Related papers: GridSim: A Toolkit for the Modeling and Simulation…

MapReduce has become a popular programming model for running data intensive applications on the cloud. Completion time goals or deadlines of MapReduce jobs set by users are becoming crucial in existing cloud-based data processing…

The increasing reliance on dynamic pricing models, such as spot instances, in public cloud environments presents new challenges for workload scheduling and reliability. While these models offer cost advantages, they introduce volatility and…

We consider a distributed computing network consisting of a master and multiple workers processing tasks of different types. The master is running multiple applications. Each application stochastically generates real-time jobs with a strict…

In this work, we design and analyze novel distributed scheduling algorithms for multi-user MIMO systems. In particular, we consider algorithms which do not require sending channel state information to a central processing unit, nor do they…

Consider the problem of a multiple access channel in a time dependent environment with a large number of users. In such a system, mostly due to practical constraints (e.g., decoding complexity), not all users can be scheduled together, and…

The scheduling literature has traditionally focused on a single type of resource (e.g., computing nodes). However, scientific applications in modern High-Performance Computing (HPC) systems process large amounts of data, hence have diverse…

In order to achieve both fast and coordinated data transfer to collaborative sites as well as to create a distribution of data over multiple sites, efficient data movement is one of the most essential aspects in distributed environment.…

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…

In this paper, we present the ADMIRE architecture; a new framework for developing novel and innovative data mining techniques to deal with very large and distributed heterogeneous datasets in both commercial and academic applications. The…

Resource selection and task placement for distributed execution poses conceptual and implementation difficulties. Although resource selection and task placement are at the core of many tools and workflow systems, the methods are ad hoc…

Distributed cloud environments hosting data-intensive applications often experience slowdowns due to network congestion, asymmetric bandwidth, and inter-node data shuffling. These factors are typically not captured by traditional host-level…

Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires…

Grid computing consists of the coordinated use of large sets of diverse, geographically distributed resources for high performance computation. Effective monitoring of these computing resources is extremely important to allow efficient use…

The Service Level Agreement~(SLA) based grid superscheduling approach promotes coordinated resource sharing. Superscheduling is facilitated between administratively and topologically distributed grid sites by grid schedulers such as…

Considering the diverse nature of real-world distributed applications that makes it hard to identify a representative subset of distributed benchmarks, we focus on their underlying distributed algorithms. We present and characterize a new…

Infrastructure as a Service model of cloud computing is a desirable platform for the execution of cost and deadline constrained workflow applications as the elasticity of cloud computing allows large-scale complex scientific workflow…

In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years…

MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are…

With the rapid development in wide area networks and low cost, powerful computational resources, grid computing has gained its popularity. With the advent of grid computing, space limitations of conventional distributed systems can be…

The problem of attaining energy efficiency in distributed systems is of importance, but a general, non-domain-specific theory of energy-minimal scheduling is far from developed. In this paper, we classify the problems of energy-minimal…