2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)最新文献

ICIN 2021 Program ICIN 2021计划

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2022-03-07 DOI: 10.1109/icin51074.2021.9385558

引用次数: 0

An Application of Kubernetes Cluster Federation in Fog Computing Kubernetes集群联合在雾计算中的应用

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385548

Francescomaria Faticanti, Daniele Santoro, Silvio Cretti, D. Siracusa

引用次数: 7

Demo: Leveraging SDN in Critical Infrastructures 演示:在关键基础设施中利用SDN

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385545

Simon Buttgereit, M. Rossberg, M. Pfeiffer, G. Schäfer

引用次数: 0

DEMO: top-k cardinality estimation with HyperLogLog sketches 演示:使用HyperLogLog草图进行top-k基数估计

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385549

V. Bruschi, S. Pontarelli, Jerome Tollet, D. Barach, G. Bianchi

引用次数: 0

Tunnelless SDN overlay architecture for flow based QoS management 基于流的QoS管理的无隧道SDN覆盖架构

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385539

I. Brugnoli, M. Bon, D. Mazzuco, G. Sena, Claudina Rattaro, Santiago Bentancur

引用次数: 1

Learning-based Incast Performance Inference in Software-Defined Data Centers 软件定义数据中心中基于学习的即时性能推断

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385546

Kokouvi Bénoît Nougnanke, Y. Labit, M. Bruyère, Simone Ferlin Oliveira, U. Aïvodji

{"title":"Learning-based Incast Performance Inference in Software-Defined Data Centers","authors":"Kokouvi Bénoît Nougnanke, Y. Labit, M. Bruyère, Simone Ferlin Oliveira, U. Aïvodji","doi":"10.1109/ICIN51074.2021.9385546","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385546","url":null,"abstract":"Incast traffic is a many-to-one communication pattern used in many applications, including distributed storage, web-search with partition/aggregation design pattern, and MapReduce, commonly in data centers. It is generally composed of short-lived flows that may be queued behind large flows’ packets in congested switches where performance degradation is observed. Smart buffering at the switch level is sensed to mitigate this issue by automatically and dynamically adapting to traffic conditions changes in the highly dynamic data center environment. But for this dynamic and smart butter management to become effectively beneficial for all the traffic, and especially for incast the most critical one, incast performance models that provide insights on how various factors affect it are needed. The literature lacks these types of models. The existing ones are analytical models, which are either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, we propose a machine-learning-based incast performance inference. With this prediction capability, smart buffering scheme or other QoS optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance. Since applying machine learning to networks managed in a distributed fashion is hard, the prediction mechanism will be deployed on an SDN control plane. We could then take advantage of SDN’s centralized global view, its telemetry capabilities, and its management flexibility.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132571490","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

Resource Tuning for Energy Efficient Slicing 能源高效切片的资源调整

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385531

P. Veitch, John J. Browne, Chris MacNamara

引用次数: 3

Slice-aware Service Chaining 感知片的服务链

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385550

Z. Zsóka, Khalil Mebarkia

引用次数: 1

The SYRROCA AI-empowered network automation platform syroca人工智能网络自动化平台

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385535

Alessio Diamanti, José Manuel Sánchez-Vílchez, Stefano Secci

引用次数: 1

Leveraging the serverless paradigm for realizing machine learning pipelines across the edge-cloud continuum 利用无服务器范例实现跨边缘云连续体的机器学习管道

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385525

Efterpi Paraskevoulakou, D. Kyriazis

{"title":"Leveraging the serverless paradigm for realizing machine learning pipelines across the edge-cloud continuum","authors":"Efterpi Paraskevoulakou, D. Kyriazis","doi":"10.1109/ICIN51074.2021.9385525","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385525","url":null,"abstract":"The exceedingly exponential-growing data rate highlighted numerous requirements and several approaches have been released to maximize the added-value of cloud and edge resources. Whereas data scientists utilize algorithmic models in order to transform datasets and extract actionable knowledge, a key challenge is oriented towards abstracting the underline layers: the ones enabling the management of infrastructure resources and the ones responsible to provide frameworks and components as services. In this sense, the serverless approach features as the novel paradigm of new cloud-related technology, enabling the agile implementation of applications and services. The concept of Function as a Service (FaaS) is introduced as a revolutionary model that offers the means to exploit serverless offerings. Developers have the potential to design their applications with the necessary scalability in the form of nanoservices without addressing themselves the way the infrastructure resources should be deployed and managed. By abstracting away the underlying hardware allocations, the data scientist concentrates on the business logic and critical problems of Machine Learning (ML) algorithms. This paper introduces an approach to realize the provision of ML Functions as a Service (i.e., ML-FaaS), by exploiting the Apache OpenWhisk event-driven, distributed serverless platform. The presented approach tackles also composite services that consist of single ones i.e., workflows of ML tasks including processes such as aggregation, cleaning, feature extraction, and analytics; thus, reflecting the complete data path. We also illustrate the operation of the approach mentioned above and assess its performance and effectiveness exploiting a holistic, end-toend anti-fraud detection machine learning pipeline.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126581627","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