Towards a Deadline-Based Simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework

GA-Par: Dependable Microservice Orchestration Framework for Geo-Distributed Clouds

Recent advances in composing Cloud applications have been driven by deployments of inter-networking heterogeneous microservices across multiple Cloud datacenters. System dependability has been of the upmost importance and criticality to both service vendors and customers. Security, a measurable attribute, is increasingly regarded as the representative example of dependability. Literally, with the increment of microservice types and dynamicity, applications are exposed to aggravated internal security threats and externally environmental uncertainties. Existing work mainly focuses on the QoS-aware composition of native VM-based Cloud application components, while ignoring uncertainties and security risks among interactive and interdependent container-based microservices. Still, orchestrating a set of microservices across datacenters under those constraints remains computationally intractable. This paper describes a new dependable microservice orchestration framework GA-Par to effectively select and deploy microservices whilst reducing the discrepancy between user security requirements and actual service provision. We adopt a hybrid (both whitebox and blackbox based) approach to measure the satisfaction of security requirement and the environmental impact of network QoS on system dependability. Due to the exponential grow of solution space, we develop a parallel Genetic Algorithm framework based on Spark to accelerate the operations for calculating the optimal or near-optimal solution. Large-scale real world datasets are utilized to validate models and orchestration approach. Experiments show that our solution outperforms the greedy-based security aware method with 42.34 percent improvement. GA-Par is roughly 4× faster than a Hadoop-based genetic algorithm solver and the effectiveness can be constantly guaranteed under different application scales

Analysis of requirements and technologies to migrate software development to the PaaS model

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementSoftware development has been evolving during the last years and, more and more, the software architecture to support this development has become more complex to meet the new requirements and new technologies. With the new cloud computing architecture and models, IT departments and ISV are developing new applications and moving the traditional software architecture to the cloud. In this context, Platform as a Service (PaaS) model can provide software development services and components within a new architecture for building a new generation of software with all benefits of cloud, like scalability and elasticity. However, currently, most companies have significant challenges to adapt and change its software development process to use the PaaS architecture and the cloud services. In this dissertation, it will first be identified and analyzed the changes and challenges for develop software with the PaaS architecture. Afterwards, will be analyzed and identified the requirements in a traditional software development and architecture (on premise) to development new software or adapt the existents software with the PaaS.Dissertation submitted as partial requirement for obtaining the Master’s degree in Information Managemen

Käytettävyyden ja kehityksen modernisointi mikropalveluilla

Vanhat ohjelmistojärjestelmät, joilla tarkoitetaan vanhoja ja vanhentuneita ohjelmistoja joita on tehty vanhentuneilla työskentelytavoilla, ovat todellisuus jonka kanssa suurin osa ohjelmistokehitysyrityksistä joutuvat kamppailemaan. Vanhat työskentelytavat ja teknologiat aiheuttavat usein ohjelmiston kehityksen ja julkaisun hidastumista, sillä niiden jatkuvassa käytössä voi piillä yhteensopivuus, turvallisuus, skaalautuvuus sekä ekonomisia ongelmia, muiden ongelmien muassa. Ohjelmistojärjestelmien modernisointi, uudelleensuunnittelu ja refaktorointi voivat lievittää vanhoista järjestelmistä nousevia ongelmia, oli se sitten työskentelytapojen muutoksella, teknologioiden päivityksellä tai ohjelmistoalustojen vaihdolla. On olemassa monia teknologioita ja metodeja jotka voivat helpottaa ohjelmistojärjestelmien modernisointia, mukaanlukien siirto käyttämään erilaista arkkitehtuuria, uusien teknologioiden käyttöönotto ja ohjelmistokehityksen tapojen vaihto. Näillä teknologioilla ja metodeilla, ja modernisaatiolla yleensäkkin, on omat riskinsä ja haasteensa, jotka tulee ottaa huomioon onnistuneen modernisaation aikaansaamiseksi; Nämä strategiset huomiot ovat avaintekijöitä modernisaatiossa. Tämä opinnäytetyö tutkii ohjelmistojen modernisaatiota yleisellä tasolla kirjallisuusarvostelun kautta, ja käyttää tietyn yrityksen tapaustutkimuksen dataa, joka on kerätty kyselyjen ja yhtiön lokien kautta, katsoen mitä teknologioita, konsepteja ja strategioita tarvitaan onnistuneeseen modernisaatioon, ja mitä vaikutuksia modernisaatiolla on modernisoitavaan ohjelmistojärjestelmään loppukäyttäjien sekä ohjelmistokehittäjien näkökulmasta. Tämän tutkimuksen lopputulos paljastaa miksi modernisaatio on monimutkainen aihe jossa on monia haasteita, mutta joka samaan aikaan tarjoaa monia hyötyjä modernisoitavalle ohjelmistojärjestelmälle. Näitä tuloksia on parasta käyttää ohjeina siihen, mihin ongelmiin kannattaa keskittyä modernisoinnin aikana, pitäen mielessä tapaustutkimuksen rajoitetun soveltamisalan.Legacy software systems, which refers to old and likely outdated software applications and practices, are a reality that most software development companies have to contend with. Old practices and technologies are often at fault for slowing down development and deployment of software, as they can have compatibility, security, scalability and economic issues with their continued use, among other issues. Software modernization, reengineering and refactoring can alleviate the issues stemming from legacy systems, whether it be in the form of altering practices, updating technologies or changing platforms. There are many technologies and methods that can facilitate the modernization of a software system, including a move to using different architectures, specific newer technologies and changing the methods of working and developing the software system. These technologies and methods, and modernization in general, come with their own risks and challenges that must be considered for a successful modernization to take place; These strategic considerations are a key factor in modernization. This thesis will explore software modernization in general through literature reviews and as a case study for a specific company using data from surveys and the case company’s logs, with a look into the technologies, concepts and strategies required for a successful modernization, and what kinds of effects modernization can have on the software system being modernized, both from a user perspective as well as from a developer perspective. The end-result of this exploration reveals that modernization is a complex subject with many challenges, but that also offers benefits to the software system being modernized. These results are best used as a guideline on what issues should be concentrated on during modernization, with a mindful consideration for the limited scope of the case study represented within

Monitoring Platform Evolution towards Serverless Computing for 5G and Beyond Systems

Fifth generation (5G) and beyond systems require flexible and efficient monitoring platforms to guarantee optimal key performance indicators (KPIs) in various scenarios. Their applicability in Edge computing environments requires lightweight monitoring solutions. This work evaluates different candidate technologies to implement a monitoring platform for 5G and beyond systems in these environments. For monitoring data plane technologies, we evaluate different virtualization technologies, including bare metal servers, virtual machines, and orchestrated containers. We show that containers not only offer superior flexibility and deployment agility, but also allow obtaining better throughput and latency. In addition, we explore the suitability of the Function-as-a-Service (FaaS) serverless paradigm for deploying the functions used to manage the monitoring platform. This is motivated by the event oriented nature of those functions, designed to set up the monitoring infrastructure for newly created services. When the FaaS warm start mode is used, the platform gives users the perception of resources that are always available. When a cold start mode is used, containers running the application"s modules are automatically destroyed when the application is not in use. Our analysis compares both of them with the standard deployment of microservices. The experimental results show that the cold start mode produces a significant latency increase, along with potential instabilities. For this reason, its usage is not recommended despite the potential savings of computing resources. Conversely, when the warm start mode is used for executing configuration tasks of monitoring infrastructure, it can provide similar execution times to a microservice-based deployment. In addition, the FaaS approach significantly simplifies the code logic in comparison with microservices, reducing lines of code to less than 38%, thus reducing development time. Thus, FaaS in warm start mode represents the best candidate technology to implements such management functions.This work has been supported by EC H2020 5GPPP projects 5G-EVE and 5GROWTH under grant agreements No. 815974 and 856709, respectively

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns