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. Povzetek: Predlagan je način dodeljevanja virov za storitve v IoT aplikacijah na osnovi večagentnih sistemov (MAS) in zadovoljevanja porazdeljenih omejitev (DCSP).

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Laser communications hold accurate data rate for ground satellite links. The laser beam is transmitted through the atmosphere. The clear-air turbulence induces a number of phase distortions that damage wave-front. Adaptive optics (AO) treats wave front correction. The nature of AO systems is iterative; it can be integrated in metaheuristic algorithms such as genetic algorithm (GA). This paper presents improved version of algorithm for wave-front corrections. The improved algorithm is based on genetic algorithm (GA) and adaptive optics approach (OA). It is implemented in a computer simulation model called object-oriented matlab adaptive optics (OOMAO). The optimisation process involves best possible GA parameters as a function of population size, iteration count, and the actuators' voltage intervals. Results show that the application of GA improves the performance of AO in wave-front corrections and the communication between satellite-to-ground laser links as well.

Cloud computing is the latest technology in both hardware and software. The cloud computing architecture offers a number of services including the SAAS, PAAS and IAAS. When customers specify their technical and economic needs, cloud computing helps them satisfy their request at the lowest possible cost and the best quality of resources. This paper presents a new approach for resource allocation (RA) in cloud computing. Its architecture is based on multi-agent system (MAS) and distributed constraint satisfaction problems (DCSP) where variables and constraints are distributed among multiple agents. In MAS, each agent makes his choice via a distributed negotiation to satisfy the global objective which is looking for the best solution to the users' needs. The present work highlights a new approach that is based on hybridizing (DCSP) and (MAS). This decentralized approach is designed to solve a number of problems such as resource allocation and planning cloud-computing systems.

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