Architectural Elements of Resource Sharing Networks

2012 ◽  
pp. 153-184 ◽  
Author(s):  
Marcos Dias de Assunção ◽  
Rajkumar Buyya

This chapter first presents taxonomies on approaches for resource allocation across resource sharing networks such as Grids. It then examines existing systems and classifies them under their architectures, operational models, support for the life-cycle of virtual organisations, and resource control techniques. Resource sharing networks have been established and used for various scientific applications over the last decade. The early ideas of Grid computing have foreseen a global and scalable network that would provide users with resources on demand. In spite of the extensive literature on resource allocation and scheduling across organisational boundaries, these resource sharing networks mostly work in isolation, thus contrasting with the original idea of Grid computing. Several efforts have been made towards providing architectures, mechanisms, policies and standards that may enable resource allocation across Grids. A survey and classification of these systems are relevant for the understanding of different approaches utilised for connecting resources across organisations and virtualisation techniques. In addition, a classification also sets the ground for future work on inter-operation of Grids.

Author(s):  
Marcos Dias de Assuncao ◽  
Rajkumar Buyya

This chapter first presents taxonomies on approaches for resource allocation across resource sharing networks such as Grids. It then examines existing systems and classifies them under their architectures, operational models, support for the life-cycle of virtual organisations, and resource control techniques. Resource sharing networks have been established and used for various scientific applications over the last decade. The early ideas of Grid computing have foreseen a global and scalable network that would provide users with resources on demand. In spite of the extensive literature on resource allocation and scheduling across organisational boundaries, these resource sharing networks mostly work in isolation, thus contrasting with the original idea of Grid computing. Several efforts have been made towards providing architectures, mechanisms, policies and standards that may enable resource allocation across Grids. A survey and classification of these systems are relevant for the understanding of different approaches utilised for connecting resources across organisations and virtualisation techniques. In addition, a classification also sets the ground for future work on inter-operation of Grids.


Author(s):  
L. Shrivastava ◽  
G. S. Tomar ◽  
S. S. Bhadauria

Grid computing came into existence as a manner of sharing heavy computational loads among multiple computers to be able to compute highly complex mathematical problems. The grid topology is highly flexible and easily scalable, allowing users to join and leave the grid without the hassle of time and resource-hungry identification procedures, having to adjust their devices or install additional software. The goal of grid computing is described as “to provide flexible, secure and coordinated resource sharing among dynamic collections of individuals, institutions and resources”. AODV is an on-demand (reactive) algorithm capable of both unicast and multicast routing. In this paper, AODV has been modified by varying some of the configuration parameters used in this algorithm to improve its performance. This modified protocol i.e. A-AODV (advanced ad hoc on demand distance vector) has been compared with AODV in grid environment. The simulations have shown that A-AODV is able to achieve high throughput and packet delivery ratio and average end-to-end delay is reduced.


Author(s):  
L. Shrivastava ◽  
G. S. Tomar ◽  
S. S. Bhadauria

Grid computing came into existence as a manner of sharing heavy computational loads among multiple computers to be able to compute highly complex mathematical problems. The grid topology is highly flexible and easily scalable, allowing users to join and leave the grid without the hassle of time and resource-hungry identification procedures, having to adjust their devices or install additional software. The goal of grid computing is described as “to provide flexible, secure and coordinated resource sharing among dynamic collections of individuals, institutions and resources”. AODV is an on-demand (reactive) algorithm capable of both unicast and multicast routing. In this paper, AODV has been modified by varying some of the configuration parameters used in this algorithm to improve its performance. This modified protocol i.e. A-AODV (advanced ad hoc on demand distance vector) has been compared with AODV in grid environment. The simulations have shown that A-AODV is able to achieve high throughput and packet delivery ratio and average end-to-end delay is reduced.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.


Author(s):  
Laura Broeker ◽  
Harald Ewolds ◽  
Rita F. de Oliveira ◽  
Stefan Künzell ◽  
Markus Raab

AbstractThe aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.


Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 821
Author(s):  
Marek Petráš ◽  
Ivana Králová Lesná ◽  
Jana Dáňová ◽  
Alexander M. Čelko

Vaccination as an important tool in the fight against infections has been suggested as a possible trigger of autoimmunity over the last decades. To confirm or refute this assumption, a Meta-analysis of Autoimmune Disorders Association With Immunization (MADAWI) was conducted. Included in the meta-analysis were a total of 144 studies published in 1968–2019 that were available in six databases and identified by an extensive literature search conducted on 30 November 2019. The risk of bias classification of the studies was performed using the Newcastle–Ottawa Quality Assessment Scale. The strength of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. While our primary analysis was conducted in terms of measures of association employed in studies with a low risk of bias, the robustness of the MADAWI outcome was tested using measures independent of each study risk of bias. Additionally, subgroup analyses were performed to determine the stability of the outcome. The pooled association of 0.99 (95% confidence interval, 0.97–1.02), based on a total of 364 published estimates, confirmed an equivalent occurrence of autoimmune disorders in vaccinated and unvaccinated persons. The same level of association reported by studies independently of the risk of bias was supported by a sufficient number of studies, and no serious limitation, inconsistency, indirectness, imprecision, and publication bias. A sensitivity analysis did not reveal any discrepancy in the primary result. Current common vaccination is not the cause of any of the examined autoimmune disorders in the medium and long terms.


2008 ◽  
Vol 31 (10) ◽  
pp. 2231-2241 ◽  
Author(s):  
Chenn-Jung Huang ◽  
Yi-Ta Chuang ◽  
Chih-Tai Guan ◽  
Yun-Cheng Luo ◽  
Kai-Wen Hu ◽  
...  

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