Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing

Cloud computing becomes increasingly popular for hosting all kinds of applications not only due to their ability to support dynamic provisioning of virtualized resources to handle workload fluctuations but also because of the usage based on pricing. This results in the adoption of data centers which store, process and present the data in a seamless, efficient and easy way. Furthermore, it also consumes an enormous amount of electrical energy, then leads to high using cost and carbon dioxide emission. Therefore, we need a Green computing solution that can not only minimize the using costs and reduce the environment impact but also improve the performance. Dynamic consolidation of Virtual Machines (VMs), using live migration of the VMs and switching idle servers to sleep mode or shutdown, optimizes the energy consumption. We propose an adaptive underloading detection method of hosts, VMs migration selecting method and heuristic algorithm for dynamic consolidation of VMs based on the analysis of the historical data. Through extensive simulation based on random data and real workload data, we show that our method and algorithm observably reduce energy consumption and allow the system to meet the Service Level Agreements (SLAs)

Performance Evaluation of Parallel Haemodynamic Computations on Heterogeneous Clouds

The article presents performance evaluation of parallel haemodynamic flow computations on heterogeneous resources of the OpenStack cloud infrastructure. The main focus is on the parallel performance analysis, energy consumption and virtualization overhead of the developed software service based on ANSYS Fluent platform which runs on Docker containers of the private university cloud. The haemodynamic aortic valve flow described by incompressible Navier-Stokes equations is considered as a target application of the hosted cloud infrastructure. The parallel performance of the developed software service is assessed measuring the parallel speedup of computations carried out on virtualized heterogeneous resources. The performance measured on Docker containers is compared with that obtained by using the native hardware. The alternative solution algorithms are explored in terms of the parallel performance and power consumption. The investigation of a trade-off between the computing speed and the consumed energy is performed by using Pareto front analysis and a linear scalarization method