Pervasive Cloud Computing Technologies: Future Outlooks and Interdisciplinary Perspectives

Technology trends may come and go, but cloud computing technologies have been gaining consideration in the commercial world due to its ability to provide on-demand access to resources, control the software environment, and supplement existing systems. Pervasive Cloud Computing Technologies: Future Outlooks and Interdisciplinary Perspectives explores the latest innovations with cloud computing and the impact of these new models and technologies. This book will present case studies and research on the future of cloud computing technologies and its ability to increase connectivity of various entities of the world. It is an essential resource for technology practitioners, engineers, managers, and academics aiming to gain the knowledge of these novel and pervasive technologies

An approximate epsilon-constraint method for a multi-objective job scheduling in the cloud

Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather than on single computers connected by networks. Through a device (e.g., either a computer or a smartphone), a browser and an Internet connection, each user accesses a cloud platform and asks for specific services. For example, a user can ask for executing some applications (jobs) on the machines (hosts) of a cloud infrastructure. Therefore, it becomes significant to provide optimized job scheduling approaches suitable to balance the workload distribution among hosts of the platform. In this paper, a multi-objective mathematical formulation of the job scheduling problem in a homogeneous cloud computing platform is proposed in order to optimize the total average waiting time of the jobs, the average waiting time of the jobs in the longest working schedule (such as the makespan) and the required number of hosts. The proposed approach is based on an approximate ϵ-constraint method, tested on a set of instances and compared with the weighted sum (WS) method. The computational results highlight that our approach outperforms the WS method in terms of a number of non-dominated solution

An Efficient Computer Simulation-Based Approach For Optimization Of Complex Polling Systems With General Arrival Distributions

This study proposes an efficient computer simulation approach for estimation and optimization of performance measures in a polling system. A single server polling system operating under exhaustive, gated, and mixed service disciplines is developed. In this system, the arrival process is a Poisson process and service and setup times are exponentially distributed. The polling model is solved through two different methods: an exact method that requires the complete characterization of the system, and a computer simulation-based solution that reduces the solving time and the complexity of the model. A set of numerical experiments are presented in which it is shown that the computer simulation model outperforms the exact method in terms of estimating a system\u27s performance measures. Moreover, it is shown that the optimizer simulation model is capable of handling general distributions and several queuing systems, whereas the exact method requires the complete characterization of the system through a Markov chain, which is a time-consuming and inefficient approach. In addition, the efficient computer simulation-based solution could be easily applied to polling systems with different numbers of queues and service disciplines

An approximate epsilon-constraint method for a multi-objective job scheduling in the cloud

Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather than on single computers connected by networks. Through a device (e.g., either a computer or a smartphone), a browser and an Internet connection, each user accesses a cloud platform and asks for specific services. For example, a user can ask for executing some applications (jobs) on the machines (hosts) of a cloud infrastructure. Therefore, it becomes significant to provide optimized job scheduling approaches suitable to balance the workload distribution among hosts of the platform. In this paper, a multi-objective mathematical formulation of the job scheduling problem in a homogeneous cloud computing platform is proposed in order to optimize the total average waiting time of the jobs, the average waiting time of the jobs in the longest working schedule (such as the makespan) and the required number of hosts. The proposed approach is based on an approximate ϵ-constraint method, tested on a set of instances and compared with the weighted sum (WS) method. The computational results highlight that our approach outperforms the WS method in terms of a number of non-dominated solutions

Removing Copper from Contaminated Water Using Activated Carbon Sorbent by Continuous Flow

Introduction: A major concern of human being is accumulation and toxicity of heavy metals in their body. Copper is a heavy metal ion that in concentration of 2 mg/l can cause numerous complications. Different treatment methods have been proposed for removing metals from contaminated water by researchers. Among these methods, sorption seems a better method with high removal efficiency. In this study, conditions for removal of copper ions by activated carbon sorbent were studied with continuous flow. Materials & Methods: This was a laboratory – experimental study. A 20mg/l solution of copper ions was prepared and passed through a 5 × 10 cm column with average output rate of 1.85 ml/min. Output of column was sampled every 30 minutes and the remaining amount of copper ion in each sample was measured by flame atomic absorption. Results: The empty bed volume (EBV) was equal to 138 ml. The highest removal efficiency was 99.7 percent at 127 minutes. From equilibrium time, the removal efficiency was constant with time. The adsorption capacity of activated carbon was 0.25mg.g-1. The isotherm study indicated that the sorption data can be obeyed by both Langmuir and Freundlich isotherms (R2>0.95) but Langmuir model had higher agreement with this experimental data (R2= 0.988). Conclusion: The binding of ions to the sorbent in the adsorption process is extremely important. For this column 62.5 minutes after filling was appropriate, so the highest removal efficiency was obtained. Equilibrium time was dependent on the speed of influent through the column in the continuous flow. For selected column, the rate of 1.85 ml/min is a good performance