Dual-Side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing

Kim, Yeongjin; Kwak, Jeongho; Chong, Song

doi:10.1109/tvt.2017.2762423

Cited by 92 publications

(34 citation statements)

References 23 publications

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“…Likewise, the average queuing delay at the server is proportional to the ratio of the average queue length to the average transmission rate. Referring to (8), we consider the probabilistic constraint as follows:…”

Section: Formulationmentioning

confidence: 99%

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Liu

Bennis

Debbah

et al. 2019

IEEE Trans. Commun.

361

183

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To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation.However, current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users' power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are re-associated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers' computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale while a dynamic task offloading and resource ). 2 allocation policy is executed in the short timescale. Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to baselines. Index Terms 5G and beyond, mobile edge computing (MEC), fog networking and computing, ultra-reliable low latency communications (URLLC), extreme value theory. I. INTRODUCTION Motivated by the surging traffic demands spurred by online video and Internet-of-things (IoT) applications, including machine type and mission-critical communication (e.g., augmented/virtual reality (AR/VR) and drones), mobile edge computing (MEC)/fog computing are emerging technologies that distribute computations, communication, control, and storage at the network edge [2]-[6]. When executing the computation-intensive applications at mobile devices, the performance and user's quality of experience are significantly affected by the device's limited computation capability. Additionally, intensive computations are energy-consuming which severely shortens the lifetime of battery-limited devices. To address the computation and energy issues, mobile devices can wirelessly offload their tasks to proximal MEC servers. On the other hand, offloading tasks incurs additional latency which cannot be overlooked and should be taken into account in the system design. Hence, the energy-delay tradeoff has received significant attention and has been studied in various MEC systems [7]-[22]. A. Related WorkIn [7], Kwak et al. focused on an energy minimization problem for local computation and task offloading in a single-user MEC system. The authors further studied a multi-user system, which takes into account both the energy cost and monetary cost of task offloading [8]. Therein, the cost-delay tradeoff was investigated in terms of competition and coo...

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Section: Formulationmentioning

confidence: 99%

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Liu

Bennis

Debbah

et al. 2019

IEEE Trans. Commun.

361

183

View full text Add to dashboard Cite

show abstract

“…be the vector of all real and virtual queue backlogs employed in the IoT system. We introduce L, which is widely used to guarantee queue stability in the Lyapunov function [23] [24], for the constraints C6 and C7. The perturbed Lyapunov function can be constructed as…”

Section: B Online Algorithm For Optimal Resource Schedulingmentioning

confidence: 99%

“…Then, by solving (28), we derive the optimalp * mn (t) = p * mn (t)d mn (t), where p * mn (t) can be given by (25). Substituting the optimalp * mn (t) into (23), the Lagrangian dual function can be also rewritten as max…”

Section: : End Whilementioning

confidence: 99%

Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI

Jiao

Dong

et al. 2020

IEEE Internet Things J.

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The Internet of Things (IoT) increases the number of connected devices and supports ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance computation capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels and imperfect estimation of channel state information (CSI). In this paper, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter β without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.

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“…Existing works from the economics viewpoint such as [33]- [35] are based on abstract utility functions (e.g., a simple logarithm function of the amount of product), and do not capture various user-side factors, such as the processing capacity of mobile device users and the amount of remaining computing tasks. The work [36] takes those user-side factors into account, and proposes Lyapunov-based algorithms for energy/monetary cost minimization while ensuring finite processing delay. Similar to other Lyapunov-based algorithms proposed by [18]- [21], the implementation of this work requires heavy signalling overhead to share the queueing states of users and the AP at each time slot.…”

Section: A Related Work and Main Contributions Of This Papermentioning

confidence: 99%

Learning-Based Pricing for Privacy-Preserving Job Offloading in Mobile Edge Computing

Siew

Quek

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Mobile edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by selfish mobile users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose learning-based pricing mechanisms where no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency.

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Dual-Side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing

Cited by 92 publications

References 23 publications

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI

Learning-Based Pricing for Privacy-Preserving Job Offloading in Mobile Edge Computing

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