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

Performance Enhancement of Android Application Testing using Android Devices as a Service Cloud Model

The recent spurt in Android devices has given rise to the development of millions of Android applications. Hence there is a need for rapid and efficient testing techniques to satiate the global app demand. Hence the paper proposes an automated testing framework by deployment testing practices on the cloud. Android devices could be tested by connecting them through wired or wireless connections. This framework can be addressed as Android Testing as a Service (ATaaS) which assists in carrying out functional testing, performance testing etc. The paper also demonstrates the use of a MAT tool to carryout automated testing, the results of which can be compared to conventional testing practices. It also gives us a gist of virtualization. Hence Cloud Testing Platform (CTP) aims at providing all the conventional testing practices at higher speed, availability and lower cost of testing these applications and the limited resources available in mobile devices.. Developers can conduct compatibility tests in various Android devices only by uploading test programs and scripts to cloud testing platforms

ERA: A Framework for Economic Resource Allocation for the Cloud

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations

Pengembangan Framework Smart Mobile Cloud Learning System Untuk Pendidikan Pembelajaran Cerdas Menuju Smart Learning Environment

Abstrak: Pembelajaran telah berkembang secara signifikan, menciptakan beberapa tantangan bagi sistem pendidikan tradisional. Pergeseran paradigma dalam pendidikan ini sudah dekat dan sejak itu menarik banyak perhatian dalam beberapa tahun terakhir, sebagai upaya untuk menjembatani kesenjangan teknologi di sektor pendidikan. Penelitian ini mencoba untuk mengembangkan framework smart mobile cloud learning system berdasarkan intelligent learning model  untuk pendidikan pembelajaran cerdas, berdasarkan desain, pengembangan, dan pengujian sistem pembelajaran berbasis mobile cloud. Sistem ini dapat memberikan pembelajaran cerdas kapan saja-di mana saja yang disesuaikan dengan individu, dan disampaikan melalui perangkat mobile pribadi. Penelitian ini membahas mobile cloud-education – sebuah penelitian mutakhir baru di bidang smart learning system, berdasarkan desain, pengembangan, dan pengujian framework smart mobile cloud learning system. Sistem ini dapat memberikan pembelajaran cerdas kapan saja-di mana saja yang disesuaikan dan disesuaikan dengan individu, dan disampaikan melalui perangkat portabel pribadi. Pengujian awal sistem mengungkapkan efektivitasnya dalam mendukung proses pengajaran dan pembelajaran.   Kata kunci: Framework Smart Mobile Cloud Learning System, Intelligent Learning Model, Smart Learning Environment, Mobile Learning, Cloud Learning   Abstract: Learning has evolved significantly, creating several challenges for traditional education systems. This paradigm shift in education is imminent and has since attracted much attention in recent years, as an attempt to bridge the technology gap in the education sector. This study tries to develop a smart mobile cloud learning system framework based on intelligent learning models for intelligent learning education, based on the design, development, and testing of mobile cloud-based learning systems. This system can provide intelligent anytime-anywhere learning tailored to the individual, and delivered via personal mobile devices. This study discusses mobile cloud-education – a new cutting-edge research in the field of smart learning systems, based on the design, development, and testing of a smart mobile cloud learning system framework. This system can provide intelligent anytime-anywhere learning that is tailored and tailored to the individual, and delivered via personal portable devices. Initial testing of the system reveals its effectiveness in supporting the teaching and learning process.   Keywords: Framework Smart Mobile Cloud Learning System, Intelligent Learning Model, Smart Learning Environment, Mobile Learning, Cloud Learnin

Automated, Systematic and Parallel Approaches to Software Testing in Bioinformatics

Software quality assurance becomes especially critical if bioinformatics tools are to be used in a translational medical setting, such as analysis and interpretation of biological data. We must ensure that only validated algorithms are used, and that they are implemented correctly in the analysis pipeline – and not disrupted by hardware or software failure. In this thesis, I review common quality assurance practice and guidelines for bioinformatics software testing. Furthermore, I present a novel cloud-based framework to enable automated testing of genetic sequence alignment programs. This framework performs testing based on gold standard simulation data sets, and metamorphic testing. I demonstrate the effectiveness of this cloudbased framework using two widely used sequence alignment programs, BWA and Bowtie, and some fault-seeded ‘mutant’ versions of BWA and Bowtie. This preliminary study demonstrates that this type of cloud-based software testing framework is an effective and promising way to implement quality assurance in bioinformatics software that is used in genomic medicine

A Certification Framework for Cloud Security Properties: The Monitoring Path

In this paper we describe the structure and functionality of a certification integrated framework aimed to support the certification of security properties of a Cloud infrastructure (IaaS), a platform (PaaS), or the software layer (SaaS). Such framework will bring service users, service providers and cloud suppliers to work together with certification authorities in order to ensure security properties and certificates validity in the continuously evolving cloud environment. For this purpose, the framework relies on multiple types of evidence gathering with respect to security, e.g., testing services, monitoring agents or trusted computing proofs. In this paper we will focus only on the monitoring case and will illustrate its use. Yet, this framework is designed to be able to follow models for hybrid, incremental and multi-layer security certification since cloud security has to build upon the entire cloud stack

A systematic review on cloud testing

A systematic literature review is presented that surveyed the topic of cloud testing over the period (2012-2017). Cloud testing can refer either to testing cloud-based systems (testing of the cloud), or to leveraging the cloud for testing purposes (testing in the cloud): both approaches (and their combination into testing of the cloud in the cloud) have drawn research interest. An extensive paper search was conducted by both automated query of popular digital libraries and snowballing, which resulted into the final selection of 147 primary studies. Along the survey a framework has been incrementally derived that classifies cloud testing research along six main areas and their topics. The paper includes a detailed analysis of the selected primary studies to identify trends and gaps, as well as an extensive report of the state of art as it emerges by answering the identified Research Questions. We find that cloud testing is an active research field, although not all topics have received so far enough attention, and conclude by presenting the most relevant open research challenges for each area of the classification framework.This paper describes research work mostly undertaken in the context of the European Project H2020 731535: ElasTest. This work has also been partially supported by: the Italian MIUR PRIN 2015 Project: GAUSS; the Regional Government of Madrid (CM) under project Cloud4BigData (S2013/ICE-2894) cofunded by FSE & FEDER; and the Spanish Government under project LERNIM (RTC-2016-4674-7) cofunded by the Ministry of Economy and Competitiveness, FEDER & AEI