Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach

Zheng, Jianchao; Cai, Yueming; Wu, Yuan; Shen, Xuemin

doi:10.1109/tmc.2018.2847337

Cited by 250 publications

(91 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Letπ i = (π i,(c) , π i,(t) , π i,(p) ) be the abstract control policy in the abstract stochastic game AG played by a SP i ∈ I over 7 From the analysis in previous sections, the payment by the SP i to the SDN-orchestrator at a scheduling slot is of finite value. 8 With the VCG mechanism, such a case exists when the number of channels in the network is sufficiently large [47].…”

Section: A Stochastic Game Abstraction Via Conjecturesmentioning

confidence: 99%

“…The tension between computation-intensive applications and resource-constrained terminal devices calls for a revolution in computing infrastructure [5]. Mobile-edge computing (MEC), which brings the computing capabilities within the RANs in close proximity to MUs, is envisioned as a promising solution [4], [6], [7]. Offloading a computation task to a MEC server for execution involves wireless transmissions.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Chen

Zhao

et al. 2019

IEEE J. Select. Areas Commun.

118

View full text Add to dashboard Cite

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

show abstract

Section: A Stochastic Game Abstraction Via Conjecturesmentioning

confidence: 99%

mentioning

confidence: 99%

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Chen

Zhao

et al. 2019

IEEE J. Select. Areas Commun.

118

View full text Add to dashboard Cite

show abstract

“…whereT th k T th k − ω k f k andη k η k + K j=1, j =k p j α k,j . 1) Solution for the problem P 4 : P 4 is still non-convex and difficult to solve due to its constraint C 1 , which can be expressed as 4 The conditions in (28) can be enforced by a proper admission control strategy [9], [22], or an appropriate choice of the fronthaul capacity or the BBU computational capability [23]…”

Section: A Hsf Design At the Xl-mimo Rrhmentioning

confidence: 99%

Computation Offloading for IoT in C-RAN: Optimization and Deep Learning

Pradhan

She

et al. 2020

IEEE Trans. Commun.

View full text Add to dashboard Cite

We consider computation offloading for Internet-of-things (IoT) applications in multiple-inputmultiple-output (MIMO) cloud-radio-access-network (C-RAN). Due to the limited battery life and computational capability in the IoT devices (IoTDs), the computational tasks of the IoTDs are offloaded to a MIMO C-RAN, where a MIMO radio resource head (RRH) is connected to a baseband unit (BBU) through a capacity-limited fronthaul link, facilitated by the spatial filtering and uniform scalar quantization. We formulate a computation offloading optimization problem to minimize the total transmit power of the IoTDs while satisfying the latency requirement of the computational tasks, and find that the problem is non-convex. To obtain a feasible solution, firstly the spatial filtering matrix is locally optimized at the MIMO RRH. Subsequently, we leverage the alternating optimization framework for joint optimization on the residual variables at the BBU, where the baseband combiner is obtained in a closed-form, the resource allocation sub-problem is solved through successive inner convexification, and the number of quantization bits is obtained by a line-search method. As a low-complexity approach, we deploy a supervised deep learning method, which is trained with the solutions to our optimization algorithm. Numerical results validate the effectiveness of the proposed algorithm and the deep learning method.

show abstract

“…A number of studies on the computation offloading have been conducted to improve the energy efficiency of energy-constraint devices [11][12][13][14][15][16][17][18][19][20][21][22]. These can be categorized into: (1) framework design [11][12][13][14][15][16]; and (2) algorithm design [17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, Chen et al [20] modeled a multi-user computation offloading game and designed a distributed computation offloading algorithm that can achieve the Nash equilibrium of the game. Zheng et al [21] formulated the mobile users' offloading decision process under a dynamic environment as a stochastic game. Then, they proposed a multi-agent stochastic learning algorithm that can run in a fully distributed manner without any information exchange.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Lee

Jang

et al. 2019

Energies

View full text Add to dashboard Cite

The limited battery capacity of Internet of Things (IoT) devices is a major deployment barrier for IoT-based computing systems. In this paper, we propose an energy efficient cooperative computation algorithm (EE-CCA). In an EE-CCA, a pair of IoT devices decide whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most of tasks before their deadlines, we formulate a constraint Markov decision process (CMDP) problem and the optimal offloading strategy is obtained by linear programming (LP). Meanwhile, an optimization problem of finding pairs of IoT devices (i.e., IoT device pairing problem) is formulated under the optimal offloading strategy. Evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78% compared with the random offloading scheme while completing tasks before their deadlines with high probability.

show abstract

Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach

Cited by 250 publications

References 34 publications

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Computation Offloading for IoT in C-RAN: Optimization and Deep Learning

Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Contact Info

Product

Resources

About