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Smart Design for Resources Allocation in IoT Application Service based on multi-agent system and CSP
Informatica ( IF 3.3 ) Pub Date : 2020-09-15 , DOI: 10.31449/inf.v44i3.2962
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.

中文翻译:

基于多Agent系统和CSP的物联网应用服务资源配置智能设计

在本文中,我们旨在解决两个问题;物联网设备(传感器、执行器等)向云服务转型时出现的第一个问题。因此,我们致力于为云保持顺畅高效的数据传输,并支持客户应用程序,例如:数据共享、存储和处理。第二个问题有两个维度。在第一维中,问题出现在向主机中的虚拟机 (VM) 提交 cloudlet(客户请求的作业)时。为了解决这个问题,我们提出了根据最低成本和负载进行资源分配的调度算法。在第二个维度中,问题在于主机中新虚拟机的托管。为了克服这个问题,我们需要考虑在不同数据中心容纳新虚拟机时的负载。在这项工作中,我们建议为面向服务的物联网应用提供资源分配方法。这种方法的架构基于两种技术:多代理系统 (MAS) 和分布式约束满足问题 (DCSP)。MAS 管理物理资源、决策和数据中心之间的通信,而 DCSP 用于简化数据中心的资源配置策略。变量和约束分布在不同层的多个代理之间。实验结果表明,我们的方法的效率体现在:平均系统负载、成本增加率和可用Mips。MAS 管理物理资源、决策和数据中心之间的通信,而 DCSP 用于简化数据中心的资源配置策略。变量和约束分布在不同层的多个代理之间。实验结果表明,我们的方法的效率体现在:平均系统负载、成本增加率和可用Mips。MAS 管理物理资源、决策和数据中心之间的通信,而 DCSP 用于简化数据中心的资源配置策略。变量和约束分布在不同层的多个代理之间。实验结果表明,我们的方法的效率体现在:平均系统负载、成本增加率和可用Mips。
更新日期:2020-09-15
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