PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI)

FIPA-based reference architecture for efficient discovery and selection of appropriate cloud service using cloud ontology

[EN] Cloud computing is considered the latest emerging computing paradigm and has brought revolutionary changes in computing technology. With the advancement in this field, the number of cloud users and service providers is increasing continuously with more diversified services. Consequently, the selection of appropriate cloud service has become a difficult task for a new cloud customer. In case of inappropriate selection of a cloud services, a cloud customer may face the vendor locked-in issue and data portability and interoperability problems. These are the major obstacles in the adoption of cloud services. To avoid these complexities, a cloud customer needs to select an appropriate cloud service at the initial stage of the migration to the cloud. Many researches have been proposed to overcome the issues, but problems still exist in intercommunication standards among clouds and vendor locked-in issues. This research proposed an IEEE multiagent Foundation for Intelligent Physical Agent (FIPA) compliance multiagent reference architecture for cloud discovery and selection using cloud ontology. The proposed approach will mitigate the prevailing vendor locked-in issue and also alleviate the portability and interoperability problems in cloud computing. To evaluate the proposed reference architecture and compare it with the state-of-the-art approaches, several experiments have been performed by utilizing the commonly used performance measures. Analysis indicates that the proposed approach enables significant improvements in cloud service discovery and selection in terms of search efficiency, execution, and response timeAbbas, G.; Mehmood, A.; Lloret, J.; Raza, MS.; Ibrahim, M. (2020). FIPA-based reference architecture for efficient discovery and selection of appropriate cloud service using cloud ontology. International Journal of Communication Systems. 33(14):1-14. https://doi.org/10.1002/dac.4504114331

An incremental approach for calculating dominance-based rough set dependency

Feature selection and classification are widely used in machine learning in the context of big data. In many data sets, both attributes and decision classes can be preference ordered. Therefore, to process the data and information based on preference-ordered attributes, dominance-based rough set approach (DRSA) has been proposed. DRSA considers dominance relation between objects and can process the information with preference-ordered attribute domains. The it should be noted that the majority of the algorithms based on DRSA use dependency as an underlying criterion measure for different tasks. However, calculating dependency using the conventional DRSA approach requires the calculation of lower and upper approximations which is a computationally expensive task. A new approach has been proposed in this paper which calculates the dominance-based rough set dependency measure without calculating the lower and upper approximations. The proposed methodology is called the “Incremental Dominance-based Dependency Calculation Method” (IDDC). To justify the proposed approach, both IDDC and conventional approaches are compared using various data sets from the UCI data set repository. Results have shown that the proposed approach outperforms the conventional approach by depicting on average 46% and 98% decrease in execution time and required runtime memory, respectively