Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment

Hyun Suk Lee; Jang Won Lee

doi:10.1109/ACCESS.2018.2812144

Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment

Hyun Suk Lee, Jang Won Lee

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

58 Citations (Scopus)

Abstract

In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider's profit while guaranteeing maximum outage probability requirements.

Original language	English
Pages (from-to)	14908-14925
Number of pages	18
Journal	IEEE Access
Volume	6
DOIs	https://doi.org/10.1109/ACCESS.2018.2812144
Publication status	Published - 2018 Mar 3

Bibliographical note

Funding Information:
This work was supported in part by the Midcareer Researcher Program through NRF grant from MSIT, South Korea, under Grant NRF-2017R1A2 B4006908, in part by the Institute for Information and communications Technology Promotion grant through by the Korea Government under Grant B0717-16-0024, and in part by the development on the core technologies of transmission, modulation and coding with low-power and low complexity for massive connectivity in the IoT environment. The work of H.-S. Lee was supported by the Graduate School of YONSEI University Research Scholarship Grants in 2017.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science(all)
Materials Science(all)
Engineering(all)

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10.1109/ACCESS.2018.2812144

Cite this

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title = "Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment",

abstract = "In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider's profit while guaranteeing maximum outage probability requirements.",

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note = "Funding Information: This work was supported in part by the Midcareer Researcher Program through NRF grant from MSIT, South Korea, under Grant NRF-2017R1A2 B4006908, in part by the Institute for Information and communications Technology Promotion grant through by the Korea Government under Grant B0717-16-0024, and in part by the development on the core technologies of transmission, modulation and coding with low-power and low complexity for massive connectivity in the IoT environment. The work of H.-S. Lee was supported by the Graduate School of YONSEI University Research Scholarship Grants in 2017. Publisher Copyright: {\textcopyright} 2013 IEEE.",

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N2 - In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider's profit while guaranteeing maximum outage probability requirements.

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Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment

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