A novel server selection approach for mobile cloud streaming service

Liu, Ran; Yuan, Xiaoqun; Xu, Jie; Chen, Jianguo; Zeng, Yuanxiang; Cao, Meng; Liu, Jinhong; Xu, Limei; Fang, Qing

doi:10.1016/j.simpat.2014.06.014

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Edge computing can be leveraged to enhance the quality of multimedia services by bringing processing and storage resources closer to user devices. Resource management and bandwidth allocation to control latency and jitter, 154,155 as well as managing cached data, 156,157 are crucial issues in this category of applications. Therefore, articles related to these applications are mostly classified in the resource management and data management topics.…”

Section: Streamingmentioning

confidence: 99%

Edge computing: A systematic mapping study

Sakhdari

Izadpanah

Zolfaghari

et al. 2023

Concurrency and Computation

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SummaryEdge computing is a new way of computing that uses resources at the edge of a network to solve the problem of communication delays in applications that require immediate responses. This field has received a lot of attention from the research community over the past few decades, leading to a significant increase in publications. To better understand the field, a systematic mapping study (SMS) was conducted using a three‐tier search method that involved defining quality criteria to extract relevant search spaces and studies. This resulted in the selection of 112 search spaces out of 805 and 1440 studies out of 8725. The SMS addressed 8 research questions to identify the main topics, architectures, techniques, and other important aspects of edge computing.

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Section: Streamingmentioning

confidence: 99%

Edge computing: A systematic mapping study

Sakhdari

Izadpanah

Zolfaghari

et al. 2023

Concurrency and Computation

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“…R. Liu et al proposed a mobility-aware framework named MACSS for mobile cloud streaming services. This framework provided dynamic and optimized server selection functions to support user mobility [14]. A. Selvaraj et al presented an optimizing VM selection solution called analogous particle swarm optimization (APSO) under a cloud computing environment, which implemented a swarm intelligence approach [15].…”

Section: A Resource Allocation Of Cloud Computingmentioning

confidence: 99%

A Dynamic Virtual Datacenter Selection Strategy for Integrated Cloud Service Platform Construction With Multiclouds

Huang

Sun

et al. 2019

IEEE Access

Self Cite

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The pay-as-you-go model and network virtualization of cloud computing allow micro and small content businesses (MSCBs) who construct their integrated cloud service platforms (ICSPs) with virtual datacenters (VDCs) to serve their end users with low service latency and construction cost. However, designing a flexible VDC selection strategy to meet the demands of MSCBs is a challenging task. To address this problem, a dynamic VDC selection strategy is designed for MSCBs to construct their ICSPs flexibly with the VDCs from different clouds. To this end, three dynamic landmark selection metrics are proposed and applied to construct the network coordinates. Then, a dynamic VDC selection algorithm is presented to determine the locations and service resources of VDCs, which are purchased from different clouds by MSCBs to construct their ICSPs. Based on our VDC selection strategy, a simulator is developed based on our designed experimental framework to evaluate our VDC selection strategy. The experimental results show that compared with previous server placement strategies, our strategy can actively and effectively determine VDCs' locations and allocate service resources for each VDC with less computing consumption and is a practical VDC selection strategy for cloud construction in a multicloud environment.

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“…In the "lost child" scenario, the author describes a situation where a child is missing and it is possible to collect and send records (or photos) from different users' mobile devices to the mobile cloud and to gather comprehensive information on the missing person. An example of another system using MCC, which provides multimedia services, is [34]. The developed mobility-aware framework for mobile cloud streaming services makes dynamic, optimized server selection functions available in order to support user mobility.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

Nawrocki

Śnieżyński

2017

J Grid Computing

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As the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question arises: how to optimally schedule services/tasks between the device and the cloud. The main objective of this paper is to investigate the possibilities for using a decision module on mobile devices in order to autonomously optimize the execution of services within the framework of Mobile Cloud Computing while taking context into account. A novel model of the decision module with learning capabilities, service-oriented architecture, and service selection optimization algorithm are proposed to solve this problem. To achieve autonomous, online learning on mobile devices, we apply supervised learning. Information about the context, task description, the decision made and its results such as calculation time or power consumption are stored and form training data for a supervised learning algorithm, which updates the knowledge used by the decision module to determine the optimal place for the execution of a given type of task. To verify the solution proposed, service-oriented mobile processing systems

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A novel server selection approach for mobile cloud streaming service

Cited by 7 publications

References 30 publications

Edge computing: A systematic mapping study

Edge computing: A systematic mapping study

A Dynamic Virtual Datacenter Selection Strategy for Integrated Cloud Service Platform Construction With Multiclouds

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

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