A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers

Cloud is an emerging technology that stores the necessary data and electronic form of data is produced in gigantic quantity. It is vital to maintain the efficacy of this data the need of data recovery services is highly essential. Cloud computing is anticipated as the vital foundation for the creation of IT enterprise and it is an impeccable solution to move databases and application software to big data centers where managing data and services is not completely reliable. Our focus will be on the cloud data storage security which is a vital feature when it comes to giving quality service. It should also be noted that cloud environment comprises of extremely dynamic and heterogeneous environment and because of high scale physical data and resources, the failure of data centre nodes is completely normal.Therefore, cloud environment needs effective adaptive management of data replication to handle the indispensable characteristic of the cloud environment. Disaster recovery using cloud resources is an attractive approach and data replication strategy which attentively helps to choose the data files for replication and the strategy proposed tells dynamically about the number of replicas and effective data nodes for replication. Thus, the objective of future algorithm is useful to help users together the information from a remote location where network connectivity is absent and secondly to recover files in case it gets deleted or wrecked because of any reason. Even, time oriented problems are getting resolved so in less time recovery process is executed.

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

Menoufia Journal of Electronic Engineering Research ◽

10.21608/mjeer.2017.63431 ◽

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

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Role of Solar Energy for Cloud Data Centers

Journal of the Association of Engineers India ◽

10.22485/jaei/2016/v86/i3-4/130884 ◽

2016 ◽

Vol 86 (3-4) ◽

pp. 78

Author(s):

Sambhunath Nan

Keyword(s):

Solar Energy ◽

Data Centers ◽

Cloud Data ◽

Cloud Data Centers

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Realistic Workload Generation for Cloud Data Centers

2020 28th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu49456.2020.9302386 ◽

2020 ◽

Keyword(s):

Data Centers ◽

Cloud Data ◽

Cloud Data Centers ◽

Workload Generation

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QiOi: Performance Isolation for Hyperledger Fabric

Applied Sciences ◽

10.3390/app11093870 ◽

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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