Application of High-Performance Techniques for Solving Linear Systems of Algebraic Equations, Journal of Telecommunications and Information Technology, 2013, nr 4

Solving many problems in mechanics, engineering, medicine and other (e.g., diffusion tensor magnetic resonance imaging or finite element modeling) requires the efficient solving of algebraic equations. In many cases, such systems are very complex with a large number of linear equations, which are symmetric positive-defined (SPD). This paper is focused on improving the computational efficiency of the solvers dedicated for the linear systems based on incomplete and noisy SPD matrices by using preconditioning technique – Incomplete Cholesky Factorization, and modern set of processor instructions – Advanced Vector Extension. Application of these techniques allows to fairly reduce the computational time, number of iterations of conventional algorithms and improve the speed of calculation

The Analysis of OpenStack Cloud Computing Platform: Features and Performance, Journal of Telecommunications and Information Technology, 2015, nr 3

Over the decades the rapid development of broadly defined computer technologies, both software and hardware is observed. Unfortunately, software solutions are regularly behind in comparison to the hardware. On the other hand, the modern systems are characterized by a high demand for computing resources and the need for customization for the end users. As a result, the traditional way of system construction is too expensive, inflexible and it doesn’t have high resources utilization. Present article focuses on the problem of effective use of available physical and virtual resources based on the OpenStack cloud computing platform. A number of conducted experiments allowed to evaluate computing resources utility and to analyze performance depending on the allocated resources. Additionally, the paper includes structural and functional analysis of the OpenStack cloud platform

Security supportive energy-aware scheduling and energy policies for cloud environments

Cloud computing (CC) systems are the most popular computational environments for providing elastic and scalable services on a massive scale. The nature of such systems often results in energy-related problems that have to be solved for sustainability, cost reduction, and environment protection. In this paper we defined and developed a set of performance and energy-aware strategies for resource allocation, task scheduling, and for the hibernation of virtual machines. The idea behind this model is to combine energy and performance-aware scheduling policies in order to hibernate those virtual machines that operate in idle state. The efficiency achieved by applying the proposed models has been tested using a realistic large-scale CC system simulator. Obtained results show that a balance between low energy consumption and short makespan can be achieved. Several security constraints may be considered in this model. Each security constraint is characterized by: (a) Security Demands (SD) of tasks; and (b) Trust Levels (TL) provided by virtual machines. SD and TL are computed during the scheduling process in order to provide proper security services. Experimental results show that the proposed solution reduces up to 45% of the energy consumption of the CC system. Such significant improvement was achieved by the combination of an energy-aware scheduler with energy-efficiency policies focused on the hibernation of VMs.COST Action IC140

Energy Efficient Scheduling Methods for Computational Grids and Clouds, Journal of Telecommunications and Information Technology, 2017, nr 1

This paper presents an overview of techniques developed to improve energy efficiency of grid and cloud computing. Power consumption models and energy usage proles are presented together with energy efficiency measuring methods. Modeling of computing dynamics is discussed from the viewpoint of system identication theory, indicating basic experiment design problems and challenges. Novel approaches to cluster and network-wide energy usage optimization are surveyed, including multi-level power and software control systems, energy-aware task scheduling, resource allocation algorithms and frameworks for backbone networks management. Software-development techniques and tools are also presented as a new promising way to reduce power consumption at the computing node level. Finally, energy-aware control mechanisms are presented. In addition, this paper introduces the example of batch scheduler based on ETC matrix approach

Non-deterministic security driven meta scheduler for distributed cloud organizations

Security is a very complex and challenging problem in Cloud organizations. Ensuring the security of operations within the cloud by also enforcing the users’ own security requirements, usually results in a complex tradeoff with the efficiency of the overall system. In this paper, we developed a novel architectural model enforcing cloud security, based on a multi-agent scheme and a security aware non-deterministic Meta Scheduler driven by genetic heuristics. Such model is explicitly designed to prevent Denial of Service and Timing Attacks over the cloud and has been demonstrated to be integrable within the well-known OpenStack platform. Additionally, we proposed two different models for assuring users security demands. The first is a scoring model that allows scheduling tasks only on the Virtual Machines offering proper security level. The second model takes into account the time spent on the necessary cryptographic operations dedicated to particular task. The above scheduling system has been simulated in order to assess the effectiveness of the proposed security architecture, resulting in an increased system safety and resiliency against attacks, without sensibly impacting the performance of the whole cloud environment

Towards Secure Non-Deterministic Meta-Scheduling For Clouds

Task scheduling in large-scale distributed High Performance Computing (HPC) systems environments remains challenging research and engineering problem. There is a need of development of novel advanced scheduling techniques in order to optimise the resource utilisation. In this work, we develop the Agent Supported Non-Deterministic Meta Scheduler for cloud environments. This scheduling model is a simple combination of intelligent agent-based monitoring model for cloud system and security-aware cloud scheduler. In our model, scheduling, monitoring and reporting are provided in nondeterministic time intervals. An empirical case study using a FastFlow task farm was presented. It has demonstrates the effectiveness of the proposed solution

Energy Efficient Scheduling Methods for Computational Grids and Clouds

This paper presents an overview of techniques developed to improve energy efficiency of grid and cloud computing. Power consumption models and energy usage profiles are presented together with energy efficiency measuring methods. Modeling of computing dynamics is discussed from the viewpoint of system identification theory, indicating basic experiment design problems and challenges. Novel approaches to cluster and network-wide energy usage optimization are surveyed, including multi-level power and software control systems, energy-aware task scheduling, resource allocation algorithms and frameworks for backbone networks management. Software-development techniques and tools are also presented as a new promising way to reduce power consumption at the computing node level. Finally, energy-aware control mechanisms are presented. In addition, this paper introduces the example of batch scheduler based on ETC matrix approach

The Production and Study of Cold Antihydrogen: ATRAP Progress in 2010

ATRAP Progress Report on 2010 Antiproton Ru

3D ActionSLAM: wearable person tracking in multi-floor environments

We present 3D ActionSLAM, a stand-alone wearable system that can track people in previously unknown multi-floor environments with sub-room accuracy. ActionSLAM stands for action-based simultaneous localization and mapping: It fuses dead reckoning data from a foot-mounted inertial measurement unit with the recognition of location-related actions to build and update a local landmark map. Simultaneously, this map compensates for position drift errors that accumulate in open-loop tracking by means of a particle filter. To evaluate the system performance, we analyzed 23 tracks with a total walked distance of 6,489 m in buildings with up to three floors. The algorithm robustly (93 % of runs converged) mapped the areas with a mean landmark positioning error of 0.59 m. As ActionSLAM is fully stand-alone and not dependent on external infrastructure, it is well suited for patient tracking in remote health care applications. The algorithm is computationally light-weight and runs in realtime on a Samsung Galaxy S4, enabling immediate location-aware feedback. Finally, we propose visualization techniques to facilitate the interpretation of tracking data acquired with 3D ActionSLAM