2021 IEEE 10th International Conference on Cloud Networking (CloudNet)最新文献_第2页

Data Analytics Using Two-Stage Intelligent Model Pipelining for Virtual Network Functions 基于两阶段智能模型流水线的虚拟网络功能数据分析

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657133

T. Miyazawa, Ved P. Kafle, H. Asaeda

{"title":"Data Analytics Using Two-Stage Intelligent Model Pipelining for Virtual Network Functions","authors":"T. Miyazawa, Ved P. Kafle, H. Asaeda","doi":"10.1109/CloudNet53349.2021.9657133","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657133","url":null,"abstract":"The use of machine learning (ML) technologies to predict server workloads and proactively adjust the amount of computational resource to maximize the quality of services is an enormous challenge. In this study, we introduce an ITU-T Y.3177 compliant framework for autonomous resource control and management of virtualized network infrastructures. Based on this framework, we propose (1) an architecture for a data analytics system consisting of learning and prediction components, and (2) a two-stage intelligent model pipelining mechanism for the learning component that cascades two ML models, namely nonlinear regression and multiple regression, to understand the trends of the fluctuations in CPU usage of a network node and predict the peak CPU usage of the node in the time granularity of seconds. We evaluated the proposed mechanism in an experimental network that installed in-network caching nodes as network functions. We prove that our ML models are capable of performing agile data analytics in the time granularity of seconds and can reduce the prediction errors of peak CPU usage.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124015540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Machine Learning Approach for Service Function Chain Embedding in Cloud Datacenter Networks 云数据中心网络中业务功能链嵌入的机器学习方法

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657124

T. Wassing, D. D. Vleeschauwer, C. Papagianni

{"title":"A Machine Learning Approach for Service Function Chain Embedding in Cloud Datacenter Networks","authors":"T. Wassing, D. D. Vleeschauwer, C. Papagianni","doi":"10.1109/CloudNet53349.2021.9657124","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657124","url":null,"abstract":"Network Functions Virtualization (NFV) is an industry effort to replace traditional hardware middleboxes with virtualized network functions (VNFs) running on general-build hardware platforms, enabling cost reduction, operational efficiency, and service agility. A Service Function Chain (SFC) constitutes an end-to-end network service, formed by chaining together VNFs in specific order. Infrastructure providers and cloud service providers try to optimally allocate computing and network resources to SFCs, in order to reduce costs and increase profit margins. The corresponding resource allocation problem, known as SFC embedding problem, is proven to be NP-hard.Traditionally the problem has been formulated as Mixed Integer Linear Program (MILP), assuming each SFC’s requirements are known a priori, while the embedding decision is based on a snapshot of the infrastructure’s load at request time. Reinforcement learning (RL) has been recently applied, showing promising results, specifically in dynamic environments, where such assumptions are considered unrealistic. However, standard RL techniques such as Q-learning might not be appropriate for addressing the problem at scale, as they are often ineffective for high-dimensional domains. On the other hand, Deep RL (DRL) algorithms can deal with high dimensional state spaces. In this paper, a Deep Q-Learning (DQL) approach is proposed to address the SFC resource allocation problem. The DQL agent utilizes a neural network for function approximation in Q-learning with experience replay learning. The simulations demonstrate that the new approach outperforms the linear programming approach. In addition, the DQL agent can perform SFC request admission control in real time.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117029916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Characterizing network performance of single-node large-scale container deployments 描述单节点大规模容器部署的网络性能

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657138

Conrado Boeira, M. Neves, T. Ferreto, I. Haque

{"title":"Characterizing network performance of single-node large-scale container deployments","authors":"Conrado Boeira, M. Neves, T. Ferreto, I. Haque","doi":"10.1109/CloudNet53349.2021.9657138","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657138","url":null,"abstract":"Cloud services have shifted from complex monolithic designs to hundreds of loosely coupled microservices over the last years. These microservices communicate via pre-defined APIs (e.g., RPC) and are usually implemented on top of containers. To make the microservices model profitable, cloud providers often co-locate them on a single (virtual) machine, thus achieving high server utilization. Despite being overlooked by previous work, the challenge of providing high-quality network connectivity to multiple containers running on the same host becomes crucial for the overall cloud service performance in this scenario. For that reason, this paper focuses on identifying the overheads and bottlenecks caused by the increasing number of concurrent containers running on a single node, particularly from a networking perspective. Through an extensive set of experiments, we show that the networking performance is mostly restricted by the CPU capacity (even for I/O intensive workloads), that containers can largely suffer from interference originated from packet processing, and that proper core scheduling policies can significantly improve connection throughput. Ultimately, our findings can help to pave the way towards more efficient large-scale microservice deployments.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128029596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Longer Stay Less Priority: Flow Length Approximation Used In Information-Agnostic Traffic Scheduling In Data Center Networks 停留时间越长优先级越低:数据中心网络中基于信息不可知的流量调度的流量长度近似

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657148

M. S. Iqbal, Chien Chen

{"title":"Longer Stay Less Priority: Flow Length Approximation Used In Information-Agnostic Traffic Scheduling In Data Center Networks","authors":"M. S. Iqbal, Chien Chen","doi":"10.1109/CloudNet53349.2021.9657148","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657148","url":null,"abstract":"Numerous scheduling approaches have been proposed to improve user experiences in a data center network (DCN) by reducing flow completion time (FCT). Mimicking the shortest job first (SJF) has been proved to be the prominent way to improve FCT. To do so, some approaches require flow size or completion time information in advance, which is not possible in scenarios like HTTP chunk transfer or database query response. Some information-agnostic schemes require involving end-hosts for counting the number of bytes sent. We present Longer Stay Less Priority (LSLP), an information-agnostic flow scheduling scheme, like Multi-Level Feedback Queue (MLFQ) scheduler in operating systems, that aims to mimic SJF using P4 switches in a DCN. LSLP considers all the flows as short flows initially and assigns them to the highest priority queue, and flows get demoted to the lower priority queues over time. LSLP estimates the active time of a flow by leveraging the state-of-the-art P4 switch’s programmable nature. LSLP estimates the active time of a group of new flows that arrive during a time interval and assigns their packets to the highest priority. At the beginning of the next time interval, arriving packets of old flows are placed one priority lower except for those already in the lowest priority queue. Therefore, short flows can be completed in the few higher priority queues while long flows are demoted to lower priority queues. We have evaluated LSLP via a series of tests and shown that its performance is comparable to the existing scheduling schemes.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134232918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

TPC Chair Address TPC主席致辞

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/cloudnet53349.2021.9657147

引用次数: 0

GDSim: Benchmarking Geo-Distributed Data Center Schedulers GDSim:对地理分布式数据中心调度程序进行基准测试

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657143

Daniel S. F. Alves, K. Obraczka, A. Kabbani

引用次数: 0

Using Distributed Tracing to Identify Inefficient Resources Composition in Cloud Applications 使用分布式跟踪识别云应用程序中低效的资源组合

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657140

Clément Cassé, Pascal Berthou, P. Owezarski, S. Josset

{"title":"Using Distributed Tracing to Identify Inefficient Resources Composition in Cloud Applications","authors":"Clément Cassé, Pascal Berthou, P. Owezarski, S. Josset","doi":"10.1109/CloudNet53349.2021.9657140","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657140","url":null,"abstract":"Cloud-Applications are the new industry standard way of designing Web-Applications. With Cloud Computing, Applications are usually designed as microservices, and developers can take advantage of thousands of such existing microservices, involving several hundred of cross-component communications on different physical resources.Microservices orchestration (as Kubernetes) is an automatic process, which manages each component lifecycle, and notably their allocation on the different resources of the cloud infrastructure. Whereas such automatic cloud technologies ease development and deployment, they nevertheless obscure debugging and performance analysis. In order to gain insight on the composition of services, distributed tracing recently emerged as a way to get the decomposition of the activity of each component within a cloud infrastructure. This paper aims at providing methodologies and tools (leveraging state-of-the-art tracing) for getting a wider view of application behaviours, especially focusing on application performance assessment.In this paper, we focus on using distributed traces and allocation information from microservices to model their dependencies as a hierarchical property graph. By applying graph rewriting operations, we managed to project and filter communications observed between microservices at higher abstraction layers like the machine nodes, the zones or regions. Finally, in this paper we propose an implementation of the model running on a microservices shopping application deployed on a Zonal Kubernetes cluster monitored by OpenTelemetry traces. We propose using the flow hierarchy metric on the graph model to pinpoint cycles that reveal inefficient resource composition inducing possible performance issues and economic waste.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122251525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Understanding and Leveraging Cluster Heterogeneity for Efficient Execution of Cloud Services 理解和利用集群异构以实现云服务的高效执行

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657128

S. Shukla, D. Ghosal, M. Farrens

{"title":"Understanding and Leveraging Cluster Heterogeneity for Efficient Execution of Cloud Services","authors":"S. Shukla, D. Ghosal, M. Farrens","doi":"10.1109/CloudNet53349.2021.9657128","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657128","url":null,"abstract":"Cloud warehouses are becoming increasingly heterogeneous by introducing different types of processors of varying speed and energy-efficiency. Developing an optimal strategy for distributing latency-critical service (LC-service) requests across multiple instances in a heterogeneous cluster is non-trivial. In this paper, we present a detailed analysis of the impact of cluster heterogeneity on the achieved server utilization and energy footprint to meet the required service-level latency bound (SLO) of LC-services. We develop cluster-level control plane strategies to address two forms of cluster heterogeneity - capacity and energy-efficiency. First, we propose Maximum-SLO-Guaranteed Capacity (MSG-Capacity) proportional load balancing for LC-Services to address the capacity heterogeneity and show that it can achieve higher utilization than naive performance-based heterogeneity awareness. Then, we present Efficient-First (E-First) heuristic-based Instance Scaling to address the efficiency heterogeneity. Finally, to address the bi-dimensional (capacity and energy-efficiency) heterogeneity, we superimpose the two approaches to propose Energy-efficient and MSG-Capacity (E2MC) based control-plane strategy that maximizes utilization while minimizing the energy footprint.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"36 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130319480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Cloud for Holography and Augmented Reality 云全息和增强现实

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657125

Antonios Makris, Abderrahmane Boudi, M. Coppola, Luís Cordeiro, M. Corsini, Patrizio Dazzi, Ferran Diego Andilla, Yago González Rozas, Manos N. Kamarianakis, M. Pateraki, Thu Le Pham, Antonis I Protopsaltis, Aravindh Raman, Alessandro Romussi, Luis Rosa, Elena Spatafora, T. Taleb, T. Theodoropoulos, K. Tserpes, E. Zschau, U. Herzog

引用次数: 14

The Open Cloud Testbed (OCT): A Platform for Research into new Cloud Technologies 开放云测试平台(OCT):一个研究新云技术的平台

2021 IEEE 10th International Conference on Cloud Networking (CloudNet) Pub Date : 2021-11-08 DOI: 10.1109/CloudNet53349.2021.9657109

M. Zink, D. Irwin, E. Cecchet, Hakan Saplakoglu, O. Krieger, Martin C. Herbordt, M. Daitzman, Peter Desnoyers, M. Leeser, Suranga Handagala

引用次数: 6