Estimating service response time for elastic cloud applications

2012 ◽  
Author(s):  
Khaled Salah ◽  
Raouf Boutaba
Keyword(s):  
Response Time ◽  
Service Response Time ◽  
Cloud Applications

PrEstoCloud

2021 ◽  
Vol 34 (1) ◽  
pp. 66-85
Author(s):  
Yiannis Verginadis ◽  
Dimitris Apostolou ◽  
Salman Taherizadeh ◽  
Ioannis Ledakis ◽  
Gregoris Mentzas ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Software Engineering ◽  
Response Time ◽  
Fog Computing ◽  
Data Sources ◽  
Data Intensive ◽  
Service Response Time ◽  
Enabling Services ◽  
Data Intensive Applications ◽  
Multi Cloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

Factors Influencing the Emergency Medical Service Response Time for Cardiovascular Disease in Guangzhou, China

2019 ◽  
Vol 39 (3) ◽  
pp. 463-471
Author(s):  
Xiao-qian Chen ◽  
Zi-feng Liu ◽  
Shi-kun Zhong ◽  
Xing-tang Niu ◽  
Yi-xiang Huang ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Response Time ◽  
Emergency Medical Service ◽  
Medical Service ◽  
Factors Influencing ◽  
Emergency Medical ◽  
Service Response Time

scholarly journals Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

2019 ◽  
Vol 9 (17) ◽  
pp. 3550 ◽  
Author(s):  
A-Young Son ◽  
Eui-Nam Huh
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Response Time ◽  
Data Centers ◽  
Cloud Data ◽  
Multi Objective ◽  
Service Placement ◽  
Service Response Time ◽  
Cloud Data Centers ◽  
Distributed Cloud

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

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
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