Pub Date : 2024-03-27DOI: 10.1109/TCC.2024.3382132
Xinglong Diao;Huaxi Gu;Wenting Wei;Guoyong Jiang;Baochun Li
Flowlet switching has been proven to be an effective technology for fine-grained load balancing in data center networks. However, flowlet detection based on static flowlet timeout values, lacks accuracy and effectiveness in complex network environments. In this article, we propose a new deep reinforcement learning approach, called DRLet, to dynamically detect flowlets. DRLet offers two advantages: first, it provides dynamic flowlet timeout values to detect bursts into fine-grained flowlets; second, flowlet timeout values are automatically configured by the deep reinforcement learning agent, which only requires simple and measurable network states, instead of any prior knowledge, to achieve the pre-defined goal. With our approach, the flowlet timeout value dynamically matches the network load scenario, ensuring the accuracy and effectiveness of flowlet detection while suppressing packet reordering. Our results show that DRLet achieves superior performance compared to existing schemes based on static flowlet timeout values in both baseline and asymmetric topologies.
{"title":"Deep Reinforcement Learning Based Dynamic Flowlet Switching for DCN","authors":"Xinglong Diao;Huaxi Gu;Wenting Wei;Guoyong Jiang;Baochun Li","doi":"10.1109/TCC.2024.3382132","DOIUrl":"10.1109/TCC.2024.3382132","url":null,"abstract":"Flowlet switching has been proven to be an effective technology for fine-grained load balancing in data center networks. However, flowlet detection based on static flowlet timeout values, lacks accuracy and effectiveness in complex network environments. In this article, we propose a new deep reinforcement learning approach, called DRLet, to dynamically detect flowlets. DRLet offers two advantages: first, it provides dynamic flowlet timeout values to detect bursts into fine-grained flowlets; second, flowlet timeout values are automatically configured by the deep reinforcement learning agent, which only requires simple and measurable network states, instead of any prior knowledge, to achieve the pre-defined goal. With our approach, the flowlet timeout value dynamically matches the network load scenario, ensuring the accuracy and effectiveness of flowlet detection while suppressing packet reordering. Our results show that DRLet achieves superior performance compared to existing schemes based on static flowlet timeout values in both baseline and asymmetric topologies.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314972","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}
Collaborative edge computing (CEC) is an emerging computing paradigm in which edge nodes collaborate to perform tasks from end devices. Task offloading decides when and at which edge node tasks are executed. Most existing studies assume task profiles and network conditions are known in advance, which can hardly adapt to dynamic real-world computation environments. Some learning-based methods use online task offloading without considering task dependency and network flow scheduling, leading to underutilized resources and flow congestion. We study Online Dependent Task Offloading (ODTO) in CEC, jointly optimizing network flow scheduling to optimize quality of service by reducing task completion time and energy consumption. The challenge of ODTO lies in how to offload dependent tasks and schedule network flows in dynamic networks. We model ODTO as the Markov Decision Process (MDP) and propose an Asynchronous Deep Progressive Reinforcement Learning (ADPRL) approach that optimize offloading and bandwidth decisions. We design a novel dependency-aware reward mechanism to address task dependency and dynamic network. Extensive experiments on the Alibaba cluster trace dataset and synthetic dataset indicate that our algorithm outperforms heuristic and learning-based methods in average task completion time and energy consumption.
{"title":"Dynamic Task Offloading in Edge Computing Based on Dependency-Aware Reinforcement Learning","authors":"Xiangchun Chen;Jiannong Cao;Yuvraj Sahni;Shan Jiang;Zhixuan Liang","doi":"10.1109/TCC.2024.3381646","DOIUrl":"10.1109/TCC.2024.3381646","url":null,"abstract":"Collaborative edge computing (CEC) is an emerging computing paradigm in which edge nodes collaborate to perform tasks from end devices. Task offloading decides when and at which edge node tasks are executed. Most existing studies assume task profiles and network conditions are known in advance, which can hardly adapt to dynamic real-world computation environments. Some learning-based methods use online task offloading without considering task dependency and network flow scheduling, leading to underutilized resources and flow congestion. We study Online Dependent Task Offloading (ODTO) in CEC, jointly optimizing network flow scheduling to optimize quality of service by reducing task completion time and energy consumption. The challenge of ODTO lies in how to offload dependent tasks and schedule network flows in dynamic networks. We model ODTO as the Markov Decision Process (MDP) and propose an Asynchronous Deep Progressive Reinforcement Learning (ADPRL) approach that optimize offloading and bandwidth decisions. We design a novel dependency-aware reward mechanism to address task dependency and dynamic network. Extensive experiments on the Alibaba cluster trace dataset and synthetic dataset indicate that our algorithm outperforms heuristic and learning-based methods in average task completion time and energy consumption.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314864","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 market penetration of Infrastructure-as-a-Service (IaaS) in cloud computing is increasing benefiting from its flexibility and scalability. One of the most important issues for IaaS cloud service providers is to minimize the monetary cost while meeting cloud user experience requirements such as makespan and security. Prior works on cloud service cost minimization ignore either security or makespan which is very important for user experience. In this article, we propose a two-stage algorithm to solve the cloud service cost minimization problem at the premise of satisfying the security and makespan requirements of cloud users. Specifically, in the first stage, we propose a novel security service selection scheme to ensure system security by judiciously selecting security services with low cost for tasks under the constraints of time and security. In the second stage, to further reduce the cloud service cost, we design a workflow scheduling method based on an improved firefly algorithm (IFA). The IFA-based method schedules cloud service workflows to virtual machines of small cost at the premise of guaranteeing security and makespan. It can quickly find the workflow scheduling solution with minimized cost using our designed updating scheme and mapping operator. Extensive simulations are conducted on real-world workflows to verify the efficacy of the proposed two-stage method. Simulation results show that the proposed two-stage method outperforms the baseline and two benchmarking methods in terms of cost minimization without violating security and time constraints. Compared to benchmarking methods, the cloud service cost can be reduced by up to 57.6% by using our proposed approach.
基础设施即服务（IaaS）的灵活性和可扩展性使其在云计算领域的市场渗透率不断提高。对于 IaaS 云服务提供商来说，最重要的问题之一是在满足云用户体验要求（如正常运行时间和安全性）的同时最大限度地降低货币成本。之前关于云服务成本最小化的研究忽略了安全性或正常运行时间，而正常运行时间对用户体验非常重要。在本文中，我们提出了一种两阶段算法，在满足云用户安全性和时延要求的前提下解决云服务成本最小化问题。具体来说，在第一阶段，我们提出了一种新颖的安全服务选择方案，在时间和安全的约束下，为任务明智地选择成本低的安全服务，以确保系统安全。在第二阶段，为了进一步降低云服务成本，我们设计了一种基于改进萤火虫算法（IFA）的工作流调度方法。基于 IFA 的方法在保证安全和有效期的前提下，将云服务工作流调度到成本较小的虚拟机上。利用我们设计的更新方案和映射算子，它能快速找到成本最小的工作流调度方案。我们在实际工作流中进行了大量仿真，以验证所提出的两阶段方法的有效性。仿真结果表明，在不违反安全性和时间限制的前提下，所提出的两阶段方法在成本最小化方面优于基准方法和两种基准方法。与基准方法相比，使用我们提出的方法，云服务成本最多可降低 57.6%。
{"title":"Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization","authors":"Liying Li;Chengliang Zhou;Peijin Cong;Yufan Shen;Junlong Zhou;Tongquan Wei","doi":"10.1109/TCC.2024.3382351","DOIUrl":"10.1109/TCC.2024.3382351","url":null,"abstract":"The market penetration of Infrastructure-as-a-Service (IaaS) in cloud computing is increasing benefiting from its flexibility and scalability. One of the most important issues for IaaS cloud service providers is to minimize the monetary cost while meeting cloud user experience requirements such as makespan and security. Prior works on cloud service cost minimization ignore either security or makespan which is very important for user experience. In this article, we propose a two-stage algorithm to solve the cloud service cost minimization problem at the premise of satisfying the security and makespan requirements of cloud users. Specifically, in the first stage, we propose a novel security service selection scheme to ensure system security by judiciously selecting security services with low cost for tasks under the constraints of time and security. In the second stage, to further reduce the cloud service cost, we design a workflow scheduling method based on an improved firefly algorithm (IFA). The IFA-based method schedules cloud service workflows to virtual machines of small cost at the premise of guaranteeing security and makespan. It can quickly find the workflow scheduling solution with minimized cost using our designed updating scheme and mapping operator. Extensive simulations are conducted on real-world workflows to verify the efficacy of the proposed two-stage method. Simulation results show that the proposed two-stage method outperforms the baseline and two benchmarking methods in terms of cost minimization without violating security and time constraints. Compared to benchmarking methods, the cloud service cost can be reduced by up to 57.6% by using our proposed approach.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314801","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}
Pre-copy-based Virtual Machine (VM) live migration seamlessly migrates the running VM to the target physical server by pre-copying memory pages and realizing updates through loop iterations. This method, which has high reliability and robustness, can effectively achieve load balancing and reduce energy consumption. It is widely used in the industry to manage server cluster resources. However, it also involves many problems, such as many dirty memory pages resulting from repeated transmission and convergence failure of iterative transmission. Hence, pre-copy live migration cannot efficiently allocate server cluster resources. To resolve these problems, a VM pre-copy live migration technology based on the similarity of dirty memory pages is proposed in this paper. The access priority of historical dirty memory pages was determined by calculating the similarity weight based on the Hamming distance. A priority-based delay transmission scheme for high dirty pages and low dirty pages was used to decrease the frequent transmission of high dirty memory pages, increase the convergence speed of the live-migration iterative copy process, and reduce the overall migration time of VMs. A comparative analysis of experimental results based on six dimensions showed that the proposed method achieved better migration efficiency than the conventional live migration strategy.
{"title":"Live Migration of Virtual Machines Based on Dirty Page Similarity","authors":"Yucong Chen;Shuaixin Xu;Hubin Yang;Rui Zhou;Deke Guo;Qingguo Zhou","doi":"10.1109/TCC.2024.3379494","DOIUrl":"10.1109/TCC.2024.3379494","url":null,"abstract":"Pre-copy-based Virtual Machine (VM) live migration seamlessly migrates the running VM to the target physical server by pre-copying memory pages and realizing updates through loop iterations. This method, which has high reliability and robustness, can effectively achieve load balancing and reduce energy consumption. It is widely used in the industry to manage server cluster resources. However, it also involves many problems, such as many dirty memory pages resulting from repeated transmission and convergence failure of iterative transmission. Hence, pre-copy live migration cannot efficiently allocate server cluster resources. To resolve these problems, a VM pre-copy live migration technology based on the similarity of dirty memory pages is proposed in this paper. The access priority of historical dirty memory pages was determined by calculating the similarity weight based on the Hamming distance. A priority-based delay transmission scheme for high dirty pages and low dirty pages was used to decrease the frequent transmission of high dirty memory pages, increase the convergence speed of the live-migration iterative copy process, and reduce the overall migration time of VMs. A comparative analysis of experimental results based on six dimensions showed that the proposed method achieved better migration efficiency than the conventional live migration strategy.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200145","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 : 2024-03-20DOI: 10.1109/TCC.2024.3403175
Myoungsung You;Minjae Seo;Jaehan Kim;Seungwon Shin;Jaehyun Nam
Containers have become the predominant virtualization technique for deploying microservices in cloud environments. However, container networking, critical for microservice functionality, often introduces significant overhead and resource consumption, potentially degrading the performance of microservices. This challenge arises from the complexity of the software-based network data plane, responsible for network virtualization and access control within container traffic. To tackle this challenge, we propose Hyperion , a novel hardware-based container networking system that prioritizes high performance and security. Leveraging smartNICs, commonly found in cloud environments, Hyperion implements a fully-functional container network data plane, encompassing network virtualization and access control. It also has the capability to dynamically optimize its data plane for agile responses to frequent changes in container environments, ensuring up-to-date data plane operation. This hardware-based design empowers Hyperion to significantly improve the overall container networking performance without relying on the host system resources. Notably, Hyperion seamlessly integrates with existing containerized applications without necessitating modifications. Our evaluation shows that compared to state-of-the-art solutions, Hyperion achieves significant improvements in HTTP container communication latency and throughput by up to 2.25x and 4.3x, respectively. Furthermore, it reduces CPU utilization associated with container networking by up to 4x.
{"title":"Hyperion: Hardware-Based High-Performance and Secure System for Container Networks","authors":"Myoungsung You;Minjae Seo;Jaehan Kim;Seungwon Shin;Jaehyun Nam","doi":"10.1109/TCC.2024.3403175","DOIUrl":"10.1109/TCC.2024.3403175","url":null,"abstract":"Containers have become the predominant virtualization technique for deploying microservices in cloud environments. However, container networking, critical for microservice functionality, often introduces significant overhead and resource consumption, potentially degrading the performance of microservices. This challenge arises from the complexity of the software-based network data plane, responsible for network virtualization and access control within container traffic. To tackle this challenge, we propose \u0000<monospace>Hyperion</monospace>\u0000, a novel hardware-based container networking system that prioritizes high performance and security. Leveraging smartNICs, commonly found in cloud environments, \u0000<monospace>Hyperion</monospace>\u0000 implements a fully-functional container network data plane, encompassing network virtualization and access control. It also has the capability to dynamically optimize its data plane for agile responses to frequent changes in container environments, ensuring up-to-date data plane operation. This hardware-based design empowers \u0000<monospace>Hyperion</monospace>\u0000 to significantly improve the overall container networking performance without relying on the host system resources. Notably, \u0000<monospace>Hyperion</monospace>\u0000 seamlessly integrates with existing containerized applications without necessitating modifications. Our evaluation shows that compared to state-of-the-art solutions, \u0000<monospace>Hyperion</monospace>\u0000 achieves significant improvements in HTTP container communication latency and throughput by up to 2.25x and 4.3x, respectively. Furthermore, it reduces CPU utilization associated with container networking by up to 4x.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151312","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 : 2024-03-18DOI: 10.1109/TCC.2024.3376394
Junfei Wang;Jing Li;Zhen Gao;Zhu Han;Chao Qiu;Xiaofei Wang
The Internet of Things (IoT) finds applications across diverse fields but grapples with privacy and security concerns. Blockchain offers a remedy by instilling trust among IoT devices. The development of blockchain in IoT encounters hurdles due to its resource-intensive computation processing, notably in PoW-based systems. Cloud and edge computing can facilitate the application of blockchain in this environment, and the IoT users who want to mine in blockchain need to pay the computation resource rent to the Cloud Computing Service Provider (CCSP) for offloading the mining workload. In this scenario, these IoT miners can form groups to trade with CCSP to maximize their utility. In this paper, a mixed model of the Stackelberg game and coalition formation game is embraced to address the grouping and pricing issues between IoT miners and CCSP. In particular, the Stackelberg game is utilized to handle the pricing problem, and the coalition formation game is employed to tackle the best group partition problem. Moreover, a coalition formation algorithm is proposed to obtain a near-optimal solution with very low complexity. Simulation results show that our proposed algorithm can obtain a performance that is very near to the exhaustive search method, outperforms other existing schemes, and requires only a small computation overhead.
{"title":"Game-Based Low Complexity and Near Optimal Task Offloading for Mobile Blockchain Systems","authors":"Junfei Wang;Jing Li;Zhen Gao;Zhu Han;Chao Qiu;Xiaofei Wang","doi":"10.1109/TCC.2024.3376394","DOIUrl":"10.1109/TCC.2024.3376394","url":null,"abstract":"The Internet of Things (IoT) finds applications across diverse fields but grapples with privacy and security concerns. Blockchain offers a remedy by instilling trust among IoT devices. The development of blockchain in IoT encounters hurdles due to its resource-intensive computation processing, notably in PoW-based systems. Cloud and edge computing can facilitate the application of blockchain in this environment, and the IoT users who want to mine in blockchain need to pay the computation resource rent to the Cloud Computing Service Provider (CCSP) for offloading the mining workload. In this scenario, these IoT miners can form groups to trade with CCSP to maximize their utility. In this paper, a mixed model of the Stackelberg game and coalition formation game is embraced to address the grouping and pricing issues between IoT miners and CCSP. In particular, the Stackelberg game is utilized to handle the pricing problem, and the coalition formation game is employed to tackle the best group partition problem. Moreover, a coalition formation algorithm is proposed to obtain a near-optimal solution with very low complexity. Simulation results show that our proposed algorithm can obtain a performance that is very near to the exhaustive search method, outperforms other existing schemes, and requires only a small computation overhead.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165520","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 : 2024-03-18DOI: 10.1109/TCC.2024.3376716
Song Zhang;Lide Suo;Wenxin Li;Yuan Liu;Yulong Li;Keqiu Li
Many existing datacenter transports perform one-shot packet priority tagging at end-hosts and leave them fixed during the packet's transmission. In this article, we experimentally show that: 1) such fixed packet priority is not sufficient for FCT (flow completion time) minimization, and 2) adjusting packet transmission priority in the network requires effective coordination among switches. Building on these insights, we present Anole, a new datacenter transport that advocates packet re-prioritization in near-bottleneck switches to minimize FCT. To this end, Anole integrates three simple-yet-effective techniques. First, it employs an in-network telemetry (INT) based approach to dynamically detect the bottleneck for each flow. Second, it adopts an on-off rate control mechanism for each sender to pause heavily congested flows but send lightly- and non-congested ones. Last, it leverages an altruistic scheduling policy at each switch to let the flows whose next hops are bottleneck switches give way to others. We implement an Anole prototype based on DPDK and show, through both testbed experiments and simulations, that Anole delivers significant performance advantages. For example, compared to EPN, Homa, and Aeolus, it shortens the average FCT of all (small) flows by up to 61.6% (89.1%).
{"title":"Anole: Scheduling Flows for Fast Datacenter Networks With Packet Re-Prioritization","authors":"Song Zhang;Lide Suo;Wenxin Li;Yuan Liu;Yulong Li;Keqiu Li","doi":"10.1109/TCC.2024.3376716","DOIUrl":"10.1109/TCC.2024.3376716","url":null,"abstract":"Many existing datacenter transports perform one-shot packet priority tagging at end-hosts and leave them fixed during the packet's transmission. In this article, we experimentally show that: 1) such fixed packet priority is not sufficient for FCT (flow completion time) minimization, and 2) adjusting packet transmission priority in the network requires effective coordination among switches. Building on these insights, we present Anole, a new datacenter transport that advocates packet re-prioritization in near-bottleneck switches to minimize FCT. To this end, Anole integrates three simple-yet-effective techniques. First, it employs an in-network telemetry (INT) based approach to dynamically detect the bottleneck for each flow. Second, it adopts an on-off rate control mechanism for each sender to pause heavily congested flows but send lightly- and non-congested ones. Last, it leverages an altruistic scheduling policy at each switch to let the flows whose next hops are bottleneck switches give way to others. We implement an Anole prototype based on DPDK and show, through both testbed experiments and simulations, that Anole delivers significant performance advantages. For example, compared to EPN, Homa, and Aeolus, it shortens the average FCT of all (small) flows by up to 61.6% (89.1%).","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165983","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 : 2024-03-17DOI: 10.1109/TCC.2024.3402185
Dung H. P. Nguyen;Chih-Chieh Lin;Tu N. Nguyen;Shao-I Chu;Bing-Hong Liu
Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks engendering interruption of services, such as node and link failures. Thus, resiliency is one of the requirements in NFV-enabled network design for recovering network services once occurring failures. Therefore, in addition to a primary chain of virtual network functions (VNFs) for a service, one typically allocates the corresponding backup VNFs to satisfy the resiliency requirement. Nevertheless, this approach consumes network resources that can be inherently employed to deploy more services. Moreover, one can hardly recover all interrupted services due to the limitation of network backup resources. In this context, the importance of the services is one of the factors employed to judge the recovery priority. In this article, we first assign each service a weight expressing its importance, then seek to retrieve interrupted services such that the total weight of the recovered services is maximum. Hence, we also call this issue the VNF restoration for recovering weighted services (VRRWS) problem. We next demonstrate the difficulty of the VRRWS problem is NP-hard and propose an effective technique, termed online recovery algorithm (ORA), to address the problem without necessitating the backup resources. Eventually, we conduct extensive simulations to evaluate the performance of the proposed algorithm as well as the factors affecting the recovery. The experiment shows that the available VNFs should be migrated to appropriate nodes during the recovery process to achieve better results.
{"title":"Service Recovery in NFV-Enabled Networks: Algorithm Design and Analysis","authors":"Dung H. P. Nguyen;Chih-Chieh Lin;Tu N. Nguyen;Shao-I Chu;Bing-Hong Liu","doi":"10.1109/TCC.2024.3402185","DOIUrl":"10.1109/TCC.2024.3402185","url":null,"abstract":"Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks engendering interruption of services, such as node and link failures. Thus, resiliency is one of the requirements in NFV-enabled network design for recovering network services once occurring failures. Therefore, in addition to a primary chain of virtual network functions (VNFs) for a service, one typically allocates the corresponding backup VNFs to satisfy the resiliency requirement. Nevertheless, this approach consumes network resources that can be inherently employed to deploy more services. Moreover, one can hardly recover all interrupted services due to the limitation of network backup resources. In this context, the importance of the services is one of the factors employed to judge the recovery priority. In this article, we first assign each service a weight expressing its importance, then seek to retrieve interrupted services such that the total weight of the recovered services is maximum. Hence, we also call this issue the VNF restoration for recovering weighted services (VRRWS) problem. We next demonstrate the difficulty of the VRRWS problem is NP-hard and propose an effective technique, termed online recovery algorithm (ORA), to address the problem without necessitating the backup resources. Eventually, we conduct extensive simulations to evaluate the performance of the proposed algorithm as well as the factors affecting the recovery. The experiment shows that the available VNFs should be migrated to appropriate nodes during the recovery process to achieve better results.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060728","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}
Forecasting workloads and responding promptly with resource scaling and migration is critical to optimizing operations and enhancing resource management in cloud environments. However, the diverse and dynamic nature of devices within cloud environments complicates workload forecasting. These challenges often lead to service level agreement violations or inefficient resource usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload Forecasting (E-LCWF) framework designed specifically for efficient resource management in these heterogeneous and dynamic environments. The E-LCWF framework processes individual resource workloads as multivariate time series and enhances model performance through anomaly detection and handling. Additionally, the E-LCWF framework employs an error-based ensemble approach, using transformer-based models and Long-Term Time Series Forecasting (LTSF) linear models, each of which has demonstrated exceptional performance in LTSF. Experimental results obtained using virtual machine data from real-world management information systems and manufacturing execution systems show that the E-LCWF framework outperforms state-of-the-art models in forecasting accuracy.
{"title":"Enhancing Long-Term Cloud Workload Forecasting Framework: Anomaly Handling and Ensemble Learning in Multivariate Time Series","authors":"Yeong-Min Kim;Seunghwan Song;Byoung-Mo Koo;Jeena Son;Yeseul Lee;Jun-Geol Baek","doi":"10.1109/TCC.2024.3400859","DOIUrl":"10.1109/TCC.2024.3400859","url":null,"abstract":"Forecasting workloads and responding promptly with resource scaling and migration is critical to optimizing operations and enhancing resource management in cloud environments. However, the diverse and dynamic nature of devices within cloud environments complicates workload forecasting. These challenges often lead to service level agreement violations or inefficient resource usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload Forecasting (E-LCWF) framework designed specifically for efficient resource management in these heterogeneous and dynamic environments. The E-LCWF framework processes individual resource workloads as multivariate time series and enhances model performance through anomaly detection and handling. Additionally, the E-LCWF framework employs an error-based ensemble approach, using transformer-based models and Long-Term Time Series Forecasting (LTSF) linear models, each of which has demonstrated exceptional performance in LTSF. Experimental results obtained using virtual machine data from real-world management information systems and manufacturing execution systems show that the E-LCWF framework outperforms state-of-the-art models in forecasting accuracy.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063993","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 : 2024-03-13DOI: 10.1109/TCC.2024.3376996
Xin Tang;Yudan Zhu;Mingjun Fu
Cross-user deduplication is an emerging technique to eliminate uploading of redundant data in cloud storage. Even though it is able to improve storage and communication efficiency simultaneously, it suffers from the problem of privacy leakage by side channel attack, which is a major obstacle to the practical application of this technique. In order to achieve a secure cross-user deduplication, Yu et al. recently proposed a zero-knowledge response (ZEUS) scheme, together with an advanced countermeasure ZEUS $^mathrm{+}$ by combining ZEUS and the random threshold solution, each of which is claimed to be secure against side channel attack. However, in this paper we show that both ZEUS and ZEUS $^mathrm{+}$ are easily subject to a random chunk generation attack, which in turn undermines the claimed security. Furthermore, we also propose a simple but effective method to improve the existing schemes.
跨用户重复数据删除是一种新兴技术，可消除云存储中的冗余数据上传。尽管它能同时提高存储和通信效率，但却存在侧信道攻击导致隐私泄露的问题，这是该技术实际应用的一大障碍。为了实现安全的跨用户重复数据删除，Yu 等人最近提出了一种零知识响应（ZEUS）方案，并结合 ZEUS 和随机阈值方案提出了一种先进的对策 ZEUS$^mathrm{+}$，据称每种对策都能安全地抵御侧信道攻击。然而，在本文中，我们发现 ZEUS 和 ZEUS$^mathrm{+}$ 都很容易受到随机块生成攻击，这反过来又破坏了所宣称的安全性。此外，我们还提出了一种简单而有效的方法来改进现有方案。
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