Pub Date : 2018-08-01DOI: 10.1109/FiCloud.2018.00035
Perin Ünal, Yunuscan Kocak, Y. Donmez
Winter service operations are one of many responsibilities of local authorities essential for public safety. Consisting of snow removal and spreading de-icing material, operations require the assignment of significant resources in terms of vehicles and personnel. For that reason, the development of tools enabling authorities to engage in the transparent monitoring and planning of the snow removal and de-icing operations is beneficial for the community. In this work, a smart winter service platform is designed for the transparent monitoring of the operation and a vehicle route planning algorithm is designed as a decision support system for the operation center. The platform consists of an electronic control unit attached to the vehicle for monitoring and data collection, which is transferred to the cloud for analytics and reporting purposes. In the cloud, distributed big data technologies are used for fault-tolerance and fast processing. Web and mobile applications have been developed for visualization and increasing public awareness of the operations.
{"title":"A Smart Winter Service Platform and Route Planning Algorithm","authors":"Perin Ünal, Yunuscan Kocak, Y. Donmez","doi":"10.1109/FiCloud.2018.00035","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00035","url":null,"abstract":"Winter service operations are one of many responsibilities of local authorities essential for public safety. Consisting of snow removal and spreading de-icing material, operations require the assignment of significant resources in terms of vehicles and personnel. For that reason, the development of tools enabling authorities to engage in the transparent monitoring and planning of the snow removal and de-icing operations is beneficial for the community. In this work, a smart winter service platform is designed for the transparent monitoring of the operation and a vehicle route planning algorithm is designed as a decision support system for the operation center. The platform consists of an electronic control unit attached to the vehicle for monitoring and data collection, which is transferred to the cloud for analytics and reporting purposes. In the cloud, distributed big data technologies are used for fault-tolerance and fast processing. Web and mobile applications have been developed for visualization and increasing public awareness of the operations.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125171773","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}
In recent years, Internet of Things devices(IoT) and Cyber-Physicals Systems(CPS) are ubiquitous and used in many situations (e.g. avionic, vehicles, household devices, smartphones). End-user privacy and security was one of the main concerns of devices designers. Moreover, these systems are becoming more complex and opened to enable industrial to provide different services at the same time on the same device. However, the industrial worries about their data integrity and confidentiality into the devices. Each service provider is in an economic confrontation with others and End-User and service data are a significant resource. In this paper we propose an IoT device architecture based on a small separation kernel and a communication control mechanism to provide a trustworthy environment for each service provider.
{"title":"Ensuring IoT Security with an Architecture Based on a Separation Kernel","authors":"Mahieddine Yaker, Chrystel Gaber, G. Grimaud, Jean-Philippe Wary, Julien Iguchi-Cartigny, Xiao Han, Vicente Sanchez-Leighton","doi":"10.1109/FiCloud.2018.00025","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00025","url":null,"abstract":"In recent years, Internet of Things devices(IoT) and Cyber-Physicals Systems(CPS) are ubiquitous and used in many situations (e.g. avionic, vehicles, household devices, smartphones). End-user privacy and security was one of the main concerns of devices designers. Moreover, these systems are becoming more complex and opened to enable industrial to provide different services at the same time on the same device. However, the industrial worries about their data integrity and confidentiality into the devices. Each service provider is in an economic confrontation with others and End-User and service data are a significant resource. In this paper we propose an IoT device architecture based on a small separation kernel and a communication control mechanism to provide a trustworthy environment for each service provider.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126046907","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00015
Tsozen Yeh, Hsinyi Huang
Cloud computing has been widely used in many areas nowadays. It is common that large cloud systems could simultaneously service tens of thousands of users and host an excessive number of jobs running at the same time. Under such circumstances, the completion of urgent or time-critical tasks can be significantly delayed if the underlying cloud system does not offer schemes to speed up the execution of those tasks. Among the platforms adopted in cloud computing, Hadoop is one of the most widely used in the community of cloud computing. Unfortunately, Hadoop does not provide users efficient ways to expedite the course of execution for high-priority jobs which users would hope for their fast completion. We designed and implemented a new scheduling scheme enabling Hadoop to support fully prioritized scheduling. With our scheduling scheme, users can dynamically assign high priority to individual jobs so their execution could be accelerated accordingly. We evaluated our design and implementation by executing the same programs with ordinary priority versus high priority in Hadoop environments under different configurations. Experimental results show that programs can shorten their execution time by up to 82.70% if they are executed with high priority.
{"title":"Realizing Prioritized Scheduling Service in the Hadoop System","authors":"Tsozen Yeh, Hsinyi Huang","doi":"10.1109/FiCloud.2018.00015","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00015","url":null,"abstract":"Cloud computing has been widely used in many areas nowadays. It is common that large cloud systems could simultaneously service tens of thousands of users and host an excessive number of jobs running at the same time. Under such circumstances, the completion of urgent or time-critical tasks can be significantly delayed if the underlying cloud system does not offer schemes to speed up the execution of those tasks. Among the platforms adopted in cloud computing, Hadoop is one of the most widely used in the community of cloud computing. Unfortunately, Hadoop does not provide users efficient ways to expedite the course of execution for high-priority jobs which users would hope for their fast completion. We designed and implemented a new scheduling scheme enabling Hadoop to support fully prioritized scheduling. With our scheduling scheme, users can dynamically assign high priority to individual jobs so their execution could be accelerated accordingly. We evaluated our design and implementation by executing the same programs with ordinary priority versus high priority in Hadoop environments under different configurations. Experimental results show that programs can shorten their execution time by up to 82.70% if they are executed with high priority.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572102","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00020
Shay Horovitz, Yair Arian
Threshold based cloud auto-scaling is one of the most common methods used to scale cloud applications. A major drawback of this method is that the thresholds are set manually by the user in an ad hoc fashion, not optimally, and specially crafted for a specific application behavior, leading to SLA failures. We present Q-Threshold - A novel algorithm for adaptively and dynamically adjusting the thresholds with no need for user configuration while meeting SLA objectives. In this context we present new methods for improving reinforcement Q-Learning auto-scaling with faster convergence, reduced state space and reduced action space in a distributed cloud environment. We demonstrate the effectiveness of our methods both on simulations and on real applications.
{"title":"Efficient Cloud Auto-Scaling with SLA Objective Using Q-Learning","authors":"Shay Horovitz, Yair Arian","doi":"10.1109/FiCloud.2018.00020","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00020","url":null,"abstract":"Threshold based cloud auto-scaling is one of the most common methods used to scale cloud applications. A major drawback of this method is that the thresholds are set manually by the user in an ad hoc fashion, not optimally, and specially crafted for a specific application behavior, leading to SLA failures. We present Q-Threshold - A novel algorithm for adaptively and dynamically adjusting the thresholds with no need for user configuration while meeting SLA objectives. In this context we present new methods for improving reinforcement Q-Learning auto-scaling with faster convergence, reduced state space and reduced action space in a distributed cloud environment. We demonstrate the effectiveness of our methods both on simulations and on real applications.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123405117","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00056
Imen Bouzarkouna, M. Sahnoun, Nouha Sghaier, D. Baudry, C. Gout
Recently, the industries converge to the integration of the industry 4.0 paradigm to keep responding to the variable market demands. This integration is realized by the adoption of several components of the industry 4.0 such as IoT, Big Data and Cloud Computing, etc. Several difficulties concerning the integration of data management were encountered during first level of Industry 4.0 integration because of the unexpected quantity of data generated by IoT devices. The Fog computing can be considered as a new component of Industry 4.0 to resolve this kind of problem. However its implementation in the industrial field faces several challenges from different natures. This paper explains the role of Fog Computing solution to enhance the Cloud layer (distribution, low latency, real-time,. . . ) and studies its ability to be implemented in manufacturing systems. The Fog Manufacturing is introduced as the new industrial Fog vision. The challenges preventing the Fog Manufacturing implementation are studied and the links between each other are justified. A future use case is described to carry out the solutions given to satisfy the Fog Manufacturing challenges.
{"title":"Challenges Facing the Industrial Implementation of Fog Computing","authors":"Imen Bouzarkouna, M. Sahnoun, Nouha Sghaier, D. Baudry, C. Gout","doi":"10.1109/FiCloud.2018.00056","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00056","url":null,"abstract":"Recently, the industries converge to the integration of the industry 4.0 paradigm to keep responding to the variable market demands. This integration is realized by the adoption of several components of the industry 4.0 such as IoT, Big Data and Cloud Computing, etc. Several difficulties concerning the integration of data management were encountered during first level of Industry 4.0 integration because of the unexpected quantity of data generated by IoT devices. The Fog computing can be considered as a new component of Industry 4.0 to resolve this kind of problem. However its implementation in the industrial field faces several challenges from different natures. This paper explains the role of Fog Computing solution to enhance the Cloud layer (distribution, low latency, real-time,. . . ) and studies its ability to be implemented in manufacturing systems. The Fog Manufacturing is introduced as the new industrial Fog vision. The challenges preventing the Fog Manufacturing implementation are studied and the links between each other are justified. A future use case is described to carry out the solutions given to satisfy the Fog Manufacturing challenges.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115866766","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00037
Daniel Lewandowski, Diego Pareschi, Waldemar Pakos, E. Ragaini
The Future of Internet of Things, Service and People (IoTSP) for the industrial sector lies in integrating two worlds – information technology and operation technology. This article explains in details practical concepts of IT and OT integration with a usage of digitalization approach. As an example an oil and gas business is taken into account. The so-called Digital Oilfield idea is focusing on melding the IT, OT and IoTSP in order to "learn" what is working best for O&G production, to predict equipment failures, track employees in the field in order to coach them in real-time and remove from hazardous situations. The ABB Polish Corporate Research Center has developed and built the advanced test rig for ABILITY-powered machines, used as multipurpose experimental rig mainly for applications in the oil, gas and chemicals sectors. The test rig provides additional capabilities in terms of the advanced control methods and condition monitoring techniques design and evaluation for the industrial compression systems. Except the industrial view on the matter, the facility is also used to verify novel and disruptive concepts for device communication and connectivity from sensors and devices up to cloud and it has been used to validate cloud use and business cases.
{"title":"Future of IoTSP – IT and OT Integration","authors":"Daniel Lewandowski, Diego Pareschi, Waldemar Pakos, E. Ragaini","doi":"10.1109/FiCloud.2018.00037","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00037","url":null,"abstract":"The Future of Internet of Things, Service and People (IoTSP) for the industrial sector lies in integrating two worlds – information technology and operation technology. This article explains in details practical concepts of IT and OT integration with a usage of digitalization approach. As an example an oil and gas business is taken into account. The so-called Digital Oilfield idea is focusing on melding the IT, OT and IoTSP in order to \"learn\" what is working best for O&G production, to predict equipment failures, track employees in the field in order to coach them in real-time and remove from hazardous situations. The ABB Polish Corporate Research Center has developed and built the advanced test rig for ABILITY-powered machines, used as multipurpose experimental rig mainly for applications in the oil, gas and chemicals sectors. The test rig provides additional capabilities in terms of the advanced control methods and condition monitoring techniques design and evaluation for the industrial compression systems. Except the industrial view on the matter, the facility is also used to verify novel and disruptive concepts for device communication and connectivity from sensors and devices up to cloud and it has been used to validate cloud use and business cases.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132307046","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00070
K. Khan, Mahboob Shaheen, Yongge Wang
Cloud computing represents the promise of outsourcing of scientific computing such as matrix multiplication. However, this can introduce new vulnerabilities such as information leakage. Cloud server intentionally or unintentionally may reveal sensitive input matrices of the client as well as multiplication results to unauthorised entities. In this paper, we propose two protocols that use sparse matrices to prevent information leakage in outsourcing matrix multiplications to cloud computing without encryption. The protocols are considered lightweight compared to other comparable approaches. We also provide a running example to demonstrate how the protocols ensure no information leakage of client data.
{"title":"Using Sparse Matrices to Prevent Information Leakage in Cloud Computing","authors":"K. Khan, Mahboob Shaheen, Yongge Wang","doi":"10.1109/FiCloud.2018.00070","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00070","url":null,"abstract":"Cloud computing represents the promise of outsourcing of scientific computing such as matrix multiplication. However, this can introduce new vulnerabilities such as information leakage. Cloud server intentionally or unintentionally may reveal sensitive input matrices of the client as well as multiplication results to unauthorised entities. In this paper, we propose two protocols that use sparse matrices to prevent information leakage in outsourcing matrix multiplications to cloud computing without encryption. The protocols are considered lightweight compared to other comparable approaches. We also provide a running example to demonstrate how the protocols ensure no information leakage of client data.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134107764","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00009
Matteo Nardelli, V. Cardellini, E. Casalicchio
Containers are increasingly adopted, because they simplify the deployment and management of applications. Moreover, the ever increasing presence of IoT devices and Fog computing resources calls for the development of new approaches for decentralizing the application execution, so to improve the application performance. Although several solutions for orchestrating containers exist, the most of them does not efficiently exploit the characteristics of the emerging computing environment. In this paper, we propose Adaptive Container Deployment (ACD), a general model of the deployment and adaptation of containerized applications, expressed as an Integer Linear Programming problem. Besides acquiring and releasing geo-distributed computing resources, ACD can optimize multiple run-time deployment goals, by exploiting horizontal and vertical elasticity of containers. We show the flexibility of the ACD model and, using it as benchmark, we evaluate the behavior of several greedy heuristics for determining the container deployment.
{"title":"Multi-Level Elastic Deployment of Containerized Applications in Geo-Distributed Environments","authors":"Matteo Nardelli, V. Cardellini, E. Casalicchio","doi":"10.1109/FiCloud.2018.00009","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00009","url":null,"abstract":"Containers are increasingly adopted, because they simplify the deployment and management of applications. Moreover, the ever increasing presence of IoT devices and Fog computing resources calls for the development of new approaches for decentralizing the application execution, so to improve the application performance. Although several solutions for orchestrating containers exist, the most of them does not efficiently exploit the characteristics of the emerging computing environment. In this paper, we propose Adaptive Container Deployment (ACD), a general model of the deployment and adaptation of containerized applications, expressed as an Integer Linear Programming problem. Besides acquiring and releasing geo-distributed computing resources, ACD can optimize multiple run-time deployment goals, by exploiting horizontal and vertical elasticity of containers. We show the flexibility of the ACD model and, using it as benchmark, we evaluate the behavior of several greedy heuristics for determining the container deployment.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129488663","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00059
D. Abreu, Karima Velasquez, M. M. Assis, L. Bittencourt, M. Curado, E. Monteiro, E. Madeira
With the advent of the Internet of Things many applications emerged that are not suitable for well-known paradigms like the Cloud, requiring its extension to provide more features to final users. Thus, the Fog rises as an extension to the Cloud able to provide mobility support, geographical distribution, and lower latency, by moving the services closer to the users, to the edge of the network. This new environment located at the edge comes with its own orchestration challenges. Among the orchestration functions that must be adapted to this new environment is scheduling. This paper presents a simple scheduling algorithm for Fog federative environments that organizes Fog instances into divisions for task assignment. Experimental results show that this approach could be particularly beneficial for critical time applications, commonly located at the Fog.
{"title":"A Rank Scheduling Mechanism for Fog Environments","authors":"D. Abreu, Karima Velasquez, M. M. Assis, L. Bittencourt, M. Curado, E. Monteiro, E. Madeira","doi":"10.1109/FiCloud.2018.00059","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00059","url":null,"abstract":"With the advent of the Internet of Things many applications emerged that are not suitable for well-known paradigms like the Cloud, requiring its extension to provide more features to final users. Thus, the Fog rises as an extension to the Cloud able to provide mobility support, geographical distribution, and lower latency, by moving the services closer to the users, to the edge of the network. This new environment located at the edge comes with its own orchestration challenges. Among the orchestration functions that must be adapted to this new environment is scheduling. This paper presents a simple scheduling algorithm for Fog federative environments that organizes Fog instances into divisions for task assignment. Experimental results show that this approach could be particularly beneficial for critical time applications, commonly located at the Fog.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129980328","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 : 2018-08-01DOI: 10.1109/FiCloud.2018.00062
E. Marín-Tordera, X. Masip-Bruin, Beatriz Otero, Eva Rodríguez
The recent advances in fog and edge computing are driving the need to identify what a component at the edge should be. Recognized the wide heterogeneity at the edge, it is with no doubt that different hardware characteristics may be found at the edge devices, thus fueling the need to identify what is the set of resources a potential fog node must have. However, this question is not so easy to answer since some discussions are yet active on what a fog node should be. Beyond the insights about the fog node concept, it looks reasonable and certainly mandatory to overcome the envisioned diversity and heterogeneity at the edge by adopting virtualization as a key strategy to manage resources at the edge. Consequently, in this paper, we analyze possible virtualization strategies, revisit what has been done in cloud computing and discuss potential trends to be deployed in fog computing aligned to the main challenges a tentative fog node is supposed to take over.
{"title":"Virtualizing the Edge: Needs, Opportunities and Trends","authors":"E. Marín-Tordera, X. Masip-Bruin, Beatriz Otero, Eva Rodríguez","doi":"10.1109/FiCloud.2018.00062","DOIUrl":"https://doi.org/10.1109/FiCloud.2018.00062","url":null,"abstract":"The recent advances in fog and edge computing are driving the need to identify what a component at the edge should be. Recognized the wide heterogeneity at the edge, it is with no doubt that different hardware characteristics may be found at the edge devices, thus fueling the need to identify what is the set of resources a potential fog node must have. However, this question is not so easy to answer since some discussions are yet active on what a fog node should be. Beyond the insights about the fog node concept, it looks reasonable and certainly mandatory to overcome the envisioned diversity and heterogeneity at the edge by adopting virtualization as a key strategy to manage resources at the edge. Consequently, in this paper, we analyze possible virtualization strategies, revisit what has been done in cloud computing and discuss potential trends to be deployed in fog computing aligned to the main challenges a tentative fog node is supposed to take over.","PeriodicalId":174838,"journal":{"name":"2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129549331","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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