In-network placement of delay-constrained computing tasks in a softwarized intelligent edge

Lia, Gianmarco; Amadeo, Marica; Ruggeri, Giuseppe; Campolo, Claudia; Molinaro, Antonella; Loscrì, Valeria

doi:10.1016/j.comnet.2022.109432

Cited by 8 publications

(4 citation statements)

References 68 publications

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“…Cooke and Fahmy [30] demonstrated the significant efficiency of COIN for distributed streaming applications in terms of latency, throughput, bandwidth, energy, and cost. The use of machine learning, such as the decision tree, multilayer perceptron, and support vector machine, to orchestrate delay-constrained task placement in COIN has also displayed remarkable performance [31].…”

Section: Related Workmentioning

confidence: 99%

“…However, for optimal performance and load distribution in terms of energy and latency, considering the location of the COIN node and user demand under changing network conditions, a dynamic TO scheme is essential. Despite this, most previous studies [6], [31], [33][34][35][36][37] have treated tasks as a single unit and did not consider situations where the tasks could be divided and handled by different computing nodes. This situation is critical in the metaverse, where a metaverse task consists of multiple tasks that can be decomposed and offloaded to different computing nodes (e.g., a COIN node).…”

Section: Related Workmentioning

confidence: 99%

“…Unlike previous studies [6], [31], [33][34][35][36]42] focusing on atomic tasks, metaverse divisible tasks are considered. For simplicity, we assume the subtasks are independent⎯a forkjoint task type.…”

Section: B Task Modelmentioning

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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

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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“…2(a) illustrates metaverse task offloading in network computing, where users generate tasks divided into subtasks for local execution or offloading to edge COIN nodes (EINs) or fog COIN nodes (FINs). In contrast to previous studies [2,[38][39][40][41][42][43] addressing atomic tasks, our focus is on divisible tasks in the metaverse. The critical notation used in this article is summarised in Table II.…”

Section: System Modelmentioning

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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Walking on two legs: Joint service placement and computation configuration for provisioning containerized services at edges

Cao,

Wang,

Zhang

et al. 2024

Computer Networks

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In-network placement of delay-constrained computing tasks in a softwarized intelligent edge

Cited by 8 publications

References 68 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

Walking on two legs: Joint service placement and computation configuration for provisioning containerized services at edges

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