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.

Modeling And Analyzing Cloud Auto-Scaling Mechanism Using Stochastic Well-Formed Coloured Nets

2021 ◽  
Author(s):  
Mohamed M. Ould Deye ◽  
Mamadou Thiongane ◽  
Mbaye Sene
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Level ◽  
The State ◽  
Service Level Agreements ◽  
Auto Scaling

Auto-scaling is one of the most important features in Cloud computing. This feature promises cloud computing customers the ability to best adapt the capacity of their systems to the load they are facing while maintaining the Quality of Service (QoS). This adaptation will be done automatically by increasing or decreasing the amount of resources being leveraged against the workload’s resource demands. There are two types and several techniques of auto-scaling proposed in the literature. However, regardless the type or technique of auto-scaling used, over-provisioning or under-provisioning problem is often observed. In this paper, we model the auto-scaling mechanism with the Stochastic Well-formed coloured Nets (SWN). The simulation of the SWN model allows us to find the state of the system (the number of requests to be dispatched, the idle times of the started resources) from which the auto-scaling mechanism must be operated in order to minimize the amount of used resources without violating the service-level agreements (SLA).

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 An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

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 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.

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

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