Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

Internet of Things (IoT) is raised as most adaptive technologies for the end users in past few years. Indeed of being popular, security in IoT turned out to be a crucial research challenge and a sensible topic which is discussed very often. Denial of Service (DoS) attack is encountered in IoT sensor networks by perpetrators with numerous compromised nodes to flood certain targeted IoT device and thus resulting in vulnerability or service unavailability. Features that are encountered from the malicious node can be utilized effectually to recognize recurring patterns or attack signature of network based or host based attacks. Henceforth, feature extraction using machine learning approaches for modelling of Intrusion detection system (IDS) have been cast off for identification of threats in IoT devices. In this investigation, Kaggle dataset is measured as benchmark dataset for detecting intrusion is considered initially. These dataset includes 41 essential attributes for intrusion identification. Next, selection of features for classifiers is done with an improved Weighted Random Forest Information extraction (IW-RFI). This proposed WRFI approach evaluates the mutual information amongst the attributes of features and select the optimal features for further computation. This work primarily concentrates on feature selection as effectual feature selection leads to effectual classification. Finally, performance metrics like accuracy, sensitivity, specificity is computed for determining enhanced feature selection. The anticipated model is simulated in MATLAB environment, which outperforms than the existing approaches. This model shows better trade off in contrary to prevailing approaches in terms of accurate detection of threats in IoT devices and offers better transmission over those networks.