Related papers: A Computational Economy for Grid Computing and its…

Big data storage management is one of the most challenging issues for Grid computing environments, since large amount of data intensive applications frequently involve a high degree of data access locality. Grid applications typically deal…

Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a…

Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while…

Coupled models are set to become increasingly important in all aspects of science and engineering as tools with which to study complex systems in an integrated manner. Such coupled, hybrid simulations typically communicate data between the…

Cloud Computing (CC) is the most prevalent paradigm under which services are provided over the Internet. The most relevant feature for its success is its capability to promptly scale service based on user demand. When scaling, the main…

Grid computing is a type of distributed computing which allows sharing of computer resources through Internet. It not only allows us to share files but also most of the software and hardware resources. An efficient resource discovery…

Objectives: Grid-based technologies are emerging as potential solutions for managing and collaborating distributed resources in the biomedical domain. Few examples exist, however, of successful implementations of Grid-enabled medical…

This paper presents resource management techniques for allocating communication and computational resources in a distributed stream processing platform. The platform is designed to exploit the synergy of two classes of network connections…

With recent increasing computational and data requirements of scientific applications, the use of large clustered systems as well as distributed resources is inevitable. Although executing large applications in these environments brings…

Modern computing systems process jobs with resource requirements such as CPU and memory, which are described by multiresource jobs (MRJ) queueing models. In practice, job resource requirements are spread out over so many values, that it is…

Field-deployable edge computing nodes form a network and are used to complete scientific tasks for remote sensing and monitoring. The networked nodes collectively decide which scientific applications to run while they are constrained by…

This chapter focuses on the use of Grid technologies to achieve utility computing. An overview of how Grids can support utility computing is first presented through the architecture of Utility Grids. Then, utility-based resource allocation…

CPU-GPU heterogeneous systems are now commonly used in HPC (High-Performance Computing). However, improving the utilization and energy-efficiency of such systems is still one of the most critical issues. As one single program typically…

The rapid development of cloud-native architecture has promoted the widespread application of container technology, but the optimization problems in container scheduling and resource management still face many challenges. This paper…

Smart grids (SGs) enable integration of diverse power sources including renewable energy resources. They can contribute to the reduction of harmful gas emission, and support two-way information flow to enhance energy efficiency, along with…

Cloud computing is a new paradigm where data and services of Information Technology are provided via the Internet by using remote servers. It represents a new way of delivering computing resources allowing access to the network on demand.…

Coordination in distributed environments, like Grids, involves selecting the most appropriate services, resources or compositions to carry out the planned activities. Such functionalities appear at various levels of the infrastructure and…

Demand-side management presents significant benefits in reducing the energy load in smart grids by balancing consumption demands or including energy generation and/or storage devices in the user's side. These techniques coordinate the…

This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs…

Computer simulation is finding a role in an increasing number of scientific disciplines, concomitant with the rise in available computing power. Realizing this inevitably requires access to computational power beyond the desktop, making use…