ABSTRACT Background Federated learning on the edge allows the use of more computing capacity and more complex training models while providing higher accuracy and bandwidth savings. However, the deployment and management of edge cloud infrastructures is still challenging due to its highly distributed nature. Aims This paper presents the deployment of a real framework for federated learning on top of a real geo‐distributed edge cloud infrastructure. Methods The OpenNebula cloud platform is used to deploy an edge cloud infrastructure and a federated learning framework on top of it, as well as to manage and orchestrate the infrastructure. Results A global edge‐based infrastructure was deployed in 16 min for just $6 per hour, reducing execution time and increasing the accuracy of the trained model. Conclusion Results show the feasibility, performance, and cost efficiency of the solution, which provides an easy, fast, and inexpensive way to perform edge‐based federated learning.

Telecom companies and infrastructure providers are planning to deploy thousands of processing nodes at the edge of their networks to provide end users and companies with low-latency edge services for IoT, video streaming, gaming, and mobile applications. However, the efficient management of these massively distributed edge computing environments is still an open issue. In this article, we present a disaggregated cloud management model that would allow companies and institutions to deploy their own edge computing infrastructures and services by combining their on-premises cloud infrastructure (if applicable) with multiple highly-dispersed edge nodes leased from third-party providers. We compare this novel disaggregated model, with the more typical distributed one. Results show that the proposed model is suitable for managing small-to medium-sized edge computing infrastructures.

Summary The number of providers in the cloud computing market is increasing at a rapid pace. They offer a wide range of pricing schemes, different types of instance, or even different value‐added features to differ from other competitors, making the cloud market more complex. Cloud brokering enable users to choose the best cloud provider for their needs and avoid particular providers' lock in. Moreover, the use of multiple clouds offers several benefits such as scalability of services, improvement in fault tolerance, or cost reduction. However, users still find difficult the decision of where to deploy their resources as they have to handle too much information. The use of cloud brokering mechanisms is useful to reduce the complexity of using a multi‐cloud environment. In this paper, we show a cloud broker architecture for deploying virtualized servers across available clouds. This architecture uses a scheduling module to obtain an optimal placement of a virtual infrastructure while making it transparent for users. We focus our investigation on dynamic cloud scenarios in terms of variable resource prices, taking into account the virtual machine migration overhead issue. We evaluate the performance of several use cases applied in real scenarios, and we show the improvement potential of using brokering mechanisms in dynamic deployments compared with static ones. Copyright © 2012 John Wiley & Sons, Ltd.

Nowadays, Clouds are used to host a large range of services. But between different Cloud Service Providers, the pricing model and the price of individual resources can be very different. Furthermore hosting a service in one Cloud is the major cause of service outage. To increase resiliency and minimize the monetary cost of running a service, it becomes mandatory to span it between different Clouds. Moreover, due to dynamicity of both the service and Clouds, it could be required to migrate a service at run time. Accordingly, this ability must be integrated into the multi-Cloud resource manager, i.e. the Cloud broker. But, when migrating a VM to a new Cloud Service Provider, the VM disk image has to be migrated too. Accordingly, data storage and transfer must be taken into account when choosing if and where an application will be migrated. In this paper, we extend a cost-optimization algorithm to take into account storage costs to approximate the optimal placement of a service. The data storage management consists in taking two decisions: the location of the upload of an image, and keep it on-line during the experiment lifetime or delete it when unused. Based on our experimentations, we show that the storage cost of VM disk image must not be neglected as it was done in previous works. Moreover, we show that using the accurate combinations of storage policies can dramatically reduce the storage cost (from 90% to 14% of the total bill).

Abstract Cluster‐based solutions are being widely adopted for implementing flexible, scalable, low‐cost and high‐performance web server platforms. One of the main difficulties to implement these platforms is the correct dimensioning of the cluster size, so as to satisfy variable and peak demand periods. In this context, virtualization is being adopted by many organizations as a solution not only to provide service elasticity, but also to consolidate server workloads, and improve server utilization rates. A virtualized web server can be dynamically adapted to the client demands by deploying new virtual nodes when the demand increases, and powering off and consolidating virtual nodes during periods of low demand. Furthermore, the resources from the in‐house infrastructure can be complemented with a cloud provider (cloud bursting), so that peak demand periods can be satisfied by deploying cluster nodes in the external cloud, on an on‐demand basis. In this paper, we analyze the scalability of hybrid virtual infrastructures for two different distributed web server cluster implementations: a simple web cluster serving static files and a multi‐tier web server platform running the CloudStone benchmark. Copyright © 2011 John Wiley & Sons, Ltd.

Cloud computing is gaining acceptance in many IT organizations, as an elastic, flexible, and variable-cost way to deploy their service platforms using outsourced resources. Unlike traditional utilities where a single provider scheme is a common practice, the ubiquitous access to cloud resources easily enables the simultaneous use of different clouds. In this paper, we explore this scenario to deploy a computing cluster on the top of a multicloud infrastructure, for solving loosely coupled Many-Task Computing (MTC) applications. In this way, the cluster nodes can be provisioned with resources from different clouds to improve the cost effectiveness of the deployment, or to implement high-availability strategies. We prove the viability of this kind of solutions by evaluating the scalability, performance, and cost of different configurations of a Sun Grid Engine cluster, deployed on a multicloud infrastructure spanning a local data center and three different cloud sites: Amazon EC2 Europe, Amazon EC2 US, and ElasticHosts. Although the testbed deployed in this work is limited to a reduced number of computing resources (due to hardware and budget limitations), we have complemented our analysis with a simulated infrastructure model, which includes a larger number of resources, and runs larger problem sizes. Data obtained by simulation show that performance and cost results can be extrapolated to large-scale problems and cluster infrastructures.

Resource and service discovery, data transfer or replica management, are some examples of basic operations of a grid which can gain efficiency by using multicast transmission techniques. However, the sparse deployment of IP multicast is the internet today is a major limitation for exploiting this communication paradigm in grid environments. Over the last few years, Application Layer Multicast has emerged as a novel and powerful technique for providing one-to-many and many-to-many data transfer over networks without infrastructure support for IP multicast. In this approach multicast functionality is implemented at the end-host systems instead of network routers, and it is based on the construction of a logical overlay over the underlying network topology. Application layer multicast techniques have been used mainly for real-time multimedia applications (such as Internet telephony, video streaming services, videoconferencing, etc.), and P2P applications (like data delivery and dissemination, etc.). However, the application of these techniques to grid environments has not been deeply analyzed.