1. Image Reconstruction Based On Compressive Sampling Using IRLS and OMP Algorithm
Indrarini Dyah Irawati dan Andriyan B. Suksmono
Abstrak
We proposed compressive sampling to reduce the sampling rate of the image and improve the accuracy of image reconstruction. Compressive sampling requires that the representation of the image is sparse on a certain basis. We use wavelet transformation to provide sparsity matrix basis. Meanwhile, to get a projection matrix using a random orthonormal process. The algorithm used to reconstruct the image is orthogonal matching pursuit (OMP) and Iteratively Reweighted Least Squares (IRLS). The test result indicates that a high quality image is obtained along with the number of coefficients M. IRLS has a good performance on PSNR than OMP while OMP takes the least time for reconstruction.
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2. Internet Traffic Matrix Estimation Based on Compressive Sampling
Indrarini Dyah Irawati, Andriyan Bayu Suksmono dan Ian Joseph Matheus Edward
Abstrak
A method for estimating traffic matrix is needed for network operator. Traffic matrix is an essential parameter for network maintenance, network planning, and network monitoring. However, it is very difficult to measure it directly. Hence, traffic matrix is inferred from direct link measurement by estimation. There are various tech- niques for traffic matrix estimation. This paper proposed SVD-l1 (Singular Value Decomposition-l1) for traffic matrix estimation based on compressive sampling. The proposed method is compared with IRLS (Iteratively Reweighted Least Square), SRSVD (Sparsity Regularized Singular Value Decomposition), OMP (Orthogonal Matching Pursuit), and interpolation techniques. The result showed that the NMAE (Normalized Mean Abso- lute Error) performance parameter of SVD-l1 as good as SRSVD and better than the other algorithms. The computational time of SVD-l1 is the longest compared to others.
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3. Missing Internet Traffic Reconstruction using Compressive Sampling
Indrarini Dyah Irawati, Andriyan Bayu Suksmono dan Ian Joseph Matheus Edward
Abstrak
Missing traffic is a commonly problem in large- scale network. Because the traffic information is needed by network engineering task for network monitoring, there are several methods that recover the missing problem. In this paper, we proposed missing internet traffic reconstruction based on compressive sampling. The main contributions of this study are as follows: (i) explore the influence of the six missing patterns on the performance of the traffic matrix reconstruction algorithm; (ii) trace the link sensitivity; and (iii) detect the time sensitivity of the network. Using Abilene data, the simulation results show that compressive sampling can perform internet traffic monitoring such as reconstruction from missing traffic, finding link sensitivity, and detecting time sensitivity.
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4. Low-Rank Representation for Internet Traffic Reconstruction using Compressive Sampling
Indrarini Dyah Irawati, Ian Joseph Matheus Edward dan Andriyan Bayu Suksmono
Abstrak
We study compressive sampling for internet traffic reconstruction. Compressive Sampling (CS) requires that the traffic satisfies the low-rank feature. Low-rank states that traffic matrix can be represented in the right domain which the entire necessary information is concentrated in a low number of coefficients. In this paper, we compared three low-rank representation, which are Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Singular Value Decomposition Mean (SVDM). This low-rank representation is applied to four CS reconstruction algorithms, namely: Sparsity Regularized Singular Value Decomposition (SRSVD), Singular Value Decomposition L1 (SVDL1), Iteratively Reweighted Least Square (IRLS), Orthogonal Matching Pursuit (OMP), and Interpolation. The SVD outperforms the others low-rank representation techniques when used together with SRSVD, SVDL1, IRLS, and Interpolation. The SVDM gives the best NMAE when applied to the OMP. The computational times is linear with the number of the rank matrix. For all reconstruction algorithms, SVDM takes the least computational times.
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5. Routing Implementation Based-on Software Defined Network Using Ryu Controller and OpenvSwitch
Open Flow is a standard protocol for differentiating forward function and control functions to facilitate the management of big network of SDN. The research have been carried out before using the emulator SDN Mininet. However Mininet has many shortcomings, such as the performance of which is less than the maximum due to simulation. Then some researchers also use the Net-FPGA as device. This device is less suitable for small scale because the prices are quite expensive and programming is quite complicated. In this study, SDN implementation carried out using OpenvSwitch as forwarding function mounted on TP-Link that has modificated using openwrt as firmware and Raspberry Pi with Ryu SDN Controller as control functions. The result shows that routing static can be implemented on SDN Network which use Raspberry Pi with Ryu Controller as control function with average bandwith 536.0909 Mbits/sec and average uptime network is 10.45 second.
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6. Link Aggregation Control Protocol on Software Defined Network
A physical connection of computer network must be made reliably. Breaking connection will cause communication between nodes (for example routers, switches, hosts) can be disconnected. One of the solutions is implemention of link aggregation (LA). LA integrates several of physical ports together to make a single logical communication link. Accordingly, there is load sharing traffic among the member port of the group, high-throughput increasing via a single link, and redundancy providing for broken links. We present the implementation of link aggregation using Ryu controller on Software Defined Network (SDN) topology. The results show that the implementation of SDN with OpenvSwitch and Ryu controller can successfully run link aggregation function to solve the problem of link failure.
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