ANALISIS PERBANDINGAN GATEWAY BALANCING PADA SOFTWARE DEFINED NETWORK DENGAN ALGORITMA SCHEDULING TAMBAHAN

Pada proses pengiriman paket data pada suatu jaringan, tentu dibutuhkan arsitektur yang memiliki keandalan yang tinggi. Hal ini dibutuhkan agar dapat menjamin kecepatan pengiriman dan tidak adanya data yang gagal dikirimkan ke tujuan. Tersedianya banyak jalur tentu akan meningkatkan availability pada jaringan tersebut, namun tidak menjamin akan meningkatkan kecepatan atau kualitas pengiriman data dan keberhasilan data tersebut sampai ke tujuan. Adanya dua atau lebih jalur memungkinkan akan terjadinya penyebaran trafik yang tidak merata dan penumpukan trafik pada suatu gateway. Untuk menghindari hal ini, maka akan dilakukan metode Gateway Balancing. Pada penelitian ini dilakukan penerapan gateway balancing pada jaringan Software Defined Network menggunakan controller OpenDayLight dengan menerapkan dua algoritma scheduling yaitu algoritma Ant Colony Optimizaion dan algoritma Random Early Detection. Dengan menerapkan gateway balancing pada dua algoritma ini, maka akan dilakukan pengujian performansi trafik dengan mengalirkan UDP Flows. Parameter pengujian yang digunakan adalah Throughput, Delay, Jitter, dan Packeloss. Disimpulkan bahwa perfomansi trafik menggunakan gateway balancing dengan algortima Ant Colony Optimization menawarkan kecepatan data lebih baik dibandingkan algoritma Random Early Detection dengan selisih delay sebesar 4,46%. Sedangkan pada algoritma Random Early Detection, menghasilkan performa yang lebih baik pada keutuhan datanya, dengan selisih 3,55% pada throughput dan 5,85% pada packetloss yang dihasilkan.

Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets

Formal modeling is considered one of the fundamental phases in the design of network algorithms, including Active Queue Management (AQM) schemes. This article focuses on modeling with Petri nets (PNs) a new scheme of AQM. This innovative AQM is based on a discrete dynamical model of random early detection (RED) for controlling bifurcations and chaos in Internet congestion control. It incorporates new parameters (α,β) that make possible better stability control over oscillations of an average queue length (AQL) at the router. The PN is validated through the matrix equation approach, reachability tree, and invariant analysis. The correctness is validated through the key properties of reachability, boundedness, reversibility, deadlock, and liveness.

Internet congestion control: From stochastic to dynamical models

Since its inception, control of data congestion on the Internet has been based on stochastic models. One of the first such models was Random Early Detection. Later, this model was reformulated as a dynamical system, with the average queue sizes at a router’s buffer being the states. Recently, the dynamical model has been generalized to improve global stability. In this paper we review the original stochastic model and both nonlinear models of Random Early Detection with a two-fold objective: (i) illustrate how a random model can be “smoothed out” to a deterministic one through data aggregation and (ii) how this translation can shed light into complex processes such as the Internet data traffic. Furthermore, this paper contains new materials concerning the occurrence of chaos, bifurcation diagrams, Lyapunov exponents and global stability robustness with respect to control parameters. The results reviewed and reported here are expected to help design an active queue management algorithm in real conditions, that is, when system parameters such as the number of users and the round-trip time of the data packets change over time. The topic also illustrates the much-needed synergy of a theoretical approach, practical intuition and numerical simulations in engineering.

An Exponential Active Queue Management Method Based on Random Early Detection

Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.