Cloud service composition using minimal unsatisfiability and genetic algorithm

2019 ◽  
Vol 32 (15) ◽  
Author(s):  
Abderrahim Ait Wakrime ◽  
Mouna Rekik ◽  
Said Jabbour
Keyword(s):  
Genetic Algorithm ◽  
Service Composition ◽  
Cloud Service ◽  
Cloud Service Composition ◽  
Minimal Unsatisfiability

Transportation Cloud Service Composition Based on Fuzzy Programming and Genetic Algorithm

2018 ◽  
Vol 2672 (45) ◽  
pp. 64-75
Author(s):  
Weibin Zhang ◽  
Haifeng Guo ◽  
Ziqiang Zeng ◽  
Yong Qi ◽  
Yinhai Wang
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Web Service ◽  
Service Composition ◽  
Big Data Analytics ◽  
Fuzzy Programming ◽  
Cloud Service ◽  
Multiple Objective Linear Programming ◽  
And Performance ◽  
Cloud Service Composition

The push toward smarter transportation management and decision-making has increased significantly in recent years. Toward this end, it is constructive to establish a traffic intelligence platform leveraging web services, big data analytics, and cloud computing. The developers of any such platform always face the challenge of selecting or creating composition plans from among numerous possible plans, often with unclear requirements, that satisfy their quality-of-service (QoS) requirements. Typical QoS properties associated with a web service are execution cost and time, availability, successful evaluation, usage frequency, and accuracy. Most of these factors are vague and/or difficult to quantity. To address the need to handle vague inputs, a constraint satisfaction-based web service composition algorithm is proposed, which is based on fuzzy multi-objective linear programming. A genetic algorithm with Pareto optimization evaluation with weighted standardized Euclidean distance is used that demonstrates good performance as the quantity of service candidate increases. The algorithm is capable of finding a satisfactory solution under input of vague QoS requirements, which shows better capability than multiple-objective linear programming. The applicability and performance of the proposed algorithm are validated in an online transportation analytics platform environment.

A dynamic ant-colony genetic algorithm for cloud service composition optimization

2019 ◽  
Vol 102 (1-4) ◽  
pp. 355-368 ◽  
Author(s):  
Yefeng Yang ◽  
Bo Yang ◽  
Shilong Wang ◽  
Feng Liu ◽  
Yankai Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Service Composition ◽  
Cloud Service ◽  
Ant Colony ◽  
Composition Optimization ◽  
Cloud Service Composition

scholarly journals MultiCuckoo: Multi-Cloud Service Composition Using a Cuckoo-Inspired Algorithm for the Internet of Things Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 56737-56749 ◽  
Author(s):  
Heba Kurdi ◽  
Fadwa Ezzat ◽  
Lina Altoaimy ◽  
Syed Hassan Ahmed ◽  
Kamal Youcef-Toumi
Keyword(s):  
Internet Of Things ◽  
Service Composition ◽  
Cloud Service ◽  
The Internet ◽  
Cloud Service Composition ◽  
The Internet Of Things ◽  
Multi Cloud

Combining reputation and QoS measures to improve cloud service composition

2017 ◽  
Vol 8 (2) ◽  
pp. 142 ◽  
Author(s):  
Fabrizio Messina ◽  
Giuseppe Pappalardo ◽  
Antonello Comi ◽  
Lidia Fotia ◽  
Domenico Rosaci ◽  
...  
Keyword(s):  
Service Composition ◽  
Cloud Service ◽  
Cloud Service Composition

scholarly journals An Efficient Multi-Cloud Service Composition Using A Distributed Multiagent-based, Memory-driven Approach

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Phillip Kendrick ◽  
Thar Baker ◽  
Zakaria Maamar ◽  
Abir Hussain ◽  
Rajkumar Buyya ◽  
...  
Keyword(s):  
Service Composition ◽  
Cloud Service ◽  
Cloud Service Composition ◽  
Multi Cloud

Client-Centric Cloud Service Composition

2017 ◽  
pp. 144-166
Author(s):  
Vivek Gaur ◽  
Praveen Dhyani ◽  
Om Prakash Rishi
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Business Processes ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Quality Parameters ◽  
End User ◽  
Cloud Service Composition ◽  
Tabu Search Methods

Recent computing world has seen rapid growth of the number of middle and large scale enterprises that deploy business processes sharing variety of services available over cloud environment. Due to the advantage of reduced cost and increased availability, the cloud technology has been gaining unbound popularity. However, because of existence of multiple cloud service providers on one hand and varying user requirements on the other hand, the task of appropriate service composition becomes challenging. The conception of this chapter is to consider the fact that different quality parameters related to various services might bear varied importance for different user. This chapter introduces a framework for QoS-based Cloud service selection to satisfy the end user needs. A hybrid algorithm based on genetic algorithm (GA) and Tabu Search methods has been developed, and its efficacy is analysed. Finally, this chapter includes the experimental analysis to present the performance of the algorithm.

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