Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

2019 ◽  
Vol 7 (3) ◽  
pp. 705-718 ◽  
Author(s):  
Yaser Mansouri ◽  
Adel Nadjaran Toosi ◽  
Rajkumar Buyya
Keyword(s):  
Data Centers ◽  
Cost Optimization ◽  
Cloud Data ◽  
Dynamic Replication ◽  
Cloud Data Centers ◽  
And Migration

Cost Optimization for Migration of Data in Cloud Data Centers

2021 ◽  
pp. 279-287
Author(s):  
S. Spoorthi ◽  
N. Rakshitha ◽  
K. S. Chandraprabha
Keyword(s):  
Data Centers ◽  
Cost Optimization ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks

2020 ◽  
Author(s):  
Sedighe Zakeri ◽  
Karthikeyan Krishnasamy ◽  
Bernard Gomathi
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Suggested Method ◽  
Cloud Data ◽  
Java Programming ◽  
The Status ◽  
Cloud Data Centers ◽  
Evolution Algorithm ◽  
And Migration ◽  
Planning Problems

Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.

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.

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