Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Load-balanced Genetic Algorithm (CLGA), respectively.

In today’s age, where data is of prime importance and rate at which data generated and sources of data generation is quite huge. The attacks on the data have increased manifold due to which its security is of prime importance. The traditional defense mechanisms are proving to be inadequate. In this paper, a new method to analyses network logs using R and Big data technologies is introduced. The approach detects fraudulent ip address in the network logs based on a set of conditions using pig scripting language. The results of using this method to detect the attack on the dataset is also presented.

Complex huge-scale scientific applications are simplified by workflow to execute in the cloud environment. The cloud is an emerging concept that effectively executes workflows, but it has a range of issues that must be addressed for it to progress. Workflow scheduling using a nature-inspired metaheuristic algorithm is a recent central theme in the cloud computing paradigm. It is an NP-complete problem that fascinates researchers to explore the optimum solution using swarm intelligence. This is a wide area where researchers work for a long time to find an optimum solution but due to the lack of actual research direction, their objectives become faint. Our systematic and extensive analysis of scheduling approaches involves recently high-cited metaheuristic algorithms like Genetic Algorithms (GA), Whale Search Algorithm (WSA), Ant Colony Optimization (ACO), Bat Algorithm, Artificial Bee Colony (ABC), Cuckoo Algorithm, Firefly Algorithm and Particle Swarm Optimization (PSO). Based on various parameters, we do not only classify them but also furnish a comprehensive striking comparison among them with the hope that our efforts will assist recent researchers to select an appropriate technique for further undiscovered issues. We also draw the attention of present researchers towards some open issues to dig out unexplored areas like energy consumption, reliability and security for considering them as future research work.

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a persons abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.