Desktop grids have evolved to combine Peer-to-Peer and Grid computing techniques to improve the robustness, reliability and scalability of job execution infrastructures. However, efficiently matching incoming jobs to available system resources and achieving good load balance in a fully decentralized and heterogeneous computing environment is a challenging problem. In this paper, we extend our prior work with a new decentralized algorithm for maintaining approximate global load information, and a job pushing mechanism that uses the global information to push jobs towards underutilized portions of the system. The resulting system more effectively balances load and improves overall system throughput. Through a comparative analysis of experimental results across different system configurations and job profiles, performed via simulation, we show that our system can reliably execute Grid applications on a distributed set of resources both with low cost and with good load balance.

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.

No abstract

No abstract

Desktop grids use opportunistic sharing to exploit large collections of personal computers and workstations across the Internet, achieving tremendous computing power at low cost. Traditional desktop grid systems are typically based on a clientserver architecture, which has inherent shortcomings with respect to robustness, reliability and scalability. In this paper, we propose a decentralized, robust, highly available, and scalable infrastructure to match incoming jobs to available resources. Through a comparative analysis on the experimental results obtained via simulation of three different types of matchmaking algorithms under different workload scenarios, we show the trade-offs between efficient matchmaking and good load balancing in a fully decentralized, heterogeneous computational environment.