Exploring computation offloading in IoT systems

Shahhosseini, Sina; Anzanpour, Arman; Azimi, Iman; Labbaf, Sina; Seo, Dongjoo; Lim, Sung-Soo; Liljeberg, Pasi; Dutt, Nikil; Rahmani, Amir M.

doi:10.1016/j.is.2021.101860

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…Since we assume that there are m wireless channels and n WSDs, we can use the k-means to divide WSDs into m clusters according to their channel gains. The cluster centers are denoted as {h 1 channel , h 2 channel , h 3 channel , . .…”

Section: Channel Gainmentioning

confidence: 99%

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Computation Offloading Game for Multi-Channel Wireless Sensor Networks

Wang

2022

Sensors

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Computation offloading for wireless sensor devices is critical to improve energy efficiency and maintain service delay requirements. However, simultaneous offloadings may cause high interferences to decrease the upload rate and cause additional transmission delay. It is thus intuitive to distribute wireless sensor devices in different channels, but the problem of multi-channel computation offloading is NP-hard. In order to solve this problem efficiently, we formulate the computation offloading decision problem as a decision-making game. Then, we apply the game theory to address the problem of allowing wireless sensor devices to make offloading decisions based on their own interests. In the game theory, not only are the data size of wireless sensor devices and their computation capability considered but the channel gain of each wireless sensor device is also included to improve the transmission rate. The consideration could evenly distribute wireless sensor devices to different channels. We prove that the proposed offloading game is a potential game, where the Nash equilibrium exists in each game after all device states converge. Finally, we extensively evaluate the performance of the proposed algorithm based on simulations. The simulation results demonstrate that our algorithm can reduce the number of iterations to achieve Nash equilibrium by 16%. Moreover, it improves the utilization of each channel to effectively increase the number of successful offloadings and lower the energy consumption of wireless sensor devices.

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Section: Channel Gainmentioning

confidence: 99%

“…Wireless sensor devices (WSDs) have emerged in various applications of smart cities, remote healthcare, unmanned aerial vehicle (UAV) and smart homes [1][2][3][4][5], where WSDs generate and transmit remote sensing data to sink nodes. Various types of modern sensor devices may generate a great volume of data [6,7].…”

Section: Introductionmentioning

confidence: 99%

Computation Offloading Game for Multi-Channel Wireless Sensor Networks

Wang

2022

Sensors

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“…The edge paradigm increases oloading opportunities for resource-constrained user-end devices. Oloading DL services in a 3-tier end-edge-cloud architecture is a complex optimization problem considering: (i) diversity in system parameters including heterogeneous computing resources, network constraints, and application characteristics, and (ii) dynamicity of DL service environment including workload arrival rate, user traic, and multi-dimensional performance requirements (e.g., application accuracy, response time) [11,37,38].…”

Section: Introductionmentioning

confidence: 99%

Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks

Shahhosseini

Seo

Kanduri

et al. 2022

ACM Trans. Embed. Comput. Syst.

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Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed in order to meet the latency and energy requirements of computationally-intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). On the other hand, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.

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“…Edge computing brings compute capacity closer to end-user devices, and complement the cloud infrastructure in providing low latency services [4]. Collaborative end-edge-cloud (EEC) architecture enables on-demand computational offloading of DL kernels from resource-constrained end-user devices to resourceful edge and cloud nodes [5], [6]. Orchestrating DL services in multilayered EEC architecture primarily focus on i) selecting an edge node onto which a task can be offloaded, and ii) selecting an appropriate learning model to accomplish the DL task.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks

Shahhosseini¹,

Hu²,

Seo³

et al. 2022

Preprint

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Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). Identifying optimal orchestration considering the cross-layer opportunities and requirements in the face of varying system dynamics is a challenging multi-dimensional problem. While Reinforcement Learning (RL) approaches have been proposed earlier, they suffer from a large number of trial-and-errors during the learning process resulting in excessive time and resource consumption. We present a Hybrid Learning orchestration framework that reduces the number of interactions with the system environment by combining modelbased and model-free reinforcement learning. Our Deep Learning inference orchestration strategy employs reinforcement learning to find the optimal orchestration policy. Furthermore, we deploy Hybrid Learning (HL) to accelerate the RL learning process and reduce the number of direct samplings. We demonstrate efficacy of our HL strategy through experimental comparison with stateof-the-art RL-based inference orchestration, demonstrating that our HL strategy accelerates the learning process by up to 166.6×.

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Exploring computation offloading in IoT systems

Cited by 17 publications

References 19 publications

Computation Offloading Game for Multi-Channel Wireless Sensor Networks

Computation Offloading Game for Multi-Channel Wireless Sensor Networks

Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks

Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks

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