Reasoning Based Workload Performance Prediction in Cloud Data Centers Cloud computing provides utilitybased and scalable services to endusers. In the past decade, the demands for resource management in cloud computing have increased substantially which lead to certain challenges such as optimal resource utilization, power consumption, and service level agreement violations. Workload performance prediction serves as an assistance to address these issues. In this paper, we propose a prediction model based on clustered CaseBased Reasoning (CBR). The proposed model determines the performance metrics for workload prior to the cooperation of autonomic computing characteristics. Thus, CBR provides optimal scheduling of resources and workload monitoring for cloud data centers. In order to validate the proposed CBRbased prediction model, we perform a series of experiments and evaluate the effectiveness in terms of precision, recall, fmeasure, and mean square error rate. We generate the cases for CBR using traces from the Google cluster data center. Moreover, we also validate our proposed prediction model against Support Vector Machine (SVM) prediction scheme. Experimental results show that the proposed CBR outperforms the SVMbased approach and yields 10% improvement in terms of precision.

Efficient resource management and workload allocation in fogxe2x80x93cloud computing paradigm in IoT using learning classifier systems Abstract   With the rapid growth in networkconnected computing devices, the Internet of Things (IoT) has progressed in terms of size and speed. Subsequently, the amount of produced data and computation loads has increased dramatically. A solution to handle this huge volume of workloads is cloud computing in which a considerable delay exists in the processing load and this has remained a concern in the field of distributed computing networks. Processing workloads at the edge of the network can reduce the response time while at the same time imposing energy constraints by bringing the task of load processing from data centers, which are supplied by electrical energy sources, to the network edges which are only supported by limited energies of batteries. Therefore, workloads need to be distributed evenly between the clouds and the edges of the network. In this paper, two methods based on XCS learning classifier systems (LCS), namely, XCS and BCMXCS, are proposed to balance the power consumption at the edge of the network and to reduce delays in the processing of workloads. The results of our experiments are indicative of the superiority of BCMXCS over the basic XCSbased method. The proposed methods distribute the workloads in a way that the delay in their processing and the communication delay between the cloud and fog nodes are both minimized. In addition to considerable advantages in controlling the fluctuations of the processing delay, the proposed methods can simultaneously reduce the processing delay by 42% by using a moderate power consumption at the edge of the network. The proposed methods can also recharge the renewable batteries used at the edge of the network about 18 percent more than the best stateoftheart method.