The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset’s usage through effectively spreading the workload across the system’s different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

Cloud Computing is the dynamic provisioning of resources to provide services to end-users over the internet. The realization of cloud computing requires addressing several challenges, such as resource discovery, security, scheduling, and load balancing. Among these research issues, load balancing is the most challenging one. Therefore, in the past few years, research into various static and dynamic algorithms to achieve optimal results is gaining importance. This research proposes Swarm Intelligence (SI) as a load-balancing solution for cloud computing. Several alternatives in the literature (like genetic algorithm, ACO, PSO, BAT, GWO and many others) are investigated, but none consider the load balancing convergence time with global optimization. Among these algorithms, this research emphasizes Particle Swarm Optimization (PSO) and GreyWolf Optimization (GWO). A combined approach of GWO-PSO that capitalizes on the benefits of fast convergence and global optimization is proposed in this paper. These two techniques enhance system efficiency and resource allocation, working together to solve the load-balancing challenge. Compared to other traditional approaches, the findings of this research are promising while achieving globally optimized fast convergence and reducing overall response time. On average, the overall response time of the proposed technique is reduced to 12% as compared to other algorithms. Furthermore, the best optimal value obtained from the objective function of the proposed GWO-PSO algorithm improves PSO to 97.253% in terms of convergence.

Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%.