PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

Sun, Xiang; Ansari, Nirwan

doi:10.1109/icc.2016.7511131

Cited by 69 publications

(34 citation statements)

References 21 publications

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“…Basically, the related papers try to find an optimal decision policy whether the VM migration should be initiated or not to minimize overall system cost (up to 32% and up to 50% reduction of average cost is achieved compared to never and always migrate options, respectively [117]). Moreover, some papers aim to find a proper trade-off between VM migration cost and VM migration gain [112], minimizing execution delay [115], minimizing VM migration time [119], or maximizing overall throughput [120].…”

Section: Summary Of Work Focused On Mobility Managementmentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

2,618

1,204

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Abstract-Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

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Section: Summary Of Work Focused On Mobility Managementmentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

2,618

1,204

View full text Add to dashboard Cite

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“…Finally, we update the ratio r and optimal solutionα as shown in Algorithm 1. We repeat this procedure until there is no optimal solution for problem (13). The convexity of problem (13) is proven in the following theorem.…”

Section: ) G/d/1 Loss Probability Approachmentioning

confidence: 99%

Hierarchical Capacity Provisioning for Fog Computing

Kiani

Ansari

Khreishah

2019

IEEE/ACM Trans. Networking

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The concept of fog computing is centered around providing computation resources at the edge of network, thereby reducing the latency and improving the quality of service. However, it is still desirable to investigate how and where at the edge of the network the computation capacity should be provisioned. To this end, we propose a hierarchical capacity provisioning scheme. In particular, we consider a two-tier network architecture consisting of shallow and deep cloudlets and explore the benefits of hierarchical capacity based on queueing analysis. Moreover, we explore two different network scenarios in which the network delay between the two tiers is negligible as well as the case that the deep cloudlet is located somewhere deeper in the network and thus the delay is significant.More importantly, we model the first network delay scenario with bufferless shallow cloudlets as well as the second scenario with finite-size buffer shallow cloudlets, and formulate an optimization problem for each model. We also use stochastic ordering to solve the optimization problem formulated for the first model and an upper bound based technique is proposed for the second model.The performance of the proposed scheme is evaluated via simulations in which we show the accuracy of the proposed upper bound technique as well as the queue length estimation approach for both randomly generated input and real trace data.

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“…In order to keep the E2E delays between mobile devices and their proxy VMs low, the location proxy VMs can be dynamically changed based on the locations of the mobile devices [24]. As shown in Fig.…”

Section: Solution 3: Latency Aware Proxy Vm Migration Among Cloudletsmentioning

confidence: 99%

“…From the networking perspective, the migration overheads incur additional proxy VM migration traffic, which is determined by the size of the proxy VM, the provisioned bandwidth, etc. [24]. From the energy consumption perspective, the migration overheads refer to the amount of energy incurred by the proxy VM migration.…”

Section: Future Workmentioning

confidence: 99%

“…Instead of applying the traditional cellular core network architecture, which leads to inefficient, inflexible, and unscalable packet forwarding, the SDN based mobile core network [22][23][24][25][26][27][28] is adopted in MEIoT. The SDN based mobile core network is essentially decoupling the control plane from the switches, which only run data plane functionalities.…”

Section: Sdn Based Mobile Core Networkmentioning

confidence: 99%

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Mobile Edge Computing Empowers Internet of Things

Ansari

Sun

2018

IEICE Trans. Commun.

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SUMMARY In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in realtime. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

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PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

Cited by 69 publications

References 21 publications

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Hierarchical Capacity Provisioning for Fog Computing

Mobile Edge Computing Empowers Internet of Things

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