Resource Management is continually assuming a significant part in cloud computing, contrasting with other computing paradigms. Due to the accessibility of finite shared resources, it is extremely challenging for cloud...
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Improving energy efficiency has become necessary to enable sustainable computational science. At the same time, scientific workflows are key in facilitating distributed computing in virtually all domain sciences. As d...
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Energy-aware scheduling algorithms are emerging as important components in economic-conscious heterogeneous computing systems such as IoT-enabled edge, fog, or cloud environments. Most of the IoT applications utilize ...
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This paper presents an efficient algorithm for scheduling vaccination process which is based on the CPU scheduling algorithms of an operating system. Based on a custom-designed scoring system of the given schedule, an...
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作者:
RupaliMangla, NeerajMMDU
Department of Computer Scienece and Engineering Mullana Haryana Amabla133203 India
Cloud computing has gained so much of attention due to its features such as pay-per-use basis, resource pooling, and scalability that has forced the professionals to use cloud services for executing various applicatio...
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scheduling is an essential part of an operating system. The main aim of scheduling is to schedule jobs and allot them to the CPU in a balanced way. It follows a set of rules to give prioritization to various processes...
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Resource scheduling has always been a hot and difficult problem in systems engineering and management at home and abroad, and it is also very important in the research of satellite earth station system. Due to the lim...
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The appearance of power-adjustable sensors provides an opportunity for latency reduction in Power Adjustable Sensor Network. In wireless network with power-adjustable sensors, the node may prefer to wait rather than t...
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Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies ...
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Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas of big data. The widespread popularity of big data processing platforms using MapReduce framework is the growing demand to further optimize their performance for various purposes. In particular, enhancing resources and jobs scheduling are becoming critical since they fundamentally determine whether the applications can achieve the performance goals in different use cases. scheduling plays an important role in big data, mainly in reducing the execution time and cost of processing. This paper aims to survey the research undertaken in the field of scheduling in big data platforms. Moreover, this paper analyzed scheduling in MapReduce on two aspects: taxonomy and performance evaluation. The research progress in MapReduce scheduling algorithms is also discussed. The limitations of existing MapReduce scheduling algorithms and exploit future research opportunities are pointed out in the paper for easy identification by researchers. Our study can serve as the benchmark to expert researchers for proposing a novel MapReduce scheduling algorithm. However, for novice researchers, the study can be used as a starting point.
This study examined the developments in the field of scheduling algorithms in the last 30 years (1992-2021) to help researchers gain new insight and uncover the emerging areas of growth for further research in this fi...
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This study examined the developments in the field of scheduling algorithms in the last 30 years (1992-2021) to help researchers gain new insight and uncover the emerging areas of growth for further research in this field. This study, therefore, carried out a bibliometric analysis of 12,644 peer-reviewed documents extracted from the Scopus database using the Bibliometrix R package for bibliometric analysis via the Biblioshiny web interface. The results of this study established the development status of the field of scheduling algorithms, the growth rate, and emerging thematic areas for further research, institutions, and country collaborations. It also identified the most impactful and leading authors, keywords, sources, and publications in this field. These findings can help both budding and established researchers to find new research focus and collaboration opportunities and make informed decisions as they research the field of scheduling algorithms and their applications.
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