Pub Date : 2019-05-01DOI: 10.1109/cfec.2019.8733143
O. Rana, M. Villari, Haiying Shen, Yogesh L. Simmhan, M. Fazio, Mohsen Amini, Nitin Auluck, P. Bellavista, Antonio Brogi, V. Cardellini, Kang-Peng Chen, Anacleto Ferrer, Jinwei Liu, Aravinda Rao, Byungchul Tak, Zichuan Xu
{"title":"ICFEC 2019 Organization","authors":"O. Rana, M. Villari, Haiying Shen, Yogesh L. Simmhan, M. Fazio, Mohsen Amini, Nitin Auluck, P. Bellavista, Antonio Brogi, V. Cardellini, Kang-Peng Chen, Anacleto Ferrer, Jinwei Liu, Aravinda Rao, Byungchul Tak, Zichuan Xu","doi":"10.1109/cfec.2019.8733143","DOIUrl":"https://doi.org/10.1109/cfec.2019.8733143","url":null,"abstract":"","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125771523","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}
Pub Date : 2019-05-01DOI: 10.1109/cfec.2019.8733139
Vasileios Karagiannis, Stefan Schulte, J. Leitao, Nuno M. Preguiça, Haiying Shen, C. Hochreiner, Patrick Wiener, Dominik Riemer, N. Dragoni
Techniques • Enabling Fog Computing using Self-Organizing Compute Nodes Vasileios Karagiannis, Stefan Schulte, Joao Leitao and Nuno Preguica • Machine Learning based Timeliness-guaranteed and Energy-efficient Task Assignment in Edge Computing Systems Tanmoy Sen and Haiying Shen • Optimal Placement of Stream Processing Operators in the Fog Thomas Hiessl, Vasileios Karagiannis, Christoph Hochreiner, Stefan Schulte, Matteo Nardelli • Using Virtual Events for Edge-based Data Stream Reduction in Distributed Publish/Subscribe Systems Philipp Zehnder, Patrick Wiener and Dominik Riemer • Edge-to-Edge Resource Discovery using Metadata Replication (Short) Ilir Murturi, Cosmin Avasalcai, Christos Tsigkanos and Schahram Dustdar
•使用自组织计算节点实现雾计算•边缘计算系统中基于机器学习的时效性保证和节能任务分配Tanmoy Sen和Haiying Shen•流处理算子在雾中的优化布局Thomas Hiessl, Vasileios Karagiannis, Christoph Hochreiner, Stefan Schulte，•使用元数据复制的边缘到边缘资源发现(简称)Ilir Murturi, Cosmin Avasalcai, Christos Tsigkanos和Schahram Dustdar
{"title":"ICFEC 2019 Papers","authors":"Vasileios Karagiannis, Stefan Schulte, J. Leitao, Nuno M. Preguiça, Haiying Shen, C. Hochreiner, Patrick Wiener, Dominik Riemer, N. Dragoni","doi":"10.1109/cfec.2019.8733139","DOIUrl":"https://doi.org/10.1109/cfec.2019.8733139","url":null,"abstract":"Techniques • Enabling Fog Computing using Self-Organizing Compute Nodes Vasileios Karagiannis, Stefan Schulte, Joao Leitao and Nuno Preguica • Machine Learning based Timeliness-guaranteed and Energy-efficient Task Assignment in Edge Computing Systems Tanmoy Sen and Haiying Shen • Optimal Placement of Stream Processing Operators in the Fog Thomas Hiessl, Vasileios Karagiannis, Christoph Hochreiner, Stefan Schulte, Matteo Nardelli • Using Virtual Events for Edge-based Data Stream Reduction in Distributed Publish/Subscribe Systems Philipp Zehnder, Patrick Wiener and Dominik Riemer • Edge-to-Edge Resource Discovery using Metadata Replication (Short) Ilir Murturi, Cosmin Avasalcai, Christos Tsigkanos and Schahram Dustdar","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131934894","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}
Pub Date : 2019-05-01DOI: 10.1109/CFEC.2019.8733144
Michele De Donno, N. Dragoni
The Internet of Things (IoT) has been one of the key disruptive technologies over the last few years, with its promise of optimizing and automating current manual tasks and evolving existing services. From the security perspective, the increasing adoption of IoT devices in all aspects of our society has exposed businesses and consumers to a number of threats, such as Distributed Denial of Service (DDoS) attacks. To tackle this IoT security problem, we proposed AntibIoTic 1.0 [1]. However, this solution has some limitations that make it difficult (when not impossible) to be implemented in a legal and controlled manner. Along the way, Fog computing was born: a novel paradigm that aims at bridging the gap between IoT and Cloud computing, providing a number of benefits, including security. As a result, in this paper, we present AntibIoTic 2.0, an anti-malware that relies upon Fog computing to secure IoT devices and to overcome the main issues of its predecessor (AntibIoTic 1.0). First, we present AntibIoTic 1.0 and its main problem. Then, after introducing Fog computing, we present AntibIoTic 2.0, showing how it overcomes the main issues of its predecessor by including Fog computing in its design.
{"title":"Combining AntibIoTic with Fog Computing: AntibIoTic 2.0","authors":"Michele De Donno, N. Dragoni","doi":"10.1109/CFEC.2019.8733144","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733144","url":null,"abstract":"The Internet of Things (IoT) has been one of the key disruptive technologies over the last few years, with its promise of optimizing and automating current manual tasks and evolving existing services. From the security perspective, the increasing adoption of IoT devices in all aspects of our society has exposed businesses and consumers to a number of threats, such as Distributed Denial of Service (DDoS) attacks. To tackle this IoT security problem, we proposed AntibIoTic 1.0 [1]. However, this solution has some limitations that make it difficult (when not impossible) to be implemented in a legal and controlled manner. Along the way, Fog computing was born: a novel paradigm that aims at bridging the gap between IoT and Cloud computing, providing a number of benefits, including security. As a result, in this paper, we present AntibIoTic 2.0, an anti-malware that relies upon Fog computing to secure IoT devices and to overcome the main issues of its predecessor (AntibIoTic 1.0). First, we present AntibIoTic 1.0 and its main problem. Then, after introducing Fog computing, we present AntibIoTic 2.0, showing how it overcomes the main issues of its predecessor by including Fog computing in its design.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128507541","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}
Pub Date : 2019-05-01DOI: 10.1109/CFEC.2019.8733147
Thomas Hiessl, Vasileios Karagiannis, C. Hochreiner, Stefan Schulte, Matteo Nardelli
Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the cloud. However, the cloud follows a centralized approach which is prone to high latency delay. For avoiding this delay, we leverage on the fog computing paradigm which extends the cloud to the edge of the network.In order to design a stream processing solution for the fog, we first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments. Then, we build a plugin (for stream processing frameworks) which solves the optimization problem periodically in order to support the dynamic resources of the fog. We evaluate this approach by performing experiments on an OpenStack testbed. The results show that our plugin reduces the response time and the cost by 31.5% and 8.8% respectively, compared to optimizing the placement of operators only upon initialization.
{"title":"Optimal Placement of Stream Processing Operators in the Fog","authors":"Thomas Hiessl, Vasileios Karagiannis, C. Hochreiner, Stefan Schulte, Matteo Nardelli","doi":"10.1109/CFEC.2019.8733147","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733147","url":null,"abstract":"Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the cloud. However, the cloud follows a centralized approach which is prone to high latency delay. For avoiding this delay, we leverage on the fog computing paradigm which extends the cloud to the edge of the network.In order to design a stream processing solution for the fog, we first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments. Then, we build a plugin (for stream processing frameworks) which solves the optimization problem periodically in order to support the dynamic resources of the fog. We evaluate this approach by performing experiments on an OpenStack testbed. The results show that our plugin reduces the response time and the cost by 31.5% and 8.8% respectively, compared to optimizing the placement of operators only upon initialization.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130969743","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}
Pub Date : 2019-05-01DOI: 10.1109/cfec.2019.8733140
It gives us great pleasure in welcoming you to the proceedings of the 3 IEEE International Conference on Fog and Edge Computing (ICFEC), and to the conference itself for those who are joining us at Larnaca, Cyprus! The Internet of Things (IoT) paradigm is incorporating “things” from the physical world into the Internet environment to enhance the monitoring and intelligent control of physical, digital and social systems. Traditionally, data from such systems have been analyzed centrally by applications hosted on public and private clouds, and the responses communicated back to the things. However, such cloud data centers are being supplemented with micro data centers, also called Fogs, located at the edge of the network and closer to a user (or thing) than the cloud. Further, Edge devices such as smart phones and IoT gateways are available even closer to the user or data source. These offer alternative computing resources to the cloud, on which to deploy and orchestrate applications. The Fog and Edge computing paradigm can improve the agility of service deployments, offer opportunistic and cheap computing, and leverage the diversity in network latency and bandwidth across resources. But this requires research and development into Fog and Edge fabrics and middleware for resource management; novel application platforms and programming models to ease composition; the means to handle security, privacy and trust of such heterogeneous resources; and adapting to emerging domains like autonomous vehicles and deep learning using such infrastructure. ICFEC 2019 sought original research contributions to address these challenges, covering both theory and practice over system software and applications. We received 23 papers in two rounds, an early submission and a regular submission – a new model we trialled this year. Articles from both rounds were rigorously reviewed by our Technical Program Committee with 21 members, in a timely and efficient manner. Papers received an average of 3.7 reviews each. Based on these detailed reviews, we have accepted 6 full papers – an acceptance rate of 26%, and a further 5 as short papers, to be presented at the conference and which are included in these proceedings. We also shepherded some papers this year to ensure a high quality of the final manuscripts. The accepted papers span Fog and Edge computing topics on stream processing, resource discovery, scheduling, machine learning, smart metering and security, and offer a glimpse of the stateof-the-art in this emerging research domain. We thank all the authors who submitted their research work for consideration to ICFEC 2019. Without their support, a high-quality conference program and proceedings would not be possible. We are also grateful for the service of our Technical Program Committee members, who worked hard across two rounds of submissions to offer valuable feedback to all the authors to enhance the quality of their submission. We also thank the presenters and attende
{"title":"Message from the ICFEC 2019 Chairs","authors":"","doi":"10.1109/cfec.2019.8733140","DOIUrl":"https://doi.org/10.1109/cfec.2019.8733140","url":null,"abstract":"It gives us great pleasure in welcoming you to the proceedings of the 3 IEEE International Conference on Fog and Edge Computing (ICFEC), and to the conference itself for those who are joining us at Larnaca, Cyprus! The Internet of Things (IoT) paradigm is incorporating “things” from the physical world into the Internet environment to enhance the monitoring and intelligent control of physical, digital and social systems. Traditionally, data from such systems have been analyzed centrally by applications hosted on public and private clouds, and the responses communicated back to the things. However, such cloud data centers are being supplemented with micro data centers, also called Fogs, located at the edge of the network and closer to a user (or thing) than the cloud. Further, Edge devices such as smart phones and IoT gateways are available even closer to the user or data source. These offer alternative computing resources to the cloud, on which to deploy and orchestrate applications. The Fog and Edge computing paradigm can improve the agility of service deployments, offer opportunistic and cheap computing, and leverage the diversity in network latency and bandwidth across resources. But this requires research and development into Fog and Edge fabrics and middleware for resource management; novel application platforms and programming models to ease composition; the means to handle security, privacy and trust of such heterogeneous resources; and adapting to emerging domains like autonomous vehicles and deep learning using such infrastructure. ICFEC 2019 sought original research contributions to address these challenges, covering both theory and practice over system software and applications. We received 23 papers in two rounds, an early submission and a regular submission – a new model we trialled this year. Articles from both rounds were rigorously reviewed by our Technical Program Committee with 21 members, in a timely and efficient manner. Papers received an average of 3.7 reviews each. Based on these detailed reviews, we have accepted 6 full papers – an acceptance rate of 26%, and a further 5 as short papers, to be presented at the conference and which are included in these proceedings. We also shepherded some papers this year to ensure a high quality of the final manuscripts. The accepted papers span Fog and Edge computing topics on stream processing, resource discovery, scheduling, machine learning, smart metering and security, and offer a glimpse of the stateof-the-art in this emerging research domain. We thank all the authors who submitted their research work for consideration to ICFEC 2019. Without their support, a high-quality conference program and proceedings would not be possible. We are also grateful for the service of our Technical Program Committee members, who worked hard across two rounds of submissions to offer valuable feedback to all the authors to enhance the quality of their submission. We also thank the presenters and attende","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132246101","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}
Pub Date : 2019-05-01DOI: 10.1109/CFEC.2019.8733154
Vaughan Veillon, Chavit Denninnart, M. Salehi
Video streaming is growing in popularity and has become the most bandwidth-consuming Internet service. As such, robust streaming in terms of low latency and uninterrupted streaming experience, particularly for viewers in distant areas, has become a challenge. The common practice to reduce latency is to pre-process multiple versions of each video and use Content Delivery Networks (CDN) to cache videos that are popular in a geographical area. However, with the fast-growing video repository sizes, caching video contents in multiple versions on each CDN is becoming inefficient. Accordingly, in this paper, we propose the architecture for Fog Delivery Networks (FDN) and provide methods to federate them (called F-FDN) to reduce video streaming latency. In addition to caching, FDNs have the ability to process videos in an on-demand manner. F-FDN leverages cached contents on the neighboring FDNs to further reduce latency. In particular, F-FDN is equipped with methods that aim at reducing latency through probabilistically evaluating the cost benefit of fetching video segments either from neighboring FDNs or by processing them. Experimental results against alternative streaming methods show that both on-demand processing and leveraging cached video segments on neighboring FDNs can remarkably reduce streaming latency (on average 52%).
{"title":"F-FDN: Federation of Fog Computing Systems for Low Latency Video Streaming","authors":"Vaughan Veillon, Chavit Denninnart, M. Salehi","doi":"10.1109/CFEC.2019.8733154","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733154","url":null,"abstract":"Video streaming is growing in popularity and has become the most bandwidth-consuming Internet service. As such, robust streaming in terms of low latency and uninterrupted streaming experience, particularly for viewers in distant areas, has become a challenge. The common practice to reduce latency is to pre-process multiple versions of each video and use Content Delivery Networks (CDN) to cache videos that are popular in a geographical area. However, with the fast-growing video repository sizes, caching video contents in multiple versions on each CDN is becoming inefficient. Accordingly, in this paper, we propose the architecture for Fog Delivery Networks (FDN) and provide methods to federate them (called F-FDN) to reduce video streaming latency. In addition to caching, FDNs have the ability to process videos in an on-demand manner. F-FDN leverages cached contents on the neighboring FDNs to further reduce latency. In particular, F-FDN is equipped with methods that aim at reducing latency through probabilistically evaluating the cost benefit of fetching video segments either from neighboring FDNs or by processing them. Experimental results against alternative streaming methods show that both on-demand processing and leveraging cached video segments on neighboring FDNs can remarkably reduce streaming latency (on average 52%).","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129037637","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}
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