CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

Energy efficiency considerations in integrated IT and optical network resilient infrastructures

The European Integrated Project GEYSERS - Generalised Architecture for Dynamic Infrastructure Services - is concentrating on infrastructures incorporating integrated optical network and IT resources in support of the Future Internet with special emphasis on cloud computing. More specifically GEYSERS proposes the concept of Virtual Infrastructures over one or more interconnected Physical Infrastructures comprising both network and IT resources. Taking into consideration the energy consumption levels associated with the ICT today and the expansion of the Internet in size and complexity, that incurring increased energy consumption of both IT and network resources, energy efficient infrastructure design becomes critical. To address this need, in the framework of GEYSERS, we propose energy efficient design of infrastructures incorporating integrated optical network and IT resources, supporting resilient end-to-end services. Our modeling results quantify significant energy savings of the proposed solution by jointly optimizing the allocation of both network and IT resources

Fog computing : enabling the management and orchestration of smart city applications in 5G networks

Fog computing extends the cloud computing paradigm by placing resources close to the edges of the network to deal with the upcoming growth of connected devices. Smart city applications, such as health monitoring and predictive maintenance, will introduce a new set of stringent requirements, such as low latency, since resources can be requested on-demand simultaneously by multiple devices at different locations. It is then necessary to adapt existing network technologies to future needs and design new architectural concepts to help meet these strict requirements. This article proposes a fog computing framework enabling autonomous management and orchestration functionalities in 5G-enabled smart cities. Our approach follows the guidelines of the European Telecommunications Standards Institute (ETSI) NFV MANO architecture extending it with additional software components. The contribution of our work is its fully-integrated fog node management system alongside the foreseen application layer Peer-to-Peer (P2P) fog protocol based on the Open Shortest Path First (OSPF) routing protocol for the exchange of application service provisioning information between fog nodes. Evaluations of an anomaly detection use case based on an air monitoring application are presented. Our results show that the proposed framework achieves a substantial reduction in network bandwidth usage and in latency when compared to centralized cloud solutions

Linear integrated location-inventory models for service parts logistics network design

We present two integrated network design and inventory control problems in service-parts logistics systems. Such models are complicated due to demand uncertainty and highly nonlinear time-based service level constraints. Exploiting unique properties of the nonlinear constraints, we provide an equivalent linear formulation under part-warehouse service requirements, and an approximate linear formulation under part service requirements. Computational results indicate the superiority of our approach over existing approaches in the literature

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines

Business Modeling Framework For Personalization In Mobile Business Services

Is presented the structure of a formal framework for personalizationfeatures for mobile business services, which can be used to drive thebusiness modeling of M-business services from a service provider pointof view. It also allows to compute the revenue as linked topersonalization levels and features. A case study has been performedin the area of personalized location based mobile servicespersonalization;individual profiles;location based services;mobile business;mobile services