A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling

Shelf and coastal sea processes extend from the atmosphere through the water column and into the sea bed. These processes are driven by physical, chemical, and biological interactions at local scales, and they are influenced by transport and cross strong spatial gradients. The linkages between domains and many different processes are not adequately described in current model systems. Their limited integration level in part reflects lacking modularity and flexibility; this shortcoming hinders the exchange of data and model components and has historically imposed supremacy of specific physical driver models. We here present the Modular System for Shelves and Coasts (MOSSCO, http://www.mossco.de), a novel domain and process coupling system tailored---but not limited--- to the coupling challenges of and applications in the coastal ocean. MOSSCO builds on the existing coupling technology Earth System Modeling Framework and on the Framework for Aquatic Biogeochemical Models, thereby creating a unique level of modularity in both domain and process coupling; the new framework adds rich metadata, flexible scheduling, configurations that allow several tens of models to be coupled, and tested setups for coastal coupled applications. That way, MOSSCO addresses the technology needs of a growing marine coastal Earth System community that encompasses very different disciplines, numerical tools, and research questions.Comment: 30 pages, 6 figures, submitted to Geoscientific Model Development Discussion

Using a desktop grid to support simulation modelling

Simulation is characterized by the need to run multiple sets of computationally intensive experiments. We argue that Grid computing can reduce the overall execution time of such experiments by tapping into the typically underutilized network of departmental desktop PCs, collectively known as desktop grids. Commercial-off-the-shelf simulation packages (CSPs) are used in industry to simulate models. To investigate if Grid computing can benefit simulation, this paper introduces our desktop grid, WinGrid, and discusses how this can be used to support the processing needs of CSPs. Results indicate a linear speed up and that Grid computing does indeed hold promise for simulation

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Grid-enabling FIRST: Speeding up simulation applications using WinGrid

The vision of grid computing is to make computational power, storage capacity, data and applications available to users as readily as electricity and other utilities. Grid infrastructures and applications have traditionally been geared towards dedicated, centralized, high performance clusters running on UNIX flavour operating systems (commonly referred to as cluster-based grid computing). This can be contrasted with desktop-based grid computing which refers to the aggregation of non-dedicated, de-centralized, commodity PCs connected through a network and running (mostly) the Microsoft Windowstrade operating system. Large scale adoption of such Windowstrade-based grid infrastructure may be facilitated via grid-enabling existing Windows applications. This paper presents the WinGridtrade approach to grid enabling existing Windowstrade based commercial-off-the-shelf (COTS) simulation packages (CSPs). Through the use of a case study developed in conjunction with Ford Motor Company, the paper demonstrates how experimentation with the CSP Witnesstrade and FIRST can achieve a linear speedup when WinGridtrade is used to harness idle PC computing resources. This, combined with the lessons learned from the case study, has encouraged us to develop the Web service extensions to WinGridtrade. It is hoped that this would facilitate wider acceptance of WinGridtrade among enterprises having stringent security policies in place