Decentralized Edge Clouds

Chandra, Abhishek; Weissman, Jon; Heintz, Benjamin

doi:10.1109/mic.2013.93

Cited by 75 publications

(35 citation statements)

References 2 publications

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“…We also intend to investigate how the approach behaves in presence of different cloud platforms (e.g., federated clouds [21]), services, and alternative ways of expressing the SLOs. Finally, another possible follow-up work is to extend our approach to decentralized cloud systems to improve the scalability and resistance to dynamism, which may contribute to support new emerging cloud paradigms such as volunteer clouds [26] and edge clouds [7,25].…”

Section: Discussionmentioning

confidence: 99%

OptiSpot: minimizing application deployment cost using spot cloud resources

Dubois

Casale

2016

Cluster Comput

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The spot instance model is a virtual machine pricing scheme in which some resources of cloud providers are offered to the highest bidder. This leads to the formation of a spot price, whose fluctuations can determine customers to be overbid by other users and lose the virtual machine they rented. In this paper we propose OptiSpot, a heuristic to automate application deployment decisions on cloud providers that offer the spot pricing model. In particular, with our approach it is possible to determine: (i) which and how many resources to rent in order to run a cloud application, (ii) how to map the application components to the rented resources, and (iii) what spot price bids to use to minimize the total cost while maintaining an acceptable level of performance. To drive the decision making, our algorithm combines a multi-class queueing network model of the application with a Markov model that describes the stochastic evolution of the spot price and its influence on virtual machine reliability. We show, using a model developed for a real enterprise application and historical traces of the Amazon EC2 spot instance prices, that our heuristic finds low cost solutions that indeed guarantee the required levels of performance. The performance of our heuristic method is compared to that of nonlinear programming and shown to markedly accelerate the finding of low-cost optimal solutions.

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Section: Discussionmentioning

confidence: 99%

OptiSpot: minimizing application deployment cost using spot cloud resources

Dubois

Casale

2016

Cluster Comput

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“…Data processing for the IoT domain, by conducting dataintensive computing on distributed and decentralized clouds at the network edge [9], is another area under active research. To perform such data operations, microclouds formed by edge devices were presented recently [10].…”

Section: Related Workmentioning

confidence: 99%

On Edge Cloud Service Provision with Distributed Home Servers

Khan

Freitag²

2017

2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

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Abstract-Edge computing has been proposed for new types of cloud services, which need computing infrastructure at the network edge. Driven by important use cases from the Internet of Things (IoT) domain, edge cloud computing has also a huge business potential. Edge computing devices are already operational in many industrial and consumer-oriented scenarios. A typical characteristic of these solutions is, however, that the hardware and software platforms in use are proprietary and closed. The interaction among the different vendor platforms, and their extension with third party services, in general, is not well supported. As a consequence, the opportunities for third party providers to build new tailored edge services on these platforms face significant barriers. In this paper we argue to build a collaborative edge cloud deployed on home servers. We position the proposed system and describe its building elements, where we use Docker containers for provisioning the services.

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“…These nodes at the network edge are part of a larger centralized cloud service. They can perform, for instance, initial data processing tasks for an Internet of Things (IoT) application [5]. Thus, we can consider that this model of centralized edge cloud services for processing data from edge sources consists in connecting, through the Internet, lightweight edge computing devices to centralized cloud data centers.…”

Section: A Centralized Edge Computing Architecturesmentioning

confidence: 99%

On edge microclouds to provide local container-based services

Baig

Centelles

Freitag

et al. 2017

2017 Global Information Infrastructure and Networking Symposium (GIIS)

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Abstract-Edge computing has been proposed to enable more user-centric cloud-based services. Nowadays, edge computing is operational in industrial and consumer-oriented scenarios. An important limitation of today's solutions, however, is that the used hardware and software platforms are proprietary and closed, and cannot easily be leveraged to perform other services beyond the specific business case. For instance, the interaction among different edge platforms or service extension by third parties is in general not supported. As a consequence, the opportunity for local stakeholders to provide innovative tailored edge service with these platforms face important barriers. In this paper we present edge microclouds deployed on local servers and an implementation using containers for service provision. We show how the adopted container approach facilitates the users to create and share services at the network edge. With the presented approach, third parties can deploy more tailored and customized services at the network edge, enabling to better fulfill specific local needs and constraints. It is also an opportunity for building cloudbased service provision with a pool of local resource-constraint edge devices.

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Decentralized Edge Clouds

Cited by 75 publications

References 2 publications

OptiSpot: minimizing application deployment cost using spot cloud resources

OptiSpot: minimizing application deployment cost using spot cloud resources

On Edge Cloud Service Provision with Distributed Home Servers

On edge microclouds to provide local container-based services

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