Framework for conference management system.

This paper presents a Web-based Conference Management System framework that is intended to support multiple conferences and facilitate the conference management starting from the conference preparation until the attendance tracking process during conference day. The framework derived from the analysis of several web-based related applications. The framework is presents and the results are discussed

A proposed framework for managing environmental causes and consequences of ocean traffic and ports

Paper presented at European Decision Sciences Institute (ED2016), 7th Annual Conference, 24th to 27th May 2016, Helsinki. Abstract The cumulative and in-combination effects of ocean shipping and port operations need addressing via a detailed, rigorous and holistic framework of risk assessment and risk management. This aims to protect the natural system while at the same time obtaining societal benefits from the seas. This paper proposes a conceptual framework that integrates both an ISO industry standard risk assessment and management framework (Bow-tie analysis) and the DAPSI(W)R(M) analysis supported by the ten-tenets criteria to provide guidance for all stakeholders, including industry and government, to address these issues. Water pollution stemming from maritime logistics and SCM are used to illustrate this framework

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

A framework for assessing crop production from rotations

This report was presented at the UK Organic Research 2002 Conference of the Colloquium of Organic Researchers (COR). Organic farming systems rely on the management of biological cycles for the provision of nutrients, which are crucial to maximising the production from the system. Rotations based on the use of grass-legume leys are central to the concept of organic farming systems, because they have the potential to support both animal production, and a subsequent, exploitative, arable cropping phase. A major challenge in organic farming is managing the supply of nitrogen, since it has a key role in governing both productivity and environmental impact. Hence, within a rotational system, there is a need to understand the complex interactions that are occurring between crop species and management, livestock production system and the impact of soil and climate on these processes. To understand these interactions, a framework is being developed for rotational farming systems that describes the soil nitrogen, crop growth and livestock production. The framework must address questions that are relevant to researchers and extensions workers. Typical questions relate to the management of nutrients in the short and long-term. Additionally, there are concerns over the impact of weeds, pests and diseases on productivity, as well as the impact of adopting new strategies or crops on the farming system

SARA: Self-Aware Resource Allocation for Heterogeneous MPSoCs

In modern heterogeneous MPSoCs, the management of shared memory resources is crucial in delivering end-to-end QoS. Previous frameworks have either focused on singular QoS targets or the allocation of partitionable resources among CPU applications at relatively slow timescales. However, heterogeneous MPSoCs typically require instant response from the memory system where most resources cannot be partitioned. Moreover, the health of different cores in a heterogeneous MPSoC is often measured by diverse performance objectives. In this work, we propose a Self-Aware Resource Allocation (SARA) framework for heterogeneous MPSoCs. Priority-based adaptation allows cores to use different target performance and self-monitor their own intrinsic health. In response, the system allocates non-partitionable resources based on priorities. The proposed framework meets a diverse range of QoS demands from heterogeneous cores.Comment: Accepted by the 55th annual Design Automation Conference 2018 (DAC'18

DIRAC framework evaluation for the Fermi\boldsymbol{Fermi}-LAT and CTA experiments

DIRAC (Distributed Infrastructure with Remote Agent Control) is a general framework for the management of tasks over distributed heterogeneous computing environments. It has been originally developed to support the production activities of the LHCb (Large Hadron Collider Beauty) experiment and today is extensively used by several particle physics and biology communities. Current ($Fermi$ Large Area Telescope -- LAT) and planned (Cherenkov Telescope Array -- CTA) new generation astrophysical/cosmological experiments, with very large processing and storage needs, are currently investigating the usability of DIRAC in this context. Each of these use cases has some peculiarities: $Fermi$-LAT will interface DIRAC to its own workflow system to allow the access to the grid resources, while CTA is using DIRAC as workflow management system for Monte Carlo production and analysis on the grid. We describe the prototype effort that we lead toward deploying a DIRAC solution for some aspects of $Fermi$-LAT and CTA needs.Comment: proceedings to CHEP 2013 conference : http://www.chep2013.org