Pub Date : 2025-01-02DOI: 10.1109/TCC.2024.3525076
Xiaofeng Ji;Faming Gong;Nuanlai Wang;Junjie Xu;Xing Yan
Offshore drilling platforms (ODPs) are critical infrastructure for exploring and developing marine oil and gas resources. As these platforms’ capabilities expand, deploying intelligent surveillance services to ensure safe production has become increasingly important. However, the unique geographical locations and harsh environmental conditions of ODPs pose significant challenges for processing large volumes of video data, complicating the implementation of efficient surveillance systems. This study proposes a Cloud-Edge Large-Tiny Model Collaborative (CELTC) architecture grounded in deep reinforcement learning to optimize the processing and decision-making of surveillance data in offshore drilling platform scenarios. CELTC architecture leverages edge-cloud computing, deploying complex, high-precision large models on cloud servers and lightweight tiny models on edge devices. This dual deployment strategy capitalizes on tiny models’ rapid response and large cloud models’ high-precision capabilities. Additionally, the architecture integrates a deep reinforcement learning algorithm designed to optimize the scheduling and offloading of computational tasks between large and tiny models in the cloud-edge environment. The efficacy of the proposed architecture is validated using real-world surveillance data from ODPs through simulations and comparative experiments.
海洋钻井平台是勘探开发海洋油气资源的重要基础设施。随着这些平台功能的扩展，部署智能监控服务以确保安全生产变得越来越重要。然而，odp独特的地理位置和恶劣的环境条件对处理大量视频数据构成了重大挑战，使高效监控系统的实施复杂化。本研究提出了一种基于深度强化学习的Cloud-Edge Large-Tiny Model Collaborative （CELTC）架构，以优化海上钻井平台场景中监控数据的处理和决策。CELTC架构利用边缘云计算，在云服务器上部署复杂、高精度的大型模型，在边缘设备上部署轻量级的微型模型。这种双重部署策略利用了微型模型的快速响应能力和大型云模型的高精度能力。此外，该架构集成了一种深度强化学习算法，旨在优化云边缘环境中大型和小型模型之间计算任务的调度和卸载。通过模拟和对比实验，利用来自odp的真实世界监控数据验证了所提出架构的有效性。
{"title":"Cloud-Edge Collaborative Service Architecture With Large-Tiny Models Based on Deep Reinforcement Learning","authors":"Xiaofeng Ji;Faming Gong;Nuanlai Wang;Junjie Xu;Xing Yan","doi":"10.1109/TCC.2024.3525076","DOIUrl":"https://doi.org/10.1109/TCC.2024.3525076","url":null,"abstract":"Offshore drilling platforms (ODPs) are critical infrastructure for exploring and developing marine oil and gas resources. As these platforms’ capabilities expand, deploying intelligent surveillance services to ensure safe production has become increasingly important. However, the unique geographical locations and harsh environmental conditions of ODPs pose significant challenges for processing large volumes of video data, complicating the implementation of efficient surveillance systems. This study proposes a Cloud-Edge Large-Tiny Model Collaborative (CELTC) architecture grounded in deep reinforcement learning to optimize the processing and decision-making of surveillance data in offshore drilling platform scenarios. CELTC architecture leverages edge-cloud computing, deploying complex, high-precision large models on cloud servers and lightweight tiny models on edge devices. This dual deployment strategy capitalizes on tiny models’ rapid response and large cloud models’ high-precision capabilities. Additionally, the architecture integrates a deep reinforcement learning algorithm designed to optimize the scheduling and offloading of computational tasks between large and tiny models in the cloud-edge environment. The efficacy of the proposed architecture is validated using real-world surveillance data from ODPs through simulations and comparative experiments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"288-302"},"PeriodicalIF":5.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/TCC.2024.3524310
Shijing Yuan;Yuxin Liu;Song Guo;Jie Li;Hongyang Chen;Chentao Wu;Yang Yang
Cloud-Edge Collaborative Architecture (CEA) is a prominent framework that provides low-latency and energy-efficient solutions for video stream processing. In Cloud-Edge Collaborative Video Streaming Systems (CEAVS), efficient online offloading strategies for video tasks are crucial for enhancing user experience. However, most existing works overlook budget constraints, which limits their applicability in real-world scenarios constrained by finite resources. Moreover, they fail to adequately address the heterogeneity of video task redundancies, leading to suboptimal utilization of CEAVS's limited resources. To bridge these gaps, we propose an Efficient Online Computing framework for CEAVS (EOCA) that jointly optimizes accuracy, energy consumption, and latency performance through adaptive online offloading and redundancy compression, without requiring future task information. Technically, we formulate computing offloading and adaptive compression under budget constraints as a stochastic optimization problem that maximizes system satisfaction, defined as a weighted combination of accuracy, latency, and energy performance. We employ Lyapunov optimization to decouple the long-term budget constraint. We prove that the decoupled problem is a generalized ordinal potential game and propose algorithms based on generalized Benders decomposition (GBD) and the best response to obtain Nash equilibrium strategies for computing offloading and task compression. Finally, we analyze EOCA's performance bound, convergence rate, and worst-case performance guarantees. Evaluations demonstrate that EOCA effectively improves satisfaction while effectively balancing satisfaction and computational overhead.
{"title":"Efficient Online Computing Offloading for Budget- Constrained Cloud-Edge Collaborative Video Streaming Systems","authors":"Shijing Yuan;Yuxin Liu;Song Guo;Jie Li;Hongyang Chen;Chentao Wu;Yang Yang","doi":"10.1109/TCC.2024.3524310","DOIUrl":"https://doi.org/10.1109/TCC.2024.3524310","url":null,"abstract":"Cloud-Edge Collaborative Architecture (CEA) is a prominent framework that provides low-latency and energy-efficient solutions for video stream processing. In Cloud-Edge Collaborative Video Streaming Systems (CEAVS), efficient online offloading strategies for video tasks are crucial for enhancing user experience. However, most existing works overlook budget constraints, which limits their applicability in real-world scenarios constrained by finite resources. Moreover, they fail to adequately address the heterogeneity of video task redundancies, leading to suboptimal utilization of CEAVS's limited resources. To bridge these gaps, we propose an Efficient Online Computing framework for CEAVS (EOCA) that jointly optimizes accuracy, energy consumption, and latency performance through adaptive online offloading and redundancy compression, without requiring future task information. Technically, we formulate computing offloading and adaptive compression under budget constraints as a stochastic optimization problem that maximizes system satisfaction, defined as a weighted combination of accuracy, latency, and energy performance. We employ Lyapunov optimization to decouple the long-term budget constraint. We prove that the decoupled problem is a generalized ordinal potential game and propose algorithms based on generalized Benders decomposition (GBD) and the best response to obtain Nash equilibrium strategies for computing offloading and task compression. Finally, we analyze EOCA's performance bound, convergence rate, and worst-case performance guarantees. Evaluations demonstrate that EOCA effectively improves satisfaction while effectively balancing satisfaction and computational overhead.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"273-287"},"PeriodicalIF":5.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advent of network function virtualization (NFV), many users resort to network service provisioning through virtual network function instances (VNFIs) run on the standard physical server in clouds. Following this trend, NFV markets are emerging, which allow a user to procure VNFIs from cloud service providers (CSPs). In such procurement process, it is a significant challenge to ensure differential privacy and truthfulness while explicitly considering dynamic resource provisioning, location sensitiveness and budget of each VNFI. As such, we design a differentially private and truthful reverse auction with dynamic resource provisioning (PTRA-DRP) to resolve the VNFI procurement (VNFIP) problem. To allow dynamic resource provisioning, PTRA-DRP enables CSPs to submit a set of bids and accept as many as possible, and decides the provisioning VNFIs based on the auction outcomes. To be specific, we first devise a greedy heuristic approach to select the set of the winning bids in a differentially privacy-preserving manner. Next, we design a pricing strategy to compute the charges of CSPs, aiming to guarantee truthfulness. Strict theoretical analysis proves that PTRA-DRP can ensure differential privacy, truthfulness, individual rationality, computational efficiency and approximate social cost minimization. Extensive simulations also demonstrate the effectiveness and efficiency of PTRA-DRP.
{"title":"Differentially Private and Truthful Reverse Auction With Dynamic Resource Provisioning for VNFI Procurement in NFV Markets","authors":"Xueyi Wang;Xingwei Wang;Zhitong Wang;Rongfei Zeng;Ruiyun Yu;Qiang He;Min Huang","doi":"10.1109/TCC.2024.3522963","DOIUrl":"https://doi.org/10.1109/TCC.2024.3522963","url":null,"abstract":"With the advent of network function virtualization (NFV), many users resort to network service provisioning through virtual network function instances (VNFIs) run on the standard physical server in clouds. Following this trend, NFV markets are emerging, which allow a user to procure VNFIs from cloud service providers (CSPs). In such procurement process, it is a significant challenge to ensure differential privacy and truthfulness while explicitly considering dynamic resource provisioning, location sensitiveness and budget of each VNFI. As such, we design a differentially private and truthful reverse auction with dynamic resource provisioning (PTRA-DRP) to resolve the VNFI procurement (VNFIP) problem. To allow dynamic resource provisioning, PTRA-DRP enables CSPs to submit a set of bids and accept as many as possible, and decides the provisioning VNFIs based on the auction outcomes. To be specific, we first devise a greedy heuristic approach to select the set of the winning bids in a differentially privacy-preserving manner. Next, we design a pricing strategy to compute the charges of CSPs, aiming to guarantee truthfulness. Strict theoretical analysis proves that PTRA-DRP can ensure differential privacy, truthfulness, individual rationality, computational efficiency and approximate social cost minimization. Extensive simulations also demonstrate the effectiveness and efficiency of PTRA-DRP.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"259-272"},"PeriodicalIF":5.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data Center Network (DCN) flexibility is critical for providing adaptive and dynamic bandwidth while optimizing network resources to manage variable traffic patterns generated by heterogeneous applications. To provide flexible bandwidth, this work proposes a machine learning approach with a new Scalable and Reconfigurable Optical DCN (SROdcn) architecture that maintains dynamic and non-uniform network traffic according to the scale of the high-performance optical interconnected DCN. Our main device is the Fiber Optical Switch (FOS), which offers competitive wavelength resolution. We propose a new top-of-rack (ToR) switch that utilizes Wavelength Selective Switches (WSS) to investigate Software-Defined Networking (SDN) with machine learning-enabled flow prediction for reconfigurable optical Data Center Networks (DCNs). Our architecture provides highly scalable and flexible bandwidth allocation. Results from Mininet experimental simulations demonstrate that under the management of an SDN controller, machine learning traffic flow prediction and graph connectivity allow each optical bandwidth to be automatically reconfigured according to variable traffic patterns. The average server-to-server packet delay performance of the reconfigurable SROdcn improves by 42.33% compared to inflexible interconnects. Furthermore, the network performance of flexible SROdcn servers shows up to a 49.67% latency improvement over the Passive Optical Data Center Architecture (PODCA), a 16.87% latency improvement over the optical OPSquare DCN, and up to a 71.13% latency improvement over the fat-tree network. Additionally, our optimized Unsupervised Machine Learning (ML-UnS) method for SROdcn outperforms Supervised Machine Learning (ML-S) and Deep Learning (DL).
{"title":"SROdcn: Scalable and Reconfigurable Optical DCN Architecture for High-Performance Computing","authors":"Kassahun Geresu;Huaxi Gu;Xiaoshan Yu;Meaad Fadhel;Hui Tian;Wenting Wei","doi":"10.1109/TCC.2024.3523433","DOIUrl":"https://doi.org/10.1109/TCC.2024.3523433","url":null,"abstract":"Data Center Network (DCN) flexibility is critical for providing adaptive and dynamic bandwidth while optimizing network resources to manage variable traffic patterns generated by heterogeneous applications. To provide flexible bandwidth, this work proposes a machine learning approach with a new Scalable and Reconfigurable Optical DCN (SROdcn) architecture that maintains dynamic and non-uniform network traffic according to the scale of the high-performance optical interconnected DCN. Our main device is the Fiber Optical Switch (FOS), which offers competitive wavelength resolution. We propose a new top-of-rack (ToR) switch that utilizes Wavelength Selective Switches (WSS) to investigate Software-Defined Networking (SDN) with machine learning-enabled flow prediction for reconfigurable optical Data Center Networks (DCNs). Our architecture provides highly scalable and flexible bandwidth allocation. Results from Mininet experimental simulations demonstrate that under the management of an SDN controller, machine learning traffic flow prediction and graph connectivity allow each optical bandwidth to be automatically reconfigured according to variable traffic patterns. The average server-to-server packet delay performance of the reconfigurable SROdcn improves by 42.33% compared to inflexible interconnects. Furthermore, the network performance of flexible SROdcn servers shows up to a 49.67% latency improvement over the Passive Optical Data Center Architecture (PODCA), a 16.87% latency improvement over the optical OPSquare DCN, and up to a 71.13% latency improvement over the fat-tree network. Additionally, our optimized Unsupervised Machine Learning (ML-UnS) method for SROdcn outperforms Supervised Machine Learning (ML-S) and Deep Learning (DL).","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"245-258"},"PeriodicalIF":5.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a remote attestation approach based on multiple verifiers named CARE. CARE aims to enhance the practicality and efficiency of remote attestation while addressing trust issues within environments involving multiple stakeholders. Specifically, CARE adopts the concept of swarm verification, and employs a circular collaboration model with multiple verifiers to collect and validate evidence, thereby resolving trust issues and enhancing verification efficiency. Moreover, CARE introduces a meticulously designed filtering mechanism to address the issue of false positives in verification outcomes non-invasively. CARE utilizes a multiway tree structure to construct the baseline value library, which enhances the flexibility and fine-grained management capability of the system. Security analysis indicates that CARE can effectively resist collusion attacks. Further, detailed simulation experiments have validated its capability to convincingly attest to the trustworthiness of the dynamically constructed environment. Notably, CARE is also suitable for the remote attestation of large-scale virtual machines, achieving an efficiency 9 times greater than the classical practice approach. To the best of our knowledge, CARE is the first practical solution to address inaccuracies in remote attestation results caused by the activation of Integrity Measurement Architecture (IMA) at the application layer.
{"title":"Enhancing the Availability and Security of Attestation Scheme for Multiparty-Involved DLaaS: A Circular Approach","authors":"Miaomiao Yang;Guosheng Huang;Honghai Chen;Yongyi Liao;Qixu Wang;Xingshu Chen","doi":"10.1109/TCC.2024.3522993","DOIUrl":"https://doi.org/10.1109/TCC.2024.3522993","url":null,"abstract":"In this paper, we propose a remote attestation approach based on multiple verifiers named CARE. CARE aims to enhance the practicality and efficiency of remote attestation while addressing trust issues within environments involving multiple stakeholders. Specifically, CARE adopts the concept of swarm verification, and employs a circular collaboration model with multiple verifiers to collect and validate evidence, thereby resolving trust issues and enhancing verification efficiency. Moreover, CARE introduces a meticulously designed filtering mechanism to address the issue of false positives in verification outcomes non-invasively. CARE utilizes a multiway tree structure to construct the baseline value library, which enhances the flexibility and fine-grained management capability of the system. Security analysis indicates that CARE can effectively resist collusion attacks. Further, detailed simulation experiments have validated its capability to convincingly attest to the trustworthiness of the dynamically constructed environment. Notably, CARE is also suitable for the remote attestation of large-scale virtual machines, achieving an efficiency 9 times greater than the classical practice approach. To the best of our knowledge, CARE is the first practical solution to address inaccuracies in remote attestation results caused by the activation of Integrity Measurement Architecture (IMA) at the application layer.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"227-244"},"PeriodicalIF":5.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growth in cloud computing, Big Data, AI and high-performance computing (HPC) necessitate the deployment of additional data centers (DC’s) with high energy demands. The unprecedented increase in the Thermal Design Power (TDP) of the computing chips will require innovative cooling techniques. Furthermore, DC’s are increasingly limited in their ability to add powerful GPU servers by power capacity constraints. As cooling energy use accounts for up to 40% of DC energy consumption, creative cooling solutions are urgently needed to allow deployment of additional servers, enhance sustainability and increase energy efficiency of DC’s. The information in this study is provided from Start Campus’ Sines facility supported by Alfa Laval for the heat exchanger and CO2 emission calculations. The study evaluates the performance and sustainability impact of various data center cooling strategies including an air-only deployment and a subsequent hybrid air/water cooling solution all utilizing sea water as the cooling source. We evaluate scenarios from 3 MW to 15+1 MW of IT load in 3 MW increments which correspond to the size of heat exchangers used in the Start Campus’ modular system design. This study also evaluates the CO2 emissions compared to a conventional chiller system for all the presented scenarios. Results indicate that the effective use of the sea water cooled system combined with liquid cooled systems improve the efficiency of the DC, plays a role in decreasing the CO2 emissions and supports in achieving sustainability goals.
{"title":"Advancing Sustainability in Data Centers: Evaluation of Hybrid Air/Liquid Cooling Schemes for IT Payload Using Sea Water","authors":"Imran Latif;Muhammad Mubashar Ashraf;Umaima Haider;Gemma Reeves;Alexandrina Untaroiu;Fábio Coelho;Denis Browne","doi":"10.1109/TCC.2024.3521666","DOIUrl":"https://doi.org/10.1109/TCC.2024.3521666","url":null,"abstract":"The growth in cloud computing, Big Data, AI and high-performance computing (HPC) necessitate the deployment of additional data centers (DC’s) with high energy demands. The unprecedented increase in the Thermal Design Power (TDP) of the computing chips will require innovative cooling techniques. Furthermore, DC’s are increasingly limited in their ability to add powerful GPU servers by power capacity constraints. As cooling energy use accounts for up to 40% of DC energy consumption, creative cooling solutions are urgently needed to allow deployment of additional servers, enhance sustainability and increase energy efficiency of DC’s. The information in this study is provided from Start Campus’ Sines facility supported by Alfa Laval for the heat exchanger and CO<sub>2</sub> emission calculations. The study evaluates the performance and sustainability impact of various data center cooling strategies including an air-only deployment and a subsequent hybrid air/water cooling solution all utilizing sea water as the cooling source. We evaluate scenarios from 3 MW to 15+1 MW of IT load in 3 MW increments which correspond to the size of heat exchangers used in the Start Campus’ modular system design. This study also evaluates the CO<sub>2</sub> emissions compared to a conventional chiller system for all the presented scenarios. Results indicate that the effective use of the sea water cooled system combined with liquid cooled systems improve the efficiency of the DC, plays a role in decreasing the CO<sub>2</sub> emissions and supports in achieving sustainability goals.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"184-197"},"PeriodicalIF":5.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1109/TCC.2024.3521657
Junhong Chen;Yanying Lin;Shijie Peng;Shuaipeng Wu;Kenneth Kent;Hao Dai;Kejiang Ye;Yang Wang
Although the emerging serverless paradigm has the potential to become a dominant way of deploying cloud-service tasks across millions of mobile and IoT devices, the overhead characteristics of executing these tasks on such a volume of mobile devices remain largely unclear. To address this issue, this paper conducts a deep analysis based on the OpenFaaS platform—a popular open-source serverless platform for mobile edge environments—to investigate the overhead of performing deep learning inference tasks on mobile devices. To thoroughly evaluate the inference overhead, we develop a performance benchmark, named ESBench, whereby a set of comprehensive experiments are conducted with respect to a bunch of simulated mobile devices associated with an edge cluster. Our investigation reveals that the performance of deep learning inference tasks is significantly influenced by the model size and resource contention in mobile devices, leading to up to $3times$ degradation in performance. Moreover, we observe that the network environment can negatively impact the performance of mobile inference, increasing the CPU overhead under poor network conditions. Based on our findings, we further propose some recommendations for designing efficient serverless platforms and resource management strategies as well as for deploying serverless computing in the mobile edge environment.
{"title":"Understanding Serverless Inference in Mobile-Edge Networks: A Benchmark Approach","authors":"Junhong Chen;Yanying Lin;Shijie Peng;Shuaipeng Wu;Kenneth Kent;Hao Dai;Kejiang Ye;Yang Wang","doi":"10.1109/TCC.2024.3521657","DOIUrl":"https://doi.org/10.1109/TCC.2024.3521657","url":null,"abstract":"Although the emerging serverless paradigm has the potential to become a dominant way of deploying cloud-service tasks across millions of mobile and IoT devices, the overhead characteristics of executing these tasks on such a volume of mobile devices remain largely unclear. To address this issue, this paper conducts a deep analysis based on the OpenFaaS platform—a popular open-source serverless platform for mobile edge environments—to investigate the overhead of performing deep learning inference tasks on mobile devices. To thoroughly evaluate the inference overhead, we develop a performance benchmark, named <i>ESBench</i>, whereby a set of comprehensive experiments are conducted with respect to a bunch of simulated mobile devices associated with an edge cluster. Our investigation reveals that the performance of deep learning inference tasks is significantly influenced by the model size and resource contention in mobile devices, leading to up to <inline-formula><tex-math>$3times$</tex-math></inline-formula> degradation in performance. Moreover, we observe that the network environment can negatively impact the performance of mobile inference, increasing the CPU overhead under poor network conditions. Based on our findings, we further propose some recommendations for designing efficient serverless platforms and resource management strategies as well as for deploying serverless computing in the mobile edge environment.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"198-212"},"PeriodicalIF":5.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1109/TCC.2024.3521978
Jun Lu;Zhenya Ma;Yinggang Gao;Sheng Yue;Ju Ren;Yaoxue Zhang
Edge computing brings several challenges when it comes to data movement. First, moving large data from edge devices to the server is likely to waste bandwidth. Second, complex data patterns (e.g., traffic cameras) on devices require flexible handling. An ideal approach is to move code to data instead. However, since only a small portion of code is required, moving the executable as well as their libraries to the devices can be an overkill. While loading code on demand from remote such as NFS can be a stopgap, but on the other hand leads to low efficiency for irregular access patterns. This article presents StreamSys, a lightweight executable delivery system that loads code on demand by redirecting the local disk IO to the server through optimized network IO. We employ a Markov-based prefetch mechanism on the server side. It learns the access pattern of code and predicts the block sequence for the client to reduce the network round trip. Meanwhile, server-side StreamSys asynchronously prereads the block sequence from the disk to conceal disk IO latency beforehand. Evaluation shows that the latency of StreamSys is up to 71.4% lower than the native Linux file system based on SD card and up to 62% lower than NFS in wired environments.
{"title":"StreamSys: A Lightweight Executable Delivery System for Edge Computing","authors":"Jun Lu;Zhenya Ma;Yinggang Gao;Sheng Yue;Ju Ren;Yaoxue Zhang","doi":"10.1109/TCC.2024.3521978","DOIUrl":"https://doi.org/10.1109/TCC.2024.3521978","url":null,"abstract":"Edge computing brings several challenges when it comes to data movement. First, moving large data from edge devices to the server is likely to waste bandwidth. Second, complex data patterns (e.g., traffic cameras) on devices require flexible handling. An ideal approach is to move code to data instead. However, since only a small portion of code is required, moving the executable as well as their libraries to the devices can be an overkill. While loading code on demand from remote such as NFS can be a stopgap, but on the other hand leads to low efficiency for irregular access patterns. This article presents <sc>StreamSys</small>, a lightweight executable delivery system that loads code on demand by redirecting the local disk IO to the server through optimized network IO. We employ a Markov-based prefetch mechanism on the server side. It learns the access pattern of code and predicts the block sequence for the client to reduce the network round trip. Meanwhile, server-side <sc>StreamSys</small> asynchronously prereads the block sequence from the disk to conceal disk IO latency beforehand. Evaluation shows that the latency of <sc>StreamSys</small> is up to 71.4% lower than the native Linux file system based on SD card and up to 62% lower than NFS in wired environments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"213-226"},"PeriodicalIF":5.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growth, development, and commercialization of artificial intelligence-based technologies such as self-driving cars, augmented-reality viewers, chatbots, and virtual assistants are driving the need for increased computing power. Most of these applications rely on Deep Neural Networks (DNNs), which demand substantial computing capacity to meet user demands. However, this capacity cannot be fully provided by users’ local devices due to their limited processing power, nor by cloud data centers due to high transmission latency from long distances. Edge cloud computing addresses this issue by processing user requests through 5G, which reduces transmission latency from local devices to computing resources and allows the offloading of some computations to cloud back-ends. This paper introduces a model for a Mobile Edge Cloud system designed for an application based on a DNN. The interaction among multiple mobile users and the edge platform is formulated as a one-leader multi-follower Stackelberg game, resulting in a challenging non-convex mixed integer nonlinear programming (MINLP) problem. To tackle this, we propose a heuristic approach based on Karush-Kuhn-Tucker conditions, which solves the MINLP problem significantly faster than the commercial state-of-the-art solvers (up to 50,000 times). Furthermore, we present an algorithm to estimate optimal platform profit when sensitive user parameters are unknown. Comparing this with the full-knowledge scenario, we observe a profit loss of approximately 1%. Lastly, we analyze the advantages for an edge provider to engage in a Stackelberg game rather than setting a fixed price for its users, showing potential profit increases ranging from 16% to 66%.
{"title":"AI Applications Resource Allocation in Computing Continuum: A Stackelberg Game Approach","authors":"Roberto Sala;Hamta Sedghani;Mauro Passacantando;Giacomo Verticale;Danilo Ardagna","doi":"10.1109/TCC.2024.3521213","DOIUrl":"https://doi.org/10.1109/TCC.2024.3521213","url":null,"abstract":"The growth, development, and commercialization of artificial intelligence-based technologies such as self-driving cars, augmented-reality viewers, chatbots, and virtual assistants are driving the need for increased computing power. Most of these applications rely on Deep Neural Networks (DNNs), which demand substantial computing capacity to meet user demands. However, this capacity cannot be fully provided by users’ local devices due to their limited processing power, nor by cloud data centers due to high transmission latency from long distances. Edge cloud computing addresses this issue by processing user requests through 5G, which reduces transmission latency from local devices to computing resources and allows the offloading of some computations to cloud back-ends. This paper introduces a model for a Mobile Edge Cloud system designed for an application based on a DNN. The interaction among multiple mobile users and the edge platform is formulated as a one-leader multi-follower Stackelberg game, resulting in a challenging non-convex mixed integer nonlinear programming (MINLP) problem. To tackle this, we propose a heuristic approach based on Karush-Kuhn-Tucker conditions, which solves the MINLP problem significantly faster than the commercial state-of-the-art solvers (up to 50,000 times). Furthermore, we present an algorithm to estimate optimal platform profit when sensitive user parameters are unknown. Comparing this with the full-knowledge scenario, we observe a profit loss of approximately 1%. Lastly, we analyze the advantages for an edge provider to engage in a Stackelberg game rather than setting a fixed price for its users, showing potential profit increases ranging from 16% to 66%.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"166-183"},"PeriodicalIF":5.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1109/TCC.2024.3520101
Lilu Zhu;Kai Huang;Yanfeng Hu;Yang Wang
Container-based task scheduling is critical for ensuring a reliable, flexible and cost-effective cloud computing mode. However, in different business cloud systems, state-of-the-art scheduling models are not as effective as those in the simulated world due to the sparsity issues associated with sample sizes and features. Herein, we propose a novel containerized task scheduling framework (SA2CTS) based on reinforcement learning (RL) that incorporates cross-modal contrastive learning (CL) loss. This framework optimizes the scheduler's understanding of the container-based cloud state in RL by adding a pretraining stage, promoting accurate scheduling action inference. Specifically, we design a two-stage learning pipeline. The initial stage involves pretraining the model on a large collection of aligned image-text pairs to extract fine-grained scheduling affinity features, and the high-level semantic representations of scheduling tasks are learned in the multimodal space. In the second stage, we fine-tune the pretrained model with multisource cluster feedback, i.e., build a mapping from state representations to scheduling actions through the RL paradigm, achieving task-oriented and semantic-aware scheduling. The experimental results obtained on three large-scale production cluster datasets substantiate that the proposed SA2CTS method can provide average convergence efficiency and resource utilization improvements of 17.57% and 10.42%, respectively, over the state-of-the-art RL scheduling methods.
{"title":"Two-Stage Learning Approach for Semantic-Aware Task Scheduling in Container-Based Clouds","authors":"Lilu Zhu;Kai Huang;Yanfeng Hu;Yang Wang","doi":"10.1109/TCC.2024.3520101","DOIUrl":"https://doi.org/10.1109/TCC.2024.3520101","url":null,"abstract":"Container-based task scheduling is critical for ensuring a reliable, flexible and cost-effective cloud computing mode. However, in different business cloud systems, state-of-the-art scheduling models are not as effective as those in the simulated world due to the sparsity issues associated with sample sizes and features. Herein, we propose a novel containerized task scheduling framework (SA2CTS) based on reinforcement learning (RL) that incorporates cross-modal contrastive learning (CL) loss. This framework optimizes the scheduler's understanding of the container-based cloud state in RL by adding a pretraining stage, promoting accurate scheduling action inference. Specifically, we design a two-stage learning pipeline. The initial stage involves pretraining the model on a large collection of aligned image-text pairs to extract fine-grained scheduling affinity features, and the high-level semantic representations of scheduling tasks are learned in the multimodal space. In the second stage, we fine-tune the pretrained model with multisource cluster feedback, i.e., build a mapping from state representations to scheduling actions through the RL paradigm, achieving task-oriented and semantic-aware scheduling. The experimental results obtained on three large-scale production cluster datasets substantiate that the proposed SA2CTS method can provide average convergence efficiency and resource utilization improvements of 17.57% and 10.42%, respectively, over the state-of-the-art RL scheduling methods.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"148-165"},"PeriodicalIF":5.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：