Energy Prediction for Cloud Workload Patterns

The excessive use of energy consumption in Cloud infrastructures has become one of the major cost factors for Cloud providers to maintain. In order to enhance the energy efficiency of Cloud resources, proactive and reactive management tools are used. However, these tools need to be supported with energy-awareness not only at the physical machine (PM) level but also at virtual machine (VM) level in order to enhance decision-making. This paper introduces an energy-aware profiling model to identify energy consumption for heterogeneous and homogeneous VMs running on the same PM and presents an energy-aware prediction framework to forecast future VMs energy consumption. This framework first predicts the VMs’ workload based on historical workload patterns using Autoregressive Integrated Moving Average (ARIMA) model. The predicted VM workload is then correlated to the physical resources within this framework in order to get the predicted VM energy consumption. Compared with actual results obtained in a real Cloud testbed, the predicted results show that this energy-aware prediction framework can get up to 2.58 Mean Percentage Error (MPE) for the VM workload prediction, and up to −4.47 MPE for the VM energy prediction based on periodic workload pattern

Toolkit for Simulation Modeling of Logistics Warehouse in Distributed Computing Environment

We address an important problem of an automation of logistics warehouses simulation modeling in distributed service-oriented computing environments. To this end, we propose new approach for adjusting management system parameters of real warehouse in production use. It is based on the integration of the conceptual, wireframe and service-oriented programming used to develop parameter sweep applications and data analysis in the simulation modeling process. We develop a toolkit for supporting modeling of warehouse logistics. The practical experiments are focused upon the refrigerated warehouse. The developed application demonstrates high efficiency and scalability for optimizing nine criteria to cope with different production demands.The study was supported by the Russian Foundation of Basic Research, projects no. 15-29-07955 and no. 16-07-00931, and Program 1.33P of fundamental research of Presidium RAS, project “Development of new approaches to creation and study of complex models of information-computational and dynamic systems with applications”

Comprehensive Aerological Reference Data Set (CARDS)

The possibility of anthropogenic climate change has reached the attention of Government officials and researchers. However, one cannot study climate change without climate data. The CARDS project will produce high-quality upper-air data for the research community and for policy-makers. The authors intend to produce a dataset which is: easy to use, as complete as possible, as free of random errors as possible. They will also attempt to identify biases and remove them whenever possible. In this report, they relate progress toward their goal. They created a robust new format for archiving upper-air data, and designed a relational database structure to hold them. The authors have converted 13 datasets to the new format and have archived over 10,000,000 individual soundings from 10 separate data sources. They produce and archive a metadata summary of each sounding they load. They have researched station histories, and have built a preliminary upper-air station history database. They have converted station-sorted data from their primary database into synoptic-sorted data in a parallel database. They have tested and will soon implement an advanced quality-control procedure, capable of detecting and often repairing errors in geopotential height, temperature, humidity, and wind. This unique quality-control method uses simultaneous vertical, horizontal, and temporal checks of several meteorological variables. It can detect errors other methods cannot

Василий Алексеевич Бильбасов и его труд «Кирилл и Мефодий»

For the first time there is presented the significant value of the research work of the outstanding Russian historian V.A. Bilbasov, contributed to the revelation of the lesser-known pages of life and activities of Slavonic Apostles Constantine (Cyril) and Methodius. The article demonstrates the role of V.A. Bilbasov in publication of historical monuments connected to Cyril - Methodius problematics and his role in the development of slavistics not only in Russia, but also abroad. There is given the analysis of critical historical miscellanea collection of Latin monuments “Cyril and Methodius in historical documents”, which was compiled by V.A. Bilbasov in 1868.В статье впервые показано значение научного творчества выдающегося русского ученого В.А. Бильбасова, способствовавшего раскрытию малоизвестных страниц жизни и деятельности славянских просветителей Константина (Кирилла) Философа и Мефодия. Освещена роль В.А. Бильбасова в публикации источников по кирилло-мефодиевской проблематике. Дан анализ критико-исторического сборника латиноязычных памятников «Кирилл и Мефодий по документальным источникам»

Editorial: Collaborative Computing for Data-Driven Systems

Over the last few years, owing to the development, deployment, and use of Internet of Things (IoT) systems and smart devices, a large volume of data has been generated from various operation systems. High speed 4G networks and low cost of data usage foster the commercialization of a few heavy data driven social networks, such as Tik Tok and Instagram

Lightweight Computation to Robust Cloud Infrastructure for Future Technologies (Workshop Paper)

Hardware and software lightweight solutions became the mainstream for current and future emerging technologies. Container-based virtualization provides more efficient and faster solutions than traditional virtual machines, offering good scalability, flexibility, and multi-tenancy. They are capable of serving in a heterogeneous and dynamic environment across multiple domains, including IoT, cloud, fog, and multi-access edge computing. In this paper, we propose a lightweight solution for LCC (Live Container Cloud) that permits the user to access live/remote cloud resources faster. LCC can be embedded as a fog/edge node to permit the users to allocate and deallocate cloud resources. The performance of such a containerization technology is presented