An efficient elastic distributed SDN controller for follow-me cloud

Aissioui, Abdelkader; Ksentini, Adlen; Guéroui, Abdelhak Mourad

doi:10.1109/wimob.2015.7348054

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…The FMCC may be deployed either centrally (as shown in Fig. 5) or hierarchically [61] with global FMCC (G-FMCC) and local FMCC (L-FMCC) for better scalability (controlled similarly as in the SCC as explained in Section III-A1). Note that the FMC itself may be also decentrally controlled by omitting the FMCC altogether.…”

Section: ) Follow Me Cloud (Fmc)mentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

2,624

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: ) Follow Me Cloud (Fmc)mentioning

confidence: 99%

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mach

Bečvář

2017

IEEE Commun. Surv. Tutorials

2,624

1,204

View full text Add to dashboard Cite

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“…The Mobile Edge Computing (MEC) concept presented by the Industry Specification Group (ISG) within the European Telecommunications Standards Institute (ETSI) is focused on integrating computation resources within the Radio Access Network (RAN) in very close proximity to mobile subscribers [16]. According to A. Shakarami et al [19], several MEC architectures have been proposed in order to integrate and manage computation resources at the RAN level, such as Small Cell Cloud (SCC) [11], Mobile Micro Cloud (MMC) [21], Fast Moving Personal Cloud (MobiScud) [20], Follow Me Cloud (FMC) [2], CONCERT [10], and eTOMEC [13].…”

Section: Related Workmentioning

confidence: 99%

Task Offloading Optimization in Mobile Edge Computing based on Deep Reinforcement Learning

Silva,

Magaia,

Grilo

2023

Proceedings of the Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems

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The Cloud Computing (CC) paradigm has risen in recent years as a solution to a need for computation and battery constrained User Equipment (UE) to run increasingly intensive computation tasks. Nevertheless, given the centralized nature of the CC paradigm, this option introduces significant network congestion problems and unpredictable communication delays unsuitable for real-time applications. In order to cope with these problems, the Mobile Edge Computing (MEC) concept has been introduced, which proposes to bring computation resources closer to the edge of the mobile networks in a distributed way. However, given that these edge computation resources are limited, this paradigm comes with its set of challenges that need to be solved in order to make it viable. This work proposes to innovate by presenting a network management agent capable of making offloading decisions from a heterogeneous network of UEs to a heterogeneous network of MEC servers. This agent is the orchestrator of a group of 5G Small Cells (SCeNBs), enhanced with computation and storage capabilities. In order to solve this high complexity problem, an Advantage Actor-Critic (A2C) agent is implemented and tested against several baselines. The proposed solution is shown to beat the baselines by making intelligent decisions taking into account computation, battery, delay and communication constraints ignored by the baselines. The solution is also shown to be scalable, data-efficient, robust, stable and adjustable to address not only the overall system performance but also to take into account the worst-case scenario.

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“…The advantages and disadvantages of both a centralized and distributed control plane in the SDN architectures have been widely researched. [9][10][11][12] The main disadvantages of the centralized SDN control plane are scalability limitations, performance issues, and single point of failure. On the other hand, distribution of the control plane introduces complexity and possible packet delay.…”

Section: Introductionmentioning

confidence: 99%

Analytical modeling of distributed network functions virtualization architectures and the impact of the distribution on the packet sojourn time

Ilievski

Latkoski

2022

Trans Emerging Tel Tech

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Distribution of network functions virtualization (NFV) environment on multiple locations and datacenters is a hot topic in research and engineering communities. In this article, we are making analytical models to calculate the network packet sojourn time in three different scenarios: classical NFV environment where all NFV elements are on the same location, distributed architecture of the data plane with centralized management and orchestration (MANO) environment, and distributed environment where every location has its own data plane and MANO elements. The analytical models are tested and discussed through Matlab based simulations. Through the simulation results, we discuss the impact of changes of different parameters on the packet sojourn time, and we compare the architectures in order to see how they can be fitted in practical services provided in an NFV environment.

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An efficient elastic distributed SDN controller for follow-me cloud

Cited by 9 publications

References 12 publications

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Task Offloading Optimization in Mobile Edge Computing based on Deep Reinforcement Learning

Analytical modeling of distributed network functions virtualization architectures and the impact of the distribution on the packet sojourn time

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