Sri Astuti
@uhamka.ac.id
Education Management and Economic Education
Universitas Muhammadiyah Prof. DR. HAMKA
Scopus Publications
Scopus Publications
Reza Maharani Susilo, Farraz Rizky Kusumaputra, Muhammad Hendrawan Adiwijaya, Ratna Mayasari, Ridha Muldina Negara, and Sri Astuti
IEEE
Named Data Networking (NDN) represents a forward-looking networking concept that addresses various challenges within the current internet architecture, particularly the reliance on IP addresses for data transmission between devices. In response, Named Data Networking (NDN) and Software-Defined Networking (SDN) architectures introduce a novel approach to data delivery by shifting from a host-centric to a data-centric model. This transition not only enhances data distribution efficiency but also leverages SDN advantages stemming from its segregation of the data and control planes. To implement this convergence, we incorporated the SDN paradigm into the NDN environment. By doing so, we harnessed the capabilities of both SDN and NDN, enhancing network efficiency and reducing data retrieval time for consumers. We utilized Named Data Link State Routing (NLSR) as a routing protocol within the default NDN environment. However, NDN encompasses diverse forwarding strategies tailored to specific network conditions. This research specifically investigates Best-Route Forwarding, Multicast Forwarding, and Adaptive Smoothed RTT Forwarding strategies. The objective is to evaluate the disparities and appropriateness of these strategies within NLSR-NDN and SDN-NDN environments when applied to wireless networks. To assess the efficacy of these strategies, our analysis employs Quality of Service (QoS) parameters, encompassing Average Round Trip Time (RTT), Throughput, Packet Loss, and Satisfied Interest Ratio. These additional metrics provide a comprehensive evaluation of interest satisfaction. Our findings reveal that the SDN-NDN environment remarkably enhances network efficiency by approximately 50-70% compared to the NLSR-NDN environment.
Fatma Nur Afifah, Nasrullah Pandu Dewantara, Ahmad Faris Faiz, Syahda Romansyah, Ridha Muldina Negara, Ratna Mayasari, and Sri Astuti
IEEE
This paper proposes to automate server usage monitoring for resource metrics using Prometheus and Grafana. The main contribution of this work is to create a cost-effective web-based application monitoring system combining Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA), called Anbu, suitable for scalable startups. The web-based application monitoring system is achieved by comparing the monthly fee with the existing monitoring system. To validate the accuracy of Anbu, services in the Kubernetes cluster are tested in two cases using a load test. The first case is to scale the number of replicas set for the service according to CPU and memory. The second is to provide recommendations for the ideal number of CPUs and memory according to the history of their actual use. The results show that scalable startups can do automation monitoring with an Anbu usage fee for 24 hours of uptime of around $200 per month. It will save $139.04 less per month than the existing monitoring system. This price is suitable for scalable startups who still manually automate server usage with an annual salary of less than $100.000.
Zhafirah Naghmah Ahmad, Fika Triana, Revita Rachel, Ridha Muldina Negara, Ratna Mayasari, Sri Astuti, and Syamsul Rizal
IEEE
In the current network architecture, IP addresses are used, where data transmission uses the host address on each device. From this data delivery method, NDN emerges as a new paradigm in data transmission from being host-centric to becoming data-centric. There is a strategy used in research with the weakness of congestion in the forwarding strategy. Therefore, modeling the forwarding strategy using Reinforcement Learning is designed to overcome this problem. In the run simulation, an environment will be created in the Reinforcement Learning system with several scenarios in the NDN network. To measure the success of the system, testing is carried out to achieve maximum results, such as the Reinforcement Learning process, which is trial and error in nature, which means that several experiments are carried out, such as the exploration process carried out by the agent in the environment to achieve the goal and get the expected maximum reward. The components used in Reinforcement Learning in the training process are agents, actions, policies, and rewards. The tests aim to make NDN an efficient network system, simplify network performance automatically using Reinforcement Learning, and make NDN a network system that can overcome congestion for forwarding.
Reza Maharani Susilo, Farraz Rizky Kusumaputra, Muhammad Hendrawan Adiwijaya, Ratna Mayasari, Ridha Muldina Negara, and Sri Astuti
IEEE
Along with the time, the development of the internet has grown so rapidly that it cannot be controlled. This has resulted in the current internet architecture that no longer being able to meet existing needs. The emergence of Named Data Networking (NDN) architecture can help to overcome previous problems. However, NDN architecture also has its own shortcomings or problems such as Broadcast Storm, which causes packets to be sent in the network to spin continuously that make requiring more energy and consuming considerable time. In this final project, the author implements a comparison of Forwarding Strategy methods focused on Analysis of Forwarding Strategies in Wireless Network Based Named Data Network to find the Best Strategies to prevent the Broadcast Storm and improve the efficiency of Network. Performance testing is carried out by experimenting with several different Forwarding Strategies. After experimenting with the Forwarding Strategies, the authors can find which is the best Forwarding Strategy to prevent the Broadcast Storm that occurs in Wireless Networks.
Sri Astuti, Rendy Munadi, and Istikmal
IEEE
Routing mechanism is one of the many ways to optimize the performance of Zigbee network that used battery-powered device. Therefore, a variety of routing algorithms are developed to obtain the optimal performance. One of the examples is Power-Source Aware Routing (PSAR) algorithm. However, PSAR algorithm has problem that the reconfiguration of battery powered node from communication path is not always possible to be conducted due to the availability of lots of battery-powered devices, or node located outside the communication range, etc. We proposed the modified PSAR algorithm by adding a fuzzy logic. This was to select the optimal battery powered node to forward the message to their neighbors. The results of the simulation indicate that for overall battery powered node, PSAR algorithm has 10.5 percent smaller energy consumption in average compared to Tree algorithm. In addition as a result of the reconfiguration, PSAR algorithm has longer lifetime node. For overall battery-powered node, the modified PSAR algorithm has 6.1 percent smaller energy consumption compared to PSAR algorithm. The modified PSAR algorithm has also more balance lifetime node.