2020 IEEE 9th International Conference on Cloud Networking (CloudNet)最新文献

Identifying Relevant Data Center Telemetry Using Change Point Detection 使用变化点检测识别相关数据中心遥测

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335800

Daniel S. F. Alves, K. Obraczka, Rick Lindberg

引用次数: 5

Profit-Driven Service-Chain Deployment For EDA Requests On Private Cloud 私有云上EDA请求的利润驱动服务链部署

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335794

Willy Y. -W. Hsu, Jiun-Cheng Tsai, John Z.-L. Tang, Charles H.-P. Wen

引用次数: 0

A Deep Reinforcement Learning Approach for Anomaly Network Intrusion Detection System 异常网络入侵检测系统的深度强化学习方法

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335796

Ying-Feng Hsu, Morito Matsuoka

引用次数: 25

Predicting Quality of Delivery Metrics for Adaptive Video Codec Sessions 预测自适应视频编解码器会话的交付质量指标

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335813

Obinna Izima, R. Fréin, M. Davis

{"title":"Predicting Quality of Delivery Metrics for Adaptive Video Codec Sessions","authors":"Obinna Izima, R. Fréin, M. Davis","doi":"10.1109/CloudNet51028.2020.9335813","DOIUrl":"https://doi.org/10.1109/CloudNet51028.2020.9335813","url":null,"abstract":"Predicting video quality will continue to be an active area of research given the dominance of video traffic for years to come. Network service practitioners that are poised to handle the strain on the existing limited bandwidth constraints are better placed to be SLA-compliant. The dynamic and time-varying nature of cloud-hosted services require improved techniques to realize accurate models of the systems. To address this challenge: (1) we propose Codec-aware Network Adaptation Agent (cNAA), an online light-weight data learning engine that achieves accurate and correct predictions of quality of delivery (QoD) metrics, namely jitter for video services. cNAA achieves this prediction accuracy by leveraging the available network information in the face of congestion and adaptive codecs; (2) we highlight the short-comings of some baseline machine learning techniques that fail to capture network dynamics and demonstrate their failure in comparison with cNAA; and finally, (3) we demonstrate the efficacy of cNAA under varying network and codec conditions and provide evidence showing that machine learning approaches that incorporate network dynamics are better placed to realize accurate and correct predictions.","PeriodicalId":156419,"journal":{"name":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394700","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

Efficient Virtual Network Embedding with Node Ranking and Intelligent Link Mapping 基于节点排序和智能链路映射的高效虚拟网络嵌入

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335801

Khoa T. D. Nguyen, Qiao Lu, Changcheng Huang

{"title":"Efficient Virtual Network Embedding with Node Ranking and Intelligent Link Mapping","authors":"Khoa T. D. Nguyen, Qiao Lu, Changcheng Huang","doi":"10.1109/CloudNet51028.2020.9335801","DOIUrl":"https://doi.org/10.1109/CloudNet51028.2020.9335801","url":null,"abstract":"Network virtualization (NV) is emerged as a key enabler for the success of the future virtualized networks (e.g. 5G networks and smart Internet of Things (IoT)). Virtual Network Embedding (VNE) that addresses the embedding problems of heterogeneous virtual networks (VNs) onto a physical infrastructure is a main challenge in NV. Network topology attributes and network resource-considered (NTANRC) algorithm is a virtual node mapping mechanism that considers essential network features and global network resources for ranking both substrate and virtual nodes prior to embedding each given virtual network request (VNR). In this paper, we propose NTANRC combined with a distributed parallel Genetic Algorithm (GA) for virtual link mapping, namely NTANRC-GA, to solve online VNE problem. Extensive evaluation results show that our proposed solution not only achieves better performance compared to state-of-the-art VNE algorithms, but also challenges the rapid speed of shortest path (SP) method. NTANRC algorithm and the parallel GA-based algorithm are reverse compliments of each other to achieve an efficient VNE solution.","PeriodicalId":156419,"journal":{"name":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130922962","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: 7

Scheduling Service Function Chains with Dependencies in the Cloud 在云中调度具有依赖关系的业务功能链

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335790

Tarek Menouer, Amina Khedimi, C. Cérin, Mohammed Chahbar

引用次数: 4

Collaborative Container-based Parked Vehicle Edge Computing Framework for Online Task Offloading 基于协同集装箱的停放车辆边缘计算框架在线任务卸载

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335809

Khoa T. D. Nguyen, S. Drew, Changcheng Huang, Jiayu Zhou

{"title":"Collaborative Container-based Parked Vehicle Edge Computing Framework for Online Task Offloading","authors":"Khoa T. D. Nguyen, S. Drew, Changcheng Huang, Jiayu Zhou","doi":"10.1109/CloudNet51028.2020.9335809","DOIUrl":"https://doi.org/10.1109/CloudNet51028.2020.9335809","url":null,"abstract":"As most vehicles spend over 95% of their time in the parking lots, the powerful computing resources of parked vehicles (PVs) are underutilized, that can be considered as available computing nodes to run tasks as well as an extension of the existing infrastructure. In this paper, we propose EdgePV, a collaborative computing paradigm to efficiently improve online heterogeneous task scheduling. To guarantee service reliability, a container orchestration (e.g. Kubernetes) is advocated to be integrated into this proposed architecture due to its notable advanced features such as load-balancing, auto-healing, resource isolation, security, etc,. Kubernetes coordinates PVs to run sufficient numbers of task replicas, providing high service availability against possible failure caused by the mobility of PVs. We investigate how efficient PVs can handle the online computational tasks during peak hours. We also present the dual cost and utility-aware heuristic algorithm, compared with a set of heuristics to solve the problem of task scheduling, that can be devised for replacing the default scheduler in Kubernetes platform. Extensive simulation results show that our proposed design improves the task acceptance ratios and average costs at least 23% and 64%, respectively, at lowest task arrival rate compared to the cooperated cloud-edge architecture. Furthermore, owners of PVs can significantly benefit from incentives received by sharing the idle resources of their PVs.","PeriodicalId":156419,"journal":{"name":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272135","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: 7

Minimizing the Age of Information in Intelligent Transportation Systems 最小化智能交通系统的信息时代

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335793

Ibrahim Sorkhoh, Dariush Ebrahimi, S. Sharafeddine, C. Assi

{"title":"Minimizing the Age of Information in Intelligent Transportation Systems","authors":"Ibrahim Sorkhoh, Dariush Ebrahimi, S. Sharafeddine, C. Assi","doi":"10.1109/CloudNet51028.2020.9335793","DOIUrl":"https://doi.org/10.1109/CloudNet51028.2020.9335793","url":null,"abstract":"In many applications offered by intelligent transportation systems (ITS), maintaining the freshness of real-time information is a key requirement for the successful delivery of their services. The age of information (AoI) is a new metric recently proposed to capture the data freshness. This paper considers intelligent vehicles in a Vehicle to Infrastructure (V2I) network where each vehicle has a stream of data sampled by on-board sensors to communicate with a road side unit (RSU). Many vehicles demand access and vehicles stay in range for a short period of time. The objective of the RSU, then, is to schedule transmissions of these vehicles with the objective of maintaining data freshness upon receiving these packets. We first formulate the scheduling problem as a mixed integer linear program (MILP) to minimize the weighted AoI, accounting for the dynamic nature of the environment (vehicles' arrivals and their speeds, channel reliability, etc.). We also propose a scalable greedy algorithm that solves the problem in a polynomial time, and we prove that it obtains the optimal solution. We generate synthetic data using SUMO and evaluate numerically the performance of our algorithms.","PeriodicalId":156419,"journal":{"name":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129736554","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

Quantifying Cloud Misbehavior 量化云的不当行为

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335812

R. Tandon, J. Mirkovic, Pithayuth Charnsethikul

{"title":"Quantifying Cloud Misbehavior","authors":"R. Tandon, J. Mirkovic, Pithayuth Charnsethikul","doi":"10.1109/CloudNet51028.2020.9335812","DOIUrl":"https://doi.org/10.1109/CloudNet51028.2020.9335812","url":null,"abstract":"Clouds have gained popularity over the years as they provide on-demand resources without associated long-term costs. Cloud users often gain superuser access to cloud machines, which is necessary to customize them to user needs. But superuser access to a vast amount of resources, without support or oversight of experienced system administrators, can create fertile ground for accidental or intentional misuse. Attackers can rent cloud machines or hijack them from cloud users, and leverage them to generate unwanted traffic, such as spam and phishing, denial of service, vulnerability scans, drive-by downloads, etc. In this paper, we analyze 13 datasets, containing various types of unwanted traffic, to quantify cloud misbehavior and identify clouds that most often and most aggressively generate unwanted traffic. We find that although clouds own only 5.4% of the routable IPv4 address space (with 94.6% going to non-clouds), they often generate similar amounts of scans as non-clouds, and contribute to 22–96% of entries on blocklists. Among /24 prefixes that send vulnerability scans, a cloud's /24 prefix is 20–100 times more aggressive than a non-cloud's. Among /24 prefixes whose addresses appear on blocklists, a cloud's /24 prefix is almost twice as likely to have its address listed, compared to a non-cloud's /24 prefix. Misbehavior is heavy-tailed among both clouds and non-clouds. There are 25 clouds that contribute 90% of all the cloud scans, and 10 clouds contribute more than 20% of blocklist entries from clouds.","PeriodicalId":156419,"journal":{"name":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128426129","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

Resource Inference for Task Migration in Challenged Edge Networks with RITMO 基于RITMO的挑战边缘网络任务迁移资源推断

2020 IEEE 9th International Conference on Cloud Networking (CloudNet) Pub Date : 2020-11-09 DOI: 10.1109/CloudNet51028.2020.9335807

Alessio Sacco, Flavio Esposito, G. Marchetto

{"title":"Resource Inference for Task Migration in Challenged Edge Networks with RITMO","authors":"Alessio Sacco, Flavio Esposito, G. Marchetto","doi":"10.1109/CloudNet51028.2020.9335807","DOIUrl":"https://doi.org/10.1109/CloudNet51028.2020.9335807","url":null,"abstract":"Edge computing, combined with the proliferation of IoT devices, is generating new business model opportunities and applications. Among those applications, Unmanned Aerial Vehicles (UAVs) have been deployed in several scenarios, from surveillance and monitoring to disaster response, to precision agriculture. To support such applications, however, edge network managers and application programmers need to overcome a few challenges, e.g., unstable network conditions, high loss rate, and node failures. Existing solutions designed to mitigate such inefficiencies by predicting future network conditions are often computationally intensive and hence less portable on constrained devices. In this paper, we propose RITMO, a distributed and adaptive task planning algorithm that aims at solving these challenges while running on a network of UAV devices. We model our system as a network of queues, and we exploit a simple yet effective ARIMA regressor, to dynamically predict the length of future UAV task queues. Such prediction is then used to proactively migrate the tasks in case of a failure or unbalanced loads. Our simulation results demonstrate how RITMO helps to reduce the overall latency perceived by the application and anticipates the node overloading by avoiding agents that are likely to exhaust their computational resources.","PeriodicalId":156419,"journal":{"name":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125634855","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: 6