AI-oriented Workload Allocation for Cloud-Edge Computing

Hao, Tianshu; Zhan, Jianfeng; Hwang, Kai; Gao, Wanling; Xu, Wen

doi:10.1109/ccgrid51090.2021.00065

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…2. In [126] and [127], the authors present two algorithms to optimize the workload allocation for AI applications. The first algorithm is for a single workload, and the second is for a multi-job workload.…”

Section: Methods For Workload Allocationmentioning

confidence: 99%

AI Workload Allocation Methods for Edge-Cloud Computing: A Review

2023

IJSER

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Edge computing is used with cloud computing as an extension to increase the performance of delay-sensitive applications such as autonomous vehicles, healthcare systems, video surveillance systems, ..etc. The fast increase in the Internet of Things (IoT) devices increases the amount of data transferred in the network. IoT devices are resource-constrained in terms of energy consumption and computation capability. Data processing near IoT devices enabled by edge devices. Hence reduces the transmission power of sending data to the cloud and causes delays due to the cloud being placed far away from the devices. Most real-time applications depend on artificial intelligence (AI) techniques, increasing the computations on IoT-edge devices. Conversely, if this AI workload is executed on the cloud, the delay increase causes degradation in application performance. How to decide where the computation is done in an IoT, edge and cloud network is an important issue. The purpose of optimizing the workload allocation decision is to increase the application performance in terms of Quality of Experience (QoE) and Quality of Service (QoS); hence, the major goal is to reduce the delay time while maintaining the accuracy of the AI systems. As presented in this review, many researchers focus on proposing a workload allocation decision based on AI techniques. In contrast, other research focuses on the AI workload, hence presenting a method for partitioning the AI model to increase the system's accuracy in the resource constraint devices (end device and edge server). Many other researches also used the AI model for resource allocation and provisioning between edge servers and the cloud. In this review, the integration between AI and edge-cloud environment is investigated, the AI workload allocation methods are presented and analyzed, a brief overview of the application of deep learning in edge-cloud computing is also presented, and many challenges that need to be addressed for the AI application are discussed. Many issues and challenges are also presented for optimizing the edge.

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Section: Methods For Workload Allocationmentioning

confidence: 99%

AI Workload Allocation Methods for Edge-Cloud Computing: A Review

2023

IJSER

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“…Ref. [19] proposed a method for AI-optimized workload allocation at the Cloud-Edge-Client computing framework to achieve the lowest response time for delay-sensitive applications. The proposed approach has two scenarios, a single workload scenario and a multi-job scenario, which are efficient allocation algorithms to reduce endto-end response time.…”

Section: Load Balancing Methods In Cloud-edge-client Communicationsmentioning

confidence: 99%

A Q-Learning-Based Load Balancing Method for Real-Time Task Processing in Edge-Cloud Networks

Peng

Yoshinaga

et al. 2023

Electronics

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Edge computing has emerged as a promising solution to reduce communication delays and enhance the performance of real-time applications. However, due to the limited processing power of edge servers, it is challenging to achieve efficient task processing. In this paper, we propose a Q-learning-based load-balancing method that optimizes the distribution of real-time tasks between edge servers and cloud servers to reduce processing time. The proposed method is dynamic and adaptive, taking into account the constantly changing network status and server usage. To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are conducted in an Edge-Cloud network environment. The simulation results demonstrate that the proposed method significantly reduces processing time compared to traditional static load-balancing methods. The Q-learning algorithm enables the load-balancing system to dynamically learn the optimal decision-making strategy to allocate tasks to the most appropriate server. Overall, the proposed Q-learning-based load-balancing method provides a dynamic and efficient solution to balance the workload between edge servers and cloud servers. The proposed method effectively achieves real-time task processing in edge computing environments and can contribute to the development of high-performance edge computing systems.

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