A Strategic Game for Task Offloading among Capacitated UAV-Mounted Cloudlets

Ma, Weibin; Liu, Xuanzhang; Mashayekhy, Lena

doi:10.1109/iciot.2019.00022

Cited by 23 publications

(18 citation statements)

References 17 publications

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“…However, we can make a distinction among the collected papers, whether they focus only on edge servers in MEC, e.g., [81], [85], [94], [112], [113], [115], [117], [154], [157]- [160], [173], [183], or they also consider the possibility to offload computational tasks to a central cloud, e.g., [101]- [104], [148], [151], [152]. Furthermore, one can find papers in the related literature that apply moving devices, e.g., UAVs [84], or robots [173], as edge nodes in order to position them to optimal locations anytime the terminals change their location. In these latter the optimization usually involves finding those optimal locations, besides the offloading decisions.…”

Section: A Single Componentmentioning

confidence: 99%

“…In these latter the optimization usually involves finding those optimal locations, besides the offloading decisions. For example, in [84] the authors formulate a computational offloading problem among a swarm of UAVs acting as small flying cloudlets that receive compute-intensive tasks from IoT devices via the FDMA technique. UAVs, as MEC resources, aim to optimize their energy consumption while satisfying the requirements of the IoT tasks under execution.…”

Section: A Single Componentmentioning

confidence: 99%

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Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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Section: A Single Componentmentioning

confidence: 99%

Section: A Single Componentmentioning

confidence: 99%

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…The objective function (4) is to minimize the overhead cost for all tasks. Constraints (5) and (6) ensure that the assignment of tasks to each resource-rich UAV does not exceed the capacity of that UAV. Constraints (7) ensure that each task is offloaded to exactly one UAV obeying the accessibility restrictions.…”

Section: Optimization Formationmentioning

confidence: 99%

“…Request permissions from permissions@acm.org. to form an interconnected computing system and improve quality of edge services [4,5]. Such a perspective, however, opens new research challenges on account of the current lack of efficient data protection mechanisms for offloading.…”

Section: Introductionmentioning

confidence: 99%

Privacy-by-design task offloading for UAV-mounted cloudlets

Mashayekhy

2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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We consider a network of capacitated Unmanned Aerial Vehicles (UAVs) cooperating as an aerial edge computing system. Due to limited onboard energy and computation capabilities of UAV-mounted cloudlets, a single UAV might not be able to execute a large number of tasks and guarantee their desired quality of services. The overloaded UAV can fulfill its overwhelming workload by offloading its tasks to other UAVs. However, data privacy and accessibility are of critical importance that need to be considered for offloading. We propose a privacy-by-design offloading solution for an overloaded UAV aiming to minimize the offloading cost while satisfying the quality of services.

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“…These resources-rich components placed closer to the users are called Cloudlets [5]. Cloudlets enable task offloading for resource-intensive mobile applications to provide realtime and location-aware services, while reducing traffic to the conventional cloud [3,7,8]. Strategically placing these geo-distributed cloudlets to provide low-latency computing services is a major challenge in edge computing.…”

Section: Introductionmentioning

confidence: 99%

Cost-aware cloudlet placement in edge computing systems

Bhatta

Mashayekhy

2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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One of the well-known challenges in Edge Computing is strategic placement of cloudlets. The fundamental goals of this challenge are to minimize the deployment cost and to guarantee minimum latency for the users of edge services. We address this challenge by designing a cost-aware cloudlet placement approach that fully maps user applications to appropriate cloudlets while ensuring their latency requirements. We investigate the effectiveness of our proposed approach by performing extensive experiments based on New York City OpenData. The results show that our approach obtains close to optimal cost solutions with significantly reduced execution time.

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A Strategic Game for Task Offloading among Capacitated UAV-Mounted Cloudlets

Cited by 23 publications

References 17 publications

Survey on Placement Methods in the Edge and Beyond

Survey on Placement Methods in the Edge and Beyond

Privacy-by-design task offloading for UAV-mounted cloudlets

Cost-aware cloudlet placement in edge computing systems

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