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Resource-Efficient DNN Inference With Early Exiting in serverless edge computing

IEEE TRANSACTIONS ON MOBILE computing 2025年第5期24卷 3650-3666页

作者： Guo, Xiaolin Dong, Fang Shen, Dian Huang, Zhaowu Zhang, Jinghui Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China

serverless edge computing (SEC) has gained widespread adoption in improving resource utilization due to its triggered event-driven model. However, deploying deep neural network (DNN) inference services directly in SEC leads to resource inefficiencies, which stem from two key factors. First, existing methods adopt model-wise function encapsulation, which requires the entire DNN model to occupy memory throughout its execution lifecycle. This increases both memory footprint and occupancy time. Second, uniform DNN inference for diversity input leads to redundant computations and additional inference time. To this end, we propose REDI, a novel framework that leverages fine-grained block-wise function encapsulation and progressive inference to provide resource-efficient DNN inference while ensuring latency requirements. REDI enables the release of memory from already inferred shallow networks and allows each request to exit early based on input data complexity, eliminating redundant computations. To fully unleash the potential, REDI jointly considers resource heterogeneity, data diversity, and environment dynamics to investigate the block-wise function placement problem. We introduce an uncertainty-aware online learning-driven algorithm with bounded regret. Finally, we conduct extensive trace-driven experiments to evaluate our methods, demonstrating that REDI achieves a significant speedup of up to 6.52x in terms of resource usage cost compared to state-of-the-art methods.

关键词： DNN inference resource-efficient serverless edge computing serverless edge computing serverless edge computing

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Joint resource autoscaling and request scheduling for serverless edge computing

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-27页

作者： Choupani, Armin Azizi, Sadoon Aslanpour, Mohammad Sadegh Univ Kurdistan Dept Comp Engn & IT Sanandaj Iran Monash Univ Dept Software Syst & Cybersecur Clayton Vic Australia CSIRO Data61 Eveleigh Australia

Integrating serverless and edge computing has revolutionized the deployment of software functions on edge devices with limited resources. Nevertheless, the limitations of computing resources, the dynamic nature of users' requests, and the latency constraints of serverless functions present several challenges for achieving operational serverless edge computing. Motivated by these challenges, this paper proposes a self-adaptive approach through joint resource autoscaling and request scheduling in serverless edge computing environments. Our contributions are two-fold: (1) utilizing a Q-learning algorithm with a customized reward function to dynamically scale the number of function instances handling user requests, and (2) employing a heuristic algorithm to select the warmest instance for scheduling requests among active instances. The proposed Q-learning mechanism considers both workload and resource dynamics to determine the optimal number of active instances. Additionally, the heuristic algorithm improves the performance of Q-learning by choosing the warmest function instance for each incoming request, ensuring minimal instance usage. This research aims to achieve a win-win outcome, benefiting both users and providers. Extensive experiments demonstrate that the proposed approach outperforms baseline methods in terms of request success rate, minimizing utilized instances, and reducing cold start occurrences.

关键词： serverless edge computing Resource autoscaling Request scheduling Reinforcement learning Q-learning Quality of service Resource utilization

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An efficient function placement approach in serverless edge computing

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computing 2025年第3期107卷 1-58页

作者： Zahed, Atiya Ghobaei-Arani, Mostafa Esmaeili, Leila Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

serverless computing has gained significant attention due to its promise of simplifying application development and deployment. Application providers in this computing model must implement their applications using primarily stateless functions, and they do not need complex infrastructure management. Due to the ever-increasing expansion of IoT devices and real-time services, serverless computing has become popular at the edge. IoT devices use many applications in serverless edge computing. In serverless edge computing, we face requests with different requirements and workloads for executing functions that must be placed and executed on heterogeneous edge devices in such a way that they meet the user's requirements and the quality of service. This problem is known as dynamic function placement in serverless edge computing, and it is one of the critical challenges in this computing. In this paper, we introduce an autonomous dynamic function placement approach using the autonomic computing model and the deep reinforcement learning technique to make decisions about dynamic deploying functions in heterogeneous and dynamic edge infrastructure. An autonomous function placement framework is also designed based on the three-layer architecture of the public edge environment. When comparing the proposed solution with the other methods, simulation results indicate that the proposed solution reduces average Cost by 27.6% and delays by 28.8% while increasing edge node utilization by 18.6%.

关键词： serverless edge computing Function placement Autonomic computing Deep reinforcement learning

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Multi-Objective Deep Reinforcement Learning for Function Offloading in serverless edge computing

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IEEE TRANSACTIONS ON SERVICES computing 2025年第1期18卷 288-301页

作者： Yang, Yaning Du, Xiao Ye, Yutong Ding, Jiepin Wang, Ting Chen, Mingsong Li, Keqin East China Normal Univ MoE Engn Res Ctr Hardware Software Codesign Techno Shanghai 200062 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Function offloading problems play a crucial role in optimizing the performance of applications in serverless edge computing (SEC). Existing research has extensively explored function offloading strategies based on optimizing a single objective. However, a significant challenge arises when users expect to optimize multiple objectives according to the relative importance of these objectives. This challenge becomes particularly pronounced when the relative importance of the objectives dynamically shifts. Consequently, there is an urgent need for research into multi-objective function offloading methods. In this paper, we redefine the SEC function offloading problem as a dynamic multi-objective optimization issue and propose a novel approach based on Multi-objective Reinforcement Learning (MORL) called MOSEC. MOSEC can coordinately optimize three objectives, i.e., application completion time, User Device (UD) energy consumption, and user cost. To reduce the impact of extrapolation errors, MOSEC integrates a Near-on Experience Replay (NER) strategy during the model training. Furthermore, MOSEC adopts our proposed Earliest First (EF) scheme to maintain the policies learned previously, which can efficiently mitigate the catastrophic policy forgetting problem. Extensive experiments conducted on various generated applications demonstrate the superiority of MOSEC over state-of-the-art multi-objective optimization algorithms.

关键词： Servers Optimization Energy consumption Costs edge computing Heuristic algorithms Vectors serverless computing Computational modeling Wireless communication serverless edge computing function offloading multi-objective optimization deep reinforcement learning

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Joint Service Request Scheduling and Container Retention in serverless edge computing for Vehicle-Infrastructure Collaboration

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IEEE TRANSACTIONS ON MOBILE computing 2024年第6期23卷 6508-6521页

作者： Hu, Shihong Qu, Zhihao Tang, Bin Ye, Baoliu Li, Guanghui Shi, Weisong Houhai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing 211100 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA

Lightweight and layered structure containers in serverless edge computing (SEC) provide flexible service configurations and computing for vehicles with diverse service requests in the Vehicle-Infrastructure Collaboration (VIC) environment. Despite progress in service request scheduling for the VIC system, the effect of layer sharing between different service images on request scheduling has not been fully explored. Additionally, the cold-start latency of service containers in SEC can significantly degrade the responsiveness of vehicle services, and container retention is proposed to minimize its impact and improve overall system performance. However, the existing research neglects the complex coupling relationship between request scheduling and container retention decisions, while focusing on the single decision optimization problem. Consequently, minimizing system costs by single decision optimization may not achieve the effect of joint decision optimization. To bridge this gap, we study the joint service request scheduling and container retention problem based on layer sharing and container caching. First, we model the joint decision problem with specific constraints and aim to minimize the long-term system cost while considering vehicle mobility. Second, an online co-decision scheme called Onco is proposed to solve the problem, which incorporates request scheduling and container retention for multiple vehicle services. Finally, both synthetic and real trace-driven simulation experiments have been conducted to evaluate the performance of Onco. The experimental results show that Onco outperforms state-of-the-art baselines in terms of system cost reduction and response time improvement.

关键词： Container retention serverless edge computing service request scheduling vehicle-infrastructure collaboration

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REPFS: Reliability-Ensured Personalized Function Scheduling in Sustainable serverless edge computing

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE computing 2024年第3期9卷 494-511页

作者： Cao, Kun Weng, Jian Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 201804 Peoples R China

In recent years, serverless edge computing has been widely employed in the deployments of Internet-of-things (IoT) applications. Despite considerable research efforts in this field, existing works fail to jointly consider essential factors such as energy, reliability, personalized user requirements, and stochastic application executions. This oversight results in an inefficient utilization of computation and communication resources within serverless edge computing networks, subsequently diminishing the profit of service providers and degrading the quality-of-experience (QoE) of end users. In this paper, we explore the problem of reliability-ensured personalized function scheduling (REPFS) to jointly optimize the profit of service providers and the holistic QoE of end users in sustainable serverless edge computing. A personality-driven user QoE prediction method is first designed to accurately estimate the QoE of individual end users with differentiated personality types. Afterward, a deterministic function scheduling policy is developed on the problem-specific augmented non-dominated sorting genetic algorithm II (PSA-NSGA-II). Given the inherent uncertainty of application executions, a stochastic function scheduling strategy that can be easily parallelized for modern multicore scheduler platforms is also devised to accelerate solution generation for stochastic applications. Experimental results show that our deterministic function scheduling policy achieves 15% performance enhancement compared with representative multiobjective evolutionary algorithms. Furthermore, our stochastic function scheduling strategy promotes the service profit by 78% and the holistic user QoE by 118% on average compared with the developed deterministic scheduling policy.

关键词： edge computing Reliability Quality of experience Processor scheduling Scheduling Optimization Job shop scheduling Personalized scheduling reliability serverless edge computing stochastic Internet-of-Things (IoT) applications sustainable energy

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ATOM: AI-Powered Sustainable Resource Management for serverless edge computing Environments

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE computing 2024年第6期9卷 817-829页

作者： Golec, Muhammed Gill, Sukhpal Singh Cuadrado, Felix Parlikad, Ajith Kumar Xu, Minxian Wu, Huaming Uhlig, Steve Queen Mary Univ London Sch Elect Engn & Comp Sci London E14NS England Abdullah Gul Univ Comp Engn Dept TR-38080 Kayseri Turkiye Tech Univ Madrid UPM Madrid 28040 Spain Univ Cambridge Inst Mfg Dept Engn Cambridge CB2 1TN England Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China

serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and CO2 emission amount of these models are evaluated and compared for the training and prediction phases.

关键词： Containers edge computing Computational modeling Internet of Things Green computing Scalability Predictive models serverless edge computing cold start deep reinforcement learning sustainable resource management

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PRICELESS: Privacy enhanced AI-driven scalable framework for IoT applications in serverless edge computing environments

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INTERNET TECHNOLOGY LETTERS 2025年第1期8卷

作者： Golec, Muhammed Golec, Mustafa Xu, Minxian Wu, Huaming Gill, Sukhpal Singh Uhlig, Steve Queen Mary Univ London Sch Elect Engn & Comp Sci Mile End Rd London E1 4NS England Abdullah Gul Univ Kayseri Turkiye Dumlupinar Univ Fac Engn Comp Engn Kutahya Turkiye Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Tianjin Univ Ctr Appl Math Tianjin Peoples R China

serverless edge computing has emerged as a new paradigm that integrates the serverless and edge computing. By bringing processing power closer to the edge of the network, it provides advantages such as low latency by quickly processing data for time-sensitive Internet of Things (IoT) applications. Additionally, serverless edge computing also brings inherent problems of edge and serverless computing such as cold start, security and privacy that are still waiting to be solved. In this paper, we propose a new Blockchain-based AI-driven scalable framework called PRICELESS, to offer security and privacy in serverless edge computing environments while performing cold start prediction. In PRICELESS framework, we used deep reinforcement learning for the cold start latency prediction. For experiments, a cold start dataset is created using a heart disease risk-based IoT application and deployed using Google Cloud Functions. Experimental results show the additional delay that the blockchain module brings to cold start latency and its impact on cold start prediction performance. Additionally, the performance of PRICELESS is compared with the current state-of-the-art method based on energy cost, computation time and cold start prediction. Specifically, it has been observed that PRICELESS causes 19 ms of external latency, 358.2 watts for training, and 3.6 watts for prediction operations, resulting in additional energy consumption at the expense of security and privacy.

关键词： artificial intelligence cold start IoT privacy security serverless edge computing

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Online Learning for Rate-Adaptive Task Offloading Under Latency Constraints in serverless edge computing

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第2期31卷 695-709页

作者： Tutuncuoglu, Feridun Josilo, Sladana Dan, Gyorgy KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Network & Syst Engn S-10044 Stockholm Sweden

We consider the interplay between latency constrained applications and function-level resource management in a serverless edge computing environment. We develop a game theoretic model of the interaction between rate adaptive applications and a load balancing operator under a function-oriented pay-as-you-go pricing model. We show that under perfect information, the strategic interaction between the applications can be formulated as a generalized Nash equilibrium problem, and use variational inequality theory to prove that the game admits an equilibrium. For the case of imperfect information, we propose an online learning algorithm for applications to maximize their utility through rate adaptation and resource reservation. We show that the proposed algorithm can converge to equilibria and achieves zero regret asymptotically, and our simulation results show that the algorithm achieves good system performance at equilibrium, ensures fast convergence, and enables applications to meet their latency constraints.

关键词： Task analysis Computational modeling edge computing FAA Wireless communication Data models Wireless sensor networks Generalized Nash equilibrium problem online learning serverless edge computing resource allocation

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Workflow Scheduling in serverless edge computing for the Industrial Internet of Things: A Learning Approach

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第7期19卷 8242-8252页

作者： Xie, Renchao Gu, Dier Tang, Qinqin Huang, Tao Yu, Fei Richard Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Shenzhen Univ Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. In addition, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as directed acyclic graph and formulate the scheduling problem as a multiobjective optimization problem. A dueling double deep Q-network-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

关键词： Deep reinforcement learning (DRL) multi-objective optimization serverless edge computing workflow scheduling

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