Expert Cloud is a new class of cloud computing which enables the users to achieve their requirements from a collection containing experts and skills created by the Human Resources (HRs). The acquisition of these skills and experts from this collection is possible by using the internet and cloud computing concepts without consideration of the HRs location. The Load Balancing in cloud computing means equal Load distribution among resources, Virtual Human Resources (VHRs) and servers. The effective Load distribution in a heterogeneous environment such as cloud is an important challenge. The increase in the number of users, the differences of request types and also different resources capabilities and capacities cause that some resources become overLoad and some others become idle. This paper presents a dynamic Load balanced task scheduling algorithm in expert cloud. In this method, we utilize the Genetic Algorithm (GA) as a ranking for making distinction among the VHRs capabilities. In the proposed method, interval estimation and specification matrix are used to allocate the VHRs and also to determine the service rate. The Load Balancing and mapping process are Modeled based on Simple Exponential Smoothing and Probability Theory. This Statistical Load Balancing Model allows allocating the VHRs based on service rate and Poisson Model. Thus, each task is delivered to the VHR; which is capable to execute it. The simulation results have shown that the expert cloud could reduce the execution and tardiness time and improve VHR utilization. The cost of using resources as an effective factor is also observed.

The anomalous behavior of angular distribution of fission fragments reported by different authors during last years. We can explain some part of these anomalous behavior by TSM, also associated it on large value spins of targets or projectiles or the fissionability parameter (Z2/ A) of compound nucleus. For large value of anisotropy, the SSM is more useful and the parameter S02, is obtained by best fitting the analytical relation to experimental data. In this research work the SSM is employed for explaining the anisotropies of these reactions 10B(64Mev)+237Np , 10B(60Mev)+232Th" 12C(72Mev)+237Np , 12 C(79Mev)+232Th " 160(88Mev)+232Th 16 O(94Mev)+237Np. And we have show that we can explain the angular distribution of fission fragments by SSM when at least the spins of projectile or target is nonzero at moderate energies, Also the ratio of spherical moment of inertia to effective moment of transition nucleus is calculated by this Model.

Task scheduling in cloud computing is a complex problem. As it is clear, Load Balancing in clouds is a NP-Complete problem and gradient-based methods which search for an optimal solution to NP-Complete problems cannot converge to the best solution in an appropriate time. Therefore, in order to solve Load Balancing problem, evolutionary and meta-heuristic methods should be used. Thus, in this study, in order to find a solution for Load Balancing in cloud computing, Cuckoo Optimization Algorithm (COA) is used and it is compared with other methods including evolutionary and non-evolutionary algorithms. In order to prove efficiency of the method, COA is presented and simulated in Cloud-Sim simulator. Obtained results are better than results of GA and RoundRobin scheduling. Finally, it is found that the leader presented in this study gives more optimal outputs in heterogeneous (cloud) environments and user’ s request is processed in an acceptable time. Thus, agreement is achieved at service level and user’ s satisfaction is increased.

Fog computing is an emerging research field for providing cloud computing services to the edges of the network. Fog nodes process data stream and user requests in real-time. In order to optimize resource efficiency and response time, increase speed and performance, tasks must be evenly distributed among the fog nodes. Therefore, in this paper, a new method is proposed to improve the Load Balancing in the fog computing environment. In the proposed algorithm, when a task is sent to the fog node via mobile devices, the fog node using reinforcement learning decides to process that task itself, or assign it to one of the neighbor fog nodes or cloud for processing. The evaluation shows that the proposed algorithm, with proper distribution of tasks between nodes, has less delay to tasks processing than other compared methods.

In recent years, Software Defined Networks (SDN) have been raised as a promising approach to improve the network programmability and management of computer networks. It consists in separating the control plane from the data plane and centralizing the control part of the network. Due to the rapid growth of computer networks in terms of number of switches and the amount of transiting traffic, the distributed architecture with centralized view on the network has been designed for control plane, enhancing the scalability, availability, fault tolerance and reliability. In such a distributed architecture, the Load Balancing between controllers plays an important role towards the optimal use of networking resources. To address the aforementioned challenges, we propose a stable distributed solution for Load Balancing between controllers in software defined networks. The proposed solution collects information on the amount of Load of controllers and their corresponding switches. Based on this knowledge, the controller with the highest overLoad migrates the switch leading to the best enhancement in Load Balancing of the network to the least-Loaded controller, if the network Load is not balanced and the migration benefit is significant compared to its cost. The proposed solution inhibits simultaneous migrations triggered by distributed controllers to avoid cascade re-migrations and ensures the network stability. The results of the test-bed study of the proposed approach show that out solution outperforms other counterparts up to 15% in terms of average memory consumption, 50% in terms of controller traffic throughput and 70% in terms of processing time of the overLoaded controllers.

Since the workLoad of the end users and the provisioned cloud resources are dynamically changed over time, the workLoad is not evenly distributed over the cloud. Therefore, designing appropriate mechanisms to detect the status of the cloud and properly distribute the Load on each host can play an effective role in improving system performance and energy consumption in cloud data centers. Reactive Load Balancing approaches don’t prevent Load-imbalance in cloud and make virtual machines (VM) migrate after Load imbalance and increase energy consumption and job response time. Also, in proactive Load Balancing methods, some problems, such as host state detection with insufficient accuracy and fixed threshold of cpu utilization without considering the host current and future states in VM migrations, prevent the optimal number of balanced hosts and energy consumption in datacenters. In this paper, a proactive approach to the early detection of host states is presented which is based on Extreme Learning Machine (ELM). The proposed approach predict the CPU utilization of each host over time and applies an adaptive threshold to determine the future status of each host (i. e., overLoad, underLoad, secure and normal state). Then, a subset of VMs are migrated to hosts with minimum overLoad probability in future to avoid overLoaded hosts. Implementation of the proposed method and its evaluation on the real data sets in Cloudsim show that the proposed method improves energy consumption, response time, the number of VM migrations and non-violation of the Service Level Agreement (SLA) in comparison to competitive algorithms including RF-LB [7] and ANN-LB [13].

The instantaneous increase in users and their need for internet services caused that, in a short time, the companies that provided this type of service faced problems such as the inability to respond quickly to users and the increase in their costs. Therefore, many of these companies, with a lot of investments in research fields, thought of effective and cost-effective ways to serve a high volume of users, and in this way, new technology and an efficiency system called cloud computing were created. With the increase in users using cloud computing services and therefore the increase in the number of requests, in order to achieve the mentioned benefits, there is a need to establish appropriate mechanisms for Load Balancing, work scheduling and virtualization. Sazi is in cloud computing. This Load can include memory capacity, network Load or delay. Load Balancing is the process of distributing Load among different nodes of a distributed system in order to improve the utilization of resources and response time, while it is a situation in which some nodes have a heavy Load while the node Others avoid being unemployed or having very little work to do. Considering the necessity and importance of Load Balancing in cloud computing, in this article, a comprehensive review of static, dynamic and nature-inspired algorithms for Load Balancing in a cloud space to handle the response time of data centers and their overall performance is given. We pay by analyzing the Load Balancing algorithms. We show that the ant colony algorithm, the genetic algorithm and the particle swarm optimization algorithm with optimal allocation of tasks can play a more effective role in Balancing the Load in the cloud space. Also, the results show that CloudSim software has been used the most in simulating Load Balancing algorithms in the cloud space.

Numerous works have been done for Load Balancing of web servers in grid environment. Reason for popularity of grid environment is to allow accessing distributed resources which are located at remote locations. For effective utilization, Load must be balanced with all resources. Importance of Load Balancing is discussed by distinguishing the system between without Load Balancing and with Load Balancing. Various performance metrics that needed to be considered for designing an efficient Load Balancing algorithm are also described. Intensive review of literature of different Load Balancing approaches for web servers had been carried out and presented in this paper.