OpenStack Extension for Fog-Powered Personal Services Deployment

Bruschi, Roberto; Genovese, Giacomo; Lago, Paolo; Lamanna, Guerino; Lombardo, Chiara; Mangialardi, Sergio

doi:10.23919/itc.2017.8065705

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…To deal with the aforementioned issues and make the system more performant and scalable, the C-based Socat tool was modified 7 to meet the requirements of our use case. Specifically, the Socat packet buffer size was increased to avoid packets overwriting, then the rest of the code was adapted accordingly.…”

Section: Resultsmentioning

confidence: 99%

“…Afterward, the Neutron server sends a notification about the port to the DHCP agent to update the lease file. (7) The S4T IoTronic Conductor receives the response for the REST call about the Neutron port creation. Then it (i.e., the Conductor) generates a random port number that will be used afterward for the Socat tunneling.…”

Section: Workflow Of Attaching An Iot Node To a Virtual Networkmentioning

confidence: 99%

“…The approach presented aims at virtualizing instances using Virtual Objects (VOs) created on the provider infrastructure; then overlay networks get instantiated among the VOs by exploiting in-network programmability capabilities. In Reference [7], an OpenStack extension, based on the previously cited platform, is introduced for Fog-powered personal service deployment. The integration is realized at the highest layer of the architecture, named the data collection and configuration layer, where OpenStack is exploited only to describe the requirements of the services.…”

Section: Related Workmentioning

confidence: 99%

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Cloud-based Network Virtualization in IoT with OpenStack

Benomar

Longo

Merlino

et al. 2021

ACM Trans. Internet Technol.

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In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

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Section: Resultsmentioning

confidence: 99%

Section: Workflow Of Attaching An Iot Node To a Virtual Networkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cloud-based Network Virtualization in IoT with OpenStack

Benomar

Longo

Merlino

et al. 2021

ACM Trans. Internet Technol.

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“…While MCO has multiple possible applications (e.g., massscale services), we evaluate its performance by considering the INPUT use case of "PN-as-a-service" (PNaaS) [49] [50]. Particularly, a PN is a special type of VTN that interfaces the user's private network with the VTN(s) of service providers (referred to as BNs henceforth).…”

Section: Resultsmentioning

confidence: 99%

A Multi-Clustering Approach to Scale Distributed Tenant Networks for Mobile Edge Computing

Bruschi

Davoli

Lago

et al. 2019

IEEE J. Select. Areas Commun.

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Fifth generation (5G) mobile networks will lead to a deep integration between networks and applications. Through novel paradigms like Network Functions Virtualization (NFV) and Edge Computing, new classes of heterogeneous application services will be enabled to run close to mobile end-user devices with zero-perceived latency and fully-cognitive dynamic reconfiguration capabilities. Such "vertical" applications exhibit diverse performance/scalability requirements, and will rely on highly distributed, extremely virtualized, multi-tenant and software-defined infrastructures. In such context, handling the required operations in a scalable and dynamic fashion will be of paramount importance. A specific aspect, addressed by Software Defined Networking (SDN), regards the provision of suitable communication channels, once resource allocation mechanisms have performed the most efficient deployment of Virtual Network Function (VNF) instances, and VNF chaining needs to be implemented to enable network services. In this respect, this paper introduces the Multi-Cluster Overlay (MCO) network paradigm: a tunnel-less SDN scheme for scalable realization of Virtual Tenant Networks (VTNs) across the 5G distributed infrastructure, able to support (bulk) migrations of software instances among geo-distributed computing resources in a seamless and effective fashion. Numerical simulation and experimental results show that the MCO achieves up to over one order of magnitude smaller number of forwarding rules than other state-of-the-art SDN mechanisms, while also assuring high performance during reconfiguration operations.

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“…We take into account personal services designed in the form of a service chain. Such service chain is stored in a service template managed by a dedicated control plane module [11] and instantiated only upon user subscription. The service chain is composed of individual service components (ServiceApps) potentially deployed in different Points of Presence (PoPs).…”

Section: Deployment Of Personal Cloud Services In the Edge Networkmentioning

confidence: 99%

Personal Services Placement and Low-Latency Migration in Edge Computing Environments

Bruschi

Davoli

Lago

et al. 2018

2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

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Low latency and integration with edge computing facilities are two of the main enablers of the upcoming fifthgeneration mobile network. The geographical distribution of virtualized compute and storage resources (e.g., NFVI PoPs) allows to deploy application/service instances/components closer to mobile subscribers, and to provide much higher performance levels in terms of latency. These new degrees of freedom should be suitably managed in order to place and to migrate application components along with subscribers' move. Given the mass-scale of subscribers and the non-negligible overhead to move application components over resource-constrained edge facilities, this management should be performed in a highly scalable and effective way. In this respect, this paper presents the design of a comprehensive edge computing framework, including an orchestration algorithm, able to place and to move services in few milliseconds (<25 ms). The algorithm provides scalability levels able to support services instantiated on a per-user basis (i.e., personal services) also in the presence of complex network and service environments. The performance evaluation has been carried out by considering different service chains within diverse software live migration technologies.

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OpenStack Extension for Fog-Powered Personal Services Deployment

Cited by 5 publications

References 4 publications

Cloud-based Network Virtualization in IoT with OpenStack

Cloud-based Network Virtualization in IoT with OpenStack

A Multi-Clustering Approach to Scale Distributed Tenant Networks for Mobile Edge Computing

Personal Services Placement and Low-Latency Migration in Edge Computing Environments

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