A proposal for a modular and application-aware autonomic manager of private cloud infrastructures

Recientemente, el paradigma de la computación en la nube ha recibido mucho interés por parte tanto de la industria como del mundo académico. Las infraestructuras cloud públicas están posibilitando nuevos modelos de negocio y ayudando a reducir costes. Sin embargo, una compañía podría desear ubicar sus datos y servicios en sus propias instalaciones, o tener que atenerse a leyes de protección de datos. Estas circunstancias hacen a las infraestructuras cloud privadas ciertamente deseables, ya sea para complementar a las públicas o para sustituirlas por completo. Por desgracia, las carencias en materia de estándares han impedido que las soluciones para la gestión de infraestructuras privadas se hayan desarrollado adecuadamente. Además, la multitud de opciones disponibles ha creado en los clientes el miedo a depender de una tecnología concreta (technology lock-in). Una de las causas de este problema es la falta de alineación entre la investigación académica y los productos comerciales, ya que aquella está centrada en el estudio de escenarios idealizados sin correspondencia con el mundo real, mientras que éstos consisten en soluciones desarrolladas sin tener en cuenta cómo van a encajar con los estándares más comunes o sin preocuparse de hacer públicos sus resultados. Con objeto de resolver este problema, propongo un sistema de gestión modular para infraestructuras cloud privadas enfocado en tratar con las aplicaciones en lugar de centrarse únicamente en los recursos hardware. Este sistema de gestión sigue el paradigma de la computación autónoma y está diseñado en torno a un modelo de información sencillo, desarrollado para ser compatible con los estándares más comunes. Este modelo divide el entorno en dos vistas, que sirven para separar aquello que debe preocupar a cada actor involucrado del resto de información, pero al mismo tiempo permitiendo relacionar el entorno físico con las máquinas virtuales que se despliegan encima de él. En dicho modelo, las aplicaciones cloud están divididas en tres tipos genéricos (Servicios, Trabajos de Big Data y Reservas de Instancias), para que así el sistema de gestión pueda sacar partido de las características propias de cada tipo. El modelo de información está complementado por un conjunto de acciones de gestión atómicas, reversibles e independientes, que determinan las operaciones que se pueden llevar a cabo sobre el entorno y que es usado para hacer posible la escalabilidad en el entorno. También describo un motor de gestión encargado de, a partir del estado del entorno y usando el ya mencionado conjunto de acciones, la colocación de recursos. Está dividido en dos niveles: la capa de Gestores de Aplicación, encargada de tratar sólo con las aplicaciones; y la capa del Gestor de Infraestructura, responsable de los recursos físicos. Dicho motor de gestión obedece un ciclo de vida con dos fases, para así modelar mejor el comportamiento de una infraestructura real. El problema de la colocación de recursos es atacado durante una de las fases (la de consolidación) por un resolutor de programación entera, y durante la otra (la online) por un heurístico hecho ex-profeso. Varias pruebas han demostrado que este acercamiento combinado es superior a otras estrategias. Para terminar, el sistema de gestión está acoplado a arquitecturas de monitorización y de actuadores. Aquella estando encargada de recolectar información del entorno, y ésta siendo modular en su diseño y capaz de conectarse con varias tecnologías y ofrecer varios modos de acceso. ABSTRACT The cloud computing paradigm has raised in popularity within the industry and the academia. Public cloud infrastructures are enabling new business models and helping to reduce costs. However, the desire to host company’s data and services on premises, and the need to abide to data protection laws, make private cloud infrastructures desirable, either to complement or even fully substitute public oferings. Unfortunately, a lack of standardization has precluded private infrastructure management solutions to be developed to a certain level, and a myriad of diferent options have induced the fear of lock-in in customers. One of the causes of this problem is the misalignment between academic research and industry ofering, with the former focusing in studying idealized scenarios dissimilar from real-world situations, and the latter developing solutions without taking care about how they f t with common standards, or even not disseminating their results. With the aim to solve this problem I propose a modular management system for private cloud infrastructures that is focused on the applications instead of just the hardware resources. This management system follows the autonomic system paradigm, and is designed around a simple information model developed to be compatible with common standards. This model splits the environment in two views that serve to separate the concerns of the stakeholders while at the same time enabling the traceability between the physical environment and the virtual machines deployed onto it. In it, cloud applications are classifed in three broad types (Services, Big Data Jobs and Instance Reservations), in order for the management system to take advantage of each type’s features. The information model is paired with a set of atomic, reversible and independent management actions which determine the operations that can be performed over the environment and is used to realize the cloud environment’s scalability. From the environment’s state and using the aforementioned set of actions, I also describe a management engine tasked with the resource placement. It is divided in two tiers: the Application Managers layer, concerned just with applications; and the Infrastructure Manager layer, responsible of the actual physical resources. This management engine follows a lifecycle with two phases, to better model the behavior of a real infrastructure. The placement problem is tackled during one phase (consolidation) by using an integer programming solver, and during the other (online) with a custom heuristic. Tests have demonstrated that this combined approach is superior to other strategies. Finally, the management system is paired with monitoring and actuators architectures. The former able to collect the necessary information from the environment, and the later modular in design and capable of interfacing with several technologies and ofering several access interfaces.

A real case of a cognitive multiplayer game: design and development of the iOS client in Swift and its cloud architecture

as tecnologías emergentes como el cloud computing y los dispositivos móviles están creando una oportunidad sin precedentes para mejorar el sistema educativo, permitiendo tanto a los educadores personalizar y mejorar la experiencia de aprendizaje, como facilitar a los estudiantes que adquieran conocimientos sin importar dónde estén. Por otra parte, a través de técnicas de gamificacion será posible promover y motivar a los estudiantes a que aprendan materias arduas haciendo que la experiencia sea más motivadora. Los juegos móviles pueden ser el camino correcto para dar soporte a esta experiencia de aprendizaje mejorada. Este proyecto integra el diseño y desarrollo de una arquitectura en la nube altamente escalable y con alto rendimiento, así como el propio cliente de iOS, para dar soporte a una nueva version de Temporis, un juego móvil multijugador orientado a reordenar eventos históricos en una línea temporal (e.j. historia, arte, deportes, entretenimiento y literatura). Temporis actualmente está disponible en Google Play. Esta memoria describe el desarrollo de la nueva versión de Temporis (Temporis v.2.0) proporcionando detalles acerca de la mejora y adaptación basados en el Temporis original. En particular se describe el nuevo backend hecho en Go sobre Google App Engine creado para soportar miles de usuarios, asó como otras características por ejemplo como conseguir enviar noticaciones push desde la propia plataforma. Por último, el cliente de iOS en Temporis v.2.0 se ha desarrollado utilizando las últimas y más relevantes tecnologías, prestando especial atención a Swift (el lenguaje de programación nuevo de Apple, que es seguro y rápido), el Paradigma Funcional Reactivo (que ayuda a construir aplicaciones altamente interactivas además de a minimizar errores) y la arquitectura VIPER (una arquitectura que sigue los principios SOLID, se centra en la separación de asuntos y favorece la reutilización de código en otras plataformas). ABSTRACT Emerging technologies such as cloud computing and mobile devices are creating an unprecedented opportunity for enhancing the educational system, letting both educators customize and improve the learning experience, and students acquire knowledge regardless of where they are. Moreover, through gamification techniques it would be possible to encourage and motivate students to learn arduous subjects by making the experience more motivating. Mobile games can be a perfect vehicle to support this enhanced learning experience. This project integrates the design and development of a highly scalable and performant cloud architecture, as well as the iOS client that uses it, in order to provide support to a new version of Temporis, a mobile multiplayer game focused on ordering time-based (e.g. history, art, sports, entertainment and literature) in a timeline that currently is available on Google Play. This work describes the development of the new Temporis version (Temporis v.2.0), providing details about improvements and details on the adaptation of the original Temporis. In particular, the new Google App Engine backend is described, which was created to support thousand of users developed in Go language are provided, in addition to other features like how to achieve push notications in this platform. Finally, the mobile iOS client developed using the latest and more relevant technologies is explained paying special attention to Swift (Apple's new programming language, that is safe and fast), the Functional Reactive Paradigm (that helps building highly interactive apps while minimizing bugs) and the VIPER architecture (a SOLID architecture that enforces separation of concerns and makes it easy to reuse code for other platforms).

Towards Automation in Digital Investigations : Seeking Efficiency in Digital Forensics in Mobile and Cloud Environments

Cybercrime and related malicious activity in our increasingly digital world has become more prevalent and sophisticated, evading traditional security mechanisms. Digital forensics has been proposed to help investigate, understand and eventually mitigate such attacks. The practice of digital forensics, however, is still fraught with various challenges. Some of the most prominent of these challenges include the increasing amounts of data and the diversity of digital evidence sources appearing in digital investigations. Mobile devices and cloud infrastructures are an interesting specimen, as they inherently exhibit these challenging circumstances and are becoming more prevalent in digital investigations today. Additionally they embody further characteristics such as large volumes of data from multiple sources, dynamic sharing of resources, limited individual device capabilities and the presence of sensitive data. These combined set of circumstances make digital investigations in mobile and cloud environments particularly challenging. This is not aided by the fact that digital forensics today still involves manual, time consuming tasks within the processes of identifying evidence, performing evidence acquisition and correlating multiple diverse sources of evidence in the analysis phase. Furthermore, industry standard tools developed are largely evidence-oriented, have limited support for evidence integration and only automate certain precursory tasks, such as indexing and text searching. In this study, efficiency, in the form of reducing the time and human labour effort expended, is sought after in digital investigations in highly networked environments through the automation of certain activities in the digital forensic process. To this end requirements are outlined and an architecture designed for an automated system that performs digital forensics in highly networked mobile and cloud environments. Part of the remote evidence acquisition activity of this architecture is built and tested on several mobile devices in terms of speed and reliability. A method for integrating multiple diverse evidence sources in an automated manner, supporting correlation and automated reasoning is developed and tested. Finally the proposed architecture is reviewed and enhancements proposed in order to further automate the architecture by introducing decentralization particularly within the storage and processing functionality. This decentralization also improves machine to machine communication supporting several digital investigation processes enabled by the architecture through harnessing the properties of various peer-to-peer overlays. Remote evidence acquisition helps to improve the efficiency (time and effort involved) in digital investigations by removing the need for proximity to the evidence. Experiments show that a single TCP connection client-server paradigm does not offer the required scalability and reliability for remote evidence acquisition and that a multi-TCP connection paradigm is required. The automated integration, correlation and reasoning on multiple diverse evidence sources demonstrated in the experiments improves speed and reduces the human effort needed in the analysis phase by removing the need for time-consuming manual correlation. Finally, informed by published scientific literature, the proposed enhancements for further decentralizing the Live Evidence Information Aggregator (LEIA) architecture offer a platform for increased machine-to-machine communication thereby enabling automation and reducing the need for manual human intervention.

CloudMPL:a domain specific language for describing management policies for an autonomic cloud infrastructure

To benefit from the advantages that Cloud Computing brings to the IT industry, management policies must be implemented as a part of the operation of the Cloud. Among others, for example, the specification of policies can be used for the management of energy to reduce the cost of running the IT system or also for security policies while handling privacy issues of users. As cloud platforms are large, manual enforcement of policies is not scalable. Hence, autonomic approaches for management policies have recently received a considerable attention. These approaches allow specification of rules that are executed via rule-engines. The process of rules creation starts by the interpretation of the policies drafted by high-rank managers. Then, technical IT staff translate such policies to operational activities to implement them. Such process can start from a textual declarative description and after numerous steps terminates in a set of rules to be executed on a rule engine. To simplify the steps and to bridge the considerable gap between the declarative policies and executable rules, we propose a domain-specific language called CloudMPL. We also design a method of automated transformation of the rules captured in CloudMPL to the popular rule-engine Drools. As the policies are changed over time, code generation will reduce the time required for the implementation of the policies. In addition, using a declarative language for writing the specifications is expected to make the authoring of rules easier. We demonstrate the use of the CloudMPL language into a running example extracted from a management energy consumption case study.

Cumulon: Simplified Matrix-Based Data Analytics in the Cloud

Cumulon is a system aimed at simplifying the development and deployment of statistical analysis of big data in public clouds. Cumulon allows users to program in their familiar language of matrices and linear algebra, without worrying about how to map data and computation to specific hardware and cloud software platforms. Given user-specified requirements in terms of time, monetary cost, and risk tolerance, Cumulon automatically makes intelligent decisions on implementation alternatives, execution parameters, as well as hardware provisioning and configuration settings -- such as what type of machines and how many of them to acquire. Cumulon also supports clouds with auction-based markets: it effectively utilizes computing resources whose availability varies according to market conditions, and suggests best bidding strategies for them. Cumulon explores two alternative approaches toward supporting such markets, with different trade-offs between system and optimization complexity. Experimental study is conducted to show the efficiency of Cumulon's execution engine, as well as the optimizer's effectiveness in finding the optimal plan in the vast plan space.

Investigation into a best practice model for providing an integrated user experience with mobile cloud applications

Mobile Cloud Computing promises to overcome the physical limitations of mobile devices by executing demanding mobile applications on cloud infrastructure. In practice, implementing this paradigm is difficult; network disconnection often occurs, bandwidth may be limited, and a large power draw is required from the battery, resulting in a poor user experience. This thesis presents a mobile cloud middleware solution, Context Aware Mobile Cloud Services (CAMCS), which provides cloudbased services to mobile devices, in a disconnected fashion. An integrated user experience is delivered by designing for anticipated network disconnection, and low data transfer requirements. CAMCS achieves this by means of the Cloud Personal Assistant (CPA); each user of CAMCS is assigned their own CPA, which can complete user-assigned tasks, received as descriptions from the mobile device, by using existing cloud services. Service execution is personalised to the user's situation with contextual data, and task execution results are stored with the CPA until the user can connect with his/her mobile device to obtain the results. Requirements for an integrated user experience are outlined, along with the design and implementation of CAMCS. The operation of CAMCS and CPAs with cloud-based services is presented, specifically in terms of service description, discovery, and task execution. The use of contextual awareness to personalise service discovery and service consumption to the user's situation is also presented. Resource management by CAMCS is also studied, and compared with existing solutions. Additional application models that can be provided by CAMCS are also presented. Evaluation is performed with CAMCS deployed on the Amazon EC2 cloud. The resource usage of the CAMCS Client, running on Android-based mobile devices, is also evaluated. A user study with volunteers using CAMCS on their own mobile devices is also presented. Results show that CAMCS meets the requirements outlined for an integrated user experience.

Radio over fiber enabling PON fronthaul in a two-tiered cloud

Avec l’avènement des objets connectés, la bande passante nécessaire dépasse la capacité des interconnections électriques et interface sans fils dans les réseaux d’accès mais aussi dans les réseaux coeurs. Des systèmes photoniques haute capacité situés dans les réseaux d’accès utilisant la technologie radio sur fibre systèmes ont été proposés comme solution dans les réseaux sans fil de 5e générations. Afin de maximiser l’utilisation des ressources des serveurs et des ressources réseau, le cloud computing et des services de stockage sont en cours de déploiement. De cette manière, les ressources centralisées pourraient être diffusées de façon dynamique comme l’utilisateur final le souhaite. Chaque échange nécessitant une synchronisation entre le serveur et son infrastructure, une couche physique optique permet au cloud de supporter la virtualisation des réseaux et de les définir de façon logicielle. Les amplificateurs à semi-conducteurs réflectifs (RSOA) sont une technologie clé au niveau des ONU(unité de communications optiques) dans les réseaux d’accès passif (PON) à fibres. Nous examinons ici la possibilité d’utiliser un RSOA et la technologie radio sur fibre pour transporter des signaux sans fil ainsi qu’un signal numérique sur un PON. La radio sur fibres peut être facilement réalisée grâce à l’insensibilité a la longueur d’onde du RSOA. Le choix de la longueur d’onde pour la couche physique est cependant choisi dans les couches 2/3 du modèle OSI. Les interactions entre la couche physique et la commutation de réseaux peuvent être faites par l’ajout d’un contrôleur SDN pour inclure des gestionnaires de couches optiques. La virtualisation réseau pourrait ainsi bénéficier d’une couche optique flexible grâce des ressources réseau dynamique et adaptée. Dans ce mémoire, nous étudions un système disposant d’une couche physique optique basé sur un RSOA. Celle-ci nous permet de façon simultanée un envoi de signaux sans fil et le transport de signaux numérique au format modulation tout ou rien (OOK) dans un système WDM(multiplexage en longueur d’onde)-PON. Le RSOA a été caractérisé pour montrer sa capacité à gérer une plage dynamique élevée du signal sans fil analogique. Ensuite, les signaux RF et IF du système de fibres sont comparés avec ses avantages et ses inconvénients. Finalement, nous réalisons de façon expérimentale une liaison point à point WDM utilisant la transmission en duplex intégral d’un signal wifi analogique ainsi qu’un signal descendant au format OOK. En introduisant deux mélangeurs RF dans la liaison montante, nous avons résolu le problème d’incompatibilité avec le système sans fil basé sur le TDD (multiplexage en temps duplexé).

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Enabling efficient multi-keyword ranked search over encrypted mobile cloud data through blind storage

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.

An epidemic model based temporal violation prediction strategy for large batch of parallel business cloud workflows

Temporal violations often take place during the running of large batch of parallel business cloud workflow, which have a serious impact on the on-time completion of massive concurrent user requests. Existing studies have shown that local temporal violations (namely the delays of workflow activities) occurring during cloud workflow execution are the fundamental causes for failed on-time completion. Therefore, accurate prediction of temporal violations is a very important yet challenging task for business cloud workflows. In this paper, based on an epidemic model, a novel temporal violation prediction strategy is proposed to estimate the number of local temporal violations and the number of violations that must be handled so as to achieve a certain on-time completion rate before the execution of workflows. The prediction result can be served as an important reference for temporal violation prevention and handling strategies such as static resource reservation and dynamic provision. Specifically, we first analyze the queuing process of the parallel workflow activities, then we predict the number of potential temporal violations based on a novel temporal violation transmission model inspired by an epidemic model. Comprehensive experimental results demonstrate that our strategy can achieve very high prediction accuracy under different situations.

Temporal verification for scientific cloud workflows: State-of-the-art and research challenges

Cloud computing is establishing itself as the latest computing paradigm in recent years. As doing science in the cloud is becoming a reality, scientists are now able to access public cloud centers and employ high-performance computing resources to run scientific applications. However, due to the dynamic nature of the cloud environment, the usability of scientific cloud workflow systems can be significantly deteriorated if without effective service quality assurance strategies. Specifically, workflow temporal verification as the major approach for workflow temporal QoS (Quality of Service) assurance plays a critical role in the on-time completion of large-scale scientific workflows. Great efforts have been dedicated to the area of workflow temporal verification in recent years and it is high time that we should define the key research issues for scientific cloud workflows in order to keep our research on the right track. In this paper, we systematically investigate this problem and present four key research issues based on the introduction of a generic temporal verification framework. Meanwhile, state-of-the-art solutions for each research issue and open challenges are also presented. Finally, SwinDeW-V, an ongoing research project on temporal verification as part of our SwinDeW-C cloud workflow system, is also demonstrated.

Automating performance and energy consumption analysis for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.

Providing fairer resource allocation for multi-tenant cloud-based systems

A fundamental premise in cloud computing is trying to provide a more sophisticated computing resource sharing capability. In order to provide better allocation, the Dominant Resource Fairness (DRF) approach has been developed to address the "fair resource allocation problem" at the application layer for multi-tenant cloud applications. Nevertheless conventional DRF only considers the interplay of CPU and memory, which may result in over allocation of resources to one tenant's application to the detriment of others. In this paper, we propose an improved DRF algorithm with 3-dimensional demand vector to support disk resources as the third dominant shared resource, enhancing fairer resource sharing. Our technique is integrated with LINUX 'group' controls resource utilisation and realises data isolation to avoid undesirable interactions between co-located tasks. Our method ensures all tenants receive system resources fairly, which improves overall utilisation and throughput as well as reducing traffic in an over-crowded system. We evaluate the performance of different types of workload using different algorithms and compare ours to the default algorithm. Results show an increase of 15% resource utilisation and a reduction of 59% completion time on average, indicating that our DRF algorithm provides a better, smoother, fairer high-performance resource allocation scheme for both continuous workloads and batch jobs.

Building cloud-based healthcare data mining services

The linkage between healthcare service and cloud computing techniques has drawn much attention lately. Up to the present, most works focus on IT system migration and the management of distributed healthcare data rather than taking advantage of information hidden in the data. In this paper, we propose to explore healthcare data via cloud-based healthcare data mining services. Specifically, we propose a cloud-based healthcare data mining framework for healthcare data mining service development. Under such framework, we further develop a cloud-based healthcare data mining service to predict patients future length of stay in hospital.

Approaches to Provisioning Network Topology of Virtual Machines in Cloud Systems

The current infrastructure as a service (IaaS) cloud systems, allow users to load their own virtual machines. However, most of these systems do not provide users with an automatic mechanism to load a network topology of virtual machines. In order to specify and implement the network topology, we use software switches and routers as network elements. Before running a group of virtual machines, the user needs to set up the system once to specify a network topology of virtual machines. Then, given the user’s request for running a specific topology, our system loads the appropriate virtual machines (VMs) and also runs separated VMs as software switches and routers. Furthermore, we have developed a manager that handles physical hardware failure situations. This system has been designed in order to allow users to use the system without knowing all the internal technical details.