The advent of cloud computing as a new model of service provisioning in distributed systems, encourages researchers to investigate its benefits and drawbacks in executing scientific applications such as workflows. In this research, we attempt to investigate the use of a Divisible Load Theory (DLT) to design efficient strategies to minimize the overall processing time for scheduling jobs in compute cloud environments. We consider homogeneous processors in our analysis and we derive a closed-form solution for the load fractions to be assigned to each processors. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user's job. Finally, we quantify the performance of the strategies via rigorous simulation studies

Task Scheduling is one of the most challenging problems in cloud computing. It is an NP-Hard and plays an important role in optimizing the use of available resources. Recently, Multi-Objectives Genetic Algorithm (MOGA) is proposed for cloud tasks scheduling. However, the execution time of the GA is higher than Particle Swarm Optimization (PSO), and the convergence is slower. PSO converges fast because it can be implemented without too many parameters and operators. In this paper, Multi-Objectives PSO (MOPSO) and MOPSO with Importance Strategy (IS) (MOPSO_IS) algorithms are proposed. MOPSO algorithm is integrated with the IS to select the global best leader. Furthermore, incorporating a mutation operator in MOPSO_IS resolved the problem of premature convergence to the local Pareto-optimal front. The performance of the proposed algorithms was compared with MOGA and produced better results. The results of the experiments showed that the proposed MOPSO and MOPSO_IS significantly minimized the total task time and average task time and obtained better distribution for tasks on the available resources in a minimal time.

Energy theft is a significant problem that needs to be addressed for effective energy management in smart cities. Smart meters are highly utilized in smart cities that help in monitoring the energy utilization level and provide information to the users. However, it is not able to detect energy theft or over-usage. Therefore, we have proposed a multi-objective diagnosing structure named an Energy Theft Prevention System (ETPS) to detect energy theft. The proposed system utilizes a combination of machine learning techniques Gated Recurrent Unit (GRU), Grey Wolf Optimization (GWO), Deep Recurrent Convolutional Neural Network (DDRCNN), and Long Short-Term Memory (LSTM). The statistical validation has been performed using the simple moving average (SMA) method. The results obtained from the simulation have been compared with the existing technique in terms of delivery ratio, throughput, delay, overhead, energy conversation, and network lifetime. The result shows that the proposed system is more effective than existing systems.