This research considers human impacts on three components of biodiversity (composition, spatial structure and function). Given the relict character and unusual biogeochemical balance of tropical montane cloud forests in Mexico, logging poses a pressing threat to their survival. Specifically, this thesis explores the effect of selective logging and above-ground secondary succession on the biogeochemical cycling and soil macroinvertebrate community in a cloud forest in Oaxaca, Mexico. The research investigates: (1) whether the above-ground chain of successional changes in tree dominance, litterfall, litter diversity and soil microenvironmental conditions are coupled with a below-ground succession of soil nutrient availability and macroinvertebrate communities, (2) the role of spatial structuring of environmental conditions and litter resources as determinates of the nutrient availability and macroinvertebrate taxa abundance, (3) the implications of successional changes for decomposition and (4) whether the local influence of single trees explains the spatial structure of macroinvertebrate communities in late successional forests. The work was carried out in three chronosequences (c.15, 45, 75 and 100 year-old stages) of high altitude (1500-2000m) tropical montane cloud forest, two recently logged sites and two pristine sites. the macroinvertebrates in the litter and mineral soil were hand sorted from monoliths. Parametric statistics and canonical correspondance analysis were used to determine mean successional trends, and Spatial Analysis by Distance Indices and geostatistical methods were used in combination to determine spatial patterns. Two decompositional experiments were performed to explore the relationship between decompositional rate, litter quality and macroinvertebrate community higher taxa composition in different successional stages and under the canopy of different tree species. The research showed that: 1. The macroinvertebrate community composition in both recently logged sites and pristine forests were distinct compared to secondary successional stages. A decrease in soil temperature and nutrient availablity but increase in litter diversity and soil organic matter recorded through succession were accompanied by an increase in the number of macroinvertebrate taxa in the soil. For exampl, Collembola were most abundant in recently logged sites and earthworms (Megascolecidae) were almost excluvely found in pristine forests. 2. The oldest secondary forest (100 year-old) showed the highest frequency of aggregation in the abundance of individual macroinvertebrate taxa, and the highest and most uniform value of Shannon's diversity. This suggests that high levels of diversity in litter resources and soil chemistry in late succession are associated with complex spatial structuring of highly diverse macroinvertebrate communities. 3. The leaves of a late successional species (Persea americana)decomposed at a slightly slower rate than an early successional species (Pinus chiapensis) in all successional stages, yet the number and Shannon's diversity of macroinvertebrate taxa that invaded decomposing P.Americana leaves was consistently higher. The preference of macroinvertebrate taxa for the late-successional leaves was ultimately explained by differences in leaf quality during decomposition. P.americana leaves had higher concentrations of cations throughout decomposition and their concentration of lignin and nitrogen became higher. 4. In the 100-year-old forest, the effect of seasonal variation on soil microenvironmental conditions and litter availability was different under the canopy of different tree species. Furthermore, the chemical evolution of the same leaf type (e.g. Oreopanax xalapensis) was differentwhen decomposing under different canopies. The highly diverse and spatially complex macroinvertebrate community found in late succession (and experimental litter) was largely explained by the interactive effects of seasonal variation, tree species, litter quality and availability of the decomposing leaf type. The results provide the first analysis of the relationship between soil biodiversity and the tight biogeochemical cycling in this relict ecosystem type. Overall the results indicate that mature cloud forests sustain a diverse and spatially heterogenous macroinvertebrate community. The compositional and spatial components of soil biodiversity are compromised by logging and full recovery may take mopre than 100 years.

Nos últimos anos, observamos o alvorecer de uma grande quantidade de aplicações que utilizam robôs autônomos. Para que um robô seja considerado verdadeiramente autônomo, é primordial que ele possua a capacidade de aprender sobre o ambiente no qual opera. Métodos de SLAM (Localização e Mapeamento Simultâneos) constroem um mapa do ambiente por onde o robô trafega ao mesmo tempo em que estimam a trajetória correta do robô. No entanto, para obter um mapa completo do ambiente de forma autônoma é preciso guiar o robô por todo o ambiente, o que é feito no problema de exploração. Câmeras são sensores baratos que podem ser utilizadas para a construção de mapas 3D. Porém, o problema de exploração em mapas gerados por métodos de SLAM monocular, i.e. que extraem informações de uma única câmera, ainda é um problema em aberto, pois tais métodos geram mapas esparsos ou semi-densos, que são inadequados para navegação e exploração. Para tal situação, é necessário desenvolver métodos de exploração capazes de lidar com a limitação das câmeras e com a falta de informação nos mapas gerados por SLAMs monoculares. Propõe-se uma estratégia de exploração que utilize mapas volumétricos locais, gerados através das linhas de visão, permitindo que o robô navegue em segurança. Nestes mapas locais, são definidos objetivos que levem o robô a explorar o ambiente desviando de obstáculos. A abordagem proposta visa responder a questão fundamental em exploração: "Para onde ir?". Além disso, busca determinar corretamente quando o ambiente está suficientemente explorado e a exploração deve parar. A abordagem proposta é avaliada através de experimentos em um ambiente simples (i.e. apenas uma sala) e em um ambiente compostos por diversas salas. / In recent years, we have seen the dawn of a large number of applications that use autonomous robots. For a robot to be considered truly autonomous, it is primordial that it has the ability to learn about the environment in which it operates. SLAM (Simultaneous Location and Mapping) methods build a map of the environment while estimating the robot’s correct trajectory. However, to autonomously obtain a complete map of the environment, it is necessary to guide the robot throughout the environment, which is done in the exploration problem. Cameras are inexpensive sensors that can be used for building 3D maps. However, the exploration problem in maps generated by monocular SLAM methods (i.e. that extract information from a single camera) is still an open problem, since such methods generate sparse or semi-dense maps that are ill-suitable for navigation and exploration. For such a situation, it is necessary to develop exploration methods capable of dealing with the limitation of the cameras and the lack of information in the maps generated by monocular SLAMs. We proposes an exploration strategy that uses local volumetric maps, generated using the lines of sight, allowing the robot to safely navigate. In these local maps, objectives are defined to lead the robot to explore the environment while avoiding obstacles. The proposed approach aims to answer the fundamental question in exploration: "Where to go?". In addition, it seeks to determine correctly when the environment is sufficiently explored and the exploration must stop. The effectiveness of the proposed approach is evaluated in experiments on single and multi-room environments.

When deploying software engineering applications in the cloud there are two similar software components used. These are Virtual Machines and Containers. In recent years containers have seen an increase in popularity and usage, in part because of tools such as Docker and Kubernetes. Virtual Machines (VM) have also seen an increase in usage as more companies move to solutions in the cloud with services like Amazon Web Services, Google Compute Engine, Microsoft Azure and DigitalOcean. There are also some solutions using auto-scaling, a technique where VMs are commisioned and deployed to as load increases in order to increase application performace. As the application load decreases VMs are decommisioned to reduce costs. In this thesis we implement and evaluate auto-scaling policies that use both Virtual Machines and Containers. We compare four different policies, including two baseline policies. For the non-baseline policies we define a policy where we use a single Container for every Virtual Machine and a policy where we use several Containers per Virtual Machine. To compare the policies we deploy an image serving application and run workloads to test them. We find that the choice of deployment strategy and policy matters for response time and error rate. We also find that deploying applications as described in the methodis estimated to take roughly 2 to 3 minutes.

Database outsourcing is an emerging paradigm for data management in which data are stored in third-party servers. With the advance of cloud computing, database outsourcing has become popular and highly adopted. However, as a result, many technology challenges have arisen. / In this thesis, we study two problems with respect to the challenges, and propose solutions for each problem with the consideration of access patterns. The first problem is raised from theviewpoint of service providers. We study the problem of data allocation in scalable distributed database systems for achieving the high availability feature of cloud services. We propose a data allocation algorithm, which makes use of time series models from previous access patterns to perform load forecasting and reallocate data fragments to balance the workload within the system. Simulation results show that, with accurate forecasting, the proposed algorithm gives a better performance than general threshold-based algorithms. / The second problem addresses the clients' concern that service providers may not be trustworthy. We first illustrate how service providers can infer sensitive information through query access patterns even when data are encrypted. Then, we propose techniques that break down large queries and randomize query access patterns such that service providers cannot infer sensitive information with a high degree of certainty. Experiments on benchmark data show that a high level of access privacy can be achieved by the proposed techniques with a reasonable overhead. / 數據庫外包是近年新興的一種數據管理服務，其特點是數據儲存於第三方的伺服器內。隨著雲端科技的發展，數據庫外包服務日趨普及，同時亦產生不少技術問題。 / 本文著重探討兩個問題。首先，從服務供應商的角度研究可擴展的分布式數據庫系統如何分配數據來提供高可用性的雲端服務。鑑於用戶訪問模式會隨著時間轉變，我們提出以時間序列模型預測負荷的算法重新分配數據，以平衡系統的工作量。通過模擬實驗可知在準確的負荷預測下，我們提出的算法比基於闆值的算法有更好的表現。 / 第二個探討的問題是如何保障用戶私隱，避免洩露給服務供應商。文中列舉了數據加密的情況下，服務供應商如何通過分析用戶訪問模式獲取資料，進而提出相應的保障技術。透過用戶訪問模式的隨機化，能使服務供應商無法準確比對用戶的資料。基準數據實驗指出此項技術可有效保護私隱，而且不會對訪問速度造成太大影響。 / Li, Shun Pun. / Thesis M.Phil. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 86-93). / Abstracts also in Chinese. / Title from PDF title page (viewed on 11, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

The new approach of cloud robotics takes advantage of cloud computing as a vast resource pool for massively parallel computation and sharing of data. Besides, the cloud robotic system removes overheads for maintenance and updates, and reduces dependence on user middleware. This is of particular interest for service robots, because on-board computation entails additional power requirements which may reduce operating duration and constrain robot mobility as well as costs. In order to utilize the cloud technology in service robots, it is crucial to allow different types of robots to share information and to develop new skills on the cloud. / In general, it is cast as a dynamic resource allocation problem. Given a set of resources and a sequence of agents, the goal is to distribute resources to agents in an optimal manner. The resource allocation problem is an NP-hard problem in general. This thesis strives to minimize the resource usage and task completion time by scheduling a number of requests from robots. However, actual realization of fully distributed cloud robotic systems is rarely found in the community. Moreover, unconstrained resources in the cloud are not commonly implemented. Therefore, the optimization of autonomously implemented resource allocation is the primary focus of the thesis. / While a respectable amount of work is done on both resource and task allocation, there is still the need for research towards the integration of problems in a typical cloud robotic system. For the outlined difficulties, this thesis addresses novel research on the following aspects: At first, the underlying architecture of Multi Sensor Data Retrieval (MSDR) is implemented on the twisted-based socket for asynchronous data transmission, which is also investigated as effective decentralized methods for multi-robot coordination, task assignment and service contract establishing. Second, the market-based scheduling mechanism is proposed for the dynamic resource allocation problem in cloud robotics. A set of criteria as empirical Quality of Service (QoS) is optimized, especially Time to Response (ToR) is minimized to fulfill Firm Real-Time (FRT) requirement of robotic tasks. Third, a Link Quality Matrix (LQM) auction-based negotiation strategy is proposed to relieve the competition among multi-robot systems in Mobile Ad-hoc Networks (MANETs). Besides, an incremental auction-based strategy is proposed considering hops, time delay as well as link quality. Both fair allocation when unmanned interference and biased allocation when users have preferences are optimized among multi-robot systems in MANETs. By tackling all these issues, this thesis contributes to general implementation of cloud robotic system into daily. / Future research will focus on task-oriented problems, such as smart home surveillance, guiding and etc., which could be better solved benefiting from cloud robotics. Solutions will proposed in a bidirectional way considering both data uploading and downloading. / 雲機器人是利用雲計算作為龐大的資源池進行大規模并行計算和資源共用。此外，雲機器人系統避免了用於維護和更新各個機器人客戶端的開銷，並降低了機器人客戶端對中間件的依賴。這對於服務機器人尤其有益，因為大量計算所需要的能量會減少機器人運行的持續時間，並且約束機器人移動性能以及增加機器人的成本。爲了更好的利用雲技術提高機器人的服務性能，最重要的是允許不同類型的機器人共用資源，尤其是多傳感器的信息，並在雲上開發新的功能和新的應用。 / 此類問題一般被規劃為動態資源配置問題，即給定一個資源集合和一個多客戶端的序列，最優化地進行資源的分配。資源配置問題是一個非確定性多項式複雜 (NP-hard) 問題 。本文通過優化調度多個資源請求，力求最大限度地減少資源的使用和任務完成的時間。目前很少有真正實現了完全分布式的雲機器人系統。此外，在實際的雲系統中並不存在無限的不受約束的資源。因此，自主地優化雲機器人系統的資源配置是雲機器人系統的關鍵問題，有著重要的現實意義。 / 雖然在資源配置和任務分配領域已經有大量的研究工作，當這兩者在典型的雲機器人系統中結合時，仍然有大量新的問題需要研究。本文著重於以下幾個方面：首先，建立多傳感器資料的檢索架構，即基於twisted的socket 框架建立任務分配和服務合同的構建方法，用於實現多傳感器信息的異步傳輸，同時將其用於研究有效的分布式多機器人協作。其次，提出基於市場范式的調度機制，用於解決雲計算機器人系統的動態資源配置問題；並針對一系列服務品質指標進行優化和驗證，特別是實現回應時間的最小化，以滿足機器人任務即時性的要求。第三，提出基於鏈路信號強度矩陣的協商策略以減輕在移動自組網路中多個機器人的通信競爭；此外，考慮到多跳、時延和鏈路品質等問題，本文提出了增量式的拍賣算法策略；當移動自組網中存在多機器人系統時，所涉算法對無人干擾情況下的公平分配和當有使用者有偏好情況下的偏好分配分別進行了優化。通過解決以上問題，本文的貢獻有助於通用的雲機器人系統融入到人類日常生活和工作中。 / 未來的研究將側重于面向機器人任務分配的問題，例如監控，多機器人嚮導等，及其他受益于雲機器人平臺的各類應用解決方案。 / Wang, Lujia. / Thesis Ph.D. Chinese University of Hong Kong 2014. / Includes bibliographical references (leaves 115-127). / Abstracts also in Chinese. / Title from PDF title page (viewed on 07, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Open-source cloud platforms provide a feasible alternative of deploying cloud computing in low-cost commodity hardware and operating systems. To enhance the reliability of an open-source cloud, we design and implement CloudVS, a practical add-on system that enables version control for virtual machines (VMs). CloudVS targets a commodity cloud platform that has limited available resources. It exploits content similarities across different VM versions using redundancy elimination (RE), such that only non-redundant data chunks of a VM version are transmitted over the network and kept in persistent storage. Using RE as a building block, we propose a suite of performance adaptation mechanisms that make CloudVS amenable to different commodity settings. Specifically, we propose a tunable mechanism to balance the storage and disk seek overheads, as well as various I/O optimization techniques to minimize the interferences to other co-resident processes. We further exploit a higher degree of content similarity by applying RE to multiple VM images simultaneously, and support the copy-on-write image format. Using real-world VM snapshots, we experiment CloudVS in an open-source cloud testbed built on Eucalyptus. We demonstrate how CloudVS can be parameterized to balance the performance trade-offs between version control and normal VM operations. / 開源雲端平台為供低成本硬件及作業系統提供一個可行的替代方案。為了提高開源雲的可靠性，我們設計及實踐了CloudVS，一個針對虛擬機的實用版本控制系統。CloudVS針對有限資源的低成本硬件雲平台，利用內容相似性，在不同的虛擬機版本使用冗餘消除。這樣，在虛擬機版本數據中只有非冗餘的部分在網絡上傳輸，並保存在持久存儲。使用冗餘消除作為構建塊，我們提出了一套性能適應機制，使CloudVS適合於不同的低成本硬件配置。具體而言，我們提出了一種可調諧的機制來平衡存儲和磁盤尋道開銷，以及應用各種I/O優化技術去最大限度地減少對其他同時運行進程的干擾。我們應用冗餘消除多個虛擬機影像去進一步利用其內容相似度，同時，我們更進一步支持寫時複製格式。使用來自真實世界的虛擬機快照，我們嘗試在開放源碼的雲測試平台Eucalyptus中測試CloudVS。我們演示CloudVS如何可以參數化，以平衡版本控制和正常的虛擬機操作之間的性能取捨。 / Tang, Chung Pan. / Thesis M.Phil. Chinese University of Hong Kong 2013. / Includes bibliographical references (leaves 57-65). / Abstracts also in Chinese. / Title from PDF title page (viewed on 07, October, 2016). / Detailed summary in vernacular field only.

Yes / Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular checkpointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classi cation and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results show that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This fi nding means that our method can effectively predict all possible future system and application failures within the system. / The full-text of this article will be released for public view a year after publication.

Review of Getting Started with Cloud Computing: A LITA Guide. Eds. Edward M. Corrado and Heather Lea Moulaison. New York: Neal-Schuman Publisher, Inc., 2011. 214p. alk. paper, $65 (ISBN 9781555707491).

The master thesis introduces the Kylo Data Lake which deployed in the public cloud environment,provides a perspective of datalake configuration and data ingestion experiment. This paper reveals the underlying architecture of Kylo data lake.

Atmospheric particles represent a component of air pollution that has been identified as a major contributor to adverse health effects and mortality. Aerosols also interact with solar radiation and clouds perturbing the atmosphere and generating responses in a wide range of scales, such as changes to severe weather and climate. Thus, being able to accurately predict aerosols and its effects on atmospheric properties is of upmost importance. This thesis presents a collection of studies with the global objective to advance in science and operations the use of WRF-Chem, a regional model able to provide weather and atmospheric chemistry predictions and simultaneously representing aerosol effects on climate. Different strategies are used to obtain accurate predictions, including finding an adequate model configuration for each application (e.g., grid resolution, parameterizations choices, processes modeled), using accurate forcing elements (e.g., weather and chemical boundary conditions, emissions), and developing and applying data assimilation techniques for different observational sources. Several environments and scales are simulated, including complex terrain at a city scale, meso-scale over the southeast US for severe weather applications, and regional simulations over the three subtropical persistent stratocumulus decks (off shore California and southeast Pacific and Atlantic) and over North America. Model performance is evaluated against a large spectrum of observations, including field experiments and ground based and satellite measurements. Overall, very positive results were obtained with the WRF-Chem system once it had been configured properly and the inputs chosen. Also, data assimilation of aerosol and cloud satellite observations contributed to improve model performance even further. The model is proven to be an excellent tool for forecasting applications, both for local and long range transported pollution. Also, advances are made to better understand aerosol effects on climate and its uncertainties. Aerosols are found to generate important perturbations, ranging from changes in cloud properties over extensive regions, up to playing a role in increasing the likelihood of tornado occurrence and intensity. Future directions are outline to keep advancing in better predictions of aerosols and its feedbacks.