Cluster computers are a viable and less expensive alternative to symmetric multiprocessor systems. However, a serious difficulty in concurrent programming of a cluster computer system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. Self-scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been designed in the past. These schemes, such as FSS, GSS and TSS, can achieve load balancing in SMP, even in a moderate heterogeneous environment, but are not suitable in extremely heterogeneous environments. In this paper, we propose a heuristic approach to solve parallel loop scheduling problems on an extremely heterogeneous PC cluster environment.
Several co-allocation strategies have been coupled and used to exploit rate differences among various client-server links and to address dynamic rate fluctuations by dividing files into multiple blocks of equal sizes. However, a major obstacle, the idle time of faster servers having to wait for the slowest server to deliver the final block, makes it important to reduce differences in finishing time among replica servers. In this paper, we propose a dynamic co-allocation scheme, namely RecursiveAdjustment Co-Allocation scheme, to improve the performance of data transfer in Data Grids. Our approach reduces the idle time spent waiting for the slowest server and decreases data transfer completion time.
The influenza problem has always been an important global issue. It not only affects people's health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenzalike illness emergency department visits to the Taiwan Centers for Disease Control, and the PM 2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.