Mobility-Aware Optimal Task Offloading in Distributed Edge Computing

Jeon, Youbin; Baek, Hosung; Pack, Sangheon

doi:10.1109/icoin50884.2021.9334008

Cited by 15 publications

(4 citation statements)

References 16 publications

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“…On the user side, task offloading aims to respect the latency constraints and extend the battery lifetime. The success of task offloading depends mainly on the user's mobility and the quality of wireless connection [1]. On the provider side, the primary goal is the minimization of the energy consumption of the data center, which is mainly affected by the number of active servers and the amount of their allocated resources [7,8].…”

Section: Motivation and Challengesmentioning

confidence: 99%

“…Although the evolution of wireless communications is accompanied with computationally powerful devices, applications still need to fully or partially offload the involved computational tasks. The reason is that mobile applications are becoming more complex and more demanding in terms of Quality of Service (QoS) and Quality of Experience (QoE) [1,2]. An efficient way to enable task-offloading and energy savings is to leverage the abundant resources available in the Cloud.…”

Section: Introductionmentioning

confidence: 99%

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ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Avgeris

Spatharakis

Dechouniotis

et al. 2022

Sensors

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Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

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Section: Motivation and Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Avgeris

Spatharakis

Dechouniotis

et al. 2022

Sensors

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show abstract

“…To this end, we design the CIODE Planning algorithm (Algorithm 1), which first divides the DNN model into partitions and then collects information, including estimated waiting time, network speed, available nodes, and prediction files. Due to the limited computing resource of edge nodes, it is difficult for a normal edge node to execute multiple DNN queries at the same time [34,35]. We design the waiting lock in which an edge node can only execute one DNN query at a time; the other DNN queries are queued in order and return the estimated waiting time to the requester.…”

Section: E Planning Phasementioning

confidence: 99%

Conflict-Resilient Incremental Offloading of Deep Neural Networks to the Edge of Smart Environment

Chen

Wan

et al. 2021

Mathematical Problems in Engineering

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Novel smart environments, such as smart home, smart city, and intelligent transportation, are driving increasing interest in deploying deep neural networks (DNN) in edge devices. Unfortunately, deploying DNN at resource-constrained edge devices poses a huge challenge. These workloads are computationally intensive. Moreover, the edge server-based approach may be affected by incidental factors, such as network jitters and conflicts, when multiple tasks are offloaded to the same device. A rational workload scheduling for smart environments is highly desired. In this work, we propose a Conflict-resilient Incremental Offloading of Deep Neural Networks at Edge (CIODE) for improving the efficiency of DNN inference in the edge smart environment. CIODE divides the DNN model into several partitions by layer and incrementally uploads them to local edge nodes. We design a waiting lock-based scheduling paradigm to choose edge devices for DNN layers to be offloaded. In detail, an advanced lock mechanism is proposed to handle concurrency conflicts. Real-world testbed-based experiments demonstrate that, compared with other state-of-the-art baselines, CIODE outperforms the DNN inference performance of these popular baselines by 20 % to 70 % and significantly improves the robustness under the insight of neighboring collaboration.

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“…Network congestion and high delay have thus emerged as problems faced by intelligent applications with low delay and low energy consumption requirements [3,4] . Computation-intensive applications consume considerable amounts of energy, which leads to a sharp decline in the endurance of Internet of things (IoT) devices [5] . Given the certain distance between the Ying Chen, Fengjun Zhao, Yangguang Lu, and Xin Chen are with the Computer School, Beijing Information Science and Technology University, Beijing 100101, China.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Chen

Zhao

et al. 2023

Tsinghua Sci. Technol.

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Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

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Mobility-Aware Optimal Task Offloading in Distributed Edge Computing

Cited by 15 publications

References 16 publications

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Conflict-Resilient Incremental Offloading of Deep Neural Networks to the Edge of Smart Environment

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

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