The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information. However, it is difficult to predict the congestion state of the link-end accurately at the source. In this paper, we presented an improved NUMFabric algorithm for calculating the overall congestion price. In the proposed scheme, the whole network structure had been obtained by the central control server in the Software Defined Network, and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated. In this scheme, the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information. Based on the historical data, the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm. The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm. We evaluate this evolved NUMFabric algorithm in NS3, which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.
The Internet of Things (IoT) was one of the main sources of big data [
In the traditional Internet business, network traffic usually takes place between the client outside the IoT datacenter and the requested server inside the datacenter (It is called “north-south traffic”), where the traffic between the servers (“east-west traffic”) was low [
Different applications in the IoT datacenter have different bandwidth and latency requirements. For example, online query services such as responding to search engines are obviously more urgent than virtual machine migration or log backup. In order to satisfy the application’s disparate demands with limited bandwidth, the server needs to decide how fast to send the packet. On the other hand, the link needs to determine the queue order of packets coming from different connections [
Recently, there have been a lot of research studied on transmission control protocol [
Contrasted to the traditional networks which suffer from low bandwidth and long delay time, with a centralized design, the cloud computing IoT date center network is capable of providing massive traffic bandwidth, quick response and high throughput. It is worth noting that due to the difference in network architecture, to guarantee the data transmission performance, the transfer control algorithms used in traditional networks cannot be directly applied in the networks of cloud computing IoT datacenters.
The function model of multi-source and multi-link utility maximization was presented based on nonlinear programming theory in Jalaparti and Bliznets’s Dynamic Pricing and Transmitting [
Until now, congestion pricing of the enhanced transmission control protocol for the current cloud computing IoT datacenter and the active queuing management approach in the link were involved in these theoretical nonlinear programming structures [
In each cloud computing IoT datacenter network, the rates of transfer can be related by a utility function. The goal is to maximize network utility within the limit of (subject to) link capacity as expressed by
where x is the rate vector of traffic TCP, R is the
The main technical contribution of NUMFabric is a TCP congestion control which using the NUM method converges on balance faster than previous research work. The kernel idea of NUMFabric is decoupling the methods for network utility maximization and for maximum congestion link bandwidth sharing among common transport flows. The former NUM algorithms combine these goals and try to solve both of these problems at the same time via price changes at the links. This process is no longer robust because of the requirement to balance between the goal of speedy convergence towards optimal link bandwidth sharing and of avoiding a state of traffic jam or low-utilization. Although the NUMFabric achieved better performance than others, the method of calculating link price with the average residual can be improved upon using Openflow in the cloud computing IoT datacenter.
In this paper, the whole network structure has been obtained by the central control server in the SDN, and in order to calculate link price, we propose a kind of dual hierarchy algorithm based on non-linear programming. The idea of the upper layer method is applied in the kernel controller of the SDN (i.e., Opendaylight) network for the cloud computing IoT datacenter and the guiding parameter B is given by the improved NUMFabric method in the first layer based on all link statuses (net topological structure, state of link end and size of packet queuing are included). The second hierarchy method is applied in links and the web congestion signal price is calculated based on the guiding parameter B given by the first layer method.
The rest of this paper is organized as follows. We present the improved NUM framework for enhanced NUMFabric congestion signal calculation algorithm in Section 2. In Section 3, we present the detail for the dual-hierarchy network of enhanced NUM Fabric algorithm. Particularly, Subsection 3.1 introduces the first hierarchy algorithm in the central controller; the second hierarchy algorithm in Openflow link is proposed in Subsection 3.2. Scheme comparison is given in Section 4. Efficiency analysis and throughput verifiability analysis are given in Sections 5 and 6 respectively. At last, Section 7 concludes the paper.
The improved NUM Fabric algorithm under the Software Defined Network structure is illustrated in
In the Software Defined Network structure, the information in the second hierarchy kernel link including new transport flow connection and network link status will be submitted to the top hierarchy method in the kernel central service controller. The network structure control module and statistic control module capture switch status information and propose theses link state operating information to the overall net status gathering module, so the first hierarchy method allocated in the kernel central controller in the cloud computing network can obtain real-time overall link state in web link. When the state of a small number of link load is low, the kernel service controller can change the routing flow table to balance the link load while the network transport flow will continue to send the packets. Therefore, it is not necessary to promote the link congestion price and restrain the transmitting rate of the sending TCP. The first hierarchy algorithm will not provide the radical guiding parameter B. But when all links are in peak load, it is out of the question to change the forward transmitting routing table. Therefore, in order to avoid all link states becoming the peak load state and to keep the cloud datacenter from becoming crowded, the kernel service controller will calculate the radical guiding parameter B and promote the link congestion signal price to restrict the sending window sizes of the source TCP.
The key idea of the improved algorithm is that the first hierarchy algorithm in kernel controller in cloud computing network provides the guiding parameter B by applying the switch link congestion signal price calculation method based on all switch link state. When small link switch status are in lower load, the central controller in the cloud computing IoT datacenter can instruct the data packet to turn to the lower load. In this case, the first hierarchy algorithm will not provide the radical guiding parameter B. However, when all links are in peak load it is out of the question to change the forward transmitting routing table. Therefore, in order to avoid all link state becoming the peak load state and to keep the cloud IoT datacenter from getting crowded, the kernel service controller will calculate the radical guiding parameter B and promote the link congestion signal price to restrict the sending windows size of the source TCP.
See
At the start time of the price calculation algorithm, based on the programming and openness of the Software Defined Network framework, the mapping relation among the network structure B of calculating link price parameter B and overall network link price are updated by the cloud computing IoT datacenter operator according to occupation history. When part Openflow link loads are light, the first hierarchy algorithm in central control servers give loose network structure B. But when all the links are under heavy load, the central control servers will provide the non-loose network structure B and promote the link congestion price to restrain the of the TCP source sending rate and avoid network congestion. The link load is
xWI is an innovative decentralized method for solving NUM problems; it operates with weighted max–min and realizes a transport layer like Swift. xWI is applied by iterative algorithm. For each iterating step, TCP flow communicates with the Openflow link to calculate the weights to set for their traffic flows in Swift. xWI iteratively obtains the weights to arrive at the balanced state of Network Utility Maximization. The important issues in xWI are to iteratively solve the KKT equations for the NUM problem.
As shown in
The network utility optimization model is described as followed:
Define the Lagrangian function
The dual problem of
We can obtain the dual problem as follows:
where
where
Substituting
At the time of
where
The second part based on underutilization form is given by
For each constant time interval, the central control servers will acquire map of the relationship between the link running status and vector B.
As shown in
The second algorithm in the openflow switch:
At time
Step 1: Receives rates
Step 2: Calculates a new link price
This is the refinement calculation method for link price.
Combining these two terms, we obtain as follows:
Here,
Step 3: Communicate new price to all sources
Source algorithm:
Step 1: Receives rates
Step 2: Chooses a new transmission rate
Step 3: Communicate new rate
The calculation results are shown in
No. | s1 | s2 | s3 | s4 | Load | B | s1/B | s2/B | s3/B | s4/B |
---|---|---|---|---|---|---|---|---|---|---|
1 | 0.2 | 0.3 | 0.2 | 0.3 | Lighter | 0.25 | 0.8 | 1.2 | 0.8 | 1.2 |
2 | 0.4 | 0.5 | 0.7 | 0.6 | Light | 0.55 | 0.72 | 0.9 | 1.27 | 1.11 |
3 | 0.7 | 0.6 | 0.8 | 0.5 | Low heavy | 0.65 | 1.07 | 0.92 | 1.23 | 0.78 |
4 | 0.8 | 0.9 | 0.7 | 0.6 | Heavy | 0.75 | 1.06 | 1.2 | 0.94 | 0.8 |
The improved NUMF method makes full use of the trait that the core server control in SDN can obtain the global link status and calculate the guiding parameter B. Compared with related methods, the link calculation price rule is divided into two terms and therefore more refined. The update rule for calculating price consists of two terms, namely formula
We design a semi-dynamic scenario to quantify the throughput per-flow. In the semi-dynamic case, we can trigger network incident in a controlled method and meter the network throughput.
We make the simulation at a cloud computing IoT datacenter network which is built using a leaf-spine structure with NS3. There are 32 servers connected to 4 leaf openflow links and the link bandwidths is 1000 Mbps. Each leaf openflow link is connected to 4 spine links and the link bandwidth is 10 Gbps, so it can guarantee full bisection bandwidth. The openflow links are intended to be standard output-queued links, with a buffer of size 1 MB per port. We have compared the convergence time with the following method.
Fast TCP optimizes TCP traffic over wide area networks and wireless data networks, especially in TCP environments with high latency and packet loss. Fast TCP does not change the standard format of TCP Packet Header, but the traffic control algorithm is optimized, which greatly improves the efficiency of TCP traffic and the utilization of WAN bandwidth.
We will extensively deploy TCP’s congestion control algorithm like DCTCP. DCTCP adopts Explicit Congestion Notification (ECN) in switches to detect and respond to network congestion signal by sequencing ECN marks via the switch [
This improved NUMFabric method propose a dual hierarchy method. The first hierarchy algorithm in the kernel controller produce the guiding parameter B for calculating switch link congestion signal price by taking full advantage of all switch link state information. The update rule for calculating price in the second hierarchy method consists of two terms, corresponding to the two optimality conditions.
In this section, according to evaluation of improved NUMFabric, we give a NS3 simulation. The goal is to simulate the throughput of NUMFabric to maximize the allocation in dynamic settings and to achieve bandwidth allocation goals precisely and robustly. We choose one-to-one communication, where our server communicates randomly with another server once a time. The results are demonstrated in
Convergence:
The main reason for the improvement in NUMFarbic convergence speed is that the link price is calculated by the global guiding parameter B which takes into consideration the global network running status. Further, the update rule for price calculation was also more refined designed; it made up with two terms, each according with two optimum conditions. From above CDF of convergence time, we can see that the improved NUMFabric achieves better convergence performance.
Compared to
The IoT data aggregation scheme based on SDN designed in this paper can improve NUMFabric algorithm for calculating overall congestion signal. We get the most advantage out of the trait that the whole network structure has been obtained by the central control server in the Software Defined Network and proposed a kind of dual hierarchy method for calculating overall network congestion signal. The first hierarchy method is set up in a central control server like Opendaylight and obtains the guiding parameter B based on the overall link state information. The second hierarchy method is assigned in Openflow link and the link price is calculated based on guiding parameter B given by the first method. The update rule for calculating price in the second hierarchy method consists of two terms, which correspond to the two optimality conditions. The simulation results demonstrate that this improved NUMFabric method can indeed provide better rate stability, reduce the time delay of data aggregation, improve the accuracy of data aggregation and the performance of the network.