Virtual Network Embedding Approximations: Leveraging Randomized Rounding

Rost, Matthias; Schmid, Stefan

doi:10.23919/ifipnetworking.2018.8696623

Cited by 29 publications

(37 citation statements)

References 20 publications

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“…Given that = 1 from Lemma 1, it follows that at the end of the decomposition procedure, ¯− = 0, ∀ , and hence allr esidual values are zero. It is clear from constraints (6) and 10 Proof. We first bound the probability that the computation load at a given node exceeds capacity by a factor .…”

Section: Performance Analysismentioning

confidence: 99%

“…The problem is further complicated, however, if one considers order constraints in the execution of the functions known as chaining constraints. Chained service functions can be modeled as service graphs to be embedded into a substrate network and various algorithms have been proposed for this purpose using techniques such as randomized rounding [6], relaxation and successive convex approximation [7], and shortest path routing on a multi-layer graph [8]. Alternative modeling and solution approaches are also known based on mixed integer linear programming [17], [18], column generation [19], sampling-based Markov approximation [20] and queuing models [21].…”

Section: Services Without Intensive Data Requirementsmentioning

confidence: 99%

“…To model the embedding decisions, we introduce two sets of optimization variables: (i) ∈ {0, 1} which indicates whether vertex is mapped to physical node ( = 1) or not ( = 0), and (ii) ∈ {0, 1} which indicates whether edge ( , ) is mapped to a path that uses physical link ( , ) ( = 1) or not ( = 0). Differently from traditional service embedding problem formulations (e.g., [6]), in our case, the network operator needs to also decide the data placement decisions: ∈ {0, 1} which indicates whether data object is stored at physical node ( = 1) or not ( = 0). The respective vectors of variables are the following:…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…Despite the above industry efforts, a fundamental algorithmic methodology for optimally instantiating chains of data-intensive services into the network is still missing. Existing algorithms based on SC Embedding (SCE) (e.g., [6], [7], [8]) have been effective in addressing this problem by provisioning computation together with appropriate communication resources to minimize network costs or maximize accepted service requests. However, the shareable nature of the stored data breaks the suitability of these algorithms.…”

Section: Introduction 1motivationmentioning

confidence: 99%

See 3 more Smart Citations

Approximation algorithms for data-intensive service chain embedding

Poularakis

Llorca

Tulino

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Recent advances in network virtualization and programmability enable innovative service models such as Service Chaining (SC), where flows can be steered through a pre-defined sequence of service functions deployed at different cloud locations. A key aspect dictating the performance and efficiency of a SC is its instantiation onto the physical infrastructure. While existing SC Embedding (SCE) algorithms can effectively address the instantiation of SCs consuming computation and communication resources, they lack efficient mechanisms to handle the increasing data-intensive nature of next-generation services. Differently from computation and communication resources, which are allocated in a dedicated per request manner, storage resources can be shared to satisfy multiple requests for the same data. To fill this gap, in this paper, we formulate the data-intensive SCE problem with the goal of minimizing storage, computation, and communication resource costs subject to resource capacity, service chaining, and data sharing constraints. Using a randomized rounding technique that exploits a novel dataaware linear programming decomposition procedure, we develop a multi-criteria approximation algorithm with provable performance guarantees. Evaluation results show that the proposed algorithm achieves near-optimal resource costs with up to 27.8% of the cost savings owed to the sharing of the data. CCS CONCEPTS • Networks → Cloud computing; • Theory of computation → Rounding techniques.

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Section: Performance Analysismentioning

confidence: 99%

Section: Services Without Intensive Data Requirementsmentioning

confidence: 99%

Section: Mathematical Formulationmentioning

confidence: 99%

Section: Introduction 1motivationmentioning

confidence: 99%

See 2 more Smart Citations

Approximation algorithms for data-intensive service chain embedding

Poularakis

Llorca

Tulino

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

View full text Add to dashboard Cite

show abstract

“…A heuristic algorithm based on a multistage directed graph and the Viterbi algorithm is proposed in , which takes each SFC as a virtual network and maps each virtual network onto the given physical substrate network. Rost and Schmid study multiple SFCs (forwarding graph) embedding and propose a polynomial‐time approximation algorithm based on random routing techniques with linear programming (LP) relaxation. Even et al provide a randomized approximation algorithm leveraging multicommodity flows for path computation and function placement.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust network function virtualization

Lin

Zhou²

2020

Networks

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Network function virtualization (NFV) enables on‐demand network function (NF) deployment providing agile and dynamic network services. Through an evaluation metric that quantifies the minimal reliability among all NFs for all demands, service providers and operators may better facilitate flexible NF service recovery and migration, thus offering higher service reliability. In this paper, we present evaluation metrics on NFV reliability and solution approaches to solve robust NFV under random NF‐enabled node failure(s). We demonstrate how to construct an auxiliary NF‐enabled network and its mapping onto the physical substrate network. With the constructed NF‐enabled network, we develop pseudo‐polynomial algorithms to solve the robust NF and SFC s − t path problems: subproblems of robust NFV. We also present approximation algorithms for robust NFV with the SFC‐Fork as the NF forwarding graph. Furthermore, we propose exact solution approaches via mixed‐integer linear programming under the general setting. Computational results show that our proposed solution approaches are capable of managing robust NFV in a large‐size network.

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Software defined service function chaining with failure consideration for fog computing

Tajiki

Shojafar

Akbari

et al. 2018

Concurrency and Computation

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Middleboxes have become a vital part of modern networks by providing services such as load balancing, optimization of network traffic, and content filtering. A sequence of middleboxes comprising a logical service is called a Service Function Chain (SFC). In this context, the main issues are to maintain an acceptable level of network path survivability and a fair allocation of the resource between different demands in the event of faults or failures. In this paper, we focus on the problems of traffic engineering, failure recovery, fault prevention, and SFC with reliability and energy consumption constraints in Software Defined Networks (SDN). These types of deployments use Fog computing as an emerging paradigm to manage the distributed small-size traffic flows passing through the SDN-enabled switches (possibly Fog Nodes). The main aim of this integration is to support service delivery in real-time failure recovery in an SFC context. First, we present an architecture for Failure Recovery called FRFP; this is a multi-tier structure in which the real-time traffic flows pass through SDN-enabled switches to jointly decrease the network side-effects of flow rerouting and energy consumption of the Fog Nodes. We then mathematically formulate an optimization problem called the Optimal Fast Failure Recovery algorithm (OFFR) and propose a near-optimal heuristic called Heuristic HFFR to solve the corresponding problem in polynomial time. In this way, the reliability of the selected paths are optimized, while the network congestion is minimized. KEYWORDS failure recovery, fog computing (FC), network function virtualization (NFV), resource reallocation, service function chaining (SFC), software defined network (SDN) 1 INTRODUCTION Network Function Virtualization (NFV) paradigm decouples network functions (NFs) from dedicated hardware equipment and provides more flexibility to the owner of an IT infrastructure. NFV consists in a set of different services running on generic hardware (eg, IDS, proxy, deep packet inspection, and firewall) that are chained using the so called Service Function Chaining (SFC) concept. This concept can be applied to industrial environments; in particular, NFV is used in industrial network design in order to reduce exposure to risks, reduce CAPEX/OPEX costs, and minimize future performance issues by basing the infrastructure upon commodity servers and switches. 1 NFV and SFCs offer a great flexibility in the chaining and placement of NFs. The European Telecommunications Standards Institute (ETSI) is driving the standardization of NFV. Software Defined Networking (SDN) complements NFV and is concerned with the decoupling of control plane functionality from packet forwarding devices, allowing a controller to flexibly configure the networking operations in the infrastructure. NFV and SDN require attention to avoid cascading threats as well ascontroller protections, especially for the software applications across the SDN switches that interoperate with the server virtualization and virtual machines (VMs). 2...

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Virtual Network Embedding Approximations: Leveraging Randomized Rounding

Cited by 29 publications

References 20 publications

Approximation algorithms for data-intensive service chain embedding

Approximation algorithms for data-intensive service chain embedding

Robust network function virtualization

Software defined service function chaining with failure consideration for fog computing

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