Single system image: A survey

Single system image is a computing paradigm where a number of distributed computing resources are aggregated and presented via an interface that maintains the illusion of interaction with a single system. This approach encompasses decades of research using a broad variety of techniques at varying levels of abstraction, from custom hardware and distributed hypervisors to specialized operating system kernels and user-level tools. Existing classification schemes for SSI technologies are reviewed, and an updated classification scheme is proposed. A survey of implementation techniques is provided along with relevant examples. Notable deployments are examined and insights gained from hands-on experience are summarized. Issues affecting the adoption of kernel-level SSI are identified and discussed in the context of technology adoption literature

Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe

How open is open enough?: Melding proprietary and open source platform strategies

Computer platforms provide an integrated architecture of hardware and software standards as a basis for developing complementary assets. The most successful platforms were owned by proprietary sponsors that controlled platform evolution and appropriated associated rewards. Responding to the Internet and open source systems, three traditional vendors of proprietary platforms experimented with hybrid strategies which attempted to combine the advantages of open source software while retaining control and differentiation. Such hybrid standards strategies reflect the competing imperatives for adoption and appropriability, and suggest the conditions under which such strategies may be preferable to either the purely open or purely proprietary alternatives

Multimedia Content Distribution Management Using a Distributed Topology

Advertising plays an important role in order for many companies to promote their products and services. It can be expensive to place advertisements with no guarantees that the message will reach the intended persons. In this field, targeted advertising is the mainstream strategy to captivate the potential consumer. People are used to see advertisements everywhere they go in many different forms. One of those is the use of screen displays that are believed to make the ads more engaging. However, using digital screens to advertise may lead to some issues, like down times or unwanted error messages from the device that controls the screens. This can cause a bad experience for both the target audience and the advertiser. This thesis was developed within the scope of a project called Vixtape. It’s a platform with the goal of turning any public screen into an ads displaying device and in the process reward the screen owner by exposing ads to the target audience. It also has the mission of giving the end user a optimal technological experience, no flaws and highly efficient. All these characteristics are accomplished by the use of a new open source technology called Interplanetary File System (IPFS), that allow devices to share content between them in a Peer-to-Peer (P2P) topology. This content distribution method saves Internet bandwidth to the end user (i.e., the Vixtape service client) and also enables the devices to work offline in case their Internet connection drops. This will greatly reduce the common problems seen with ads screen, thus giving a better experience to both the audience and the end user. By the end of this document one can see that, adding a distributed topology to the Vixtape platform increased the Internet usage efficiency of the ads devices by always having up-to-date content available. This avoids that a device unnecessarily requests content from any of the other devices that had previously requested it. Additionally, a strategy to target a given audience was employed in order to choose the right ads to play. This further increases the maximum potential consumers the advertisements are shown to

NetServ: Reviving Active Networks

In 1996, Tennenhouse and Wetherall proposed active networks, where users can inject code modules into network nodes. The proposal sparked intense debate and follow-on research, but ultimately failed to win over the networking community. Fifteen years later, the problems that motivated the active networks proposal persist. We call for a revival of active networks. We present NetServ, a fully integrated active network system that provides all the necessary functionality to be deployable, addressing the core problems that prevented the practical success of earlier approaches. We make the following contributions. We present a hybrid approach to active networking, which combines the best qualities from the two extreme approaches — integrated and discrete. We built a working system that strikes the right balance between security and performance by leveraging current technologies. We suggest an economic model based on NetServ between content providers and ISPs. We built four applications to illustrate the model

Unikraft:Fast, Specialized Unikernels the Easy Way

Unikernels are famous for providing excellent performance in terms of boot times, throughput and memory consumption, to name a few metrics. However, they are infamous for making it hard and extremely time consuming to extract such performance, and for needing significant engineering effort in order to port applications to them. We introduce Unikraft, a novel micro-library OS that (1) fully modularizes OS primitives so that it is easy to customize the unikernel and include only relevant components and (2) exposes a set of composable, performance-oriented APIs in order to make it easy for developers to obtain high performance. Our evaluation using off-the-shelf applications such as nginx, SQLite, and Redis shows that running them on Unikraft results in a 1.7x-2.7x performance improvement compared to Linux guests. In addition, Unikraft images for these apps are around 1MB, require less than 10MB of RAM to run, and boot in around 1ms on top of the VMM time (total boot time 3ms-40ms). Unikraft is a Linux Foundation open source project and can be found at www.unikraft.or

The Sea of Stuff: a model to manage shared mutable data in a distributed environment

Managing data is one of the main challenges in distributed systems and computer science in general. Data is created, shared, and managed across heterogeneous distributed systems of users, services, applications, and devices without a clear and comprehensive data model. This technological fragmentation and lack of a common data model result in a poor understanding of what data is, how it evolves over time, how it should be managed in a distributed system, and how it should be protected and shared. From a user perspective, for example, backing up data over multiple devices is a hard and error-prone process, or synchronising data with a cloud storage service can result in conflicts and unpredictable behaviours. This thesis identifies three challenges in data management: (1) how to extend the current data abstractions so that content, for example, is accessible irrespective of its location, versionable, and easy to distribute; (2) how to enable transparent data storage relative to locations, users, applications, and services; and (3) how to allow data owners to protect data against malicious users and automatically control content over a distributed system. These challenges are studied in detail in relation to the current state of the art and addressed throughout the rest of the thesis. The artefact of this work is the Sea of Stuff (SOS), a generic data model of immutable self-describing location-independent entities that allow the construction of a distributed system where data is accessible and organised irrespective of its location, easy to protect, and can be automatically managed according to a set of user-defined rules. The evaluation of this thesis demonstrates the viability of the SOS model for managing data in a distributed system and using user-defined rules to automatically manage data across multiple nodes."This work was supported by Adobe Systems, Inc. and EPSRC [grant number EP/M506631/1]" - from the Acknowledgements pag

Design and implementation of a platform for predicting pharmacological properties of molecules

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019O processo de descoberta e desenvolvimento de novos medicamentos prolonga-se por vários anos e implica o gasto de imensos recursos monetários. Como tal, vários métodos in silico são aplicados com o intuito de dimiuir os custos e tornar o processo mais eficiente. Estes métodos incluem triagem virtual, um processo pelo qual vastas coleções de compostos são examinadas para encontrar potencial terapêutico. QSAR (Quantitative Structure Activity Relationship) é uma das tecnologias utilizada em triagem virtual e em optimização de potencial farmacológico, em que a informação estrutural de ligandos conhecidos do alvo terapêutico é utilizada para prever a actividade biológica de um novo composto para com o alvo. Vários investigadores desenvolvem modelos de aprendizagem automática de QSAR para múltiplos alvos terapêuticos. Mas o seu uso está dependente do acesso aos mesmos e da facilidade em ter os modelos funcionais, o que pode ser complexo quando existem várias dependências ou quando o ambiente de desenvolvimento difere bastante do ambiente em que é usado. A aplicação ao qual este documento se refere foi desenvolvida para lidar com esta questão. Esta é uma plataforma centralizada onde investigadores podem aceder a vários modelos de QSAR, podendo testar os seus datasets para uma multitude de alvos terapêuticos. A aplicação permite usar identificadores moleculares como SMILES e InChI, e gere a sua integração em descritores moleculares para usar como input nos modelos. A plataforma pode ser acedida através de uma aplicação web com interface gráfica desenvolvida com o pacote Shiny para R e directamente através de uma REST API desenvolvida com o pacote flask-restful para Python. Toda a aplicação está modularizada através de teconologia de “contentores”, especificamente o Docker. O objectivo desta plataforma é divulgar o acesso aos modelos criados pela comunidade, condensando-os num só local e removendo a necessidade do utilizador de instalar ou parametrizar qualquer tipo de software. Fomentando assim o desenvolvimento de conhecimento e facilitando o processo de investigação.The drug discovery and design process is expensive, time-consuming and resource-intensive. Various in silico methods are used to make the process more efficient and productive. Methods such as Virtual Screening often take advantage of QSAR machine learning models to more easily pinpoint the most promising drug candidates, from large pools of compounds. QSAR, which means Quantitative Structure Activity Relationship, is a ligand-based method where structural information of known ligands of a specific target is used to predict the biological activity of another molecule against that target. They are also used to improve upon an existing molecule’s pharmacologic potential by elucidating the structural composition with desirable properties. Several researchers create and develop QSAR machine learning models for a variety of different therapeutic targets. However, their use is limited by lack of access to said models. Beyond access, there are often difficulties in using published software given the need to manage dependencies and replicating the development environment. To address this issue, the application documented here was designed and developed. In this centralized platform, researchers can access several QSAR machine learning models and test their own datasets for interaction with various therapeutic targets. The platform allows the use of widespread molecule identifiers as input, such as SMILES and InChI, handling the necessary integration into the appropriate molecular descriptors to be used in the model. The platform can be accessed through a Web Application with a full graphical user interface developed with the R package Shiny and through a REST API developed with the Flask Restful package for Python. The complete application is packaged up in container technology, specifically Docker. The main goal of this platform is to grant widespread access to the QSAR models developed by the scientific community, by concentrating them in a single location and removing the user’s need to install or set up software unfamiliar to them. This intends to incite knowledge creation and facilitate the research process