Online End-to-End Quality of Service Monitoring for Service Level Agreement Verification

2006 ◽  
Author(s):  
Xiaoyuan Ta ◽  
Guoqiang Mao
Keyword(s):  
Quality Of Service ◽  
Service Level Agreement ◽  
Service Level ◽  
Service Monitoring ◽  
End To End

scholarly journals AN ANALYSIS OF THE APPLICATION END TO END QUALITY OF SERVICE ON 3G TELECOMMUNICATION NETWORK

2012 ◽  
Vol 6 (1) ◽  
pp. 20
Author(s):  
Cahya Lukito ◽  
Rony Baskoro Lukito ◽  
Deddy Arifin
Keyword(s):  
Quality Of Service ◽  
Telecommunication Network ◽  
Service Level Agreement ◽  
Service Level ◽  
Analysis Method ◽  
Voice Service ◽  
End To End ◽  
The Impact

End to End Quality of Service is a way to provide data package service in a telecommunication network that based on Right Price, Right Service Level, and Right Quality. The goal of this research is to analyze the impact of End to End QoS use on 3G telecommunication network for voice service and data. This research uses an analysis method by doing the application on the lab. The result that is achieved in this research shows that End to End QoS is very influental to the Service Level Agreement to the users of the telecommunication service.Keywords: End to End Qos, SLA, Diffserv

Quality of Service Monitoring, Diagnosis, and Adaptation for Service Level Management

2011 ◽  
pp. 41-58
Author(s):  
Guijun Wang ◽  
Changzhou Wang ◽  
Haiqin Wang ◽  
Rodolfo A. Santiago ◽  
Jingwen Jin ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Real Time ◽  
Service Level ◽  
Management Framework ◽  
Qos Management ◽  
Service Monitoring ◽  
Service Level Management ◽  
Qos Monitoring ◽  
Monitoring Service

A key requirement in Service Level Management (SLM) is managing the Quality of Services (QoS) demanded by clients and offered by providers. This managing process is complicated by the globalization and Internet scale of enterprise services and their compositions. This chapter presents two contributions to the QoS management task for SLM. First, instead of considering monitoring as an isolated service, it incorporates a monitoring service as an integral part of a comprehensive QoS management framework for SLM. Second, it includes a diagnosis service as an integral part of the QoS management framework. Using the data fed from monitoring service, diagnosis service detects system condition changes and reasons about the causes of detected degradation in networked enterprise system. With condition detection and situation understanding, the QoS management framework can then proactively activate adaptation mechanisms to maximize the system’s ability to meet QoS contract requirements of concurrent clients. Using this framework, enterprise systems can provide real time automated QoS management to optimize system resources in meeting contract requirements. This approach is validated using QoS management services integrated in a publish/subscribe style of SOA. Benefits of QoS monitoring, diagnosis, and adaptation services for responsiveness SLM are demonstrated via experiments.

scholarly journals Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

2020 ◽  
Vol 178 ◽  
pp. 375-385
Author(s):  
Ismail Zahraddeen Yakubu ◽  
Zainab Aliyu Musa ◽  
Lele Muhammed ◽  
Badamasi Ja’afaru ◽  
Fatima Shittu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Delivery ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment

scholarly journals A Survey on Machine Learning and Deep Learning based Quality of Service aware Protocols for Software Defined Networks

2021 ◽  
Author(s):  
Hiren Kumar Deva Sarma
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Deep Learning ◽  
Service Level Agreement ◽  
Service Level ◽  
Future Research ◽  
Traffic Classification ◽  
Software Defined Networks ◽  
Research Issues

<p>Quality of Service (QoS) is one of the most important parameters to be considered in computer networking and communication. The traditional network incorporates various quality QoS frameworks to enhance the quality of services. Due to the distributed nature of the traditional networks, providing quality of service, based on service level agreement (SLA) is a complex task for the network designers and administrators. With the advent of software defined networks (SDN), the task of ensuring QoS is expected to become feasible. Since SDN has logically centralized architecture, it may be able to provide QoS, which was otherwise extremely difficult in traditional network architectures. Emergence and popularity of machine learning (ML) and deep learning (DL) have opened up even more possibilities in the line of QoS assurance. In this article, the focus has been mainly on machine learning and deep learning based QoS aware protocols that have been developed so far for SDN. The functional areas of SDN namely traffic classification, QoS aware routing, queuing, and scheduling are considered in this survey. The article presents a systematic and comprehensive study on different ML and DL based approaches designed to improve overall QoS in SDN. Different research issues & challenges, and future research directions in the area of QoS in SDN are outlined. <b></b></p>

scholarly journals SSD Bandwidth Distributing I/O Scheduler Considering Garbage Collection

2018 ◽  
Vol 12 (1) ◽  
pp. 1-6
Author(s):  
Jung Kyu Park ◽  
Jaeho Kim
Keyword(s):  
Quality Of Service ◽  
Low Power ◽  
Garbage Collection ◽  
Service Level Agreement ◽  
Service Level ◽  
Storage Device ◽  
Hard Disks ◽  
Improve Performance ◽  
Simulation Based

There were scheduler studies for QoS(Quality of Service) or SLA(Service Level Agreement) of hard disks. The use of SSDs as storage has been increasing dramatically in recent systems due to their fast performance and low power usage. However, the studies to guarantee the SLA are based on the hard disk and do not consider SSD which is a ash storage device. In the SSD, GC(Garbae Collection) process copies data to an empty block and the corresponding block is removed by the GC. This causes SSD performance to degrade in a virtualized environment with many I/Os. We considered the Linux scheduler to take SSD characteristics into consideration and to improve I/O performance. In this paper, we propose a MTS-CFQ I/O scheduler that is implemented by modifying the existing Linux CFQ I/O scheduler. Our proposed method controls the time slice based on the I/O bandwidth for the current storage device. Real workload-driven simulation based experimental results have shown that MTS-CFQ can improve performance by up to 20% with an average of 5%, compared with the traditional CFQ I/O for the four workload considered.  

scholarly journals Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

2019 ◽  
Vol 19 (3) ◽  
pp. 94-117
Author(s):  
K. Bhargavi ◽  
B. Sathish Babu
Keyword(s):  
Quality Of Service ◽  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Soft Set ◽  
Learning Agent ◽  
Optimal Resource ◽  
Auto Scaling ◽  
Heterogeneous Workloads

Abstract Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

scholarly journals Priority-Based Virtual Machine Selection Algorithm in Cloud Computing

2019 ◽  
Vol 8 (3) ◽  
pp. 1457-1462
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Cloud Service Provider

Cloud computing technology has gained the attention of researchers in recent years. Almost every application is using cloud computing in one way or another. Virtualization allows running many virtual machines on a single physical computer by sharing its resources. Users can store their data on datacenter and run their applications from anywhere using the internet and pay as per service level agreement documents accordingly. It leads to an increase in demand for cloud services and may decrease the quality of service. This paper presents a priority-based selection of virtual machines by cloud service provider. The virtual machines in the cloud datacenter are configured as Amazon EC2 and algorithm is simulated in cloud-sim simulator. The results justify that proposed priority-based virtual machine algorithm shortens the makespan, by 11.43 % and 5.81 %, average waiting time by 28.80 % and 24.50%, and cost of using the virtual machine by 21.24% and 11.54% as compared to FCFS and ACO respectively, hence improving quality of service.

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