Taming the big to small: efficient selfish task allocation in mobile crowdsourcing systems

Li, Qingyu; Yang, Panlong; Fan, Xiaochen; Tang, Shaojie; Xiang, Chaocan; Guo, Deke; Li, Fan

doi:10.1002/cpe.4121

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…First, only when the distance between two cloudlets is within an inter-contact range R, they can establish an intermittent connection. Second, based on [24], the connecting probability of two cloudlets i and j is computed as:…”

Section: B Task Transmission Modelmentioning

confidence: 99%

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FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

Lai

Fan

et al. 2020

IEEE Access

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Mobile cloud computing has emerged as a promising paradigm to facilitate computationintensive and delay-sensitive mobile applications. Computation offloading services at the edge mobile cloud environment are provided by small-scale cloud infrastructures such as cloudlets. While offloading tasks to in-proximity cloudlets enjoys benefits of lower latency and smaller energy consumption, new issues related to the cloudlets are rising. For instance, unbalanced task distribution and huge load gaps among heterogeneous mobile cloudlets are becoming more challenging, concerning the network dynamics and distributed task offloading. In this paper, we propose 'FairEdge', a Fairness-oriented computation offloading scheme to enable balanced task distribution for mobile Edge cloudlet networks. By integrating the balls-andbins theory with fairness index, our solution promotes effective load balancing with limited information at low computation cost. The evaluation results from extensive simulations and experiments with real-world datasets show that, FairEdge outperforms conventional task offloading methods, and it can achieve a network fairness up to 0.85 and reduce the unbalanced task offload by 50%. INDEX TERMS Mobile cloudlets, load balancing, edge computing, fair task offloading.

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Section: B Task Transmission Modelmentioning

confidence: 99%

“…Second, by leveraging the '2-choice' paradigm for selecting the target for computation offloading, FairEdge only probes load information from two contactable neighboring cloudlets for comparison. According to [26] and [24], such a process would significantly reduce the complexity overhead to O(1) compared with greedy offloading's O(n) complexity.…”

mentioning

confidence: 99%

FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

Lai

Fan

et al. 2020

IEEE Access

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“…The inter-meeting time of cloudlets c i and c j is denoted as t i,j . Based on [16] and [17], the inter-contact time t i,j would follow exponential distribution with pairwise rate α ij , i.e.,…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…After querying and comparing their load states, the cloudlet choose offload a task to the one with less task load, where the computing complexity in each time interval is O(1). Similar to [17], we compare performance of proposed scheme with three benchmarks, i.e., random allocation, proportional allocation [14] and greedy allocation. In random allocation, a mobile cloudlet offloads tasks by randomly selecting another mobile cloudlet in its contact range.…”

Section: Performance Evaluationmentioning

confidence: 99%

CTOM: Collaborative Task Offloading Mechanism for Mobile Cloudlet Networks

Fan

Puthal

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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With the advance in wireless networking technologies and communication infrastructures, mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity-limited mobile devices. More specifically, at the network edge, resource-rich and trusted cloudlet system is acting as a 'data center in a box' to support computeintensive mobile applications. The mobile cloudlets can provide in-proximity services by executing the workloads for nearby devices. Nevertheless, load balancing in mobile cloudlet network is of great importance and has a huge impact on response time. Existing methods for cloudlet load balancing basically rely on the user-cooperation or strategic placement. However, above solutions require global task load information from the network, which is costly in communication and computational overhead. To achieve more efficient and low-cost load balancing, we propose 'CTOM', a Collaborative Task Offloading Mechanism for mobile cloudlet network. Our solution is based on balls-andbins theory and can balance the task load only with limited information. Extensive simulations and mobility trace based evaluation show that, the proposed 'CTOM' is effective and can achieve similar performance to the greedy algorithm with low computing complexity.

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“…Thus, neutral networks display sparse and less compact communities. Li et al investigate the selfish load balancing problem in mobile distributed crowdsourcing networks. This work leverages the d‐choice method based on Ball and Bin theory for effective balancing under limited information and the proportional allocation scheme for selfish load balancing, maintaining good load balancing property among selfish users.…”

Section: Themes Of This Special Issuementioning

confidence: 99%

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Hassan

Yang

Qiu

2017

Concurrency and Computation

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This special issue is composed of a collection of articles, which provide recent advances in some fields related to Big Data Security and Intelligent Data in Clouds. More precisely, the manuscripts investigate different topics including forensic in cloud-of-things devices, encryption in mobile cloud environments, defense against malicious URL spreading in microblog networks, social network access control model in cloud computing, dynamic resource sharing algorithms, load balancing for cloud computing, cloud-based audio/video streaming techniques, energy-aware scheduling in clouds, sensor network security in cloud computing, as well as real world applications related to industry, manufacturing, and vanets. We hope that the readers could benefit from the articles presented in this special issue and would contribute to these exciting research areas. ACKNOWLEDGMENTSThe guest editors of this special issue wish to express their sincere gratitude to all of the authors who contributed to this issue. We extend our thanks to the reviewers for their hard work and their feedbacks. Likewise, we wish to express our gratitude

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Taming the big to small: efficient selfish task allocation in mobile crowdsourcing systems

Cited by 6 publications

References 20 publications

FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

CTOM: Collaborative Task Offloading Mechanism for Mobile Cloudlet Networks

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

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