SeaClouds: An Open Reference Architecture for Multi-Cloud Governance

A. Brogi, J. Carrasco, J. Cubo, F. D'Andria, E. Di Nitto, M. Guerriero, D. Pérez, E. Pimentel, J. Soldani. "SeaClouds: An Open Reference Architecture for Multi-Cloud Governance". In B. Tekinerdogan et al. (Eds.): ECSA 2016, LNCS 9839, pp. 334–338, 2016.We present the open reference architecture of the SeaClouds solution. It aims at enabling a seamless adaptive multi-cloud management of complex applications by supporting the distribution, monitoring and reconfiguration of app modules over heterogeneous cloud providers.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Microservices Validation: Methodology and Implementation

Due to the wide spread of cloud computing, arises actual question about architecture, design and implementation of cloud applications. The microservice model describes the design and development of loosely coupled cloud applications when computing resources are provided on the basis of automated IaaS and PaaS cloud platforms. Such applications consist of hundreds and thousands of service instances, so automated validation and testing of cloud applications developed on the basis of microservice model is a pressing issue. There are constantly developing new methods of testing both individual microservices and cloud applications at a whole. This article presents our vision of a framework for the validation of the microservice cloud applications, providing an integrated approach for the implementation of various testing methods of such applications, from basic unit tests to continuous stability testing

Microservices Architecture Enables DevOps: an Experience Report on Migration to a Cloud-Native Architecture

This article reports on experiences and lessons learned during incremental migration and architectural refactoring of a commercial mobile back end as a service to microservices architecture. It explains how the researchers adopted DevOps and how this facilitated a smooth migration

Studi Komparasi Performa NGINX dan HAPROXY Sebagai Load Balancer di Cloud Menggunakan Teknologi Kontainer

High availability and scalability is a must have feature for server that owned by industrial sector. One concept that used by technology practitioner ishorizontal scaling. Horizontal scaling can be achieved by using load balancer. Even though those technology practitioner started to adopt containerization technology, those technology practitioner still using load balancer. The purpose of this research is to compare NGINX and HAPROXY performance as load balancer based on response time and error rate. Both NGINX and HAPROXY will run on docker that installed on various virtual machine type in both AWS and GCP. The result shows that based on writer’s configuration, NGINX could handle medium and heavy load better than HAPROXY. Another result shows that AWS could handle medium and heavy load better than GCP.High availability dan scalability sudah menjadi suatu keharusan bagi server yang dimiliki oleh sektor industri. Salah satu konsep yang dipakai oleh pelaku teknologi adalah horizontal scaling. Horizontal scaling ini dapat dicapai oleh load balancer. Meskipun para pelaku teknologi mulaimengadaptasi teknologi kontainerisasi, para pelaku teknologi ini tetap memakai load balancer. Penelitian ini bertujuan untuk membandingkanperforma NGINX dan HAPROXY sebagai load balancer dari sisi response time dan error rate, yang dijalankan diatas docker yang berada di virtual machine AWS dan GCP. Perbandingan ini dilakukan di berbagai spesifikasi virtual machine. Hasil penelitian menunjukkan bahwa berdasarkankonfigurasi yang sudah dibuat oleh penulis, NGINX dapat menangani beban sedang dan besar dengan lebih baik dibandingkan dengan HAPROXY.Hasil penelitian juga menunjukkan AWS dapat menangani beban sedang dan besar dengan lebih baik dibandingkan dengan GCP

An agile container-based approach to TaaS

Current cloud deployment scenarios imply a need for fast testing of user oriented software in diverse, hetero-geneous and often unknown hardware and network environ-ments, making it difficult to ensure optimal or reproducible in-site testing. The current paper proposes the use of container based lightweight virtualization with a ready-to-run, just-in-time deployment strategy in order to minimize time and resources needed for streamlined multicomponent prototyping in PaaS systems. To that end, we will study a specific case of use consisting of providing end users with pre-tested custom prepackaged and preconfigured software, guaranteeing the viability of the aforementioned custom software, the syntactical integrity of the provided deployment system, the availability of needed dependencies as well as the sanity check of the already deployed and running software. From an architectural stand-point, by using standard, common use deployment packages as Chef or Puppet hosted in parallellizable workloads over ready-to-run Docker images, we can minimize the time required for full-deployment multicomponent systems testing and valida-tion, as well as wrap the commonly provided features via a user-accessible RESTful API. The proposed infrastructure is currently available and freely accessible as part of the FIWARE EU initiative, and is open to third party collaboration and extension from a FOSS perspective

A study on performance measures for auto-scaling CPU-intensive containerized applications

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org)

Exploring Deployment Strategies for the Tor Network [Extended Version]

In response to upcoming performance and security challenges of anonymity networks like Tor, it will be of crucial importance to be able to develop and deploy performance improvements and state-of-the-art countermeasures. In this paper, we therefore explore different deployment strategies and review their applicability to the Tor network. In particular, we consider flag day, dual stack, translation, and tunneling strategies and discuss their impact on the network, as well as common risks associated with each of them. In a simulation based evaluation, which stems on historical data of Tor, we show that they can practically be applied to realize significant protocol changes in Tor. However, our results also indicate that during the transitional phase a certain degradation of anonymity is unavoidable with current viable deployment strategies