Abstract: In this paper, we propose an efficient non-linear task workload prediction mechanism incorporated with a fair scheduling algorithm
for task allocation and resource management in Grid computing. Workload prediction is accomplished in a Grid middleware approach
using a non-linear model expressed as a series of finite known functional components using concepts of functional analysis. The coefficient
of functional components are obtained using a training set of appropriate samples, the pairs of which are estimated based on
a runtime estimation model relied on a least squares approximation scheme. The advantages of the proposed non-linear task workload
prediction scheme is that (i) it is not constrained by analysis of source code (analytical methods), which is practically impossible to be
implemented in complicated real-life applications or (ii) it does not exploit the variations of the workload statistics as the statistical
approaches does. The predicted task workload is then exploited by a novel scheduling algorithm, enabling a fair Quality of Serviceoriented
resource management so that some tasks are not favored against others. The algorithm is based on estimating the adjusted fair
completion times of the tasks for task order selection and on an earliest completion time strategy for the grid resource assignment. Experimental
results and comparisons with traditional scheduling approaches as implemented in the framework of European Union funded
research projects GRIA and GRIDLAB grid infrastructures have revealed the outperformance of the proposed method.
Abstract: ServiceOrientedComputing and its most famous implementation technology Web Services (WS) are becoming an important enabler of networked business models. Discovery mechanisms are a critical factor to the overall utility of Web Services. So far, discovery mechanisms based on the UDDI standard rely on many centralized and area-specific directories, which poses information stress problems such as performance bottlenecks and fault tolerance. In this context, decentralized approaches based on Peer to Peer overlay networks have been proposed by many researchers as a solution. In this paper, we propose a new structured P2P overlay network infrastructure designed for Web Services Discovery. We present theoretical analysis backed up by experimental results, showing that the proposed solution outperforms popular decentralized infrastructures for web discovery, Chord (and some of its successors), BATON (and itĘs successor) and Skip-Graphs.
Abstract: Human mobility monitoring and respective traces are important for understanding human behavior, respective patterns and associated context. Such data can be potentially used in business intelligence-oriented systems, for providing added value commercial services or insight to internal enterprise procedures. At the same time, smartphones are rapidly becoming an indispensable tool for our everyday life, while their advanced networking and computing capabilities are increasingly being used as enablers for new applications. We discuss here a system using a stable computing and networking infrastructure along with smartphone applications, based on commodity technologies, meant to be deployed rapidly and provide analytics almost in real-time for such aspects. We also discuss a related scenario in order to provide insight as to where our system could be used. We briefly present the deployment of our system in two settings, an office building and a research exhibition event, along with our experiences. Our findings show that it is feasible and efficient to deploy and operate our system relatively easy, producing meaningful data.