Abstract:Abstract:The current mobile Cloud computing trend has set the focus on the ubiquity of computation. However, the current architecture confines the cloud to datacenters, which are generally far from the user. Distance leads to increased utilization of the broadband Wide Area Network -WAN -and poor user experience, especially for interactive applications. Decentralized architectures are emerging as an alternative, but they still fail to adapt to situations where files are concurrently modified. A semi-decentrali… Show more
“…From the latency point-ofview, we assume a 100 ms Round-Trip-Time (RTT) between the vehicles and the core cloud. This value is similar to what can be observed for accessing an Amazon Cloud for instance [27]. On the side of the solar power production, we employ a mini-scale solar power farm which is set up in the campus of University Nantes 1 .…”
Abstract-The emergence of Internet of Things (IoT) is participating to the increase of data-and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.
“…From the latency point-ofview, we assume a 100 ms Round-Trip-Time (RTT) between the vehicles and the core cloud. This value is similar to what can be observed for accessing an Amazon Cloud for instance [27]. On the side of the solar power production, we employ a mini-scale solar power farm which is set up in the campus of University Nantes 1 .…”
Abstract-The emergence of Internet of Things (IoT) is participating to the increase of data-and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.
“…From the latency point-of-view, we assume a 100 ms Round-Trip-Time (RTT) between the vehicles and the core Cloud. This value is similar to what can be observed for accessing an Amazon Cloud for instance [54].…”
Section: Setup For the Cloud And Networking Partssupporting
confidence: 87%
“…The energy consumption of the network flows depends also on the number of routers they have to cross. We consider a core Cloud located 10 hops away from the access point for a 100 ms Round-Trip-Time according to values measured in [54] between clients and Amazon Cloud. Finally, we consider a PUE of 1.7 in the access point and the network points-of-presence, which is a typical value for small data centers [55].…”
Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known simulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.
“…We have chosen to study incentives to the use of neighborhood interactive applications deployed on a MC architecture, such as described in [4]. In this scenario multiple users collaborate using applications in a MC restricted to a neighborhood.…”
Section: Scenario: Neighborhood Applications On Mobile Cloudsmentioning
Mobile Cloud Computing is a form of collaborative decentralized Cloud which allows mobile devices to unload computation to a local Cloud formed by mobile and static devices. Mobile Cloud Computing provides a better service to latency sensitive applications, due to its physical proximity to the VM host. However, in these systems, the problem of free riding users becomes more acute, for the heterogeneity of devices (from smartphones to private servers) makes the gap of contributed resources much larger. In this work, we analyze the use of incentives for Mobile Clouds, and propose a new auction system adapted to the high dynamism and heterogeneity of these systems. We compare our solution to other existing auctions systems in a Mobile Cloud use case, and show the suitability of our solution.
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