Distributed Task Offloading and Resource Purchasing in NOMA-Enabled Mobile Edge Computing: Hierarchical Game Theoretical Approaches

Chen, Ying; Zhao, Jie; Hu, Jintao; Wan, Shaohua; Huang, Jiwei

doi:10.1145/3597023

Cited by 32 publications

(8 citation statements)

References 37 publications

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“…Additionally, we explored the potential of COIN to address the computing and memory limitations faced by MEC [2,54]. Moreover, existing studies have demonstrated the effectiveness of OPG solutions for task offloading [51], but they often focus on MEC without considering the BC scalability issues [15][16][17][18][19][20]. Our work bridges this gap by addressing BC data processing tasks and BC mining problems through partial offloading in COIN.…”

Section: Discussionmentioning

confidence: 99%

“…• The OPG method considers a scenario where OPG offloading via the BC is based on full redundancy. The OPG has generally performed remarkably in solving taskoffloading problems [50][51][52][53] and we included it as a benchmark to compare against our proposed redundancyaware offloading approach.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…• The OPG method assisted by a random redundancy factor (OPG-Rand) considers a scenario where OPG method offloading via the BC is based on a randomly assigned BRF unlike existing studies [50][51][52][53]. This baseline allows us to fairly evaluate the effectiveness of our proposed agent, which learns the optimal redundancy factor based on dynamic user demand and system status.…”

Section: Numerical Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Aliyu,

Oh,

et al. 2023

2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)

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The computing in the network (COIN) paradigm has emerged as a potential solution for computation-intensive applications like the metaverse by utilizing unused network resources. The blockchain (BC) guarantees task-offloading privacy, but cost reduction, queueing delays, and redundancy elimination remain open problems. This paper presents a redundancy-aware BC-based approach for the metaverse's partial computation offloading (PCO). Specifically, we formulate a joint BC redundancy factor (BRF) and PCO problem to minimize computation costs, maximize incentives, and meet delay and BC offloading constraints. We proved this problem is NP-hard and transformed it into two subproblems based on their temporal correlation: real-time PCO and Markov decision process-based BRF. We formulated the PCO problem as a multiuser game, proposed a decentralized algorithm for Nash equilibrium under any BC redundancy state, and designed a double deep Q-networkbased algorithm for the optimal BRF policy. The BRF strategy is updated periodically based on user computation demand and network status to assist the PCO algorithm. The experimental results suggest that the proposed approach outperforms existing schemes, resulting in a remarkable 47% reduction in cost overhead, delivering approximately 64% higher rewards, and achieving convergence in just a few training episodes.

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Section: Discussionmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

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Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Aliyu,

Oh,

et al. 2023

2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)

View full text Add to dashboard Cite

show abstract

“…In traffic management, the traffic flow and average speed of a specific section at a specific time are two very important traffic characteristics that describe the traffic conditions of the section [18]. Effective analysis of these two traffic characteristics can accurately study and judge road traffic conditions [19]. Then take corresponding traffic management actions, such as adjusting the time of traffic lights, setting variable lanes, etc.…”

Section: Introductionmentioning

confidence: 99%

Traffic prediction for diverse edge IoT data using graph network

Shen,

Zhang,

Geng

et al. 2024

J Cloud Comp

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More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets, so the robustness of the models is weak. When different types of data face other model parameters, the prediction performance often varies a lot. Thus, this work starts from the perspective of data processing and algorithm models. Taking traffic data as an example, we first propose a new data processing method that processes traffic data with different attributes and characteristics into a dataset that is more common for most models. Then we will compare different types of datasets from the perspective of multiple model parameters, and further analyze the precautions and changing trends of different traffic data in machine learning. Finally, different types of data and ranges of model parameters are explored, together with possible reasons for fluctuations in forecast results when data parameters change.

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“…Using cloud computing technology, we can leverage cloud resources to facilitate distributed training and federated learning [11][12][13]. This approach entails training a global model on a central cloud server, without collecting private data on edge device.…”

Section: Introductionmentioning

confidence: 99%

Adaptive device sampling and deadline determination for cloud-based heterogeneous federated learning

Zhang,

Sun,

Zheng

et al. 2023

J Cloud Comp

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As a new approach to machine learning, Federated learning enables distributned traiing on edge devices and aggregates local models into a global model. The edge devices that participate in federated learning are highly heterogeneous in terms of computing power, device state, and data distribution, making it challenging to converge models efficiently. In this paper, we propose FedState, which is an adaptive device sampling and deadline determination technique for cloud-based heterogeneous federated learning. Specifically, we consider the cloud as a central server that orchestrates federated learning on a large pool of edge devices. To improve the efficiency of model convergence in heterogeneous federated learning, our approach adaptively samples devices to join each round of training and determines the deadline for result submission based on device state. We analyze existing device usage traces to build device state models in different scenarios and design a dynamic importance measurement mechanism based on device availability, data utility, and computing power. We also propose a deadline determination module that dynamically sets the deadline according to the availability of all sampled devices, local training time, and communication time, enabling more clients to submit local models more efficiently. Due to the variability of device state, we design an experience-driven algorithm based on Deep Reinforcement Learning (DRL) that can dynamically adjust our sampling and deadline policies according to the current environment state. We demonstrate the effectiveness of our approach through a series of experiments with the FMNIST dataset and show that our method outperforms current state-of-the-art approaches in terms of model accuracy and convergence speed.

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Distributed Task Offloading and Resource Purchasing in NOMA-Enabled Mobile Edge Computing: Hierarchical Game Theoretical Approaches

Cited by 32 publications

References 37 publications

Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Traffic prediction for diverse edge IoT data using graph network

Adaptive device sampling and deadline determination for cloud-based heterogeneous federated learning

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