An Intelligent Power Load Control/Switching System Using an Energy Meter and Relay Circuit

2016 ◽  
Vol 8 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Edward Osita Ofoegbu ◽  
Emmanuel Udoh
Keyword(s):  
Data Centers ◽  
Load Control ◽  
Automation System ◽  
Cloud Data ◽  
Energy Meter ◽  
Power Load ◽  
Building Automation System ◽  
Consumption Rates ◽  
Cloud Data Centers ◽  
The Cost

Energy conservation and its efficient utilization especially in cloud data centers has been a subject of discourse amongst numerous stakeholders. Advancement in information technology tools provides a solution to automating the process of electricity metering as well as remote load control alternatives. This paper presents an energy meter reader implemented with a microcontroller based logic methodology fused with a building automation system to implement remote load control by home owners using SMS from a GSM phone. A password based relay circuit was incorporated to ensure secure switching by the user. The system when deployed can enable users query and set energy consumption rates remotely so as to reduce the cost on final consumers as well as conserve energy. This is could be a useful system in the green-based design of cloud data centers.

Lowering Down the Cost for Green Cloud Data Centers by Using ESDs and Energy Trading

2016 ◽  
Author(s):  
Chonglin Gu ◽  
Ke Hu ◽  
Zhenlong Li ◽  
Qiang Yuan ◽  
Hejiao Huang ◽  
...  
Keyword(s):  
Data Centers ◽  
Energy Trading ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Green Cloud ◽  
The Cost

scholarly journals Optimal Switching and Load Distribution Strategy for Energy Minimization and Performance Optimization of Cloud Data Center

2021 ◽  
Author(s):  
Poobalan A ◽  
◽  
Sangeetha S ◽  
Shanthakumar P ◽  
◽  
...  
Keyword(s):  
Energy Saving ◽  
Performance Optimization ◽  
Data Center ◽  
Load Distribution ◽  
Data Centers ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Power Load ◽  
Cloud Data Centers ◽  
Manta Ray

Cloud computing is a promising computing technology utilized in every stage of the business. The cloud offers different services to cloud users from anytime to anywhere, and it is attained with different parameters, like load optimization, resource optimization. Due to the increase in data center, energy consumption has become a major issue in green data centers. The majority of data centers are function using peak load with huge scales. Thus, it is essential for carrying out energy saving in cloud data centers. This paper designed an energy-saving method using fat tree. The proposed techniques optimize the load at different zones of data center and user in the cloud platform. Here, the distribution of load in cloud data centers is performed using Taylor-based Manta Ray Foraging Optimization (Taylor-MRFO), which is an integration of Manta Ray Foraging Optimization (MRFO) and Taylor series. The method utilized different objectives that involve power, load, latency, and bandwidth. With the load distribution, the switching of cloud data center to the desired mode is performed using Actor critic neural network (ACNN). Thus, the dual strategy leads to performance optimization in cloud infrastructure and also in consolidating parallel workload in data centers more effectively. The proposed Taylor-MRFO+ACNN outperformed other methods with minimal energy of 0.553, minimal load of 0.363, and minimal fitness of 0.437, 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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