Elephant Flow Detection Mechanism in SDN-Based Data Center Networks

Tang, Qian; Zhang, Huan; Dong, Jun; Zhang, Lianming

doi:10.1155/2020/8888375

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…In order to ensure the balance of traffic in user services, our method further divides traffic into elephant flows and mice flows for dynamic scheduling. The elephant flow usually has a long survival time and carries a large amount of data, occupying 80-90% of the total network traffic of the data center, the data traffic in less than 1% of the traffic packets can reach more than 90%, and less than 0.1% of the data traffic can last for 200 s [20]. The mice flow usually has a short survival time and carries a small amount of data, the total number reaches 80% of the total flows, and the transmission time is within 10 s [21].…”

Section: Modeling Of Energy-efficient Routingmentioning

confidence: 99%

“…In order to intuitively experience the cumulative rewards, the inverse of the energy consumption and the inverse of the packet loss rate are selected as the reward value factors when the reward function is set so that the optimization goal performance and the rewards increase in direct proportion. The specific calculation process is shown in Equation (20).…”

Section: Ee-routing Neural Network Interaction With Environmentmentioning

confidence: 99%

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Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning

et al. 2022

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With the vigorous development of the Internet, the network traffic of data centers has exploded, and at the same time, the network energy consumption of data centers has also increased rapidly. Existing routing algorithms only realize routing optimization through Quality of Service (QoS) and Quality of Experience (QoE), which ignores the energy consumption of data center networks. Aiming at this problem, this paper proposes an Ee-Routing algorithm, which is an energy-saving routing algorithm based on deep reinforcement learning. First, our method takes the energy consumption and network performance of the data plane in the software-defined network as the joint optimization goal and establishes an energy-efficient traffic scheduling scheme for the elephant flows and the mice flows. Then, we use Deep Deterministic Policy Gradient (DDPG), which is a deep learning framework, to achieve continuous and energy-efficient traffic scheduling for joint optimization goals. The training process of our method is based on a Convolutional Neural Network (CNN), which can effectively improve the convergence efficiency of the algorithm. After the algorithm training converges, the energy-efficient path weights of the elephant flows and the mice flows are output, and the balanced scheduling of routing energy-saving and network performance is completed. Finally, the results show that our algorithm has good convergence and stability. Compared with the DQN-EER routing algorithm, Ee-Routing improves the energy saving percentage by 13.93%, and compared with the EARS routing algorithm, Ee-Routing reduces the delay by 13.73%, increases the throughput by 10.91%, and reduces the packet loss rate by 13.51%.

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Section: Modeling Of Energy-efficient Routingmentioning

confidence: 99%

Section: Ee-routing Neural Network Interaction With Environmentmentioning

confidence: 99%

Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning

et al. 2022

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“…The authors described a framework to assist QoS equipping for reserving the resource demands of various kinds of data flows in reference [6]. It is feasible that there would not be sufficient accessible routes to redirect QoS flows, which results in deficient QoS flow transfer performance in complicated networks [7]. However, providing a high QoS level is difficult because of the many restrictions applied in the legacy networks.…”

Section: Introductionmentioning

confidence: 99%

“…A summary of the results of the performance of the various controllers depending on specific QoS factors, such as scalability, reliability, load balancing, and consistency, is provided in reference [10]. The SDN controller performance tests are conducted using the criteria (such as Bandwidth) [7]. OpenFlow protocol is the technology and interaction channel to actualize SDN, providing interaction between the forwarding and control layers [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of QoS metrics in Software Defined Networking using Floodlight Controller

Hamad,

Yalda,

ÈšÄƒpuÈ™

2023

JOIV : Int. J. Inform. Visualization

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The quality of service is not the same in all parts of the network. Some areas experience a low level and others a higher level of fixed quality services. The shortcomings in legacy networks encouraged researchers to find a new paradigm of the network to obviate legacy networks' deficiencies. The effort to create network services is called Quality of Service (QoS). Software-Defined Networking (SDN) focuses on separating the control layer from the data layer, and their communication is done through a central controller named SDN controller. After separation, the data layer moves the packets through the network according to the commands it receives from the controller. The controller obtains applications (QoS requests), translates them to low-level instructions, and implements them in the data layer. In this paper, we create an infrastructure for Quality of Service (QoS) in tree topology using a meter table per flow in Software Defined Networking Floodlight open-source controller. Meters are introduced into the OpenFlow protocol version 1.3, which calculates the packet rates allocated to them and allows control of those packet rates. Meters are directly connected to flow entry. Any flow entry can determine a meter in its command collection, which calculates and supervises the sum of all flow entries to which it is connected. When we get statistics from the meter table in each switch, we manage the network and affect the routing algorithms.

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“…There are two types of traffic flows inside the DCN mice flow and elephant flow [3]. The mice flow comprises packets of small sizes for example text messages (e.g.…”

Section: Introductionmentioning

confidence: 99%

SD-ALB: Software Defined Adaptive load balancing in Data Center Network

Fazal,

Gilani,

Khalid

2023

KJCIS

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Network monitoring has crucial importance in data center networks to analyze the behavior of the underlying network. This analysis is used for working on multiple network parameters and load balancing is one of them. This article proposes an adaptive load-balancing approach to balance the load between the servers while changing its behavior with a change in traffic. Software Defined Networking (SDN) provides the single point of network configuration called SDN controller. This approach is facilitating the easy implementation of adaptive load balancing in Data Center Networks. The proposed approach is an extension to the LBBCLT load balancing approach that uses a dynamic probe generator to probe the servers about the response time and link bandwidth. We incorporate a path selection module in it and the path is selected using the Ant Colony Optimization. The results show that the bandwidth consumption and throughput have been improved and servers are receiving the load according to their capacities.

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Elephant Flow Detection Mechanism in SDN-Based Data Center Networks

Cited by 11 publications

References 29 publications

Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning

Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning

Performance Assessment of QoS metrics in Software Defined Networking using Floodlight Controller

SD-ALB: Software Defined Adaptive load balancing in Data Center Network

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