scholarly journals A Combinatorial Reliability Analysis of Generic Service Function Chains in Data Center Networks

2021 ◽  
Vol 6 (3) ◽  
pp. 1-24
Author(s):  
Anna Engelmann ◽  
Admela Jukan
Keyword(s):  
Reliability Analysis ◽  
Data Center ◽  
Service Reliability ◽  
Data Center Networks ◽  
Binomial Theorem ◽  
Service Function ◽  
Service Function Chain ◽  
A Chain ◽  
Application Requirements ◽  
Generic Service

In data center networks, the reliability of Service Function Chain (SFC)—an end-to-end service presented by a chain of virtual network functions (VNFs)—is a complex and specific function of placement, configuration, and application requirements, both in hardware and software. Existing approaches to reliability analysis do not jointly consider multiple features of system components, including, (i) heterogeneity, (ii) disjointness, (iii) sharing, (iv) redundancy, and (v) failure interdependency. To this end, we develop a novel analysis of service reliability of the so-called generic SFC, consisting of n = k + r sub-SFCs, whereby k ≥ 1 and r ≥ 0 are the numbers of arbitrary placed primary and backup (redundant) sub-SFCs, respectively. Our analysis is based on combinatorics and a reduced binomial theorem—resulting in a simple approach, which, however, can be utilized to analyze rather complex SFC configurations. The analysis is practically applicable to various VNF placement strategies in arbitrary data center configurations, and topologies and can be effectively used for evaluation and optimization of reliable SFC placements.

scholarly journals Online Parallelized Service Function Chain Orchestration in Data Center Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 100147-100161 ◽  
Author(s):  
Gang Sun ◽  
Zhenrong Chen ◽  
Hongfang Yu ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Data Center ◽  
Data Center Networks ◽  
Service Function ◽  
Service Function Chain

Reliability analysis of data center networks based on precise and imprecise diagnosis strategies

2020 ◽  
Vol 809 ◽  
pp. 189-203
Author(s):  
Xiaoyan Li ◽  
Xiaohua Jia ◽  
Jianxi Fan ◽  
Cheng-Kuan Lin
Keyword(s):  
Reliability Analysis ◽  
Data Center ◽  
Data Center Networks

Analysis on Buffer Occupancy of Quantized Congestion Notification in Data Center Networks

2016 ◽  
Vol E99.B (11) ◽  
pp. 2361-2372 ◽  
Author(s):  
Chang RUAN ◽  
Jianxin WANG ◽  
Jiawei HUANG ◽  
Wanchun JIANG
Keyword(s):  
Data Center ◽  
Data Center Networks ◽  
Buffer Occupancy

scholarly journals HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiawei Huang ◽  
Shiqi Wang ◽  
Shuping Li ◽  
Shaojun Zou ◽  
Jinbin Hu ◽  
...  
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Network Performance ◽  
Rooted Tree ◽  
Heterogeneous Traffic ◽  
Data Center Networks ◽  
Packet Reordering ◽  
Traffic Characteristics ◽  
Network Utilization ◽  
The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

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