Future Clients' Requests Estimation for Dynamic Resource Allocation in Cloud Data Center Using CGPANN

2013 ◽  
Author(s):  
Jawad Ali ◽  
Faheem Zafari ◽  
Gul Muhammad Khan ◽  
S. Ali Mahmud
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Dynamic Resource Allocation ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Resource

Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

2021 ◽  
Author(s):  
Sakshi Chhabra ◽  
Ashutosh Kumar Singh
Keyword(s):  
Resource Allocation ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Dynamic Resource Allocation ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Resource

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

Preference Based Resource Allocation In Cloud Data Center

2018 ◽  
Vol 6 (2) ◽  
pp. 287-292
Author(s):  
M.R. Dave ◽  
◽  
H.B. Patel ◽  
B. Shrimali ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Cloud Data Center ◽  
Cloud Data

scholarly journals Reproducible experiments on dynamic resource allocation in cloud data centers

2016 ◽  
Vol 59 ◽  
pp. 98-101 ◽  
Author(s):  
Andreas Wolke ◽  
Martin Bichler ◽  
Fernando Chirigati ◽  
Victoria Steeves
Keyword(s):  
Resource Allocation ◽  
Data Centers ◽  
Dynamic Resource Allocation ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Dynamic Resource

scholarly journals Efficient Resource Utilization Based on Energy Management in Cloud Data Center

2019 ◽  
Vol 8 (6) ◽  
pp. 340-344
Keyword(s):  
Power Consumption ◽  
Resource Utilization ◽  
Data Center ◽  
Vital Role ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Efficient Resource ◽  
Novel Method ◽  
Dynamic Resource

Efficient resource utilization plays a vital role in cloud computing since the shared computational power of the resources is offered on demand. During dynamic resource allocation sometimes a server may be over utilized or underutilized thus leading to excess of energy consumption in the data centers. So the proposed system calculates the over utilization and underutilization of a CPU and RAM usage and also considers the network bandwidth usage to reduce power consumption in the cloud data center. Hence, a novel method is used for minimizing power consumption in the data center

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