Status report on the NCRIS eResearch capability summary

Preface The period 2006 to 2014 has seen an approach to the national support of eResearch infrastructure by the Australian Government which is unprecedented. Not only has investment been at a significantly greater scale than previously, but the intent and approach has been highly innovative, shaped by a strategic approach to research support in which the critical element, the catchword, has been collaboration. The innovative directions shaped by this strategy, under the banner of the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS), have led to significant and creative initiatives and activity, seminal to new research and fields of discovery. Origin This document is a Technical Report on the Status of the NCRIS eResearch Capability. It was commissioned by the Australian Government Department of Education and Training in the second half of 2014 to examine a range of questions and issues concerning the development of this infrastructure over the period 2006-2014. The infrastructure has been built and implemented over this period following investments made by the Australian Government amounting to over $430 million, under a number of funding initiatives

An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach

Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM International Conference on Utility and Cloud Computin

Bonus Computing: An Evolution from and a Supplement to Volunteer Computing

Despite the huge success in various worldwide projects, volunteer computing also suffers from the possible lack of computing resources (one volunteered device can join one project at a time) and from the uncertain job interruptions (the volunteered device can crash or disconnect from the Internet at any time). To relieve the challenges faced by volunteer computing, we have proposed bonus computing that exploits the free quotas of public Cloud resources particularly to deal with problems composed of fine-grained, short-running, and compute-intensive tasks. In addition to explaining the loosely-coupled functional architecture and six architectural patterns of bonus computing in this paper, we also employ the Monte-Carlo approximation of Pi (π) as a use case demonstration both to facilitate understanding and to help validate its functioning mechanism. The results exhibit not only effectiveness but also multiple advantages of bonus computing, which makes it a valuable evolution from and supplement to volunteer computing

Disturbance and Predictability of Flowering Patterns in Bird-Pollinated Cloud Forest Plants

The distribution and flowering patterns of hummingbird—pollinated plants were compared from July 1981 to June 1983 in three patch types in cloud forest at Monteverde, Costa Rica. Study plots were: (1) four recent, large (1100—2500 m2) disturbances ("cutovers") produced by cutting vegetation, (2) six recent, smaller (200—600 m2) disturbances caused by treefalls, and (3) four plots (1600—1800 m2) of canopied forest. Based on published literature dealing with communities that characterize different regimes of disturbance, we tested one assumption and two hypotheses. Assumption: Plant species composition differs among the three patch types. Hypothesis 1: Phenotypic specialization by plants for co—evolved interactions with hummingbirds will be lowest in large gaps, highest in forest, and intermediate in treefalls. Hypothesis 2: Predictability of flowering phenologies and nectar production will be lowest in large gaps, highest in forest, intermediate in treefalls. Neither the assumption nor the hypotheses were supported by the results. The patch mosaic in this cloud forest was not associated with major differences in species composition of bird—pollinated plants. Most species studied were self—compatible. Most abundant in cutovers were species with long corollas, relatively specialized for attracting long—billed hummingbirds. Species with short corollas, which can be visited by many hummingbird species and some insects, were most abundant in treefalls and forest. Variation in phenological patterns showed no consistent trends among patch types. Predictability of flower and nectar production tended to be greatest in treefalls, which are foci of concentrated flowering activity by all species. Discrepancies between our results and previous studies can be ascribed to two facts. (1) Much of the literature dealing with ecological consequences of disturbance has dealt with large—scale anthropogenic disturbances such as old fields of the eastern USA, whereas we studied small, natural, or quasi—natural disturbances. (2) Studies of forest disturbance have focused on the tree layer, whereas we studied the understory herbs, shrubs, and epiphytes. Natural disturbance usually involves death and replacement of one or more trees, whereas individuals of other life forms may persist through the disturbance

Personal mobile grids with a honeybee inspired resource scheduler

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The overall aim of the thesis has been to introduce Personal Mobile Grids (PMGrids) as a novel paradigm in grid computing that scales grid infrastructures to mobile devices and extends grid entities to individual personal users. In this thesis, architectural designs as well as simulation models for PM-Grids are developed. The core of any grid system is its resource scheduler. However, virtually all current conventional grid schedulers do not address the non-clairvoyant scheduling problem, where job information is not available before the end of execution. Therefore, this thesis proposes a honeybee inspired resource scheduling heuristic for PM-Grids (HoPe) incorporating a radical approach to grid resource scheduling to tackle this problem. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are initiated. Among the other main contributions are a comprehensive taxonomy of grid systems as well as a detailed analysis of the honeybee colony and its nectar acquisition process (NAP), from the resource scheduling perspective, which have not been presented in any previous work, to the best of our knowledge. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Comparisons with optimal values and worst bounds are conducted to gain a clear insight into HoPe behaviour, in terms of stability, throughput, turnaround time and speedup, under different running conditions of number of jobs and grid scales. Experimental results demonstrate the superiority of HoPe performance where it has successfully maintained optimum stability and throughput in more than 95% of the experiments, with HoPe achieving three times better than the OSH under extremely heavy loads. Regarding the turnaround time and speedup, HoPe has effectively achieved less than 50% of the turnaround time incurred by the OSH, while doubling its speedup in more than 60% of the experiments. These results indicate the potential of both PM-Grids and HoPe in realising futuristic grid visions. Therefore considering the deployment of PM-Grids in real life scenarios and the utilisation of HoPe in other parallel processing and high throughput computing systems are recommended

Evaluation of the honey bee colonies weight gain during the intensive foraging period

Received: March 5th, 2022 ; Accepted: April 1st, 2022 ; Published: April 13th, 2022 ; Correspondence: [email protected] in Latvia has a long tradition and it is a classical branch of agriculture. In Latvia, there is no traditional beekeeping region, and beekeeping is performed in all regions. Honey yield is influenced by various factors - variety of crops (nectar plants) around the apiary, man-made changes in land/forests (deforestation), climate change, beekeepers’ actions, etc. Application of information and communication technologies (ICT) in the field of beekeeping can bring benefits to the beekeepers. To be more specific, continuous remote monitoring of certain bee colony parameters can improve beekeeper’s apiary management, by informing timely about the nectar flow (or even provide information on bee colony states, e.g., swarming). In such a way, beekeepers can plan their next actions - prepare supers or even choose to move the apiary to a different geographical location. Within this research, weight gain of the ten honey bee colonies was remotely monitored and analysed during two-week period at the beginning of the summer 2021 in Vecauce, Latvia, using the precision beekeeping approach. This monitoring period corresponded to intensive flowering of the winter rapeseed and field beans. Colonies were equipped with the automatic scales. In addition, colony and environmental temperature was monitored. Measurements were taken every thirty minutes. Analysing the obtained data, weight increase can be observed in all colonies, from 17 to 48 kg. As well, based on weight data, swarming event can be identified. Constant monitoring of weight change can also help to identify daily patterns in honey bee activity

Secondary Metabolites in Nectar-Mediated Plant-Pollinator Relationships

n recent years, our understanding of the complex chemistry of floral nectar and its ecological implications for plant-pollinator relationships has certainly increased. Nectar is no longer considered merely a reward for pollinators but rather a plant interface for complex interactions with insects and other organisms. A particular class of compounds, i.e., nectar secondary compounds (NSCs), has contributed to this new perspective, framing nectar in a more comprehensive ecological context. The aim of this review is to draft an overview of our current knowledge of NSCs, including emerging aspects such as non-protein amino acids and biogenic amines, whose presence in nectar was highlighted quite recently. After considering the implications of the different classes of NSCs in the pollination scenario, we discuss hypotheses regarding the evolution of such complex nectar profiles and provide cues for future research on plant-pollinator relationships

THE ROLE OF CONSERVATION IN PUBLIC HEALTH

Master of Public HealthPublic Health Interdepartmental ProgramRoman Reddy R. GantaAbstract This is a report of my field experience (240 hours) completed at the Soltis Center and Monteverde Institutions in Costa Rica during the summer of 2015. Under the supervision of Dr. Raymond Tarpley, the director of the Conservet Program, the course explores concepts related to conservation and facilitates collaboration amongst veterinary students, veterinarians, biologists, and ecologists to identify levels of health dysfunction in the ecosystem and establish methods of intervention to protect public health. The Conservet Program included lecture-based discussions with local professionals about the biodiversity in Costa Rica, disease ecology, drivers of global health dysfunction, and conservation strategies. The course also provided opportunities for fieldwork data collection from two different eco-zones of rain forest and local farms to explore the complexity of environmental function. The fieldwork focused on birds, bats, rodents, local livestock, fish, and mosquitoes as sentinels for disease and ecosystem health. Diseases investigated include Dengue, Chikungunya, Chagas Disease, West Nile Virus, and Avian Influenza. With a focus on conservation, this program allowed me to develop a greater understanding of public health from studying the interface between human, animal, and environmental health. This report describes my field experience in Costa Rica and discusses the relevance of conservation efforts as it affects public health. The key to understanding emerging infectious diseases is to understand the protective effects of nature intact, and recognize the consequences of destroying it. This report will also discuss the MPH core competency courses and how they relate to my field experience