Programming slick network functions

Anwer, Bilal; Benson, Theophilus; Feamster, Nick; Levin, Dave

doi:10.1145/2774993.2774998

Cited by 82 publications

(61 citation statements)

References 29 publications

(26 reference statements)

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“…a VNF altering the packet header could invalid the end-to-end chain). Higher level chaining abstractions and programming languages are needed in order to allow service developers to programmatically declare the sequence the VNF traffic should follow, leaving to the underlying runtime system the actual implementation of such rules [21,22]. For the chain routing to be deterministic, there has to be a field that keeps track of the chain hops.…”

Section: Introducing Mobile Edge Services Into Cloud-enabled Small Cellsmentioning

confidence: 99%

Introducing Mobile Edge Computing Capabilities through Distributed 5G Cloud Enabled Small Cells

et al. 2016

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Abstract:Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloudenabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

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Section: Introducing Mobile Edge Services Into Cloud-enabled Small Cellsmentioning

confidence: 99%

Introducing Mobile Edge Computing Capabilities through Distributed 5G Cloud Enabled Small Cells

et al. 2016

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“…Despite the multiple SDN-NFV architectures proposed in recent years (e.g., E2 [2], Stratos [3], Slick [4], SDNFV [5]), the problem of resource allocation and management in such environments has not received as much of attention.…”

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confidence: 99%

“…With Mininet, we compare DRENCH to a centralized approach Slick [4] on a 4K-Fat tree topology. Additionally, we compared DRENCH in a simulation environment consisting of a Rocketfuel topology (87 switches) to a custom centralized approach: Simplefying [6] on top of a E2 SDN framework [2].…”

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confidence: 99%

“…However, SIMPLE [6] assumes that the NFIs and middleboxes are statically placed and any dynamic instantiation of NFIs requires the re-run of the expensive offline ILP solver. Slick [4] provides a programming model abstraction for service chaining and supports heuristic based function placement and flow steering schemes. Slick [4] also supports dynamic scaling of NFIs.…”

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confidence: 99%

“…Slick [4] provides a programming model abstraction for service chaining and supports heuristic based function placement and flow steering schemes. Slick [4] also supports dynamic scaling of NFIs. However, it does not redirect the existing flows from the overloaded instances, but only steers the new flows to the scaled-out instances.…”

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confidence: 99%

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DRENCH: A semi-distributed resource management framework for NFV based service function chaining

Tasiopoulos

Kulkarni

Arumaithurai

et al. 2017

2017 IFIP Networking Conference (IFIP Networking) and Workshops

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Abstract-As networks grow in scale and complexity, the use of Network Function Virtualization (NFV) and the ability to dynamically instantiate network function instances (NFI) allow us to scale out the network's capabilities in response to demand. At the same time, an increasing number of computing resources, deployed closer to users, as well as network equipment are now capable of performing general-purpose computation for NFV. However, NFV management in the presence of Service Function Chaining (SFC) for arbitrary topologies is a challenging task.In this work we argue for the necessity of an algorithmic resource management framework that captures the involved tradeoffs of NFIs minimum workload, load balancing, and flow path stretch. We introduce DRENCH as a low complexity NFV and flow steering management framework. In DRENCH a NFV market is considered where a centralised SDN controller acts as market orchestrator of NFV nodes. Through competition, NFV nodes make flow steering and NFI instantiation/consolidation decisions. DRENCH design enables third party NFV nodes participation while it can coexist with other NFV management solutions. DRENCH orchestrator parameterisation strikes the right balance between path stretch and NFI load balancing, resulting in significantly lower Flow Completion Times, up to 10× less, in some cases.

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Multi-commodity Flow with In-Network Processing

Charikar

Naamad

Rexford

et al. 2019

Lecture Notes in Computer Science

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Modern networks run "middleboxes" that offer services ranging from network address translation and server load balancing to firewalls, encryption, and compression. In an industry trend known as Network Functions Virtualization (NFV), these middleboxes run as virtual machines on any commodity server, and the switches steer traffic through the relevant chain of services. Network administrators must decide how many middleboxes to run, where to place them, and how to direct traffic through them, based on the traffic load and the server and network capacity. Rather than placing specific kinds of middleboxes on each processing node, we argue that server virtualization allows each server node to host all middlebox functions, and simply vary the fraction of resources devoted to each one. This extra flexibility fundamentally changes the optimization problem the network administrators must solve to a new kind of multi-commodity flow problem, where the traffic flows consume bandwidth on the links as well as processing resources on the nodes. We show that allocating resources to maximize the processed flow can be optimized exactly via a linear programming formulation, and to arbitrary accuracy via an efficient combinatorial algorithm. Our experiments with real traffic and topologies show that a joint optimization of node and link resources leads to an efficient use of bandwidth and processing capacity. We also study a class of design problems that decide where to provide node capacity to best process and route a given set of demands, and demonstrate both approximation algorithms and hardness results for these problems.

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Programming slick network functions

Cited by 82 publications

References 29 publications

Introducing Mobile Edge Computing Capabilities through Distributed 5G Cloud Enabled Small Cells

Introducing Mobile Edge Computing Capabilities through Distributed 5G Cloud Enabled Small Cells

DRENCH: A semi-distributed resource management framework for NFV based service function chaining

Multi-commodity Flow with In-Network Processing

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