Distributing application requests across applications located in different datacenters with in cloud equally must be provided by cloud load balancing. In this paper, we compare different provisioning policies within cloud for virtual machines and workloads, where we are focusing on how to distribute the processing power between virtual machines and how to distribute workload among virtual machines. Cloudsim is the simulation plate form used to test the different distributions scenarios to check the performance on makespan, average turnaround time, bandwidth utilization and CPU utilization. Result showed the difference in performance between the three tested provisioning schemes, where the space-shared gives better readings for the selected performance metrics.

Due to centralized nature for cloud computing and some other reasons, high mobility cannot be supported and low latency requirements for some applications such as Internet of Things (IoT) that require real time and mobility support. To satisfy such requirements new technologies, fog computing is a good solution, where we use edges of network for service provisioning instead of far datacenters allocated in clouds. Low latency response is the most attractive property for fog computing, which is very suitable for IoT multi-billion devices, sensors and actuators generates huge amount of data that need processing and analysis for smart decision generation. The main objective of this article is to show the super ability of fog computing over cloud-only computing. The authors present a patient monitoring system as a case study for simulation; they evaluated the performance of the system using: latency, network usage, power consumption, cost of execution and simulation execution time performance metrics. The results show that the Fog computing is superior over Cloud-only paradigm in all performance measurements.

Cloud computing systems are considered complex systems, because of the various classes of services offered for users and the big challenges for providers to meet the increasing demands. Thus, service allocation is a critical issue in cloud computing. Fuzzy modeling is one choice to deal with such complexity. In this paper, the authors introduce a new heuristic service allocation model for cloud computing service allocation. Fuzzy sets are used to determine a candidate cloud for providing a service and crisp sets are used to serve requests from a cloud. Supply and demand are used as the fuzzy input to provide the desired heuristic allocation model for the candidate cloud, and a set of parameters are used to determine a cloud user needs.

The limitations in terms of power and processing in IoT (Internet of Things) nodes make nodes an easy prey for malicious attacks, thus threatening business and industry. Detecting malicious nodes before they trigger an attack is highly recommended. The paper introduces a special purpose IoT crawler that works as an inspector to catch malicious nodes. This crawler is deployed in the Fog layer to inherit its capabilities, and to be an intermediate connection between the things and the cloud computing nodes. The crawler collects data streams from IoT nodes, upon a priority criterion. A behavior analyzer, with a machine learning core, detects malicious nodes according to the extracted node behavior from the crawler collected data streams. The performance of the behavior analyzer was investigated using three machine learning algorithms: Adaboost, Random forest and Extra tree. The behavior analyzer produces better testing accuracy, for the tested data, when using Extra tree compared to Adaboost and Random forest; it achieved 98.3% testing accuracy with Extra tree.

Distributing application requests across applications located in different datacenters with in cloud equally must be provided by cloud load balancing. In this paper, we compare different provisioning policies within cloud for virtual machines and workloads, where we are focusing on how to distribute the processing power between virtual machines and how to distribute workload among virtual machines. Cloudsim is the simulation plate form used to test the different distributions scenarios to check the performance on makespan, average turnaround time, bandwidth utilization and CPU utilization. Result showed the difference in performance between the three tested provisioning schemes, where the space-shared gives better readings for the selected performance metrics.