A. Zamani, I. Petri, J. Montes, O. Rana, M. Parashar
With the increasing availability of Internet of Things (IoT) devices, and potential applications that make use of data from such devices, there is a need to better identify appropriate data processing techniques that can be applied to this data. The computational complexity of these applications, and the complexity of the requirements on the data processing techniques, often derives from the capabilities of current IoT devices and the need to integrate data streams across multiple IoT devices, which result in larger data sizes and loads on the computing infrastructure. Furthermore, due to the dynamics and uncertainties of edge environments, it is essential that these techniques are capable of adapting across a range of computational and data transfer requirements (such as execution performance) and infrastructure scales (processing nodes, storage needs, network requirements) to carry out a particular analysis task, in response to changing requirements and constraints. Approximate computing offers techniques that can simplify the overall analysis workflow, trading off loss in quality and optimality of the solution with time to reach a particular outcome. These techniques have two main advantages: (i) reduced time to execute a particular data analysis; (ii) reduced requirements on the computational infrastructure (i.e., lower energy, computational resource needs, etc) to carry out such analysis. With data processing capabilities available IoT devices and associated gateway nodes, such approximate computing can be achieved at or close to the network edge. In this paper, we propose in-transit and edge-supported approximation techniques, which can undertake partial/approximate data processing at the data generation/capture or aggregation site, prior to delivery to a cloud data center. We also demonstrate how such an approach can be used in practice by applying it to support energy optimization in built environments (utilizing a combination of sensors and cloud-based data analysis). Several approximation techniques that are relevant in this context are presented, and their relevance explored and evaluated in the context of an energy simulation application scenario.
{"title":"Edge-Supported Approximate Analysis for Long Running Computations","authors":"A. Zamani, I. Petri, J. Montes, O. Rana, M. Parashar","doi":"10.1109/FiCloud.2017.24","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.24","url":null,"abstract":"With the increasing availability of Internet of Things (IoT) devices, and potential applications that make use of data from such devices, there is a need to better identify appropriate data processing techniques that can be applied to this data. The computational complexity of these applications, and the complexity of the requirements on the data processing techniques, often derives from the capabilities of current IoT devices and the need to integrate data streams across multiple IoT devices, which result in larger data sizes and loads on the computing infrastructure. Furthermore, due to the dynamics and uncertainties of edge environments, it is essential that these techniques are capable of adapting across a range of computational and data transfer requirements (such as execution performance) and infrastructure scales (processing nodes, storage needs, network requirements) to carry out a particular analysis task, in response to changing requirements and constraints. Approximate computing offers techniques that can simplify the overall analysis workflow, trading off loss in quality and optimality of the solution with time to reach a particular outcome. These techniques have two main advantages: (i) reduced time to execute a particular data analysis; (ii) reduced requirements on the computational infrastructure (i.e., lower energy, computational resource needs, etc) to carry out such analysis. With data processing capabilities available IoT devices and associated gateway nodes, such approximate computing can be achieved at or close to the network edge. In this paper, we propose in-transit and edge-supported approximation techniques, which can undertake partial/approximate data processing at the data generation/capture or aggregation site, prior to delivery to a cloud data center. We also demonstrate how such an approach can be used in practice by applying it to support energy optimization in built environments (utilizing a combination of sensors and cloud-based data analysis). Several approximation techniques that are relevant in this context are presented, and their relevance explored and evaluated in the context of an energy simulation application scenario.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116390431","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}
Francois Aissaoui, G. Cooperman, T. Monteil, S. Tazi
The Internet of Things (IoT) continues to expand in terms of the number of connected devices. To handle the data produced by those devices, gateways are deployed to collect data, possibly to analyze it, and finally to send it to the cloud or to the end-user to support new services. This process involves complex software that is deployed on those gateways. Moreover, the dynamicity due to new services, mobility, etc., could be corrupted by new events that then require the deployment of software components on additional equipment. Those new events arise in at least two fundamental ways: devices that may change their geographical location; and limitations due to hardware resources and energy consumption. We propose to use autonomic monitoring and control in response to a changing environment in order to manage deployed software with little or no human intervention. A new generic approach is described, based on a semantic model of the system being monitored. Much of the power of the proposed approach is accomplished through a novel use of checkpointing in order to control the software deployed on the gateway
{"title":"Intelligent Checkpointing Strategies for IoT System Management","authors":"Francois Aissaoui, G. Cooperman, T. Monteil, S. Tazi","doi":"10.1109/FiCloud.2017.34","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.34","url":null,"abstract":"The Internet of Things (IoT) continues to expand in terms of the number of connected devices. To handle the data produced by those devices, gateways are deployed to collect data, possibly to analyze it, and finally to send it to the cloud or to the end-user to support new services. This process involves complex software that is deployed on those gateways. Moreover, the dynamicity due to new services, mobility, etc., could be corrupted by new events that then require the deployment of software components on additional equipment. Those new events arise in at least two fundamental ways: devices that may change their geographical location; and limitations due to hardware resources and energy consumption. We propose to use autonomic monitoring and control in response to a changing environment in order to manage deployed software with little or no human intervention. A new generic approach is described, based on a semantic model of the system being monitored. Much of the power of the proposed approach is accomplished through a novel use of checkpointing in order to control the software deployed on the gateway","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124509155","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}
Mohamed Abderrahim, M. Ouzzif, K. Guillouard, J. François, A. Lèbre
Although academic and industry experts are now advocating for going from large-centralized Cloud Computing infrastructures to smaller ones massively distributed at the edge of the network, management systems to operate and use such infrastructures are still missing. In this paper, we focus on the monitoring service which is a key element to any management system in charge of operating a distributed infrastructure. Several solutions have been proposed in the past for cluster, grid and cloud systems. However, none is well appropriate to the Fog/Edge context. Our goal in this study, is to pave the way towards a holistic monitoring service for a Fog/Edge infrastructure hosting next generation digital services. The contributions of our work are: (i) the problem statement, (ii) a classification and a qualitative analysis of major existing solutions, and (iii) a preliminary discussion on the impact of the deployment strategy of functions composing the monitoring service.
{"title":"A Holistic Monitoring Service for Fog/Edge Infrastructures: A Foresight Study","authors":"Mohamed Abderrahim, M. Ouzzif, K. Guillouard, J. François, A. Lèbre","doi":"10.1109/FiCloud.2017.30","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.30","url":null,"abstract":"Although academic and industry experts are now advocating for going from large-centralized Cloud Computing infrastructures to smaller ones massively distributed at the edge of the network, management systems to operate and use such infrastructures are still missing. In this paper, we focus on the monitoring service which is a key element to any management system in charge of operating a distributed infrastructure. Several solutions have been proposed in the past for cluster, grid and cloud systems. However, none is well appropriate to the Fog/Edge context. Our goal in this study, is to pave the way towards a holistic monitoring service for a Fog/Edge infrastructure hosting next generation digital services. The contributions of our work are: (i) the problem statement, (ii) a classification and a qualitative analysis of major existing solutions, and (iii) a preliminary discussion on the impact of the deployment strategy of functions composing the monitoring service.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121687786","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}
Emerging applications for the Internet of Things (IoT) are complex programs which are composed of multiple modules (or services). For scalability, reliability and performance, modular applications are distributed on infrastructures that support utility computing (e.g., Cloud, Fog). In order to simply operate such infrastructures, an Infrastructure-as-a-Service (IaaS) manager is required. OpenStack is the de-facto open-source solution to address the IaaS level of the Cloud paradigm. However, OpenStack is itself a large modular application composed of more than 150 modules that make it hard to deploy manually. To fully understand how IaaSes are deployed today, we propose in this paper an overall model of the application deployment process which describes each step with their interactions. This model then serves as the basis to analyse five different deployment tools used to deploy OpenStack in production: Kolla, Enos, Juju, Kubernetes, and TripleO. Finally, a comparison is provided and the results are discussed to extend this analysis.
{"title":"Production Deployment Tools for IaaSes: An Overall Model and Survey","authors":"Hélène Coullon, Christian Pérez, Dimitri Pertin","doi":"10.1109/FiCloud.2017.51","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.51","url":null,"abstract":"Emerging applications for the Internet of Things (IoT) are complex programs which are composed of multiple modules (or services). For scalability, reliability and performance, modular applications are distributed on infrastructures that support utility computing (e.g., Cloud, Fog). In order to simply operate such infrastructures, an Infrastructure-as-a-Service (IaaS) manager is required. OpenStack is the de-facto open-source solution to address the IaaS level of the Cloud paradigm. However, OpenStack is itself a large modular application composed of more than 150 modules that make it hard to deploy manually. To fully understand how IaaSes are deployed today, we propose in this paper an overall model of the application deployment process which describes each step with their interactions. This model then serves as the basis to analyse five different deployment tools used to deploy OpenStack in production: Kolla, Enos, Juju, Kubernetes, and TripleO. Finally, a comparison is provided and the results are discussed to extend this analysis.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131992282","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}
K. Kritikos, Chrysostomos Zeginis, F. Griesinger, Daniel Seybold, Jörg Domaschka
The notion of a BPaaS is currently taking a momentum as many organisations attempt to move and offer their business processes (BPs) in the cloud. Such BPs need to be adaptively provisioned so as to sustain the service level promised in the respective SLA. However, current cloud-based adaptation frameworks cannot cover all possible abstraction levels and usually rely on simplistic adaptation rules. As such, this paper proposes a novel BPaaS adaptation framework able to orchestrate actions on different abstraction levels so as to better address the current problematic situation. This framework can support the dynamic generation of adaptation workflows as well as the recording of the adaptation history for analysis purposes. It is also coupled with the CAMEL language which has been extended to support the specification of cross-level adaptation workflows.
{"title":"A Cross-Layer BPaaS Adaptation Framework","authors":"K. Kritikos, Chrysostomos Zeginis, F. Griesinger, Daniel Seybold, Jörg Domaschka","doi":"10.1109/FiCloud.2017.12","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.12","url":null,"abstract":"The notion of a BPaaS is currently taking a momentum as many organisations attempt to move and offer their business processes (BPs) in the cloud. Such BPs need to be adaptively provisioned so as to sustain the service level promised in the respective SLA. However, current cloud-based adaptation frameworks cannot cover all possible abstraction levels and usually rely on simplistic adaptation rules. As such, this paper proposes a novel BPaaS adaptation framework able to orchestrate actions on different abstraction levels so as to better address the current problematic situation. This framework can support the dynamic generation of adaptation workflows as well as the recording of the adaptation history for analysis purposes. It is also coupled with the CAMEL language which has been extended to support the specification of cross-level adaptation workflows.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809429","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}
Fatma Mohamed, R. Mizouni, Mohammad Abu-Matar, M. Al-Qutayri, J. Whittle
Software-as-a-Service (SaaS) has recently been adopted by many organizations to get their work done through subscription-based services. To leverage economies of scale, software and hardware resources are shared among multiple tenants who have different requirements that rapidly change with time. Responding to tenants' diverse needs requires SaaS providers to carefully manage software variability so that every tenant feels like having a distinct instance of the application. Tenants' evolvable needs require the SaaS instance to dynamically adapt. This paper presents an integrated platform that facilitates the dynamic adaptation of Multi-Tenant Single Instance SaaS applications and supports runtime tenants' configurations customization. The proposed platform is based on three concepts: Service-Orientation, Software Product Lines (SPLs), and Model Driven Architecture (MDA). The proposed solution spans over two dimensions: Feature-level variability management and runtime variability management. We propose raising the level of abstraction in which the whole adaptation process is addressed to better manage customization. The feasibility of the approach is illustrated via a functioning prototype. A realistic SaaS application was used to exercise the different adaptation scenarios and evaluate the platform prototype implementation.
{"title":"An Integrated Platform for Dynamic Adaptation of Multi-tenant Single Instance SaaS Applications","authors":"Fatma Mohamed, R. Mizouni, Mohammad Abu-Matar, M. Al-Qutayri, J. Whittle","doi":"10.1109/FiCloud.2017.39","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.39","url":null,"abstract":"Software-as-a-Service (SaaS) has recently been adopted by many organizations to get their work done through subscription-based services. To leverage economies of scale, software and hardware resources are shared among multiple tenants who have different requirements that rapidly change with time. Responding to tenants' diverse needs requires SaaS providers to carefully manage software variability so that every tenant feels like having a distinct instance of the application. Tenants' evolvable needs require the SaaS instance to dynamically adapt. This paper presents an integrated platform that facilitates the dynamic adaptation of Multi-Tenant Single Instance SaaS applications and supports runtime tenants' configurations customization. The proposed platform is based on three concepts: Service-Orientation, Software Product Lines (SPLs), and Model Driven Architecture (MDA). The proposed solution spans over two dimensions: Feature-level variability management and runtime variability management. We propose raising the level of abstraction in which the whole adaptation process is addressed to better manage customization. The feasibility of the approach is illustrated via a functioning prototype. A realistic SaaS application was used to exercise the different adaptation scenarios and evaluate the platform prototype implementation.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114347162","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}
Disasters, both natural and man-made, can occur at any time or place in the world; aid is then required to support the victims of the disaster and to provide humanitarian support within the disaster zone. The first 24-48 hours after a disaster are a critical time for first responders in administering aid to the victims. This is known as the golden 24 hours [1] where 85% to 95% of live rescues are made. To support this effort, a rapidly deployable, scalable, and low cost communication infrastructure is required. This paper proposes the use of low-cost Single Board Computers, in combination with scalable containerised network services (e.g. VOIP, Web Services, etc.), utilising Software Defined Network based control to allow the centralised management of devices. A Raspberry Pi based prototype setup is detailed, and initial performance tests are presented as a means of confirming the technological viability of the concept under a number of different topology configurations.
{"title":"A Raspberry Pi Based Scalable Software Defined Network Infrastructure for Disaster Relief Communication","authors":"Ron Austin, P. Bull, S. Buffery","doi":"10.1109/FiCloud.2017.25","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.25","url":null,"abstract":"Disasters, both natural and man-made, can occur at any time or place in the world; aid is then required to support the victims of the disaster and to provide humanitarian support within the disaster zone. The first 24-48 hours after a disaster are a critical time for first responders in administering aid to the victims. This is known as the golden 24 hours [1] where 85% to 95% of live rescues are made. To support this effort, a rapidly deployable, scalable, and low cost communication infrastructure is required. This paper proposes the use of low-cost Single Board Computers, in combination with scalable containerised network services (e.g. VOIP, Web Services, etc.), utilising Software Defined Network based control to allow the centralised management of devices. A Raspberry Pi based prototype setup is detailed, and initial performance tests are presented as a means of confirming the technological viability of the concept under a number of different topology configurations.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133085788","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}
Nadir Cherifi, T. Vantroys, A. Boé, Colombe Hérault, G. Grimaud
Surrounding autonomous embedded devices are in a constant expansion. The advent and the rise of Internet of Things (IoT) enable these objects to take a giant step forward, especially regarding their large scale deployment in real-world applications of the everyday life. A significant part of these objects are battery-powered and energy-dependent. Thus, energy is a critical resource which greatly complicates the development of the embedded software. By decomposing the energy consumption of a battery-powered IoT device, we can see that peripheral components are the major contributors among the overall consumption. Indeed, these components are exploited and repeatedly used by the object to interact and communicate with its surrounding environment during all the application lifetime. Acquire the expertise to handle accurately, during the development stage, the behaviour of every on-board peripheral component is a big challenge to improve the development of IoT embedded applications. To guide the developer in this task, we propose an automated inference procedure of energy models for peripheral components. An accurate automata-based model of the energy consumption can be generated, with only little efforts from the developer, based on real runtime measurements, providing precise energy figures. The proposed process is focused on a lightweight code generation step and simple analyses of the energy output traces, allowing a quick regeneration of the models in the case of a peripheral component modification. We show the potentials of the proposed procedure by real experiments on real peripherals. The obtained results are satisfactory, and we believe that our proposition is able to enhance the embedded development in an energy-constrained environment.
{"title":"Automatic Inference of Energy Models for Peripheral Components in Embedded Systems","authors":"Nadir Cherifi, T. Vantroys, A. Boé, Colombe Hérault, G. Grimaud","doi":"10.1109/FiCloud.2017.53","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.53","url":null,"abstract":"Surrounding autonomous embedded devices are in a constant expansion. The advent and the rise of Internet of Things (IoT) enable these objects to take a giant step forward, especially regarding their large scale deployment in real-world applications of the everyday life. A significant part of these objects are battery-powered and energy-dependent. Thus, energy is a critical resource which greatly complicates the development of the embedded software. By decomposing the energy consumption of a battery-powered IoT device, we can see that peripheral components are the major contributors among the overall consumption. Indeed, these components are exploited and repeatedly used by the object to interact and communicate with its surrounding environment during all the application lifetime. Acquire the expertise to handle accurately, during the development stage, the behaviour of every on-board peripheral component is a big challenge to improve the development of IoT embedded applications. To guide the developer in this task, we propose an automated inference procedure of energy models for peripheral components. An accurate automata-based model of the energy consumption can be generated, with only little efforts from the developer, based on real runtime measurements, providing precise energy figures. The proposed process is focused on a lightweight code generation step and simple analyses of the energy output traces, allowing a quick regeneration of the models in the case of a peripheral component modification. We show the potentials of the proposed procedure by real experiments on real peripherals. The obtained results are satisfactory, and we believe that our proposition is able to enhance the embedded development in an energy-constrained environment.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114573030","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}
The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area. With this in mind we propose a tailored generative stream model, with two main uses: stream similarity and generation. Sensor data can be organized based on pattern similarity, that can be estimated using the proposed model. The proposed stream model will be used in conjunction with our context organization model, in which we aim to provide an automatic organizational model without enforcing specific representations. Moreover, the model can be used to generate streams in a controlled environment. Useful for validating, evaluating and testing any platform that deals with IoT/M2M devices.
{"title":"Improve IoT/M2M Data Organization Based on Stream Patterns","authors":"M. Antunes, Ricardo Jesus, D. Gomes, R. Aguiar","doi":"10.1109/FiCloud.2017.33","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.33","url":null,"abstract":"The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area. With this in mind we propose a tailored generative stream model, with two main uses: stream similarity and generation. Sensor data can be organized based on pattern similarity, that can be estimated using the proposed model. The proposed stream model will be used in conjunction with our context organization model, in which we aim to provide an automatic organizational model without enforcing specific representations. Moreover, the model can be used to generate streams in a controlled environment. Useful for validating, evaluating and testing any platform that deals with IoT/M2M devices.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123308566","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}
Cloud federation allows interconnected Cloud Computing environments of different Cloud Service Providers (CSPs) to share their resources and deliver more efficient service performance. However, each CSP provides a different level of security in terms of cost and performance. Instead of consuming the whole set of Cloud services that are required to deploy an application through a single CSP, consumers could benefit from the Cloud federation and flexibly assign the services to multiple CSPs in order to satisfy all their services' security requirements. In this paper, we model the service assignment problem in federated Cloud environments as a Multi-objective optimization problem based on security. The model allows consumers to consider a trade-off between three security factors: cost, performance, and risk, when assigning their services to CSPs. The cost and performance of the delivered security services are evaluated using a set of quantitative metrics which we propose. We then solve the problem using the preemptive optimization method which permits to take into consideration the customer's priorities. Simulations showed that the model helps in reducing the rate of security and performance violations.
{"title":"Service Assignment in Federated Cloud Environments Based on Multi-objective Optimization of Security","authors":"Talal Halabi, Martine Bellaïche","doi":"10.1109/FiCloud.2017.21","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.21","url":null,"abstract":"Cloud federation allows interconnected Cloud Computing environments of different Cloud Service Providers (CSPs) to share their resources and deliver more efficient service performance. However, each CSP provides a different level of security in terms of cost and performance. Instead of consuming the whole set of Cloud services that are required to deploy an application through a single CSP, consumers could benefit from the Cloud federation and flexibly assign the services to multiple CSPs in order to satisfy all their services' security requirements. In this paper, we model the service assignment problem in federated Cloud environments as a Multi-objective optimization problem based on security. The model allows consumers to consider a trade-off between three security factors: cost, performance, and risk, when assigning their services to CSPs. The cost and performance of the delivered security services are evaluated using a set of quantitative metrics which we propose. We then solve the problem using the preemptive optimization method which permits to take into consideration the customer's priorities. Simulations showed that the model helps in reducing the rate of security and performance violations.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133691170","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：