Beyond the core: Enabling software-defined control at the network edge

Heuschkel, Jens; Stein, Michael; Wang, Lin; Mühlhäuser, Max

doi:10.1109/netsys.2017.7903963

Cited by 4 publications

(2 citation statements)

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“…While SDN today is primarily used for the management of devices in the core network, its principles of softwaredefined control can also be applied to edge environments. For example, Heuschkel et al [122] present a protocol to extend software-defined control beyond the core network to the end devices. Bi et al [123] show how user mobility can be realized by decoupling mobility control and data forwarding.…”

Section: ) Software-defined Networkingmentioning

confidence: 99%

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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Section: ) Software-defined Networkingmentioning

confidence: 99%

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

Self Cite

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“…Few reports have already explored the similarity of SDN ideas to edge conditions. Heuschkel et al [Heuschkel (2017)] present a protocol which goes beyond the core network and enables it to spread to end devices. Bi et al [Bi (2018)] demonstrate how the regulation of versatility can be decoupled from the forwarding of data and obtains with SDN.…”

Section: Future Potential Of Sdnmentioning

confidence: 99%

Resource allocation and management under priorities based on the squatting-kicking model for multi-slice 5G networks

Hamouda

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The upcoming Beyond Fifth Generation networks aim to meet network services characterized by low latency and high reliability among others in different slices to provide a high-quality user experience. However, existing best-effort networking schemes that implement traditional methods of controlling and allocating network and computing resources do not meet such strict service requirements well. In International Telecommunication Union-Telecommunication sector, future services are defined as Network 2030 Services under a chartered Focus Group on Networks 2030 (FG-NET2030). The results from the FG-NET2030 analysis suggests that current networks cannot accommodate real-time applications with low latency and high bandwidth requirements. Moreover, current networks lack the capabilities to dynamically aggregate and share network resources through multiple flows, which is essential for future services. However, to satisfy the strict requirements of those services, intelligent algorithms and techniques that incorporate 5G enablers are needed to introduce novel network management systems. These intelligent algorithms shall not only result in efficient utilization of network resources but also guarantee the required quality of service for the priority slices. Moreover, cognizant of the strict latency requirements of the different services, such algorithms should include delay constraints of requests. Despite the advantages expected from future services are real-time applications, should benefit from reduced physical and logical paths between end-users and data or service hosts. However, all the above requirements are not intended for the network slicing paradigm alone. Therefore, in addition to network slicing, we want to leverage technologies and components that have features such as network programming, dynamic network reconfiguration and orchestration to enable improved performance and efficient resource management. Such technologies include NFV and SDN among others. Consequently, the main objective of this PhD thesis is to develop a service deployment algorithm that uses Squatting and Kicking techniques intelligence to effectively allocate, manage, and control slice resources under several constraints in a real-time multi-slice scenario, such as priority, bandwidth, and E2E delay with targeting to maximize the total resource usage in the substrate network. The proposed online algorithm allocates the available resource to different priority demands from source node to destination node along the routed path according to more realistic constraints, such as links' bandwidth and E2E delay. Moreover, the benefits of the new proposed algorithm will be reflected in creating real-time demands for 5G applications that are sensitive to delay, in addition to solving the resource allocation problem for large scale networks, using fewer resources and generating lower costs. Further, the proposed algorithm is adaptable to meet various QoS requirements of services, guaranteeing high QoS levels and providing high admission for higher priority classes under congested scenarios. In terms of managing bandwidth resources in a multi-slice scenario, Bandwidth Allocation Models offer improved metrics over best effort models. The proposed algorithm outperforms the others by far especially, during congested scenarios. To this end, this thesis proposes a resource allocation model called Squatting and Kicking model (SKM) to maximize the number of successfully embedded demands while maximizing the utilization in the multi-slice networks by choosing less congested paths through the efficient allocation of demands on the network. Moreover, this thesis analyzes the impact of delay constraint on the performance of an online resource allocation algorithm based on an intelligent efficient SKM, proved in this work to be the most effective up to the present time yet.

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Richerzhagen¹

2018

Mechanism Transitions in Publish/Subscribe Systems

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Beyond the core: Enabling software-defined control at the network edge

Cited by 4 publications

References 9 publications

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Resource allocation and management under priorities based on the squatting-kicking model for multi-slice 5G networks

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