Smart and fuzzy approach based on CSP for cloud resources allocation

In this study, we proposed a resource allocation approach that aims at fulfilling two main objectives. First, it equilibrates between the different cloud infrastructure particularities including load balancing, so it enhances the performance of infrastructure. Second, our approach provides a solution for the customer needs through shortening the execution time and reducing payments of the requested resources that have a dynamic nature. This paper suggests a new hybrid resources allocation approach based on three methods: multiagent system (MAS), distributed constraints satisfaction problems (DCSP), and the fuzzy logic (FL). The MAS represents the physical infrastructure. It provides an efficient management of the resources in the distribution and the heterogeneity of this infrastructure. DCSP, on the other hand, works side by side with MAS to maintain resources allocation policies in datacenters, while the FL is used to facilitate the representation of the dynamic resource values into linguistic terms (low, medium, high …) and helps the system to determine the best solution according to the criteria of customer requests. The experimental results show the efficiency of our approach in terms of load balancing, energy consumption cost, execution time, and the rate payment gain of customers.

• cloud resource allocation
• distributed constraint satisfaction problem
• multi-agent systems
• fuzzy logic
• load balancing
• data centers
• energy consumption cost
• execution time
• linguistic terms

• Cloud Computing and Resource Management
• Distributed and Parallel Computing Systems
• Energy Efficiency in Computing
• cloud resource allocation
• distributed constraint satisfaction problem
• multi-agent systems
• fuzzy logic
• load balancing
• data centers
• energy consumption cost
• execution time
• Information Systems
• Computer Networks and Communications
• Hardware and Architecture
• Computer Science