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Timely detection of road cracks is crucial for road maintenance and traffic safety. Nevertheless, the conventional approach of manual detection is fraught with issues such as being time-consuming, labor-intensive, and having a high degree of subjective ...

Configuration tuning for big-data software systems is generally challenging due to the complex configuration space and costly performance evaluation. The workload changes over time in the context of big data making this problem even more ...

• An efficient configuration tuning algorithm for big-data software systems.
• Improving tuning efficiency through configuration space reduction.
• The tradeoff between exploitation and exploration in configuration space reduction.
• ...

Many modern software systems provide numerous configuration options to users and different configurations often lead to different performances. Due to the complex impact of a configuration on the system performance, users have to experimentally ...

• We formulate a performance prediction problem for configurable software systems.
• We introduce configuration space with a perspective of entropy on the problem.
• We design an algorithm, RSFIN, and evaluate its performance on eleven ...

As software systems become increasingly large and complex, automated parameter tuning of software systems (PTSS) has been the focus of research and many tuning algorithms have been proposed recently. However, due to the lack of a unified platform ...

Configuration tuning for large software systems is generally challenging due to the complex configuration space and expensive performance evaluation. Most existing approaches follow a two-phase process, first learning a regression-based ...

• A comparison-based performance model is more suitable for configuration tuning.
• Introducing tuning experiences in performance modeling through active learning.
• Collaborative active and semisupervised learning for performance ...

Deep reinforcement learning (DRL) is a primary machine learning approach for solving sequential decision problems. To exploit the potential vulnerabilities of DRL, we propose a poisoning attack method that injects a backdoor for the DRL model by ...

Resource provisioning for cloud computing requires adaptive and accurate prediction of cloud workloads. However, existing studies in workload prediction have faced significant challenges in predicting time-varying cloud workloads of diverse trends ...

• An encoding scheme named Pathed-Dewey Order for nodes in XML documents.
• An XML-...

Many big data applications such as smart transportation, healthcare, and e-commerce need to store and query large collections of small XML documents, which has become a fundamental problem. However, existing solutions are inadequate to ...

Big data analytics has become increasingly vital in many modern enterprise applications such as user profiling and business process optimization. Today’s big data processing systems, such as Hadoop MapReduce, Spark, and Hive, treat big data applications ...

Most classic face recognition classification algorithms need to extract enough face images with class label information as training samples. However in most practical applications, face recognition based on supervised methods are incapable to deal with ...

Complex software systems often provide a large number of parameters so that users can configure them for their specific application scenarios. However, configuration tuning requires a deep understanding of the software system, far beyond the abilities ...

Multiple-choice reading comprehension task has seen a recent surge of popularity, aiming at choosing the correct option from candidate options for the question referring to a related passage. Previous work focuses on factoid-based questions but ...

Microservice has been increasingly recognized as a promising architectural style for constructing large-scale cloud-based applications within and across organizational boundaries. This microservice-based architecture greatly increases application ...

Distributed message systems (DMSs) serve as the communication backbone for many real-time streaming data processing applications. To support the vast diversity of such applications, DMSs provide a large number of parameters to configure. However, It ...

Service composition is a convincing approach for rapidly constructing large-scale distributed applications in public clouds. With the rapid increase of the composite service requests from many concurrent clients in public clouds, it is critical to ...

Parallel computing combined with distributed data storage and management has been widely adopted by most big data analytics systems. Scheduling computing tasks to improve data locality is crucial to the performance of such systems. While existing ...

Large-scale multi-objective optimization problems (LS-MOP) are complex problems with a large number of decision variables. Due to its high-dimensional decision space, LS-MOP poses a significant challenge to multi-objective optimization methods including ...

In this paper we consider the simpler GMRES method augmented by approximate eigenvectors for solving nonsymmetric linear systems. We modify the augmented restarted simpler GMRES proposed by Boojhawon and Bhuruth to obtain a simpler GMRES with deflated ...

This paper is devoted to the convergence analysis of an iterative method for solving a nonsymmetric algebraic Riccati equation arising in transport theory. We give the convergence rate, and show that the iterative method converges linearly in one case ...

With the prevalence of SOA, an increasing number of Web services are created and composed to construct complex business processes. Selecting an appropriate service from a lot of independently developed services which have the same functionality but ...