Routing and Fault Tolerance in Z-Fat Tree

Adda, Mo; Peratikou, Adamantini

doi:10.1109/tpds.2017.2666807

Cited by 22 publications

(22 citation statements)

References 27 publications

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“…(II) [9] Xn+1 is switch sought after at the upper level. Rn+1 is the total number of switches at the upper level.…”

Section: B Switch Connectivitymentioning

confidence: 99%

“…Xp+1 = ((Xn\Rn) % Zn+1) *Rn/gcd(Rn, Rn+1)+p (III) [9] where p, set of switch ports to be mapped, and represented as:…”

Section: Port Mappingmentioning

confidence: 99%

“…Nevertheless, Extended Generalized Fat tree emerged, allowing variable number of switch ports to be used at different level of network [18,19,20]. Having said that, our work evolved from a variant of Fat tree called Z-node that provides extra degree of connectivity to utilize the extra ports per switches that are in some cases not utilized by the architectural constraints of other variants of fat trees by Adda and Peratikou [9]. We have used this variant of Fat tree in some of our previous works [1, 10, to prove that our Fat tree hybrid design is a better option to use than single design when fault tolerance and graceful performance degradation are of optimum importance in cloud data center network.…”

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confidence: 99%

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Achieving a Fault Tolerant and Reliable Cloud Data Center Network

Emesowum

Paraskelidis

Adda

2018

2018 IEEE International Conference on Services Computing (SCC)

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The need for a robust data center that is fault tolerant can never be overemphasized, especially nowadays that the advent of big data traffic, internet of things and other on-demand internet applications are on the increase. The rate at which these data are transferred across the internet is worrisome, and a thing of concern to the data center developers. The emergence of ubiquitous computing has also aided to the increase in traffic across the internet, because computing occurs more now by use of any device, in any location, and in any format. These issues have compounded the management of Cloud Data Center used for storage, transfer, and analysis of data across the cloud; as a result, the data center network devices become prone to failures, which automatically impacts on its performance. Nevertheless, several researchers have come up with solutions, though not sufficient to mitigate these issues. Therefore, on our part, we realised that architectural design of data center network is the bedrock of having a fault tolerant, reliable, robust, and congestion free network. So, this paper, which is an extension of our previous works, based on an improved version of Fat Tree (called Z-node); we proposed a Hybrid fat tree design and compared it with Single fat tree design, for client to server communication pattern such as HTTP and EMAIL applications. The simulation results obtained with different device failures and traffic rate patterns, show that the Hybrid fat tree design performed better than the Single fat tree design, hence will be the best bet for the transfer and analysis of big data in cloud data center network.

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“…(II) [9] Xn+1 is switch sought after at the upper level. Rn+1 is the total number of switches at the upper level.…”

Section: B Switch Connectivitymentioning

confidence: 99%

“…Xp+1 = ((Xn\Rn) % Zn+1) *Rn/gcd(Rn, Rn+1)+p (III) [9] where p, set of switch ports to be mapped, and represented as:…”

Section: Port Mappingmentioning

confidence: 99%

mentioning

confidence: 99%

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Achieving a Fault Tolerant and Reliable Cloud Data Center Network

Emesowum

Paraskelidis

Adda

2018

2018 IEEE International Conference on Services Computing (SCC)

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“…Furthermore, where extra links are used in the connection, we introduced the pattern used by the authors of Z-Fat tree [11]. The Z-Fat tree describes the number of root nodes per zone in its semantics and adds a degree of connectivity, with the notation: Ƶ (h; z1, z2, …, zh; r1, r2, …,rh; g1, g2, …,gh).…”

Section: Model Descriptionmentioning

confidence: 99%

“…To achieve flexible performance requirements, the extended generalized fat tree (XGFT) was introduced by Ohring et al; for more routing capacity, performance requirement, and allowing variable number of switch ports to be used at different levels of the network [7,9,10]. Nevertheless, the Z-Fat-tree, which is the bedrock of our work, is also a variant of Fat tree introduced by [11]. This improved version of fat tree helps define the number of root nodes per zone or subtree, and adds a degree of connectivity for maximal fault-tolerance.…”

Section: Introductionmentioning

confidence: 99%

Fault tolerance capability of cloud data center

Emesowum

Paraskelidis

Adda

2017

2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

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In this era of big data and internet of things, the need for performance improvement in cloud data center is unavoidable. This has led to several designs of data center network topologies with the aim of achieving a data center that has the capability of tolerating fault during multiple failures. In this paper, we proposed improved variants of fat-tree interconnections to mitigate the challenges of fault tolerance. The availability of alternative paths for congestion control and fault tolerance gave Fat-tree an edge over other data center architectures, thereby becoming a widely used architecture for data center. Our focus is on client to server communications in a cloud data center network as explained in Fig. 7, hence simulation of HTTP application was carried out on different variants of fat tree designs. The simulation results with Riverbed showed that our proposed hybrid designs outperformed the Single fat tree designs as the number of link failures increase.

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Hybrid interconnection networks for reducing hardware cost and improving path diversity based on fat‐trees and hypercubes

Wang

2023

Concurrency and Computation

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Fat‐tree topologies are widely used in interconnect network designs for parallel supercomputers. In the classic fat‐tree, compute nodes are connected to leaf stage switches by links. Given a large number of compute nodes, many switches and links are required, resulting in high hardware costs. To solve this problem, this paper proposes two hybrid topologies, k$$ k $$‐Cube k$$ k $$‐Ary n$$ n $$‐Tree (CAT) and Mirrored k$$ k $$‐Cube k$$ k $$‐Ary n$$ n $$‐Tree (MiCAT), based on fat‐tree and hypercube. Instead of connecting k$$ k $$ compute nodes directly to a leaf switch, we connect a k$$ k $$‐cube to the switch, and each switch in the k$$ k $$‐cube part connects k$$ k $$ compute nodes. That is, this k$$ k $$‐cube consists of 2kprefix−1$$ {2}^k-1 $$ switches and kfalse(2kprefix−1false)$$ k\left({2}^k-1\right) $$ compute nodes. We give the shortest path routing algorithms and evaluate the path diversity, cost, performance, and average packet latency of CAT and MiCAT. The results show that CAT and MiCAT can save up to 87%$$ 87\% $$ switches and 80%$$ 80\% $$ links in a large‐scale parallel system, k=n=8$$ k=n=8 $$ for example, compared to fat‐trees, and meanwhile, both CAT and MiCAT have higher path diversities than fat‐trees.

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Routing and Fault Tolerance in Z-Fat Tree

Cited by 22 publications

References 27 publications

Achieving a Fault Tolerant and Reliable Cloud Data Center Network

Achieving a Fault Tolerant and Reliable Cloud Data Center Network

Fault tolerance capability of cloud data center

Hybrid interconnection networks for reducing hardware cost and improving path diversity based on fat‐trees and hypercubes

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