Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

Cheng, Long; Dongen, Boudewijn F. van; Aalst, Wil M. P. van der

doi:10.1109/tsc.2019.2906203

Cited by 31 publications

(14 citation statements)

References 29 publications

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“…For example, we will try to optimize the QoS problems, in which some tasks have higher priorities than others. Additionally, we also plan to use our approach to handle more complex task jobs, such as workflows [52], the tasks in which are not independent from each other, and cloud-based deap learning workloads [53]. Our long term goal is to develop a highly adaptive and efficient scheduling system for cloud computing in the presence of different task workloads.…”

Section: Discussionmentioning

confidence: 99%

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

Self Cite

215

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Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

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Section: Discussionmentioning

confidence: 99%

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

Self Cite

215

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“…The main reason is that the platform is very well suited to parallel data processing in distributed environments. It is elastic in terms of both storage (through the use of HDFS) and computation, which is in contrast with the conventional data systems where each node has to be carefully tuned to its specifications (Cheng et al 2019). This makes Spark be able to greatly simplify the parallel programming of data applications.…”

Section: Apache Sparkmentioning

confidence: 99%

Rethinking Defeasible Reasoning: A Scalable Approach

Maher¹,

Tachmazidis²,

Antoniou³

et al. 2020

Theory and Practice of Logic Programming

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AbstractRecent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

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“…Comparatively, the control improvements about double-wire GMAW process were a bit limited when compared to other forms of GMAW process. By means of serious researching relative improvements used in other forms of GMAW process, the future improvements about this process will be more and more improved, especially can employ artificial intelligent tools [56], [57] for process analysis and control, or technique innovations, such as bypass coupling technology, and so on.…”

Section: Improvement Of Process Control Of the Double-wire Gmaw Processmentioning

confidence: 99%

Review of the Recent Trends of Process Monitoring and Control for Double- Wire GMAW Process

Zhou

Yao

2019

IEEE Access

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Recently, double-wire gas metal arc welding (GMAW) process is more and more prevalently employed in industrial applications and attracted many academic researches. This paper reviewed the recent trends of researches and applications of this process. Three main aspects were included in this paper. The first was the operational process analysis of the process, and the reviewing work was divided into two catalogues: twin-wire GMAW process and tandem GMAW process, which were two main energy delivery and control formations of the process. This part focused on some negative phenomena, such as arc interruption, during the welding process and their effects on the products quality and process stability. Some works about using different current waveforms combinations and their effects on the welding process were discussed. Also, the works of numerical simulation for this process were mentioned in this part. The second part was about process stability monitoring, and corresponding works focused on establishing quantitative process stability estimation model. The last was the improvement of process control method, and the works mainly focused on the adjustment and modulation of the current waveforms, in order to eliminate negative phenomena, improve the process stability and obtain the products with high quality. Finally, some suggestions about future works were presented. This paper can provide references and enlightens for current academic researches or actual industrial applications in the double-wire GMAW process relative areas.INDEX TERMS Double-wire GMAW process, arc interruption, process stability, current waveform.

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Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

Cited by 31 publications

References 29 publications

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Rethinking Defeasible Reasoning: A Scalable Approach

Review of the Recent Trends of Process Monitoring and Control for Double- Wire GMAW Process

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