Pub Date : 2019-05-14DOI: 10.1109/CFEC.2019.8733148
Alina Buzachis, A. Galletta, A. Celesti, M. Fazio, M. Villari
In recent years, great attention has been given to new Internet of Things (IoT) technologies. The IoT concept is nowadays intrinsic to traditional products and services. With its rapid development, more and more small smart devices are connected over the Internet in order to monitor, collect and exchange data in real-time to provide smart IoT-as-a-Services (IoTaaS). A few years ago, IoT devices exclusively sent data to a centralized Cloud data center; today it is possible to perform "on board" processing tasks at the Edge of the network and subsequently share or use the obtained results closer to users. This paper, focusing on a smart grid scenario, investigates the possibility of creating an IoTaaS for smart metering, including a microservice for IoT devices capable of acquiring and processing electrical data using the Fast Fourier Transform (FFT) algorithm. In particular, we experimentally use the smart metering IoTaaS running on a Raspberry Pi 3 device to perform a harmonic analysis of a frequency signal of the domestic electrical grid in order to characterize the non-linear loads associated to the electronic devices (e.g., smart TV, computers, etc) with the purpose of monitoring their status and preventing possible malfunctions and faults.
{"title":"Development of a Smart Metering Microservice Based on Fast Fourier Transform (FFT) for Edge/Internet of Things Environments","authors":"Alina Buzachis, A. Galletta, A. Celesti, M. Fazio, M. Villari","doi":"10.1109/CFEC.2019.8733148","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733148","url":null,"abstract":"In recent years, great attention has been given to new Internet of Things (IoT) technologies. The IoT concept is nowadays intrinsic to traditional products and services. With its rapid development, more and more small smart devices are connected over the Internet in order to monitor, collect and exchange data in real-time to provide smart IoT-as-a-Services (IoTaaS). A few years ago, IoT devices exclusively sent data to a centralized Cloud data center; today it is possible to perform \"on board\" processing tasks at the Edge of the network and subsequently share or use the obtained results closer to users. This paper, focusing on a smart grid scenario, investigates the possibility of creating an IoTaaS for smart metering, including a microservice for IoT devices capable of acquiring and processing electrical data using the Fast Fourier Transform (FFT) algorithm. In particular, we experimentally use the smart metering IoTaaS running on a Raspberry Pi 3 device to perform a harmonic analysis of a frequency signal of the domestic electrical grid in order to characterize the non-linear loads associated to the electronic devices (e.g., smart TV, computers, etc) with the purpose of monitoring their status and preventing possible malfunctions and faults.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131065536","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 : 2019-05-14DOI: 10.1109/CFEC.2019.8733153
Tanmoy Sen, Haiying Shen
The proliferation in the use of the Internet of Things (IoT) and Machine Learning (ML) techniques in edge computing systems have paved the way of using Intelligent Cognitive Assistants (ICA) for assisting people in working, learning, transportation, healthcare, and other activities. A challenge here is how to schedule application tasks between the three tiers in the edge computing system (i.e., remote cloud, fog and edge devices) according to several considered factors such as latency, energy, and bandwidth consumption. However, the state-of-the-art approaches for this challenge fall short in providing a schedule in real time for critical ICA tasks due to complex calculation phase. In this paper, we propose a novel ReInforcement Learning based Task Assignment approach, RILTA, that ensures the timeliness guaranteed execution of ICA tasks with high energy efficiency. We first formulate the task-scheduling problem in the edge computing systems considering timeliness and energy consumption in ICA applications. We then propose a heuristic for solving the problem and design the reinforcement model based on the output of the proposed heuristic. Our simulation results show that RILTA can reduce the task processing time and energy consumption with higher timeliness guarantee in comparison to other existing methods by 13 − 22% and 1 − 10% respectively.
{"title":"Machine Learning based Timeliness-Guaranteed and Energy-Efficient Task Assignment in Edge Computing Systems","authors":"Tanmoy Sen, Haiying Shen","doi":"10.1109/CFEC.2019.8733153","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733153","url":null,"abstract":"The proliferation in the use of the Internet of Things (IoT) and Machine Learning (ML) techniques in edge computing systems have paved the way of using Intelligent Cognitive Assistants (ICA) for assisting people in working, learning, transportation, healthcare, and other activities. A challenge here is how to schedule application tasks between the three tiers in the edge computing system (i.e., remote cloud, fog and edge devices) according to several considered factors such as latency, energy, and bandwidth consumption. However, the state-of-the-art approaches for this challenge fall short in providing a schedule in real time for critical ICA tasks due to complex calculation phase. In this paper, we propose a novel ReInforcement Learning based Task Assignment approach, RILTA, that ensures the timeliness guaranteed execution of ICA tasks with high energy efficiency. We first formulate the task-scheduling problem in the edge computing systems considering timeliness and energy consumption in ICA applications. We then propose a heuristic for solving the problem and design the reinforcement model based on the output of the proposed heuristic. Our simulation results show that RILTA can reduce the task processing time and energy consumption with higher timeliness guarantee in comparison to other existing methods by 13 − 22% and 1 − 10% respectively.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"3 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114033257","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.2019.8733145
Patrick Wiener, Philipp Zehnder, Dominik Riemer
Newly arising IoT-driven use cases often require low-latency anaiytics to derive time-sensitive actions, where a centralized cloud approach is not applicable. An emerging computing paradigm, referred to as fog computing, shifts the focus away from the central cloud by offloading specific computational parts of analytical stream processing pipelines (SPP) towards the edge of the network, thus leveraging existing resources close to where data is generated. However, in scenarios of mobile edge nodes, the inherent context changes need to be incorporated in the underlying fog cluster management, thus accounting for the dynamics by relocating certain processing elements of these SPP. This paper presents our initial work on a conceptual architecture for context-aware and dynamic management of SPP in the fog. We provide preliminary results, showing the general feasibility of relocating processing elements according to changes in the geolocation.
{"title":"Towards Context-Aware and Dynamic Management of Stream Processing Pipelines for Fog Computing","authors":"Patrick Wiener, Philipp Zehnder, Dominik Riemer","doi":"10.1109/CFEC.2019.8733145","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733145","url":null,"abstract":"Newly arising IoT-driven use cases often require low-latency anaiytics to derive time-sensitive actions, where a centralized cloud approach is not applicable. An emerging computing paradigm, referred to as fog computing, shifts the focus away from the central cloud by offloading specific computational parts of analytical stream processing pipelines (SPP) towards the edge of the network, thus leveraging existing resources close to where data is generated. However, in scenarios of mobile edge nodes, the inherent context changes need to be incorporated in the underlying fog cluster management, thus accounting for the dynamics by relocating certain processing elements of these SPP. This paper presents our initial work on a conceptual architecture for context-aware and dynamic management of SPP in the fog. We provide preliminary results, showing the general feasibility of relocating processing elements according to changes in the geolocation.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"58 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":"123391867","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.2019.8733141
Dominik Riemer, Vasileios Karagiannis, Stefan Schulte, J. Leitao, Nuno, Preguica, R. Hussain, M. Salehi, Mohsen, Amini Salehi, Haiying Shen
• Edge-to-Edge Resource Discovery using Metadata Replication, Ilir Murturi, Cosmin Avasalcai, Christos Tsigkanos and Schahram Dustdar • Towards Context-Aware and Dynamic Management of Stream Processing Pipelines for Fog Computing, Patrick Wiener, Philipp Zehnder and Dominik Riemer • Robust Resource Allocation Model Using Edge Computing for Vehicle to Infrastructure (V2I) Networks, Anna Kovalenko, Razin Hussain, Omid Semiari and Mohsen Amini Salehi
{"title":"ICFEC 2019 Program Schedule","authors":"Dominik Riemer, Vasileios Karagiannis, Stefan Schulte, J. Leitao, Nuno, Preguica, R. Hussain, M. Salehi, Mohsen, Amini Salehi, Haiying Shen","doi":"10.1109/cfec.2019.8733141","DOIUrl":"https://doi.org/10.1109/cfec.2019.8733141","url":null,"abstract":"• Edge-to-Edge Resource Discovery using Metadata Replication, Ilir Murturi, Cosmin Avasalcai, Christos Tsigkanos and Schahram Dustdar • Towards Context-Aware and Dynamic Management of Stream Processing Pipelines for Fog Computing, Patrick Wiener, Philipp Zehnder and Dominik Riemer • Robust Resource Allocation Model Using Edge Computing for Vehicle to Infrastructure (V2I) Networks, Anna Kovalenko, Razin Hussain, Omid Semiari and Mohsen Amini Salehi","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"2 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":"133525496","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.2019.8733142
{"title":"[Copyright notice ICFEC 2019]","authors":"","doi":"10.1109/cfec.2019.8733142","DOIUrl":"https://doi.org/10.1109/cfec.2019.8733142","url":null,"abstract":"","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":"134382377","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.2019.8733146
Philipp Zehnder, Patrick Wiener, Dominik Riemer
Distributed publish/subscribe systems are an enabling technology for Industrial Internet of Things applications. While the number of sensors increases, network bandwidth becomes a bottleneck. Existing solutions typically aim to reduce network load either by pre-processing events directly on the edge or by aggregating events into larger batches. However, these approaches are rather static and do not adequately account for the application requirements of subscribers or the actual values of sensor measurements. This paper introduces methods for publish/subscribe systems to dynamically adapt payloads of events at runtime based on i) different data reduction and transformation strategies, ii) a wrapper solution around existing message brokers and iii) a semantics-based event schema registry. Consumers are able to subscribe to various quality levels and receive virtual events, that are reconstructed directly at the subscriber based on knowledge from the semantic model and dynamic decision rules. Our evaluation shows that the concept of virtual events can reduce the network load between publishers, the message broker and subscribers compared to multiple investigated compression techniques.
{"title":"Using Virtual Events for Edge-based Data Stream Reduction in Distributed Publish/Subscribe Systems","authors":"Philipp Zehnder, Patrick Wiener, Dominik Riemer","doi":"10.1109/CFEC.2019.8733146","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733146","url":null,"abstract":"Distributed publish/subscribe systems are an enabling technology for Industrial Internet of Things applications. While the number of sensors increases, network bandwidth becomes a bottleneck. Existing solutions typically aim to reduce network load either by pre-processing events directly on the edge or by aggregating events into larger batches. However, these approaches are rather static and do not adequately account for the application requirements of subscribers or the actual values of sensor measurements. This paper introduces methods for publish/subscribe systems to dynamically adapt payloads of events at runtime based on i) different data reduction and transformation strategies, ii) a wrapper solution around existing message brokers and iii) a semantics-based event schema registry. Consumers are able to subscribe to various quality levels and receive virtual events, that are reconstructed directly at the subscriber based on knowledge from the semantic model and dynamic decision rules. Our evaluation shows that the concept of virtual events can reduce the network load between publishers, the message broker and subscribers compared to multiple investigated compression techniques.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"58 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":"134473908","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.2019.8733152
Klervie Toczé, S. Nadjm-Tehrani
In the emerging edge computing architecture, several types of devices have computational resources available. In order to make efficient use of those resources, deciding on which device a task should execute is of great importance.Existing works on task placement in edge computing focus on a resource supply side consisting of stationary devices only. In this paper, we consider the addition of mobile edge devices. We explore how mobile and stationary edge devices can augment the original task placement problem with a second placement problem: the placement of the mobile edge devices.We propose the ORCH framework in order to solve the joint problem in a distributed manner and evaluate it in the context of a spatially-changing load. Our implementation of the combined task and edge placement algorithms shows a normalized 83% delay-sensitive task completion rate compared to a perfect edge placement strategy.
{"title":"ORCH: Distributed Orchestration Framework using Mobile Edge Devices","authors":"Klervie Toczé, S. Nadjm-Tehrani","doi":"10.1109/CFEC.2019.8733152","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733152","url":null,"abstract":"In the emerging edge computing architecture, several types of devices have computational resources available. In order to make efficient use of those resources, deciding on which device a task should execute is of great importance.Existing works on task placement in edge computing focus on a resource supply side consisting of stationary devices only. In this paper, we consider the addition of mobile edge devices. We explore how mobile and stationary edge devices can augment the original task placement problem with a second placement problem: the placement of the mobile edge devices.We propose the ORCH framework in order to solve the joint problem in a distributed manner and evaluate it in the context of a spatially-changing load. Our implementation of the combined task and edge placement algorithms shows a normalized 83% delay-sensitive task completion rate compared to a perfect edge placement strategy.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"42 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":"122377100","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.2019.8733149
Ilir Murturi, Cosmin Avasalcai, Christos Tsigkanos, S. Dustdar
Edge computing has been recently introduced as an intermediary between Internet of Things (IoT) deployments and the cloud, providing data or control facilities to participating IoT devices. This includes actively supporting IoT resource discovery, something particularly pertinent when building large- scale, distributed and heterogeneous IoT systems. Moreover, edge devices supporting resource discovery are required to meet the stringent requirements prevalent in IoT systems including high availability, low-latency, and privacy. To this end, we present a resource discovery platform for IoT resources situated at the edge of the network. Our approach aims at providing a seamless discovery process that is able to (i) extend the covered area by deploying additional edge nodes and (ii) assist in the development of new IoT applications that target already available resources. Within our proposed platform, devices located in a certain proximity connect and form an edge-to-edge network that we call an edge neighborhood - our edge-to-edge metadata replication platform enables participating devices to discover available resources. Our solution is characterized by absence of centralization, as edge nodes exchange metadata about available resources within their scope in a peer-to-peer manner.
{"title":"Edge-to-Edge Resource Discovery using Metadata Replication","authors":"Ilir Murturi, Cosmin Avasalcai, Christos Tsigkanos, S. Dustdar","doi":"10.1109/CFEC.2019.8733149","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733149","url":null,"abstract":"Edge computing has been recently introduced as an intermediary between Internet of Things (IoT) deployments and the cloud, providing data or control facilities to participating IoT devices. This includes actively supporting IoT resource discovery, something particularly pertinent when building large- scale, distributed and heterogeneous IoT systems. Moreover, edge devices supporting resource discovery are required to meet the stringent requirements prevalent in IoT systems including high availability, low-latency, and privacy. To this end, we present a resource discovery platform for IoT resources situated at the edge of the network. Our approach aims at providing a seamless discovery process that is able to (i) extend the covered area by deploying additional edge nodes and (ii) assist in the development of new IoT applications that target already available resources. Within our proposed platform, devices located in a certain proximity connect and form an edge-to-edge network that we call an edge neighborhood - our edge-to-edge metadata replication platform enables participating devices to discover available resources. Our solution is characterized by absence of centralization, as edge nodes exchange metadata about available resources within their scope in a peer-to-peer manner.","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":"116859164","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.2019.8733151
A. Kovalenko, R. Hussain, Omid Semiari, M. Salehi
Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-quality services for self-driving vehicles. The majority of service requests in Vehicular Networks are delay intolerant (e.g., hazard alerts, lane change warning) and require immediate service. Therefore, Vehicular Networks, and particularly, Vehicle-to-Infrastructure (V2I) systems must provide a consistent real-time response to autonomous vehicles. During peak hours or disasters, when a surge of requests arrives at a Base Station, it is challenging for the V2I system to maintain its performance, which can lead to hazardous consequences. Hence, the goal of this research is to develop a V2I system that is robust against uncertain request arrivals. To achieve this goal, we propose to dynamically allocate service requests among Base Stations. We develop an uncertainty-aware resource allocation method for the federated environment that assigns arriving requests to a Base Station so that the likelihood of completing it on-time is maximized. We evaluate the system under various workload conditions and oversubscription levels. Simulation results show that edge federation can improve robustness of the V2I system by reducing the overall service miss rate by up to 45%.
{"title":"Robust Resource Allocation Using Edge Computing for Vehicle to Infrastructure (V2I) Networks","authors":"A. Kovalenko, R. Hussain, Omid Semiari, M. Salehi","doi":"10.1109/CFEC.2019.8733151","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733151","url":null,"abstract":"Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-quality services for self-driving vehicles. The majority of service requests in Vehicular Networks are delay intolerant (e.g., hazard alerts, lane change warning) and require immediate service. Therefore, Vehicular Networks, and particularly, Vehicle-to-Infrastructure (V2I) systems must provide a consistent real-time response to autonomous vehicles. During peak hours or disasters, when a surge of requests arrives at a Base Station, it is challenging for the V2I system to maintain its performance, which can lead to hazardous consequences. Hence, the goal of this research is to develop a V2I system that is robust against uncertain request arrivals. To achieve this goal, we propose to dynamically allocate service requests among Base Stations. We develop an uncertainty-aware resource allocation method for the federated environment that assigns arriving requests to a Base Station so that the likelihood of completing it on-time is maximized. We evaluate the system under various workload conditions and oversubscription levels. Simulation results show that edge federation can improve robustness of the V2I system by reducing the overall service miss rate by up to 45%.","PeriodicalId":340721,"journal":{"name":"2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)","volume":"112 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":"127953129","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.2019.8733150
Vasileios Karagiannis, Stefan Schulte, J. Leitao, Nuno M. Preguiça
The emergence of fog computing has led to the design of multi-layer fog computing models which are organized hierarchically. These models commonly dictate the hierarchical structure to all the participating compute nodes. However, organizing the compute nodes by adding customized connections that do not abide by the hierarchical approach, may result in improved performance due to the network’s properties i.e., latency or bandwidth between the nodes. For this reason, in this paper we propose an alternative to the hierarchical approach, which is the self-organizing compute nodes. These nodes organize themselves into a flat model which leverages on the network’s properties to provide improved performance. The results of the evaluation show that this approach reduces bandwidth utilization (~30%) by using optimized messaging instead of direct messaging. Furthermore, we show that following a flat model, enables the design of mechanisms for fault tolerance which has been mostly neglected in existing hierarchical models.
{"title":"Enabling Fog Computing using Self-Organizing Compute Nodes","authors":"Vasileios Karagiannis, Stefan Schulte, J. Leitao, Nuno M. Preguiça","doi":"10.1109/CFEC.2019.8733150","DOIUrl":"https://doi.org/10.1109/CFEC.2019.8733150","url":null,"abstract":"The emergence of fog computing has led to the design of multi-layer fog computing models which are organized hierarchically. These models commonly dictate the hierarchical structure to all the participating compute nodes. However, organizing the compute nodes by adding customized connections that do not abide by the hierarchical approach, may result in improved performance due to the network’s properties i.e., latency or bandwidth between the nodes. For this reason, in this paper we propose an alternative to the hierarchical approach, which is the self-organizing compute nodes. These nodes organize themselves into a flat model which leverages on the network’s properties to provide improved performance. The results of the evaluation show that this approach reduces bandwidth utilization (~30%) by using optimized messaging instead of direct messaging. Furthermore, we show that following a flat model, enables the design of mechanisms for fault tolerance which has been mostly neglected in existing hierarchical models.","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":"115314230","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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