INTELLIGENT DISTRIBUTED DYNAMIC FOG COMPUTING FRAMEWORK

Одним из направлений развития сетей связи 5G и сетей связи 2030 является интегрирование в сеть распределенных вычислительных структур, таких как системы пограничных и туманных вычислений (Fog), которые призваны выполнить децентрализацию вычислительной части сетей. В связи с этим необходимо исследовать и определить принципы предоставления услуг на основе распределенной вычислительной инфраструктуры, в том числе в условиях ограниченности ресурсов отдельно взятых составных частей (Fog-устройства). Предлагается новый фреймворк распределенной динамической вычислительной системы туманных вычислений на основе микросервисного архитектурного подхода к реализации, развертыванию и миграции программного обеспечения предоставляемых услуг. Исследуется типовая архитектура микросервисного подхода и ее имплементация в туманные вычисления, а также рассматриваются два алгоритма: алгоритм K-средних для нахождения центра пользовательской нагрузки и алгоритм роевой оптимизации для определения устройства тумана с необходимыми характеристиками для последующей миграции микросервиса. One of the directions of 5G and 2030 communications networks development is the network-integrated distributed structures, such as edge computing (MEC) and Fog computing, which are designed to decentralize the computing part of networks. In this regard, it is necessary to investigate and determine the principles of providing services based on a distributed computing infrastructure, including in conditions of limited resources of individual components (Fog devices). This article proposes a new framework for a distributed dynamic computing system of fog computing based on a microservice architectural approach to the implementation, deployment, and software migration of the services. The article examines the typical architecture of the microservice approach and its implementation in fog computing, and also investigates two algorithms: K-means for finding the center of user load, swarm optimization (PSO) to determine the fog device with the necessary characteristics for the subsequent migration of the microservice.

Reaching new peaks for the future of the CMS HTCondor Global Pool

The CMS experiment at CERN employs a distributed computing infrastructure to satisfy its data processing and simulation needs. The CMS Submission Infrastructure team manages a dynamic HTCondor pool, aggregating mainly Grid clusters worldwide, but also HPC, Cloud and opportunistic resources. This CMS Global Pool, which currently involves over 70 computing sites worldwide and peaks at 350k CPU cores, is employed to successfully manage the simultaneous execution of up to 150k tasks. While the present infrastructure is sufficient to harness the current computing power scales, CMS latest estimates predict a noticeable expansion in the amount of CPU that will be required in order to cope with the massive data increase of the High-Luminosity LHC (HL-LHC) era, planned to start in 2027. This contribution presents the latest results of the CMS Submission Infrastructure team in exploring and expanding the scalability reach of our Global Pool, in order to preventively detect and overcome any barriers in relation to the HL-LHC goals, while maintaining high effciency in our workload scheduling and resource utilization.

ArchaeoGRID Science Gateways for Easy Access to Distributed Computing Infrastructure for Large Data Storage and Analysis in Archaeology and History

This article describes how archaeological and historical research grew as a multidisciplinary and interdisciplinary activity due to availability of larger amount of data within the reconstruction of global historical and archaeological contexts at a global spatio-temporal scale. The increased information, also integrated with data from the Earth Sciences, has had an effect on the exponential increase of complex sets of data and of refined methods of analysis. For such purposes, this article discusses the ArchaeoGRID Science Gateway paradigm for accessing ArchaeoGRID Cyberinfrastructure (CI), a Distributed Computing Infrastructure (DCI), that can supply storage and computing resources for managing and analyzing large amount of archaeological and historical data. In fact, ArchaeoGRID Science Gateway is emerging as high-level web environment that makes easier the access, in a transparent way, to DCI, as local high-performance computing, Grids and Clouds, from no specialized Virtual Research Communities (VRC) of archaeologists and historians.

ArchaeoGRID Science Gateways for Easy Access to Distributed Computing Infrastructure for Large Data Storage and Analysis in Archaeology and History

This article describes how archaeological and historical research grew as a multidisciplinary and interdisciplinary activity due to availability of larger amount of data within the reconstruction of global historical and archaeological contexts at a global spatio-temporal scale. The increased information, also integrated with data from the Earth Sciences, has had an effect on the exponential increase of complex sets of data and of refined methods of analysis. For such purposes, this article discusses the ArchaeoGRID Science Gateway paradigm for accessing ArchaeoGRID Cyberinfrastructure (CI), a Distributed Computing Infrastructure (DCI), that can supply storage and computing resources for managing and analyzing large amount of archaeological and historical data. In fact, ArchaeoGRID Science Gateway is emerging as high-level web environment that makes easier the access, in a transparent way, to DCI, as local high-performance computing, Grids and Clouds, from no specialized Virtual Research Communities (VRC) of archaeologists and historians.