Energy-efficient path allocation heuristic for service function chaining

Tajiki, Mohammad Mahdi; Shojafar, Mohammad; Chiaraviglio, Luca; Akbari, Behzad

doi:10.1109/icin.2018.8401618

Cited by 26 publications

(21 citation statements)

References 10 publications

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“…We categorized the existing solution into two parts, in which, at first, we address the SFC algorithms, [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] and then in the second part, we describe the solutions and handle the failures and faults in SDNs/NFVs. We categorized the existing solution into two parts, in which, at first, we address the SFC algorithms, [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] and then in the second part, we describe the solutions and handle the failures and faults in SDNs/NFVs.…”

Section: Related Workmentioning

confidence: 99%

“…In the following, we briefly discuss the main literature on NFVs/SDNs and service function changing related to our work. We categorized the existing solution into two parts, in which, at first, we address the SFC algorithms, [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] and then in the second part, we describe the solutions and handle the failures and faults in SDNs/NFVs. [24][25][26][27][28][29][30][31][32][33][34][35]…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Software defined service function chaining with failure consideration for fog computing

Tajiki

Shojafar

Akbari

et al. 2018

Concurrency and Computation

Self Cite

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“…It provides spectrum efficiency by means of a strict collaboration among end-devices in the same integrated platform [5]. FC aims at distributing the self-powered data center (e.g., FNs) between resource-remote clouds and a resource-limited smartphone in the vehicles [6], in order to perform context-aware energy-efficient data mining and dissemination for the adoption of a smartphone device and clients' highly dependent mobile applications [3,7,8].…”

Section: Introductionmentioning

confidence: 99%

Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study

et al. 2017

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Abstract:In this paper, we discuss the most significant application opportunities and outline the challenges in real-time and energy-efficient management of the distributed resources available in mobile devices and at the Internet-to-Data Center. We also present an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC) that operates at the edge of a vehicular network, connected to the served Vehicular Clients (VCs) through an Infrastructure-to-Vehicular (I2V) over multiple Foglets (Fls). The scheduler optimizes the energy by leveraging the heterogeneity of Fls, where the Fl provider shapes the system workload by maximizing the task admission rate over data transfer and computation. The presented scheduling algorithm demonstrates that the resulting adaptive scheduler allows scalable and distributed implementation.

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“…They reallocate the resources in a way that minimizes the network reconfiguration overhead. Similarly, The papers [12,13] propose two rerouting algorithms to guarantee the QoS constraints while the energy consumption of the network is minimized. In this way, they provide a mathematical formulation of the problem.…”

Section: Related Workmentioning

confidence: 99%

Joint QoS and Congestion Control Based on Traffic Prediction in SDN

et al. 2017

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Due to the various network requirements of applications, quality of service (QoS)-aware routing plays an important role in the networks. Recently proposed resource allocation algorithms focus on the current traffic matrix, which is not applicable for dynamic networks. In this paper, we exploit an estimation of flow matrix that gives our scheme the ability to sufficiently reduce the total packet loss and simultaneously raise the network throughput. In this way, we mathematically formulate the QoS-aware resource reallocation in software-defined networking (SDN) networks based on the traffic prediction. To solve this optimization problem, two schemes are proposed: (i) exact solution; and (ii) fast suboptimal one. The proposed schemes are compared with the accuracy perspective. Moreover, the impact of prediction on resource reallocation is discussed. In this regard, it is shown that, compared with the conventional scheme, the proposed scheme decreases the packet loss and increases the throughput significantly.

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Energy-efficient path allocation heuristic for service function chaining

Cited by 26 publications

References 10 publications

Software defined service function chaining with failure consideration for fog computing

Software defined service function chaining with failure consideration for fog computing

Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study

Joint QoS and Congestion Control Based on Traffic Prediction in SDN

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