Energy-optimal resource scheduling and computation offloading in small cell networks

Labidi, Wael; Sarkiss, Mireille; Kamoun, Mohamed

doi:10.1109/ict.2015.7124703

Cited by 70 publications

(40 citation statements)

References 9 publications

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“…A further extension of [79] from a single-UE to a multi-UEs scenario is considered in [80]. The main objective is to jointly optimize scheduling and computation offloading strategy for each UE in order to guarantee QoE, fairness between the UEs, low energy consumption, and average queuing/delay constraints.…”

Section: A Full Offloadingmentioning

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: A Full Offloadingmentioning

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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“…In [2], an offloading strategy based on Lyapunov optimization is adopted to minimize the total cost which consists of delay and energy consumption. In [20], two offline strategies based on the constrained MDP are proposed to minimize the energy consumption under a delay constraint.…”

Section: A Computation Offloading In Mec Systemsmentioning

confidence: 99%

Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

Meng

Wang

et al. 2019

IEEE Trans. Wireless Commun.

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Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability.In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependencies and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP), and approximate it to a virtual continuous time system (VCTS) with reflections. Different to most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closedform approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). A extension is provided from single MT single MEC server scenarios to multiple MTs multiple MEC servers scenarios and several insights are derived. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.Technology, Hong Kong. 2 I. INTRODUCTION Smart mobile terminals (MTs) with advanced communication and computation capabilities facilitate us with a pervasive and powerful platform to realize many emerging computationintensive mobile applications, e.g., interactive gaming, character recognition, and natural language processing [2], [3]. These pose exigent requirements on the quality of computation experience, especially for the delay-sensitive applications. Computation offloading [4], which offloads the computation tasks to the offloading destination, is one of the fundamental services to improve the computation performance, i.e., delay performance. In computation offloading services, both the communication capability of the MT and the computation capability of the offloading destination will influence the delay performance. Specifically, • The communication capability of the MT: The offloading rate varies according to the time-varying wireless channel quality between the MT and the offloading destination. Poor communication capabilities will result in the starvation of the computation of the offloading destination, which induces a large queuing delay at the MT.• The computation capability of the offloading destination: In practical scenarios, the offloaded tasks cannot be executed immediately because the computation capability of the offloading destination is not infinity. Both the computation time and the waiting time at ...

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“…is the vector indicating the age of each packet in the data buffer, • b is the battery level, and • x is the channel gain. Due to the strict delay constraint, we describe the data buffer states using k instead of the queue length q used in the stateof-the-art [5], [7], [9]. Actually, q can be extracted from k…”

Section: A State Spacementioning

confidence: 99%

“…Optimizing the transmission strategies in MEC has attracted considerable attention during the past decade. For instance, in [5], the energy consumption of the mobile device was minimized by jointly optimizing the radio resource scheduling and the computation offloading under average delay constraints This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675891.…”

Section: Introductionmentioning

confidence: 99%

Joint Resource Scheduling and Computation Offloading for Energy Harvesting Communications

Fawaz

Sarkiss²,

Ciblar³

2018

2018 25th International Conference on Telecommunications (ICT)

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This paper studies the joint optimization of resource scheduling and computation offloading for mobile networks where energy harvesting (EH)-enabled devices are wirelessly connected to nearby base stations (BSs), which can be endowed with some computational capabilities. We consider that a mobile device may run its applications either locally or remotely at its serving base station. We also consider that its applications are strict delay constraints. Our objective is to minimize the packets' loss due to buffer overflow or delay violation of the queued packets at the mobile device. We formulate this problem as a Markov Decision Process (MDP) and exhibit an optimal deterministic offline scheduling-offloading policy. This policy makes decision on the processing location (either local or offloading) and on the number of processed packets by relying on the knowledge on the current channel, the past data and energy arrivals as well as the harvested energy available in the battery. We show through numerical results that the proposed policy can significantly improve the successfully received packets' rate and the energy consumption compared to other policies, such as immediate scheduling or only local processing or only offloading policies.

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Energy-optimal resource scheduling and computation offloading in small cell networks

Cited by 70 publications

References 9 publications

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

Joint Resource Scheduling and Computation Offloading for Energy Harvesting Communications

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