Cloud computing is a multi billion dollar industry, based around outsourcing the provisioning and maintenance of computing resources. In particular, Infrastructure as a Service (IaaS) enables customers to purchase virtual machines in order to run arbitrary software. IaaS customers are given the option to purchase priority access, while providers choose whether customers are preempted based on priority level. The customer decision is based on their tolerance for preemption. However, this decision is a reaction to the provider choice of preemption policy and cost to purchase priority. In this work, a non-cooperative game is developed for an IaaS system offering resource reservations. An unobservable $M|G|1$ queue with priorities is used to model customer arrivals and service. Customers receive a potential priority from the provider, and choose between purchasing a reservation for that priority and accepting the lowest priority for no additional cost. Customers select the option which minimizes their total cost of waiting. This decision is based purely on statistics, as customers cannot communicate with each other. This work presents the impact of the provider preemption policy choice on the cost customers will pay for a reserved instance. A provider may implement a policy in which no customers are preempted (NP); a policy in which all customers are subject to preemption (PR); or a policy in which only the customers not making reservations are subject to preemption (HPR). It is shown that only the service load impacts the equilibrium possibilities in the NP and PR policies, but that the service variance is also a factor under the HPR policy. These factors impact the equilibrium possibilities associated to a given reservation cost. This work shows that the cost leading to a given equilibrium is greater under the HPR policy than under the NP or PR policies, implying greater incentive to purchase reservations. From this it is proven that a provider maximizes their potential revenue from customer reservations under an HPR policy. It is shown that this holds in general and under the constraint that the reservation cost must correspond to a unique equilibrium. / 2020-06-03T00:00:00Z

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

In recent times Cloud Computing has become an accessible technology which makes it possible to provide online services to end user by the network of remote servers. With the increase in remote servers and resources allocated to these remote servers leads to performance degradation of service. In such a case, the environment on which service is made run plays a significant role in order to provide better performance and adds up to Quality of Service. This paper focuses on Bare metal and Linux container environments as request response time is one of the performance metrics to determine the QOS. To improve request response time platforms are customized using real-time kernel and compiler optimization flags to optimize the performance of a service. UDP packets are served to the service made run in these customized environments. From the experiments performed, it concludes that Bare metal using real-time kernel and level 3 Compiler optimization flag gives better performance of a service.

Cloud computing represents a distributed computing paradigm that gained notoriety due to its properties related to on-demand elastic and dynamic resource provisioning. These characteristics are highly desirable for the execution of workflows, in particular scientific workflows that required a great amount of computing resources and that handle large-scale data. One of the main questions in this sense is how to manage resources of one or more cloud infrastructures to execute workflows while optimizing resource utilization and minimizing the total duration of the execution of tasks (makespan). The more complex the infrastructure and the tasks to be executed are, the higher the risk of incorrectly estimating the amount of resources to be assigned to each task, leading to both performance and monetary costs. Scenarios which are inherently more complex, such as hybrid and multiclouds, rarely are considered by existing resource management solutions. Moreover, a thorough research of relevant related work revealed that most of the solutions do not address data-intensive workflows, a characteristic that is increasingly evident for modern scientific workflows. In this sense, this proposal presents MPSF, the Multiphase Proactive Scheduling Framework, a cloud resource management solution based on multiple scheduling phases that continuously assess the system to optimize resource utilization and task distribution. MPSF defines models to describe and characterize workflows and resources. MPSF also defines performance and reliability models to improve load distribution among nodes and to mitigate the effects of performance fluctuations and potential failures that might occur in the system. Finally, MPSF defines a framework and an architecture to integrate all these components and deliver a solution that can be implemented and tested in real applications. Experimental results show that MPSF is able to predict with much better accuracy the duration of workflows and workflow phases, as well as providing performance gains compared to greedy approaches. / A computação em nuvem representa um paradigma de computação distribuída que ganhoudestaque devido a aspectos relacionados à obtenção de recursos sob demanda de modo elástico e dinâmico. Estas características são consideravelmente desejáveis para a execução de tarefas relacionadas a fluxos de trabalho científicos, que exigem grande quantidade de recursos computacionais e grande fluxo de dados. Uma das principais questões neste sentido é como gerenciar os recursos de uma ou mais infraestruturas de nuvem para execução de fluxos de trabalho de modo a otimizar a utilização destes recursos e minimizar o tempo total de execução das tarefas. Quanto mais complexa a infraestrutura e as tarefas a serem executadas, maior o risco de estimar incorretamente a quantidade de recursos destinada para cada tarefa, levando a prejuízos não só em termos de tempo de execução como também financeiros. Cenários inerentemente mais complexos como nuvens híbridas e múltiplas nuvens raramente são considerados em soluções existentes de gerenciamento de recursos para nuvens. Além destes fatores, a maioria das soluções não oferece mecanismos claros para tratar de fluxos de trabalho com alta intensidade de dados, característica cada vez mais proeminente em fluxos de trabalho moderno. Neste sentido, esta proposta apresenta MPSF, uma solução de gerenciamento de recursos baseada em múltiplas fases de gerenciamento baseadas em mecanismos dinâmicos de alocação de tarefas. MPSF define modelos para descrever e caracterizar fluxos de trabalho e recursos de modo a suportar cenários simples e complexos, como nuvens híbridas e nuvens integradas. MPSF também define modelos de desempenho e confiabilidade para melhor distribuir a carga e para combater os efeitos de possíveis falhas que possam ocorrer no sistema. Por fim, MPSF define um arcabouço e um arquitetura que integra todos estes componentes de modo a definir uma solução que possa ser implementada e utilizada em cenários reais. Testes experimentais indicam que MPSF não só é capaz de prever com maior precisão a duração da execução de tarefas, como também consegue otimizar a execução das mesmas, especialmente para tarefas que demandam alto poder computacional e alta quantidade de dados.

Business intelligence (BI) has emerged as a key technology to be hosted on Cloud computing. BI offers a method to analyse data thereby enabling informed decision making to improve business performance and profitability. However, within the shared domains of Cloud computing, BI is exposed to increased security and privacy threats because an unauthorised user may be able to gain access to highly sensitive, consolidated business information. The business process contains collaborating services and users from multiple Cloud systems in different security realms which need to be engaged dynamically at runtime. If the heterogamous Cloud systems located in different security realms do not have direct authentication relationships then it is technically difficult to enable a secure collaboration. In order to address these security challenges, a new authentication framework is required to establish certain trust relationships among these BI service instances and users by distributing a common session secret to all participants of a session. The author addresses this challenge by designing and implementing a multiparty authentication framework for dynamic secure interactions when members of different security realms want to access services. The framework takes advantage of the trust relationship between session members in different security realms to enable a user to obtain security credentials to access Cloud resources in a remote realm. This mechanism can help Cloud session users authenticate their session membership to improve the authentication processes within multi-party sessions. The correctness of the proposed framework has been verified by using BAN Logics. The performance and the overhead have been evaluated via simulation in a dynamic environment. A prototype authentication system has been designed, implemented and tested based on the proposed framework. The research concludes that the proposed framework and its supporting protocols are an effective functional basis for practical implementation testing, as it achieves good scalability and imposes only minimal performance overhead which is comparable with other state-of-art methods.

E-handelsföretagen försöker ständigt sträva efter expansion. Samtidigt så vill de ha en IT-lösning som är så kostnadseffektiv som möjligt. Säkerhet är också någonting som företagen värderar väldigt högt. Cloud services är något som kan användas för detta. Men e-handelsföretagen måste först och främst känna att de har någonting att utvinna av dessa. Säkerheten är här ett hinder. Efter våra intervjuer med e-handelsföretagen inom klädesbranschen i Borås så betonade samtliga företag hur viktigt det var att hemlig företagsinformation absolut inte fick läcka ut. Det är säkerhet som är den största frågan med moln. Ansvaret för säkerheten ligger just på företaget som köper tjänstens sida. Även om en cloud provider erbjuder säkerhet, backup och bra upptid, så är det fortfarande upp till företaget att se till att de levererar detta. Samtidigt så går det mycket diskussion kring ämnet och många menar att det är nytt och bristen på kunskap som gör att företagen är rädda för riskerna. Vill företaget absolut inte att utomstående ska få komma åt viktig företagsinformation, då kan de välja att ha en kombination av privata och publika moln i det som kallas hybridmoln. Detta beror på att privata moln är byggda och driftade inom företaget. På så vis är data som finns lagrat i det privata molnet inte delat med andra företag. Det viktiga är för företaget att själva se vad de själva har för krav på säkerhet. Finns det krav på säkerhet på vissa delar men inte på andra, så kan hybridmoln vara en lösning. Genom att intervjua tre av de största e-handelsföretagen i Borås så har vi sett en tendens till detta.Det finns många områden som de företag vi intervjuat skulle kunna använda sig av cloud services på. Vi har fokuserat på kärnverksamheten, de stödsystem som de använde, webbsidan, kommunikation och kontorsprogram. Anledningen till detta är att företagen som vi intervjuat tyckte att dessa delar var viktigast. / Program: Kandidatutbildning i informatik

重覆數據删除技術是一個消除冗餘數據存儲塊的技術。尤其是，在儲存數兆位元組的虛擬機器影像時，它已被證明可以減少使用磁碟空間。但是，在會經常加入和讀取虛擬機器影像的雲端平台，部署重覆數據删除技術仍然存在挑戰。我們提出了一個在內核運行的重覆數據删除檔案系統LiveDFS，它可以在一個在低成本硬件配置的開源雲端平台中作為儲存虛擬機器影像的後端。LiveDFS有幾個新穎的特點。具體來說，LiveDFS中最重要的特點是在考慮檔案系統佈局時，它利用空間局部性放置重覆數據删除中繼資料。LiveDFS是POSIX兼容的Linux內核檔案系統。我們透過使用42個不同Linux發行版的虛擬機器影像，在實驗平台測試了LiveDFS的讀取和寫入性能。我們的工作證明了在低成本硬件配置的雲端平台部署LiveDFS的可行性。 / Deduplication is a technique that eliminates the storage of redundant data blocks. In particular, it has been shown to effectively reduce the disk space for storing multi-gigabyte virtual machine (VM) images. However, there remain challenging deployment issues of enabling deduplication in a cloud platform, where VM images are regularly inserted and retrieved. We propose a kernel-space deduplication file systems called LiveDFS, which can serve as a VM image storage backend in an open-source cloud platform that is built on low-cost commodity hardware configurations. LiveDFS is built on several novel design features. Specifically, the main feature of LiveDFS is to exploit spatial locality of placing deduplication metadata on disk with respect to the underlying file system layout. LiveDFS is POSIX-compliant and is implemented as Linux kernel-space file systems. We conduct testbed experiments of the read/write performance of LiveDFS using a dataset of 42 VM images of different Linux distributions. Our work justifies the feasibility of deploying LiveDFS in a cloud platform under commodity settings. / Detailed summary in vernacular field only. / Ng, Chun Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 39-42). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- LiveDFS Design --- p.5 / Chapter 2.1 --- File System Layout --- p.5 / Chapter 2.2 --- Deduplication Primitives --- p.6 / Chapter 2.3 --- Deduplication Process --- p.8 / Chapter 2.3.1 --- Fingerprint Store --- p.9 / Chapter 2.3.2 --- Fingerprint Filter --- p.11 / Chapter 2.4 --- Prefetching of Fingerprint Stores --- p.14 / Chapter 2.5 --- Journaling --- p.15 / Chapter 2.6 --- Ext4 File System --- p.17 / Chapter 3 --- Implementation Details --- p.18 / Chapter 3.1 --- Choice of Hash Function --- p.18 / Chapter 3.2 --- OpenStack Deployment --- p.19 / Chapter 4 --- Experiments --- p.21 / Chapter 4.1 --- I/O Throughput --- p.21 / Chapter 4.2 --- OpenStack Deployment --- p.26 / Chapter 5 --- Related Work --- p.34 / Chapter 6 --- Conclusions and Future Work --- p.37 / Bibliography --- p.39

近年雲存儲發展迅速，它具彈性的收費模式還有使用上的便利性吸引了不少用家把它當作一個備份的平台，如何保障雲端上資料的完整性也就成了一項重要的課題。我們試著探討如何能有效地在客戶端檢查雲端上資料的完整性，並且在探測到雲存儲節點故障以後如何有效地進行修復。抹除碼(Erasure codes)透過產生冗餘，令編碼過後的資料能允許一定程度的缺片。雲端使用者可以利用抹除碼把檔案分散到不同的雲節點，即使其中一些節點壞了用戶還是能透過解碼餘下的資料來得出原檔。我們的研究是基於一種叫再造編碼(Regenerating code)的新興抹除碼。再造編碼借用了網絡編碼(Network coding)的概念，使得在修復錯誤節點的時候並不需要把完整的原檔先重構一遍，相比起一些傳統的抹除碼（如里德所羅門碼Reed-Solomoncode）能減少修復節點時需要下載的資料量。其中我們在FMSR這門再造編碼上實現了一個能有效檢測錯誤的系統FMSR-DIP。FMSR-DIP的好處是在檢測的時候只需要下載一小部份的資料，而且不要求節點有任何的編碼能力，可以直接對應現今的雲存儲。為了驗證我們系統的實用性，我們在雲存儲的測試平台上運行了一系列的測試。 / To protect outsourced data in cloud storage against corruptions, enabling integrity protection, fault tolerance, and efficient recovery for cloud storage becomes critical. To enable fault tolerance from a client-side perspective, users can encode their data with an erasure code and stripe the encoded data across different cloud storage nodes. We base our work on regenerating codes, a recently proposed type of erasure code that borrows the concept of network coding and requires less repair traffic than traditional erasure codes during failure recovery. We study the problem of remotely checking the integrity of regenerating-coded data against corruptions under a real-life cloud storage setting. Specifically, we design a practical data integrity protection (DIP) scheme for a specific regenerating code, while preserving the intrinsic properties of fault tolerance and repair traffic saving. Our DIP scheme is designed under the Byzantine adversarial model, and enables a client to feasibly verify the integrity of random subsets of outsourced data against general or malicious corruptions. It works under the simple assumption of thin-cloud storage and allows different parameters to be fine-tuned for the performance-security trade-off. We implement and evaluate the overhead of our DIP scheme in a cloud storage testbed under different parameter choices. We demonstrate that remote integrity checking can be feasibly integrated into regenerating codes in practical deployment. / Detailed summary in vernacular field only. / Chen, Chuk Hin Henry. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 38-41). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Preliminaries --- p.4 / Chapter 2.1 --- FMSR Implementation --- p.4 / Chapter 2.2 --- Threat Model --- p.6 / Chapter 2.3 --- Cryptographic Primitives --- p.7 / Chapter 3 --- Design --- p.8 / Chapter 3.1 --- Design Goals --- p.8 / Chapter 3.2 --- Notation --- p.9 / Chapter 3.3 --- Overview of FMSR-DIP --- p.11 / Chapter 3.4 --- Basic Operations --- p.11 / Chapter 3.4.1 --- Upload operation --- p.11 / Chapter 3.4.2 --- Check operation --- p.13 / Chapter 3.4.3 --- Download operation --- p.15 / Chapter 3.4.4 --- Repair operation --- p.16 / Chapter 4 --- Implementation --- p.17 / Chapter 4.1 --- Integration of DIP into NCCloud --- p.17 / Chapter 4.2 --- Instantiating Cryptographic Primitives --- p.18 / Chapter 4.3 --- Trade-off Parameters --- p.19 / Chapter 5 --- Security Analysis --- p.22 / Chapter 5.1 --- Uses of Security Primitives --- p.22 / Chapter 5.2 --- Security Guarantees --- p.23 / Chapter 5.2.1 --- Corrupting an AECC Stripe --- p.23 / Chapter 5.2.2 --- Picking Corrupted Bytes for Checking --- p.25 / Chapter 5.2.3 --- Putting It All Together --- p.26 / Chapter 6 --- Evaluations --- p.27 / Chapter 6.1 --- Running Time Analysis --- p.27 / Chapter 6.2 --- Monetary Cost Analysis --- p.30 / Chapter 6.3 --- Summary --- p.33 / Chapter 7 --- Related Work --- p.34 / Chapter 8 --- Conclusions --- p.37 / Bibliography --- p.38

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.