Quality of Service in Large Scale Mobile Distributed Systems Based on Opportunistic Networks

2011 ◽  
Author(s):  
Mihai Tanase ◽  
Valentin Cristea
Keyword(s):  
Quality Of Service ◽  
Distributed Systems ◽  
Large Scale ◽  
Opportunistic Networks

A Simulation Software for the Evaluation of Vulnerabilities in Reputation Management Systems

2021 ◽  
Vol 37 (1-4) ◽  
pp. 1-30
Author(s):  
Vincenzo Agate ◽  
Alessandra De Paola ◽  
Giuseppe Lo Re ◽  
Marco Morana
Keyword(s):  
Quality Of Service ◽  
Distributed Systems ◽  
State Of The Art ◽  
Simulation Software ◽  
Reputation Management ◽  
Management Systems ◽  
Security Attacks ◽  
Vulnerability Evaluation ◽  
Multi Agent

Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent on disseminating false reputation values. Given the variety of application scenarios in which RMSs can be adopted, as well as the multitude of behaviours that agents can implement, designers need RMS evaluation tools that allow them to predict the robustness of the system to security attacks, before its actual deployment. To this aim, we present a simulation software for the vulnerability evaluation of RMSs and illustrate three case studies in which this tool was effectively used to model and assess state-of-the-art RMSs.

An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking

2020 ◽  
Vol 28 (4) ◽  
pp. 1228-1247
Author(s):  
Lucio Henrik A. Reis ◽  
Luiz Claudio S. Magalhães ◽  
Dianne Scherly V. de Medeiros ◽  
Diogo M. F. Mattos
Keyword(s):  
Quality Of Service ◽  
Large Scale ◽  
Wireless Networking ◽  
Unsupervised Approach

scholarly journals Analisis Perbandingan Quality Of Service (Qos) Pada Performa Bandwidth Jaringan Dengan Metode Hierarchical Token Bucket (Htb) Dan Per Connection Queque (Pcq).

Respati ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. 24
Author(s):  
Adytia A. Tambunan ◽  
Lukman Lukman
Keyword(s):  
Quality Of Service ◽  
Large Scale ◽  
Improve Quality ◽  
Bandwidth Management ◽  
Available Bandwidth ◽  
Service Parameter ◽  
Token Bucket ◽  
Large Scale Networks ◽  
Network Administrators

INTISARIQuality of Service (QoS) adalah cara cerdas untuk mengalokasikan bandwidth yang tersedia. Penggunaan manajemen bandwidth sebagai parameter Quality of Service tidak hanya membatasi tetapi menjaga kualitas bandwidth, sehingga semua pengguna yang terhubung dalam satu jaringan mendapatkan kualitas internet yang merata dan stabil. Ada beberapa cara untuk mengaplikasikan bandwidth managemen untuk meningkatkan Quality of Service, salah satunya yakni menggunakan mikrotik.Ada banyak metode manajemen   bandwidth yang dapat digunakan atau diterapkan pada jaringan yang menggunakan router mikrotik. Adapun yang paling sering digunakan dalam jaringan berskala menengah atau pun besar seperti metode Hierarchical Token Bucket (HTB) dan Per Connection Queue (PCQ).Penelitian ini akan melakukan analisis variabel Quality of Service (QoS) terhadap performa bandwidth dengan membandingkan dua metode antrian yaitu metode Hierarchical Token Bucket (HTB) dan metode Per Connection Queue (PCQ).  Penelitian dilakukan untuk mengetahui metode manakah lebih baik untuk di implementasikan oleh administrator jaringan agar efesien dan tepat untuk digunakan.Kata kunci: Manajemen Bandwidth, QOS, PCQ, HTB, Mikrotik. ABSTRACTQuality of Service (QoS) is a smart way to allocate available bandwidth. The use of bandwidth management as a Quality of Service parameter not only limits but maintains bandwidth quality, so that all users connected in one network get an even and stable internet quality. There are several ways to apply bandwidth management to improve Quality of Service, one of which is using a proxy.There are many bandwidth management methods that can be used or applied to networks that use a proxy router. As for what is most often used in medium or large scale networks such as the Hierarchical Token Bucket (HTB) and Per Connection Queue (PCQ) methods.This study will analyze the Quality of Service (QoS) variable on bandwidth performance by comparing two queuing methods, namely the Hierarchical Token Bucket (HTB) method and the Per Connection Queue (PCQ) method. The study was conducted to determine which method is better for network administrators to implement in order to be efficient and appropriate to use.Keywords: Bandwidth Management, QOS, PCQ, HTB, Mikrotik.

scholarly journals Enhanced Quality of Service Strategy for Improving Network Coverage in IOT Applications

2021 ◽  
Vol 23 (05) ◽  
pp. 694-707
Author(s):  
Dr. D. I. George Amalarethinam ◽  
◽  
Ms. P. Mercy ◽  
Keyword(s):  
Quality Of Service ◽  
Large Scale ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Threshold Value ◽  
Maximum Energy ◽  
Sensor Nodes ◽  
Network Coverage ◽  
Fuzzy C Means Clustering

The Internet of Things (IoT) is a network that includes physical things capable of aggregating and communicating electronic information. With the advancement in wireless sensor networks, IoT provides highly efficient communication for various real-time applications. IoT networks are large-scale networks where routing can be improved by focusing on the Quality of Service (QoS) Parameter. Network coverage can be enhanced by hierarchical clustering of the nodes which increases the network lifetime. The proposed algorithm Enhanced Fuzzy Based Clustering and Routing Algorithm (EFCRA) performs distance and energy-based cluster head selection to find a new path from source to destination. The algorithm uses Fuzzy c-means clustering to provide optimization in forming cluster centers. The cluster head (CH) is identified based on the minimum distance and maximum energy of the sensor node. The cluster head is updated when its energy is lesser than the threshold value. The distance between sensor nodes and its CH node and then to the destination is computed using Dijkstra’s algorithm. The proposed routing strategy provides improved network coverage and throughput which extends the lifetime of the IoT network.

scholarly journals Green Metascheduler Architecture to Provide QoS in Cloud Computing

2015 ◽  
Author(s):  
Osvaldo Adilson De Carvalho Junior ◽  
Sarita Mazzini Bruschi ◽  
Regina Helena Carlucci Santana ◽  
Marcos José Santana
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Response Time ◽  
Large Scale ◽  
Quality Of Services ◽  
Average Response ◽  
Average Response Time ◽  
Private Cloud

The aim of this paper is to propose and evaluate GreenMACC (Green Metascheduler Architecture to Provide QoS in Cloud Computing), an extension of the MACC architecture (Metascheduler Architecture to provide QoS in Cloud Computing) which uses greenIT techniques to provide Quality of Service. The paper provides an evaluation of the performance of the policies in the four stages of scheduling focused on energy consumption and average response time. The results presented confirm the consistency of the proposal as it controls energy consumption and the quality of services requested by different users of a large-scale private cloud.

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