Energy efficient SDN commodity switch based practical flow forwarding method

AlGhadhban, Amer; Shihada, Basem

doi:10.1109/noms.2016.7502899

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…The results described that the modified DFS multipath algorithm was better than Dijkstra multipath algorithm. In term of the comprehensive results compared to the previous similar research in [14], the simulated Dijkstra Multipath algorithm using both bandwidth and link capacity metrics produced less effective results compared to the proposed modified DFS. The notable differences was pointed in all evaluated variables (Throughput, Jitter, and Packet loss).…”

Section: Packet Loss Resultsmentioning

confidence: 74%

“…In the example from Table 2, switch will forward packet through port 3 and 4 with probabilities 0,3 on port 3 and 0,7 on port 4. The number of ports for data transmission process depends on the available paths in the topology such as the Fat-Tree topology so that the multipath routing can be implemented [11][13] [14]. In order to actualize the multipath routing concept, this research proposed a modification of the DFS algorithm so that it can find all paths in a multiple path network topology [18] shown in Table 3.…”

Section: Figure 1 Example Transmission By Multipath Routingmentioning

confidence: 99%

“…Therefore, multipath routing may become a solution for that problem [12]. In several research, multipath routing implementation has been used on networking topology which supports multiple paths such as Fat-Tree [13], [14]. In papers [15] [16], showed the benefits of multipath routing such as load balancing to data transmission that was better than single path routing.…”

Section: Introductionmentioning

confidence: 99%

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Comparison Analysis of Multipath Routing Implementation in Software Defined Network

Syaifuddin

Azis

Sumadi

2021

KINETIK

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Multipath routing is a path search method used as a data transmission process utilizing more than one available path on a network. The multipath routing concept is directed to substitute the single path routing concept for reducing network traffic congestion by distributing data transmission through several available paths. It can be implemented in the Software Defined Network (SDN) environment that separates the control plane and the data plane, which provides flexibility by deploying application-based solutions to resolve the problem. This paper is directed to implement the modified Deep First Search (DFS) multipath routing algorithm and compare the proposed method with Dijkstra and normal DFS multipath algorithm. The contribution was designed by combining the node, edge, path, and bucket weight using port statistics available in OpenFlow standard and manual calculation. The results of the system’s emulation showed that the overall algorithm could determine more than one path for the data transmission process. The average execution time on all algorithms produced 0.0903 ms for the modified DFS multipath algorithm, 0.0858 ms for DFS multipath algorithm, and 0.901 ms Dijkstra multipath algorithm, respectively. The QoS parameter testing results illustrated that the proposed method was better than another multipath routing algorithm in terms of throughput and jitter. However, based on packet loss percentage, the modified method was placed after normal DFS but still generated better results than Dijkstra. Overall, the implement multipath routing concept in SDN with all algorithms could be deployed to provide more than one data transmission path.

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Section: Packet Loss Resultsmentioning

confidence: 74%

Section: Figure 1 Example Transmission By Multipath Routingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison Analysis of Multipath Routing Implementation in Software Defined Network

Syaifuddin

Azis

Sumadi

2021

KINETIK

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“…(1) Expressiveness: According to the universal approximation theorem, a depth-2 NN with suitable activation functions and a sufficient size 27 can approximate any continuous function [39,76,97,126,189]. However, existing studies [187,189,247] also pointed out that the expressiveness of such a depth-2 NN comes at the price of the network size.…”

Section: Chapter 5 Drl For Request Dispatching In Sdnmentioning

confidence: 99%

“…Secondly, hardware technology advancement enables lightning 26 A dispatching rule is used to prioritize all the jobs in the queue such that the job with the highest priority can be processed next. 27 The size of an NN is the total number of neurons [187].…”

Section: Chapter 5 Drl For Request Dispatching In Sdnmentioning

confidence: 99%

Achieving Effective Resource Management in Distributed SDN Controller Architectures

Huang¹

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<p>As an emerging computer networking paradigm, Software-Defined Networking (SDN) empowers network operators with simplified network configuration and centralized network management. Recently, distributed controller architectures have become a notable invention where multiple controllers are jointly deployed in the network for request processing. One major research challenge for distributed controller architectures is to effectively manage the controller resources including allocating sufficient controllers to the suitable network locations and making the best use of the given controller resources. In general, existing approaches for managing the controller resources in the literature can be classified into three main directions. Designing new controller architectures belongs to the first direction, where the focus is on enabling workload shifting among controllers using switch migration. Designing controller placement algorithms to identify the number and locations of controllers is the second direction. Given the controller placement solution, the third direction is controller scheduling which aims to make the best use of the shared controllers by properly distributing requests among them. However, existing approaches have three major limitations. First, existing controller architectures feature a switch-controller binding which restricts the requests generated by a switch to only be processed by a predefined controller. Since each switch comes with different workload and the workload can be time-variant, the binding renders the bound controller susceptible to either being overloaded or underloaded. Second, existing placement algorithms have consistently underestimated the importance of controller scheduling. Due to the NP-hardness of the placement problem, Genetic Algorithm (GA) is a promising candidate. However, as a population-based approach, GA can be computationally expensive. Especially in a large network, the corresponding search space becomes too large for GA to handle effectively. Third, existing approaches for controller scheduling are mostly designed under the switch-controller binding constraint. When the scheduling is performed at a per-request level, the scheduling complexity increases significantly, rendering the efficiency and effectiveness of existing algorithms questionable. Apart from that, existing studies mainly focus on manually designing request dispatching policy which strongly relies on domain knowledge and involves a time-consuming fine-tuning process. The overall goal of this thesis is to effectively manage the controller resources in distributed SDN controller architectures. To address the three major limitations, three research objectives are established. First, this thesis aims to propose a new controller architecture to enable flexible controller placement and scheduling. Second, the thesis focuses on effectively and scalably identifying suitable controller placement while jointly taking the controller scheduling problem into consideration. Third, the thesis seeks to incorporate machine learning techniques in the request dispatching policy design to automatically learn adaptive and effective policies. To achieve the first objective, this thesis proposes a new BindingLess Architecture for distributed Controllers (BLAC) which features bindingless association between switches and controllers. With the newly introduced scheduling layer, requests can be transparently and flexibly dispatched among multiple controllers without invoking the time-consuming and complicated switch migration. Experiments conducted in this thesis show that BLAC significantly reduces the average response time and improves the throughput compared to existing SDN architectures. To achieve the second objective, this thesis proposes a Clustering-based Genetic Algorithm with Cooperative Clusters (CGA-CC) to tackle the controller placement problem. Particularly, CGA-CC partitions a large network into non-overlapping sub-networks to substantially reduce the search space of GA. Within each sub-network, GA is applied to identifying the placement solution. The quality of any given placement solution is evaluated by a gradient-descent-based scheduling algorithm which is developed to optimize the probability distribution of requests among all controllers. Moreover, a greedy load re-distribution mechanism is developed to handle unexpected demand variations by dynamically forwarding indigestible requests to adjacent sub-networks. Extensive simulations show that our algorithms can significantly outperform several existing and state-of-the-art algorithms and is more robust in handling unexpected traffic bursts. To achieve the third objective, this thesis proposes a Multi-Agent (MA) deep-reinforcement-learning-based approach with the aim to automatically learn adaptive, effective, and efficient policies used by each switch. In particular, a new adaptive policy representation is proposed to support networks with a changing number of controllers. To enable the training of an adaptive policy, a new policy gradient calculation technique is developed. Then the policy design problem is formulated as an MA Markov Decision Processing and a new MA training algorithm is proposed. The results show that the policy designed by our algorithm can easily adapt to networks with a changing number of controllers. Moreover, our policy can achieve significantly better performance compared with existing policies including the man-made policy (e.g., weighted round-robin), the model-based policy (e.g., the gradient-descent-based scheduling algorithm), and policies designed by other reinforcement learning algorithms (e.g., the proximal policy optimization algorithm).</p>

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Failure mitigation in software defined networking employing load type prediction

Bouacida

AlGhadhban

Alalmaei

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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CitationBouacida Abstract-The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new mice flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.

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Energy efficient SDN commodity switch based practical flow forwarding method

Cited by 6 publications

References 14 publications

Comparison Analysis of Multipath Routing Implementation in Software Defined Network

Comparison Analysis of Multipath Routing Implementation in Software Defined Network

Achieving Effective Resource Management in Distributed SDN Controller Architectures

Failure mitigation in software defined networking employing load type prediction

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