scholarly journals OpenDaylight and OpenNebula integration: Testing traffic management

2020 ◽  
Vol 12 (2) ◽  
pp. 74-79
Author(s):  
Omayma Belkadi ◽  
Alexandru Vulpe ◽  
Yassin Laaziz ◽  
Simona Halunga
Keyword(s):  
Traffic Management ◽  
Data Centers ◽  
Integration Testing ◽  
Common Component ◽  
Cloud Data ◽  
Cloud Management ◽  
Cloud Data Centers ◽  
Management Techniques ◽  
Deployment Time ◽  
Integrate Network

Software-Defined Networking (SDN) and Cloud Computing are now two of the most adopted technologies, on which many organizations are working to enhance every day. For instance, SDN is particularly emerging to solve networking complexity in cloud data centers, so we see many attempts to integrate Network and Cloud Managers. In this paper, we address an integration of these two technologies, particularly a yet undiscussed combination of two popular frameworks: OpenNebula and OpenDaylight. These open source solutions are widely used for cloud management and network management, yet there are no developed modules for communication between the two. Therefore, we propose a simple way for OpenDaylight to manage OpenNebula's compute nodes, using a common component they both support: OpenvSwitch. We compared OpenNebula with the popular OpenStack cloud manager, as it is attracting more attention in both academia and industry, by evaluating some relevant time metrics and discussing the differences of the proposed technologies. Then, we deployed a test topology to conduct some traffic management techniques in this integration. Our results show that OpenNebula's deployment time as well as clean-up time is significantly lower than OpenStack, but OpenStack takes less time to the running state, besides proving the simplicity of traffic management in OpenNebula using OpenDaylight.

scholarly journals Energy-Efficient Resources Management in Container- Based Clouds

2021 ◽  
pp. 361-365
Author(s):  
Sujalkumar Patel ◽  
Dr. Sanjay Patel
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
High Availability ◽  
Level Energy ◽  
Resources Management ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Cloud Environments ◽  
Management Techniques ◽  
Virtual Resources

Cloud enables access to a shared pool of virtual resources through Internet and its adoption rate is increasing because of its high availability, scalability and cost effectiveness. However, cloud data centers are one of the fastest-growing energy consumers and half of their energy consumption is wasted mostly because of inefficient allocation of the server’s resources. Therefore, this thesis focuses on software level energy management techniques that are applicable to containerized cloud environments. Containerized clouds are studied as containers are increasingly gaining popularity. And containers are going to be major deployment model in cloud environments.

Towards Scalable Traffic Management in Cloud Data Centers

2014 ◽  
Vol 62 (3) ◽  
pp. 1033-1045 ◽  
Author(s):  
Chadi Assi ◽  
Sara Ayoubi ◽  
Samir Sebbah ◽  
Khaled Shaban
Keyword(s):  
Traffic Management ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers

An Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers

2017 ◽  
Vol 26 (1) ◽  
pp. 113-128
Author(s):  
Gamal Eldin I. Selim ◽  
Mohamed A. El-Rashidy ◽  
Nawal A. El-Fishawy
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals QiOi: Performance Isolation for Hyperledger Fabric

2021 ◽  
Vol 11 (9) ◽  
pp. 3870
Author(s):  
Jeongsu Kim ◽  
Kyungwoon Lee ◽  
Gyeongsik Yang ◽  
Kwanhoon Lee ◽  
Jaemin Im ◽  
...  
Keyword(s):  
Data Centers ◽  
Component Level ◽  
Isolation Technique ◽  
Cloud Data ◽  
Cpu Scheduling ◽  
Performance Isolation ◽  
Cloud Data Centers ◽  
A New Technique ◽  
The Impact ◽  
Level Performance

This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristics of the different components in Hyperledger Fabric and show that Fabric components have different impacts on the performance of Fabric. Then, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The key idea of QiOi is to dynamically control the CPU scheduling of Fabric components to cope with the performance interference. We implement QiOi as a user-level daemon and evaluate how QiOi mitigates the performance interference of Fabric. The evaluation results demonstrate that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services. In addition, we show that QiOi can support different ordering services and chaincodes with negligible overhead to Fabric performance.

Adaptive Workload Forecasting in Cloud Data Centers

2019 ◽  
Vol 18 (1) ◽  
pp. 149-168 ◽  
Author(s):  
Eduard Zharikov ◽  
Sergii Telenyk ◽  
Petro Bidyuk
Keyword(s):  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers
Sign in / Sign up

Export Citation Format

Share Document