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Mahla Rahati-Quchani

Publications and source records attributed to Mahla Rahati-Quchani.

2 recordsLinked to original sources

Edge Computing: A Systematic Mapping Study

Edge computing is a novel computing paradigm that extends cloud resources at the edge of the network to tackle the problem of communication latency in latency-sensitive applications. For the last decades, there have been many efforts dedicated to this field. The dramatic growth in the publications, and the great attention of the research community, have made it necessary to conduct a Systematic Mapping Study (SMS) to enable researchers get a better view of the field. A three-tier search method is considered in this work. In this method, we defined some quality criteria to extract appropriate search spaces and studies. Using this methodology, we select 112 search spaces out of 805 ones, and by searching in these search spaces we select 1440 studies out of 8725. In our SMS, 8 research questions have been designed and answered to identify the main topics, architectures, techniques, etc. in the field of edge computing.

cs.NI

An Efficient Mechanism for Computation Offloading in Mobile-Edge Computing

Mobile edge computing (MEC) is a promising technology that provides cloud and IT services within the proximity of the mobile user. With the increasing number of mobile applications, mobile devices (MD) encounter limitations of their resources, such as battery life and computation capacity. The computation offloading in MEC can help mobile users to reduce battery usage and speed up task execution. Although there are many solutions for offloading in MEC, most usually only employ one MEC server for improving mobile device energy consumption and execution time. Instead of conventional centralized optimization methods, the current paper considers a decentralized optimization mechanism between MEC servers and users. In particular, an assignment mechanism called school choice is employed to assist heterogeneous users to select different MEC operators in a distributed environment. With this mechanism, each user can benefit from minimizing the price and energy consumption of executing tasks while also meeting the specified deadline. The present research has designed an efficient mechanism for a computation offloading scheme that achieves minimal price and energy consumption under latency constraints. Numerical results demonstrate that the proposed algorithm can attain efficient and successful computation offloading.

cs.DC