scholarly journals Service-Level Agreement Durability for Web Service Response Time

2015 ◽  
Author(s):  
Hiranya Jayathilaka ◽  
Chandra Krintz ◽  
Rich Wolski
Keyword(s):  
Response Time ◽  
Web Service ◽  
Service Level Agreement ◽  
Service Level ◽  
Service Response Time

scholarly journals Prospective: A Data-Driven Technique to Predict Web Service Response Time Percentiles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 127904-127919
Author(s):  
Yasaman Amannejad ◽  
Diwakar Krishnamurthy ◽  
Behrouz Far
Keyword(s):  
Response Time ◽  
Web Service ◽  
Data Driven ◽  
Service Response Time

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.

Predicting Web service response time percentiles

2016 ◽  
Author(s):  
Yasaman Amannejad ◽  
Diwakar Krishnamurthy ◽  
Behrouz Far
Keyword(s):  
Response Time ◽  
Web Service ◽  
Service Response Time
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