10 research outputs found

    An Application of Kubernetes Cluster Federation in Fog Computing

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    This demonstration aims at showcasing an application of a cluster federation to increase the elasticity and resilience of a Fog Computing system. Federation is performed by means of the Kubernetes Cluster Federation (KubeFed), a framework we augmented with a two-phase workload placement mechanism that smartly distributes applications' microservices among the federated infrastructure. Despite KubeFed has been generally used in a multi-cloud environment for workloads split on different cloud providers avoiding the lock-in, in this demonstration we show that it can also be used for implementing a decentralized control plane in a highly distributed architecture where networking issues should be taken into account

    Towards Application-Aware Provisioning of Security Services with Kubernetes

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    In network security, Network Function Virtualization can be exploited to implement flexible security services tailored to specific user needs. However, in practice this is hard to achieve due to the limitations of reference software platforms, such as Kubernetes, which are designed to orchestrate cloud-native services. In this work, we complement Kubernetes with a state-of-the-art algorithm for application-aware provisioning of security services. We demonstrate that the proposed solution improves basic provisioning mechanisms, such as the default Kubernetes scheduler, in terms of Quality of Service and security guarantees for the users

    Cutting Throughput with the Edge: App-Aware Placement in Fog Computing

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    Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects' data access is attained by displacing workloads from the central cloud to edge devices. Doing so, it reduces raw data transfers from target objects to the central cloud, thus overcoming communication bottlenecks. This is a key step towards the pervasive uptake of next generation IoT-based services. In this work we study efficient orchestration of applications in fog computing, where a fog application is the cascade of a cloud module and a fog module. The problem results into a mixed integer non linear optimisation. It involves multiple constraints due to computation and communication demands of fog applications, available infrastructure resources and it accounts also the location of target IoT objects. We show that it is possible to reduce the complexity of the original problem with a related placement formulation, which is further solved using a greedy algorithm. This algorithm is the core placement logic of FogAtlas, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed algorithm, showing performance close to the optimal solution with respect to the number of served applications

    Foggy: a platform for workload orchestration in a Fog Computing environment

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    International audienceIn this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems. Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be distributed and located close to data sources and users following the Fog Computing paradigm. Foggy provides a platform for infrastructure owners and tenants (i.e., application providers) offering functionality of negotiation, scheduling and workload placement taking into account traditional requirements (e.g. based on RAM, CPU, disk) and non-traditional ones (e.g. based on networking) as well as diversified constraints on location and access rights. Economics and pricing of resources can also be considered by the Foggy model in a near future. The ability of Foggy to find a trade-off between infrastructure owners' and tenants' needs, in terms of efficient and optimized use of the infrastructure while satisfying the application requirements, is demonstrated through three use cases in the video surveillance and vehicle tracking contexts

    Foggy: A Platform for Workload Orchestration in a Fog Computing Environment

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    In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems. Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be distributed and located close to data sources and users following the Fog Computing paradigm. Foggy provides a platform for infrastructure owners and tenants (i.e., application providers) offering functionality of negotiation, scheduling and workload placement taking into account traditional requirements (e.g. based on RAM, CPU, disk) and non-traditional ones (e.g. based on networking) as well as diversified constraints on location and access rights. Economics and pricing of resources can also be considered by the Foggy model in a near future. The ability of Foggy to find a trade-off between infrastructure owners' and tenants' needs, in terms of efficient and optimized use of the infrastructure while satisfying the application requirements, is demonstrated through three use cases in the video surveillance and vehicle tracking contexts

    Throughput-aware Partitioning and Placement of Applications in Fog Computing

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    Fog computing promises to extend cloud computing to match emerging demands for low latency, location-awareness and dynamic computation. It thus brings data processing close to the edge of the network by leveraging on devices with different computational characteristics. However, the heterogeneity, the geographical distribution, and the data-intensive profiles of IoT deployments render the placement of fog applications a fundamental problem to guarantee target performance figures. This is a core challenge for fog computing providers to offer fog infrastructure as a service, while satisfying the requirements of this new class of microservices-based applications. In this article we root our analysis on the throughput requirements of the applications while exploiting offloading towards different regions. The resulting resource allocation problem is developed for a fog-native application architecture based on containerised microservice modules. An algorithmic solution is designed to optimise the placement of applications modules either in cloud or in fog. Finally, the overall solution consists of two cascaded algorithms. The first one performs a throughput-oriented partitioning of fog application modules. The second one rules the orchestration of applications over a region-based infrastructure. Extensive numerical experiments validate the performance of the overall scheme and confirm that it outperforms state-of-the-art solutions adapted to our context

    A Blockchain-based Brokerage Platform for Fog Computing Resource Federation

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    This demonstration aims at showcasing an initial version of the DECENTER Brokerage Platform, which leverages an Ethereum blockchain to enable resource federation among different Fog Computing infrastructures. We consider a scenario where an Italian Infrastructure Provider wants to seamlessly extend its pool of resources to get access to an IP camera located in Korea, so that it can deploy an application to locally perform text recognition from a live video stream
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