Intelligent Eco Networking (IEN) III: A Shared In-network Computing Infrastructure towards Future Internet

Huang, Shuokang; Chen, Ruibiao; Liu, Yu; Zhang, Meimei; Lei, Kai; Xu, Ting; Yu, Xiquan

doi:10.1109/hoticn50779.2020.9350853

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…Because the metaverse is expected to provide an immersion experience to a large group of simultaneous users [13], optimal computing resource allocation is critical to resolving the resource demand conflict among simultaneous users. The COIN paradigm is a promising solution that leverages unused network resources to perform tasks, reducing delay and meeting QoE requirements [9,10]. However, adding computing resources or enabling COIN increases power consumption.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

“…Although mobile edge computing (MEC) offers a solution through remote task offloading (TO), it cannot simultaneously support massive user demand [5][6][7][8]. The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks, reducing delay and satisfying the quality of experience (QoE) requirements [9][10][11]. However, adding computing resources or enabling COIN increases power consumption.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Aliyu,

Oh,

et al. 2024

IEEE Access

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The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.

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Section: B Motivation and Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Aliyu,

Oh,

et al. 2024

IEEE Access

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“…For data-intensive tasks, input size 𝐼 𝑓 and software volume 𝑉 𝑓 of the four subtasks are uniformly and randomly generated from { [10][11][12][13][14][15][16][17][18][19][20] MB, [0.5-2] GB}, respectively. The required CPU cycles 𝑃 𝑓 for each data-intensive subtask were randomly chosen from { [1][2][3][4] gigacycles}. In the compute-intensive task type, 𝐼 𝑓 and 𝑉 𝑓 were generated uniformly and randomly from { [1][2][3][4] MB, [0.5-2] GB}, respectively, with corresponding 𝑃 𝑓 randomly selected from { [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] gigacycles}.Some slight adjustments to the ranges of randomly generated values were made to accommodate variations in subtask sizes.…”

Section: B Influence Of Experimental Parameters On the System Model 1...mentioning

confidence: 99%

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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“…As such, the computing-power network rises in response to the proper time and conditions for ubiquitous AI. It aims to connect the resources between clouds, edges, and ends through the network, to provide more flexible high-quality AI services while breaking down the island and monopoly of resources [249], [250].…”

Section: A Comprehensive Architecturementioning

confidence: 99%

Integrating Edge Intelligence and Blockchain: What, Why, and How

Wang

Ren

Qiu

et al. 2022

IEEE Commun. Surv. Tutorials

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Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas.Index Terms-Edge intelligence (EI), blockchain (BC), edge computing, decentralization, distributed ledger technology (DLT).

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Intelligent Eco Networking (IEN) III: A Shared In-network Computing Infrastructure towards Future Internet

Cited by 9 publications

References 15 publications

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

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

Integrating Edge Intelligence and Blockchain: What, Why, and How

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