Kazuya Okada, Shigeru Kashihara, Nao Kawanishi, N. Suzuki, K. Sugiyama, Y. Kadobayashi
5G mobile communication networks (5G) are expected to provide a wide range of new emerging services through flexible communication with high data rates and low network latencies. In 5G, Multi-access Edge Computing (MEC) is one of the key technologies contributing to performance improvement for various use cases. MEC servers are deployed at edges of mobile networks, e.g., on a base station, and enable provision of new services to users. Unlike the conventional end-to-end communication, MEC needs to allow flexible communication between a client and a server. In this paper, to feasible the flexible communication, we propose an implementation design of a scalable and stateless local breakout method for MEC. It presents a function of scalable traffic steering between a client and a server. Also, a preliminary evaluation shows the feasibility of our proposed mechanism.
{"title":"GoEdge","authors":"Kazuya Okada, Shigeru Kashihara, Nao Kawanishi, N. Suzuki, K. Sugiyama, Y. Kadobayashi","doi":"10.1145/3229774.3229776","DOIUrl":"https://doi.org/10.1145/3229774.3229776","url":null,"abstract":"5G mobile communication networks (5G) are expected to provide a wide range of new emerging services through flexible communication with high data rates and low network latencies. In 5G, Multi-access Edge Computing (MEC) is one of the key technologies contributing to performance improvement for various use cases. MEC servers are deployed at edges of mobile networks, e.g., on a base station, and enable provision of new services to users. Unlike the conventional end-to-end communication, MEC needs to allow flexible communication between a client and a server. In this paper, to feasible the flexible communication, we propose an implementation design of a scalable and stateless local breakout method for MEC. It presents a function of scalable traffic steering between a client and a server. Also, a preliminary evaluation shows the feasibility of our proposed mechanism.","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114166138","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}
Ahmed Salim Alrawahi, Kevin Lee, Jon Robinson, Ahmad Lotfi
Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT). CoT is constrained by the limited computing capabilities of IoT resources and the costly investment required to deploy IoT infrastructure. Despite the support of existing CoT implementations to various applications, IoT physical resources are still computationally limited and cannot to be shared as other Cloud resources yet. This paper proposes a new approach to improve shared access to IoT resources. The new approach relies on optimising resource trading of IoT resources to enable exclusive access to allocated resources at a given time. A generic architecture is proposed to support the proposed approach along with notations required to commoditise IoT resources. A case study of multiple application uses is presented. Simulations are carried out to evaluate the feasibility of the approach using three optimisation techniques. The evaluation of the proposed approach includes optimising the cost of resource allocation, different QoS metrics and the coverage of IoT resources.
{"title":"Enabling Exclusive Shared Access to Cloud of Things Resources","authors":"Ahmed Salim Alrawahi, Kevin Lee, Jon Robinson, Ahmad Lotfi","doi":"10.1145/3229774.3229779","DOIUrl":"https://doi.org/10.1145/3229774.3229779","url":null,"abstract":"Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT). CoT is constrained by the limited computing capabilities of IoT resources and the costly investment required to deploy IoT infrastructure. Despite the support of existing CoT implementations to various applications, IoT physical resources are still computationally limited and cannot to be shared as other Cloud resources yet. This paper proposes a new approach to improve shared access to IoT resources. The new approach relies on optimising resource trading of IoT resources to enable exclusive access to allocated resources at a given time. A generic architecture is proposed to support the proposed approach along with notations required to commoditise IoT resources. A case study of multiple application uses is presented. Simulations are carried out to evaluate the feasibility of the approach using three optimisation techniques. The evaluation of the proposed approach includes optimising the cost of resource allocation, different QoS metrics and the coverage of IoT resources.","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115327903","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}
Network Function Virtualization (NFV) is a promising approach for providing efficient and flexible network functions deployment, which also reduces the cost in the network operations compared to traditional method using dedicated hardware. Multiple virtual network functions can be chained together working as service chain to provide a specific service. When multiple network functions share computing resources, how to efficiently schedule them dynamically is a great challenge. In this paper, we first investigate the issue that a single packet drop occurs at different positions of a service chain may have different impact on the performance. In light of this, a packets dropping cost model is carefully defined based on the NF positions in a service chain. Then, according to linear programming, a Minimizing Dropping Cost (MDC) approach is proposed to optimally schedule vNFs. Simulation results show that the proposed algorithm can reduce the total incurred cost by up to 60% compared to the existing common schedule approach.
网络功能虚拟化(Network Function Virtualization, NFV)是提供高效、灵活的网络功能部署的一种很有前景的方法，与使用专用硬件的传统方法相比，NFV还降低了网络运营成本。可以将多个虚拟网络功能链接在一起，形成服务链，提供特定的服务。当多个网络功能共享计算资源时，如何有效地对计算资源进行动态调度是一个很大的挑战。在本文中，我们首先研究了在服务链的不同位置发生的单个丢包可能对性能产生不同影响的问题。在此基础上，根据业务链中NF的位置，仔细定义了丢包代价模型。然后，根据线性规划理论，提出了一种最小化下降成本(MDC)方法来优化vNFs调度。仿真结果表明，与现有的常用调度方法相比，该算法可将总成本降低60%。
{"title":"A Novel NFV Schedule Optimization Approach with Sensitivity to Packets Dropping Positions","authors":"Chengxiang Li, Lin Cui","doi":"10.1145/3229774.3229778","DOIUrl":"https://doi.org/10.1145/3229774.3229778","url":null,"abstract":"Network Function Virtualization (NFV) is a promising approach for providing efficient and flexible network functions deployment, which also reduces the cost in the network operations compared to traditional method using dedicated hardware. Multiple virtual network functions can be chained together working as service chain to provide a specific service. When multiple network functions share computing resources, how to efficiently schedule them dynamically is a great challenge. In this paper, we first investigate the issue that a single packet drop occurs at different positions of a service chain may have different impact on the performance. In light of this, a packets dropping cost model is carefully defined based on the NF positions in a service chain. Then, according to linear programming, a Minimizing Dropping Cost (MDC) approach is proposed to optimally schedule vNFs. Simulation results show that the proposed algorithm can reduce the total incurred cost by up to 60% compared to the existing common schedule approach.","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"24 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114027707","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}
Internet of Things (IoT) services and applications include Connected Vehicles, Smart Grid, Smart Cities, Health Care and, in general, Wireless Sensors and Actuators Networks. Typically, the scenarios can be captured with a Fog Computing architecture that consists of edge nodes that generate and possibly pre-process (sensor) data, fog nodes that do some processing quickly and do any actuations that may be needed, and cloud nodes that may perform further detailed analysis for long-term and archival purposes. This paradigm enables (i) quicker real time computations and actuations, avoiding the latency involved in communicating with the cloud for them, (ii) reducing the amount of data that is sent to the cloud, thus reducing network bandwidth requirement and delay in data transmission, and (iii) doing this without the need for 24/7 network connectivity to the cloud. However, the storage, compute and network connectivity capabilities of the edge and fog nodes may be limited. Hence the computations need to be distributed carefully among the processing nodes. In this paper, we develop a generic framework for distributing computations to the different nodes in a fog architecture. Our framework is applicable to an arbitrary hierarchy of the nodes, one or more homogeneous or heterogeneous source inputs, and to processing the input batches either individually or combined with other batches by way of merges and splits. It can serve initially as a schema for a given computation and later to optimize executions of instances.
{"title":"Distributing Computations in Fog Architectures","authors":"K. Vidyasankar","doi":"10.1145/3229774.3229775","DOIUrl":"https://doi.org/10.1145/3229774.3229775","url":null,"abstract":"Internet of Things (IoT) services and applications include Connected Vehicles, Smart Grid, Smart Cities, Health Care and, in general, Wireless Sensors and Actuators Networks. Typically, the scenarios can be captured with a Fog Computing architecture that consists of edge nodes that generate and possibly pre-process (sensor) data, fog nodes that do some processing quickly and do any actuations that may be needed, and cloud nodes that may perform further detailed analysis for long-term and archival purposes. This paradigm enables (i) quicker real time computations and actuations, avoiding the latency involved in communicating with the cloud for them, (ii) reducing the amount of data that is sent to the cloud, thus reducing network bandwidth requirement and delay in data transmission, and (iii) doing this without the need for 24/7 network connectivity to the cloud. However, the storage, compute and network connectivity capabilities of the edge and fog nodes may be limited. Hence the computations need to be distributed carefully among the processing nodes. In this paper, we develop a generic framework for distributing computations to the different nodes in a fog architecture. Our framework is applicable to an arbitrary hierarchy of the nodes, one or more homogeneous or heterogeneous source inputs, and to processing the input batches either individually or combined with other batches by way of merges and splits. It can serve initially as a schema for a given computation and later to optimize executions of instances.","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374077","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}
A relay is defined as an electrically controlled device that opens and closes electrical contacts, or activates and deactivates operation of other devices in the same or another electrical circuit. Two types of relay technology are available, mechanical and solid state. A mechanical relay is essentially a combination of an inductor and a switch, where the electromagnetic force of the inductor causes a switch to change position. A solid state relay accomplishes the same function with semiconductor devices changing impedance to effectively activate or deactivate a circuit open or closed. This document is intended to be a general guide to aid the designer in the appropriate selection of a relay for the intended application. Detailed information on the selection and use of relays can be found in MIL-STD-1346.
{"title":"Relays","authors":"C. Scheideler","doi":"10.1145/3229774.3229781","DOIUrl":"https://doi.org/10.1145/3229774.3229781","url":null,"abstract":"A relay is defined as an electrically controlled device that opens and closes electrical contacts, or activates and deactivates operation of other devices in the same or another electrical circuit. Two types of relay technology are available, mechanical and solid state. A mechanical relay is essentially a combination of an inductor and a switch, where the electromagnetic force of the inductor causes a switch to change position. A solid state relay accomplishes the same function with semiconductor devices changing impedance to effectively activate or deactivate a circuit open or closed. This document is intended to be a general guide to aid the designer in the appropriate selection of a relay for the intended application. Detailed information on the selection and use of relays can be found in MIL-STD-1346.","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122021602","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}
We study edge server support for multiple periodic real-time applications located in different clouds. The edge communicates both with sensor devices over wireless sensor networks and with applications over Internet type networks. The edge caches sensor data and can respond to multiple applications with different timing requirements to the data. The purpose of caching is to reduce the number of multiple direct accesses to the sensor since sensor communication is very energy expensive. However, the data will then age in the cache and eventually become stale for some application. A push update method and the concept of age of information is used to schedule data updates to the applications. An aging model for periodic updates is derived. We propose that the scheduling should take into account periodic sensor updates, the differences in the periodic application updates, the aging in the cache and communication variance. By numerical analysis we study the number of deadline misses for two different scheduling policies with respect to different periods.
{"title":"Scheduling at the Edge for Assisting Cloud Real-Time Systems","authors":"Lorenzo Corneo, P. Gunningberg","doi":"10.1145/3229774.3229777","DOIUrl":"https://doi.org/10.1145/3229774.3229777","url":null,"abstract":"We study edge server support for multiple periodic real-time applications located in different clouds. The edge communicates both with sensor devices over wireless sensor networks and with applications over Internet type networks. The edge caches sensor data and can respond to multiple applications with different timing requirements to the data. The purpose of caching is to reduce the number of multiple direct accesses to the sensor since sensor communication is very energy expensive. However, the data will then age in the cache and eventually become stale for some application. A push update method and the concept of age of information is used to schedule data updates to the applications. An aging model for periodic updates is derived. We propose that the scheduling should take into account periodic sensor updates, the differences in the periodic application updates, the aging in the cache and communication variance. By numerical analysis we study the number of deadline misses for two different scheduling policies with respect to different periods.","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"25 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120862383","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}
Respiratory and other close-contact infectious diseases, such as tuberculosis (TB), measles and pneumonia, are major killers in much of the developing world.Mathematical models are essential for understanding how these diseases spread, and for understanding how best to control them. Although central to modelling, few quantitative real-world data on relevant contact patterns are available. Capturing human interactions provides an empirical, quantitative measurement of social interaction patterns to informmathematical models of the spread of close-contact diseases.We have developed various systems to collect human contact/mobility data. The recent emergence ofwireless technology (e.g.mobile phones and sensors) makes it possible to collect real-world data on human proximity. Capturing human interactions with wireless sensors will allow us to understand complex patterns of human activities. For example, in one experiment people will carry tiny wireless sensors that record dynamic information about other devices nearby.
{"title":"Digital Epidemiology and Beyond","authors":"Eiko Yoneki","doi":"10.1145/3229774.3229782","DOIUrl":"https://doi.org/10.1145/3229774.3229782","url":null,"abstract":"Respiratory and other close-contact infectious diseases, such as tuberculosis (TB), measles and pneumonia, are major killers in much of the developing world.Mathematical models are essential for understanding how these diseases spread, and for understanding how best to control them. Although central to modelling, few quantitative real-world data on relevant contact patterns are available. Capturing human interactions provides an empirical, quantitative measurement of social interaction patterns to informmathematical models of the spread of close-contact diseases.We have developed various systems to collect human contact/mobility data. The recent emergence ofwireless technology (e.g.mobile phones and sensors) makes it possible to collect real-world data on human proximity. Capturing human interactions with wireless sensors will allow us to understand complex patterns of human activities. For example, in one experiment people will carry tiny wireless sensors that record dynamic information about other devices nearby.","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132210351","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}
{"title":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","authors":"","doi":"10.1145/3229774","DOIUrl":"https://doi.org/10.1145/3229774","url":null,"abstract":"","PeriodicalId":117201,"journal":{"name":"Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129317635","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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