Smart Design for Resources Allocation in IoT Application Service based on multi-agent system and CSP

Mouadh Bali, Abdelkamel Tari, Abdallah Almutawakel, Okba Kazar


In the present paper, we aim at solving two problems; the first problem occurring in the transformation of the IoT devices (sensors, actuators, …) to cloud service. Therefore, we work on maintaining a smooth and efficient data transmission for the cloud and support customer applications like: data sharing, storage and processing. The second problem has two dimensions. In the first dimension, the problem is arisen in the submission of cloudlets (customer requested jobs) to Virtual Machines (VMs) in the hosts. To solve this problem, we propose scheduling algorithm for resource allocation according to the lowest cost and load. In the second dimension, the problem lies in the hosting of new VMs in the hosts. To overcome this problem, we need take into account the loads when housing new VMs in different datacenters. In this work, we suggest a resource allocation approach for services oriented IoT applications. The architecture of this approach is based on two technics: Multi Agent System (MAS) and Distributed Constraint Satisfaction Problems (DCSP). The MAS manages the physical resources, making decision and the communication between datacenters, while DCSP used to simplify the policy of the resources provisioning in Datacenters. Variables and constraints are distributed among multiple agents in different layers. The experimental results show that the efficiency of our approach is manifested in: Average System Load, Cost augmentation Rate and Available Mips.

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Anithakumari, S., & Chandrasekaran, K. (2017). Interoperability based resource management in cloud computing by adaptive dimensional search. Proceedings - 2017 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2017, 77–84.

Artan, M., Minarolli, D., & Bernd, F. (2017). Distributed Resource Allocation in Cloud Computing Using Multi-Agent Systems. Telfor, 9(2), 110–115. Retrieved from

Bajo, J., De la Prieta, F., Corchado, J. M., & Rodríguez, S. (2016). A low-level resource allocation in an agent-based Cloud Computing platform. Applied Soft Computing Journal, 48, 716–728.

Baran, B., & Fabio, L.-P. (2017). Resource Allocation for Cloud Infrastructures: Taxonomies and Research Challenges. Research Advances in Cloud Computing, (January 2018), 1–465.

Chen, J., Han, X., & Jiang, G. (2014). A Negotiation Model Based on Multi-agent System under Cloud Computing. In The Ninth International Multi-Conference on Computing in the Global Information Technology, 157–164.

Ejarque, J., Álvarez, J., Sirvent, R., & Badia, R. M. (2012). Resource Allocation for Cloud Computing: A Semantic Approach. In Open Source Cloud Computing Systems (pp. 90–112).

Fayazi, M., Reza, M., & Enayatollah, S. (2016). Resource Allocation in Cloud Computing Using Imperialist Competitive Algorithm with Reliability Approach. International Journal of Advanced Computer Science and Applications, 7(3), 323–331.

Gawanmeh, A., & April, A. (2016). A Novel Algorithm for Optimizing Multiple Services Resource Allocation. International Journal of Advanced Computer Science and Applications, 7(6), 428–434.

Gutierrez-Garcia, J. O., & Sim, K. M. (2011). Agents for cloud resource allocation: An amazon EC2 case study. Communications in Computer and Information Science, 261 CCIS, 544–553.

Jing, L., Weicai, Z., & Licheng, J. (2006). A multiagent evolutionary algorithm for constraint satisfaction problems. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 36(1), 54–73.

Khan, H. M., Chan, G. Y., & Chua, F. F. (2018). A fuzzy model for detecting and predicting cloud quality of service violation. Journal of Engineering Science and Technology, 13(Special Issue on ICCSIT 2018), 58–77.

Lu, D., Ma, J., & Xi, N. (2015). A universal fairness evaluation framework for resource allocation in cloud computing. China Communications, 12(5), 113–122.

Mezache, C., Kazar, O., & Bourekkache, S. (2016). A Genetic Algorithm for Resource Allocation with Energy Constraint in Cloud Computing. Proceedings of 2016 International Conference on Image Processing, Production and Computer Science (ICIPCS’2016) London (UK), March 26-27, 2016 Pp.62-69 A, (August), 62–69.

Nair, A. S., Hossen, T., Campion, M., Selvaraj, D. F., Goveas, N., Kaabouch, N., & Ranganathan, P. (2018). Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid. Technology and Economics of Smart Grids and Sustainable Energy, 3, 1–15.

Naseri, A., & Jafari Navimipour, N. (2019). A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 10(5), 1851–1864.

Rodrigo N., C., Rajiv, R., Anton, B., Rose, C. A. F. De, & Rajkumar, B. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper, 41(1), 23–50.

Roogi, R. H. (2015). ORIENTAL JOURNAL OF Big Data Solution by Divide and Conquer technique in Parallel Distribution System using Cloud Computing. ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY, 8(1), 9–12.

Shrimali, B., Bhadka, H., & Patel, H. (2018). A fuzzy-based approach to evaluate multi-objective optimization for resource allocation in cloud. International Journal of Advanced Technology and Engineering Exploration, 5(43), 140–150.

Singh, A., Dutta, K., & Singh, A. (2014). Resource Allocation in Cloud Computing Environment using AHP Technique. International Journal of Cloud Applications and Computing, 4(1), 33–44.

Son, S., & Sim, K. M. (2012). A price-and-time-slot-negotiation mechanism for cloud service reservations. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(3), 713–728.


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