EEOA: Improving energy efficiency of mobile cloudlets using efficient offloading approach

Panigrahi, Chhabi Rani; Pati, Bibudhendu; Tiwary, Mayank; Sarkar, Joy Lal

doi:10.1109/ants.2015.7413617

Cited by 30 publications

(10 citation statements)

References 13 publications

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“…It is shown that the packets which are generated by all nodes are received by the BS where the link capacity does not cross its limit as given in (12). The relative network load (RNL) is computed by using (13).…”

Section: Nps Servicementioning

confidence: 99%

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EMC ² : an emergency management system using mobile cloud computing

Ramasamy¹,

Gomathy²,

Sarkar

et al. 2020

IET Networks

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A natural or unexpected man-made emergency may occur at any time and which affects the economy as well as human life of the country. The most important task in an emergency is to collect the essential information about the emergency places and do a proper analysis to take effective action. In those situations, mobile gadgets are very much helpful to collect the information in a significant way. The mobile devices have certain limitations such as less battery power, lack of Internet connectivity for data transmission or less computational resources. So the energy hungry applications can be offloaded to the cloud to reduce the workload of the mobile devices. To overcome these problems, the authors propose an emergency management system named as EMC 2 , which follows peer-to-peer communication using Bluetooth technology. EMC 2 first detects the available mobile devices present in a network using mobile probing service (MPS) and then it chooses the suitable mobile device among them using mobile ranking service (MRS). EMC 2 also determines the best network resources while roaming in heterogeneous access networks (HANs). The experimental results of authors-proposed EMC 2 approach shows the increased performance and out-performs in terms of receiving time as compared to the baseline algorithm.

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Section: Nps Servicementioning

confidence: 99%

“…Mobile components are fundamental for MCC. Numerous applications need to be processed with mobiles, but it has less battery and memory capacity [7][8][9][10][11][12][13][14][15][16]. Users share their photos and videos etc.…”

Section: Introductionmentioning

confidence: 99%

EMC ² : an emergency management system using mobile cloud computing

Ramasamy¹,

Gomathy²,

Sarkar

et al. 2020

IET Networks

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“…To achieve the goals, heuristic and clustering algorithms were designed for reducing delay while an integer linear programming is formulated to lessen the used edge servers. Panigrahi et al [34] proposed an energy e cient o oading approach to increase the resource utilization of edge servers for better energy usage. Analogously, Gai et al [35] designed a dynamic energy-aware edge server-based mobile cloud computing model for energy saving according to dynamic edge servers (DCL)-based model.…”

Section: Evaluation On Energy Consumptionmentioning

confidence: 99%

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Xue

Cui

et al. 2019

Complexity

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By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the computing resources across edge servers. With the consideration of this issue, a Balanced Resource Scheduling Method, named BRSM, for trade-offs between virtual machine (VM) migration cost and energy consumption of VM migrations for edge server management, named BRSM, is designed in this paper. Technically, we analyze the load conditions of edge servers and formulate the energy consumption of VM migrations and VM migration cost as a multi-objective optimization problem. Then, we propose a dynamic resource scheduling method for WMAN to deal with the multi-objective optimization problem. In addition, nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate optimal resource scheduling strategies. Finally, we conduct experiment simulations to testify the efficiency of the proposed method BRSM.

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“…The current world has undergone a big revolution with the introduction of Smart Mobile Devices (SMDs) like smart phones, tablets, laptops, etc., which can accomplish different types of activities using intelligent applications that can be run independently on the cloud [1]. The current Year on Year (YoY) growth of mobile devices is more than 50 % and their number has already surpassed the total world population [2].…”

Section: Introductionmentioning

confidence: 99%

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

et al. 2021

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In today's fast advancing world, sensors are used in various applications to provide complete data about different objects present around us. The sensor data when integrated with Mobile Cloud Computing (MCC) is used for further computation to provide useful results advanced warning, forecasting, planning, and plethora of other applications in this uncertain world. Few works have been proposed where sensor nodes are integrated with Wireless Sensor Networks (WSNs) which give a new direction in modern research for developing new technologies which help the users for fast access of sensory data over mobile devices. Herein, a systematic literature review of both MCC and WSNs are conducted and subsequently assimilated them via an architecture which is efficient and achievable within short time-span for a variety of applications. The MCC applications are reviewed from two main perspectives that are energy and information management. The communication aspect in MCC and WSNs has been discussed and the need for integration between the two has been justified in this work. The work done on mobility management in MCC and WSNs is reviewed. Also, the challenges in MCC-WSNs integration along with the comprehensive analysis and findings of the review are identified. Finally, the conclusion and specific future direction of research in this area are provided. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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EEOA: Improving energy efficiency of mobile cloudlets using efficient offloading approach

Cited by 30 publications

References 13 publications

EMC ² : an emergency management system using mobile cloud computing

EMC ² : an emergency management system using mobile cloud computing

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

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EEOA: Improving energy efficiency of mobile cloudlets using efficient offloading approach

Cited by 30 publications

References 13 publications

EMC 2 : an emergency management system using mobile cloud computing

EMC 2 : an emergency management system using mobile cloud computing

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

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Product

Resources

About

EMC ² : an emergency management system using mobile cloud computing

EMC ² : an emergency management system using mobile cloud computing