Handoff Strategy for Improving Energy Efficiency and Cloud Service Availability for Mobile Devices

Ravi, Anuradha; Peddoju, Sateesh K.

doi:10.1007/s11277-014-2119-y

Cited by 34 publications

(23 citation statements)

References 32 publications

(41 reference statements)

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“…This is to minimize the impact of RSS fluctuation. The median method [shown in (8)] is used instead of the mean method [23] because it overcomes a sudden large increase or decrease in the RSS value caused by an unintended factor. Therefore, RSS value is given as …”

Section: Proposed Vertical Handover Methodsmentioning

confidence: 99%

New Vertical Handover Method to Optimize Utilization of Wireless Local Area Network in High-Speed Environment

et al. 2016

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In heterogeneous wireless networks, wireless local area network (WLAN) is highly preferred by mobile terminals (MTs) owing to its high transmission bandwidth and low access cost. However, in high-speed environment, handover from a cellular network to a WLAN cell will lead to a high number of handover failures and unnecessary handovers due to the WLAN coverage limitation and will become worse at high speed. A new vertical handover method is proposed to minimize the probability of handover failure and unnecessary handover while maximizing the usage of WLAN in high-speed environment. The simulation results show that the proposed method kept the probability of handover failure and unnecessary handover below 0.5% and 1%, respectively. Compared with previous studies, the proposed method reduced the number of handover failures and unnecessary handovers up to 80.0% and 97.7%, respectively, while the MT is highly mobile. Using the proposed prediction method, the MT can benefit high bandwidth and low network access cost from the WLAN with minimum interruption regardless of speed.

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Section: Proposed Vertical Handover Methodsmentioning

confidence: 99%

New Vertical Handover Method to Optimize Utilization of Wireless Local Area Network in High-Speed Environment

et al. 2016

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“…The main features of TOPSIS are chosen as the alternatives that simultaneously have the shortest distance from the ideal solution and the farthest distance from the anti-ideal solution. On this basis, many decision making methods use or extend TOPSIS in order to determine the ideal solution such as [52,53,57]. Based on our survey, we found that the utilization of MCDA methods depends on the particular use at a particular step of the offloading process leading to considering a problem in part of the mobile cloud environment, such as [51], which focuses on the selection of an optimal wireless medium.…”

Section: Certainty In MCCmentioning

confidence: 99%

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Bangui

Bühnová

et al. 2017

JSAN

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Abstract:Mobile cloud computing (MCC) is becoming a popular mobile technology that aims to augment local resources of mobile devices, such as energy, computing, and storage, by using available cloud services and functionalities. The offloading process is one of the techniques used in MCC to enhance the capabilities of mobile devices by moving mobile data and computation-intensive operations to cloud platforms. Several techniques have been proposed to perform and improve the efficiency and effectiveness of the offloading process, such as multi-criteria decision analysis (MCDA). MCDA is a well-known concept that aims to select the best solution among several alternatives by evaluating multiple conflicting criteria, explicitly in decision making. However, as there are a variety of platforms and technologies in mobile cloud computing, it is still challenging for the offloading process to reach a satisfactory quality of service from the perspective of customers' computational service requests. Thus, in this paper, we conduct a literature review that leads to a better understanding of the usability of the MCDA methods in the offloading operation that is strongly reliant on the mobile environment, network operators, and cloud services. Furthermore, we discuss the challenges and opportunities of these MCDA techniques for offloading research in mobile cloud computing. Finally, we recommend a set of future research directions in MCDA used for the mobile cloud offloading process.

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“…Equation 30 in Section 3.5.4). Therefore, to be aligned with the studies on Cloud applications' energy consumption, we simplify the Internet model to be an equipment combination of switches, routers and various links, plus the Cloudlet and Cloud, as illustrated in • Device Cloud: Considering the potentially spare computing resources of surrounding devices, peer-device offloading has been proposed as an effective option to share workloads through Bluetooth ad-hoc network [33]. A simulation-based theoretical analysis even showed 63% more energy saving than traditional offloading to the Cloud [37].…”

Section: Access Pointsmentioning

confidence: 99%

“…The energy consumption influenced by different technologies is mainly discussed with regarding to client devices [66,67]. Among the popular access point technologies, WiFi and Ethernet generally consume less energy than cellular wireless networks [24,44,68,43,29,69]; although providing lower data rate, Bluetooth could be 80% to 120% more energy efficient than WiFi [31]; as for the cellular networks, LTE (4G) consumes more power than UMTS (3G), followed by EDGE (2G) [33,49]. 2) Network Bandwidth: As indicating the maximum channel capacity, the network bandwidth is considered to have a positive impact on reducing both the transmission delay and the energy consumption of Cloud applications [58,70].…”

Section: Communication Environmental Factorsmentioning

confidence: 99%

“…The network condition can be reflected by the signal strength or the signal to noise ratio [75]. When the signal strength is low, the relevant network devices will have to increase their power levels for data transmission [33], and will correspondingly end up with higher communication cost [43]. Furthermore, weak signals would lead to high chance of network unavailability [22].…”

Section: Communication Environmental Factorsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates

Zheng

Tesfatsion

Bastani

et al. 2017

IEEE Trans. Sustain. Comput.

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Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and environmental conditions, with possible overlaps between different research works. There- fore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and deployment environment of Cloud applications, and identified 18 environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.

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Handoff Strategy for Improving Energy Efficiency and Cloud Service Availability for Mobile Devices

Cited by 34 publications

References 32 publications

New Vertical Handover Method to Optimize Utilization of Wireless Local Area Network in High-Speed Environment

New Vertical Handover Method to Optimize Utilization of Wireless Local Area Network in High-Speed Environment

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates

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