345,283 research outputs found
A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System
We introduce a game-theoretic framework to ex- plore revenue sharing in an
Edge-Cloud computing system, in which computing service providers at the edge
of the Internet (edge providers) and computing service providers at the cloud
(cloud providers) co-exist and collectively provide computing resources to
clients (e.g., end users or applications) at the edge. Different from
traditional cloud computing, the providers in an Edge-Cloud system are
independent and self-interested. To achieve high system-level efficiency, the
manager of the system adopts a task distribution mechanism to maximize the
total revenue received from clients and also adopts a revenue sharing mechanism
to split the received revenue among computing servers (and hence service
providers). Under those system-level mechanisms, service providers attempt to
game with the system in order to maximize their own utilities, by strategically
allocating their resources (e.g., computing servers).
Our framework models the competition among the providers in an Edge-Cloud
system as a non-cooperative game. Our simulations and experiments on an
emulation system have shown the existence of Nash equilibrium in such a game.
We find that revenue sharing mechanisms have a significant impact on the
system-level efficiency at Nash equilibria, and surprisingly the revenue
sharing mechanism based directly on actual contributions can result in
significantly worse system efficiency than Shapley value sharing mechanism and
Ortmann proportional sharing mechanism. Our framework provides an effective
economics approach to understanding and designing efficient Edge-Cloud
computing systems
Edge-as-a-Service: Towards Distributed Cloud Architectures
We present an Edge-as-a-Service (EaaS) platform for realising distributed
cloud architectures and integrating the edge of the network in the computing
ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery
protocol that identifies edge nodes and make them publicly accessible in a
computing environment, and (ii) a scalable resource provisioning mechanism for
offloading workloads from the cloud on to the edge for servicing multiple user
requests. We validate the feasibility of EaaS on an online game use-case to
highlight the improvement in the QoS of the application hosted on our
cloud-edge platform. On this platform we demonstrate (i) low overheads of less
than 6%, (ii) reduced data traffic to the cloud by up to 95% and (iii)
minimised application latency between 40%-60%.Comment: 10 pages; presented at the EdgeComp Symposium 2017; will appear in
Proceedings of the International Conference on Parallel Computing, 201
Combining edge and cloud computing for mobility analytics
Mobility analytics using data generated from the Internet of Mobile Things
(IoMT) is facing many challenges which range from the ingestion of data streams
coming from a vast number of fog nodes and IoMT devices to avoiding overflowing
the cloud with useless massive data streams that can trigger bottlenecks [1].
Managing data flow is becoming an important part of the IoMT because it will
dictate in which platform analytical tasks should run in the future. Data flows
are usually a sequence of out-of-order tuples with a high data input rate, and
mobility analytics requires a real-time flow of data in both directions, from
the edge to the cloud, and vice-versa. Before pulling the data streams to the
cloud, edge data stream processing is needed for detecting missing, broken, and
duplicated tuples in addition to recognize tuples whose arrival time is out of
order. Analytical tasks such as data filtering, data cleaning and low-level
data contextualization can be executed at the edge of a network. In contrast,
more complex analytical tasks such as graph processing can be deployed in the
cloud, and the results of ad-hoc queries and streaming graph analytics can be
pushed to the edge as needed by a user application. Graphs are efficient
representations used in mobility analytics because they unify knowledge about
connectivity, proximity and interaction among moving things. This poster
describes the preliminary results from our experimental prototype developed for
supporting transit systems, in which edge and cloud computing are combined to
process transit data streams forwarded from fog nodes into a cloud. The
motivation of this research is to understand how to perform meaningfulness
mobility analytics on transit feeds by combining cloud and fog computing
architectures in order to improve fleet management, mass transit and remote
asset monitoringComment: Edge Computing, Cloud Computing, Mobility Analytics, Internet of
Mobile Things, Edge Fog Fabri
Gossip-based service monitoring platform for wireless edge cloud computing
Edge cloud computing proposes to support shared services, by using the infrastructure at the network's edge. An important problem is the monitoring and management of services across the edge environment. Therefore, dissemination and gathering of data is not straightforward, differing from the classic cloud infrastructure. In this paper, we consider the environment of community networks for edge cloud computing, in which the monitoring of cloud services is required. We propose a monitoring platform to collect near real-time data about the services offered in the community network using a gossip-enabled network. We analyze and apply this gossip-enabled network to perform service discovery and information sharing, enabling data dissemination among the community. We implemented our solution as a prototype and used it for collecting service monitoring data from the real operational community network cloud, as a feasible deployment of our solution. By means of emulation and simulation we analyze in different scenarios, the behavior of the gossip overlay solution, and obtain average results regarding information propagation and consistency needs, i.e. in high latency situations, data convergence occurs within minutes.Peer ReviewedPostprint (author's final draft
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Experimental determination of layer cloud edge charging from cosmic ray ionisation
The cloud-air transition zone at stratiform cloud edges is an electrically active region where droplet charging has been predicted. Cloud edge droplet charging is expected from vertical flow of cosmic ray generated atmospheric ions in the global electric circuit. Experimental confirmation of stratiform cloud edge electrification is presented here, through charge and droplet measurements made within an extensive layer of supercooled stratiform cloud, using a specially designed electrostatic sensor. Negative space charge up to 35 pC m−3 was found in a thin (<100 m) layer at the lower cloud boundary associated with the clear air-cloud conductivity gradient, agreeing closely with space charge predicted from the measured droplet concentration using ion-aerosol theory. Such charge levels carried by droplets are sufficient to influence collision processes between cloud droplets
ENORM: A Framework For Edge NOde Resource Management
Current computing techniques using the cloud as a centralised server will
become untenable as billions of devices get connected to the Internet. This
raises the need for fog computing, which leverages computing at the edge of the
network on nodes, such as routers, base stations and switches, along with the
cloud. However, to realise fog computing the challenge of managing edge nodes
will need to be addressed. This paper is motivated to address the resource
management challenge. We develop the first framework to manage edge nodes,
namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for
provisioning and auto-scaling edge node resources are proposed. The feasibility
of the framework is demonstrated on a PokeMon Go-like online game use-case. The
benefits of using ENORM are observed by reduced application latency between 20%
- 80% and reduced data transfer and communication frequency between the edge
node and the cloud by up to 95\%. These results highlight the potential of fog
computing for improving the quality of service and experience.Comment: 14 pages; accepted to IEEE Transactions on Services Computing on 12
September 201
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