to see it, and decide its significance, i.e. be able to tell a story about its behaviours. This paper describes a few of the modelling challenges that underlie monitoring and…
Implementation process ERP is complex and expensive process. Typically always be faced with many failures. Successfully implemented in an organization has many challenges. Organizations in the deployment and success of the system depends on…
Logging plays a crucial role in software engineering because it is key to perform various tasks including debugging, performance analysis, and detection of anomalies. Despite the importance of log data, the practice of logging still suffers…
Although a data processing system often works as a batch processing system, many enterprises deploy such a system as a service, which we call the service-oriented data processing system. It has been shown that in-memory data processing…
In this paper we represent a new framework for integrated distributed systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyse integrated systems and their…
Many researchers have studied the behaviour of successful developers while debugging desktop software. In this paper, we investigate the embedded-software debugging by intermediate programmers through an exploratory study. The bugs are…
In today's data-driven era, deep learning is vital for processing massive datasets, yet single-device training is constrained by computational and memory limits. Distributed deep learning overcomes these challenges by leveraging multiple…
The software is often produced under significant time constraints. Our idea is to understand the effects of various software development practices on the performance of developers working in stressful environments, and identify the best…
Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location…
The design of embedded systems is a complex activity that involves a lot of decisions. With high performance demands of present day usage scenarios and software, they often involve energy hungry state-of-the-art computing units. While…
\underline{Context:} Logging is a fundamental yet complex practice in software engineering, essential for monitoring, debugging, and auditing software systems. With the increasing integration of machine learning (ML) components into…
Context: The term reactivity is popular in two areas of research: programming languages and distributed systems. On one hand, reactive programming is a paradigm which provides programmers with the means to declaratively write event-driven…
Nowadays, several software systems rely on stream processing architectures to deliver scalable performance and handle large volumes of data in near real-time. Stream processing frameworks facilitate scalable computing by distributing the…
Time-triggered switched networks are a deterministic communication infrastructure used by real-time distributed embedded systems. Due to the criticality of the applications running over them, developers need to ensure that end-to-end…
In distributed computing systems with stragglers, various forms of redundancy can improve the average delay performance. We study the optimal replication of data in systems where the job execution time is a stochastically decreasing and…