Nowadays, while software industries are aiming to develop their software continuously, their delivery is hindered by technical debt.  Preventing technical debt would be valuable if it is considered in architectural decisions. On the other side, since microservices architecture is adaptable to build cloud applications and has other advantages, it has become a trend in the software industries. Due to the popularity of microservices and the importance of technical debt in the software industry, this research aims to find the influence of architectural decisions on technical debt in microservices applications. In this research, we explore architectural decisions in microservice applications and their qualities that impact technical debt.   We calculated the repetitiveness of selected microservices architectural decisions and the extra effort that they need to meet qualities to prevent technical debt. Spearman correlation coefficient used to calculate the relation between extra effort on the qualities of architectural decisions in microservice applications that affect technical debt. Furthermore, we calculated the correlation between the repetitiveness of selected architectural decisions and the effort for their qualities to find the effect of repetitiveness on qualities that reduce technical debt.   Our result shows that every architectural decision that we have explored for microservice applications needs some extra effort to increase the quality that can prevent technical debt. Correlation between qualities and repetitiveness of architectural decisions shows that weak correlation, which proves that increasing or decreasing of repetitiveness would not change the demand for extra effort to prevent technical debt.

The enterprise systems are built with a monolithic way of software architecture which may not be the very suitable and realistic solutions, due to the quantity and diversity of Internet of Things (IOT) devices connected to the internet. The large code is based as a single deployable unit; making it cumbersome and difficult to change, test, and deploy – typically in the monthly deployment cycles. Which could potentially be a large impact and high-risk for updates and deployment build up between releases.  The thesis aims to exploit Microservices Architecture in context of the Internet of Things. Because of scalability and rapid deployment, Microservices Architecture (MSA) naturally fits for M2M and IOT developments. Therefore, it has become increasingly popular and highly suited as a design choice in the context of developing IOT applications. We have proposed various ontologies for conceptual architecture designs. Our conceptual model encompasses all the essential elements and entities that constitute IOT; describing various characteristics of microservices and consideration to be used as a basis for developing a Reference Architecture model. The reference architecture is effectively exploiting Microservices Architectural Style for a diverse set of things composition like sensor devices, connectivity and transportation protocols for IP enabled IOT smart objects.  The thesis will contribute to the conceptual space of more successful reference architectures. The goal is to provide features and deployment information with instantiation guidelines to derive the software architecture instances in a given IOT application domain. Thus, it promises to become a valuable framework in the derivation of the Microservices IOT architectures.

Microservices are a way of decentralizing software services into smaller, isolated environments with contained, specific responsibilities. The traditional approach of monolithic applications introduces many problems regarding complexity due to scaling of functionality. Microservices emerged as a way of dealing with these problems by separating services into modules independent of one another, promoting communication between each component to fulfill the service requirements. This architectural style of software development separates concern of business logic, data models, and other domain specific modules to their respective domains where they are isolated from the rest of the system. Communication is key in the world of microservices as modules rely on transferring information to the rest of the system rather than mutating and operating on global data bound to the entirety of the system. APIs (Application Programming Interfaces) expose data from individual software modules to other parts of the application, and this can be done in a multitude of ways. This thesis focuses on APIs following the REST (Representational State Transfer) protocol as a means to exchange data between software modules. This project dives into the concept of microservices by developing a service through an iterative development process in order to incrementally implement the requirements of the service. The purpose of the microservice is to integrate third-party APIs into the existing service, Link Visualizer. Instead of directly implementing the required functionalities from the external API into the core source code of Link Visualizer, a microservice was built to isolate the responsibilities, removing co-dependence from the integrating APIs.

<p>Microservices have emerged as a popular pattern for developing large-scale applications in cloud environments for their flexibility, scalability, and agility benefits. However, microservices make management more complex due to their scale, multiple languages, and distributed nature. Orchestration and automation tools like Kubernetes help deploy microservices running simultaneously, but it can be difficult for an operator to understand their behaviors, interdependencies, and interactions. In such a complex and dynamic environment, performance problems (e.g., slow application responses and high resource usage)  require significant human effort spent on diagnosis and recovery. Moreover, manual diagnosis of cloud microservices tends to be tedious, time-consuming, and impractical. Effective and automated performance analysis and anomaly detection require an observable system, which means an application's internal state can be inferred by observing and tracking metrics, traces, and logs. Traditional APM uses libraries and SDKs to improve application monitoring and tracing but has additional overheads of rewriting, recompiling, and redeploying the applications' code base. Therefore, there is a critical need for a standardized automated microservices observability solution that does not require rewriting or redeploying the application to keep up with the agility of microservices.</p> <p><br></p> <p>This thesis studies observability for microservices and implements an automated Extended Berkeley Packet Filter (eBPF) based observability solution. eBPF is a Linux feature that allows us to write extensions to the Linux kernel for security and observability use cases. eBPF does not require modifying the application layer and instrumenting the individual microservices. Instead, it instruments the kernel-level API calls, which are common across all hosts in the cluster. eBPF programs provide observability information from the lowest-level system calls and can export data without additional performance overhead. The Prometheus time-series database is leveraged to store all the captured metrics and traces for analysis. With the help of our tool, a DevOps engineer can easily identify abnormal behavior of microservices and enforce appropriate countermeasures. Using Chaos Mesh, we inject anomalies at the network and host layer, which we can identify with root cause identification using the proposed solution. The Chameleon cloud testbed is used to deploy our solution and test its capabilities and limitations.</p>

When adopting a microservice architecture, maintenance is needed to keep the system working. Maintenance can sometimes involve more than one team that needs to collaborate in order to fix a maintenance issue. This procedure requires coordination and correspondence between teams which can have a negative impact on the autonomy of the teams. The microservice architecture does not only introduce new benefits but also new challenges. This study creates a strategy guideline that organisations can use to address these challenges. The study highlights challenges with collaboration in maintenance and gives suggestions on strategies to use to counter the challenges. This guideline can help organisations new to the microservice architecture and help organisations to make future maintenance decisions in a microservice architecture with multiple teams. The guideline is developed using the Convergent Parallel Design and by interviewing product owners, architects, and developers in three organisations working with microservices. A survey was sent out to people working with microservices in different industries to validate the findings from the interviews. The interviews and the survey is analysed using the five steps described in the book “A Step-By-Step Guide To Qualitative Data Analysis” by O'Connor and Gibson (2003). This paper is focused on how agile teams collaborate with maintenance across microservices and does not cover teamwork within the agile team. This paper does not cover any technical aspects of microservices, such as application programming interface endpoints between services, but rather how teams work with them.

Large scale Internet services are increasingly implemented as distributed systems in order to achieve fault tolerance, availability, and scalability. When requests traverse multiple services, end-to-end metrics no longer tell a clear picture. Distributed tracing emerged to break down end-to-end latency on a per service basis, but only answers where a problem occurs, not why. From user research we found that root-cause analysis of performance problems is often still done by manually correlating information from logs, stack traces, and monitoring tools. Profilers provide fine-grained information, but we found they are rarely used in production systems because of the required changes to existing applications, the substantial storage requirements they introduce, and because it is difficult to correlate profiling data with information from other sources. The proliferation of modern low-overhead profilers opens up possibilities to do online always-on profiling in production environments. We propose Protractor as the missing link that exploits these possibilities to provide distributed profiling. It features a novel approach that leverages service meshes for application-level transparency, and uses anomaly detection to selectively store relevant profiling information. Profiling information is correlated with distributed traces to provide contextual information for root-cause analysis. Protractor has support for different profilers, and experimental work shows impact on end-to-end request latency is less than 3%. The utility of Protractor is further substantiated with a survey showing the majority of the participants would use it frequently / Storskaliga Internettjänster implementeras allt oftare som distribuerade system för att uppnå feltolerans, tillgänglighet och skalbarhet. När en request spänner över flera tjänster ger inte längre end-to-end övervakning en tydlig bild av orsaken till felet. Distribuerad tracing utvecklades för att spåra end-to-end request latency per tjänst och för att ge en indikation vart problemet kan ligger med visar oftas inte orsaken. Genom user research fann vi att root-cause-analys av prestandaproblem ofta fortfarande görs genom att manuellt korrelera information från loggar, stack traces och övervakningsverktyg. Kod-profilering tillhandahåller detaljerad information, men vi fann att den sällan används i produktionssystem på grund av att de kräver ändringar i den befintliga koden, de stora lagringskraven som de introducerar och eftersom det är svårt att korrelera profilerings data med information från andra källor. Utbredning av moderna kodprofilerare med låg overhead öppnar upp möjligheten att kontinuerligt köra dem i produktionsmiljöer. Vi introducerar Protractor som kombinerar kodprofilering och distribuerad tracing. Genom att utnyttja och bygga på koncept så som service meshes uppnår vi transparens på applikationsnivå och använder anomalitetsdetektering för att selektivt lagra relevant profileringsinformation. Den informationen korreleras med distribuerade traces för att ge kontext för root-cause-analys. Protractor har stöd för olika kodprofilerare och experiment har visat att påverkan på end-to-end request latency är mindre än 3Användbarheten av Protractor är ytterligare underbyggd med en undersökning som visar att majoriteten av deltagarna skulle använda den ofta.

With great advancements in cloud computing, the microservice architecture has become a promising architectural style for enterprise software. It has been proposed to cope with problems of the traditional monolithic architecture which includes slow release cycles, limited scalability and low developer productivity. Therefore, this thesis aims to investigate the affordances and challenges of adopting microservices as well as the difference in performance compared to the monolithic approach at one of Sweden’s largest banks, SEB - the Scandinavian Individual Bank. The investigation consisted of a literature study of research papers and official documentation of microservices. Moreover, two applications were developed and deployed using two different system architectures - a monolithic architecture and a microservice architecture. Performance tests were executed on both systems to gather quantitative data for analysis. The two metrics investigated in this study were response time and error rate. The results indicate the microservice architecture has a significantly higher error rate but a slower response time than the monolithic approach, further strengthening the results of Ueda et. al. [47] and Villamizar et. al. [48]. The findings have then been discussed with regards to the challenges and complexity involved in implementing distributed systems. From this study, it becomes clear the complexity shifts from inside the application out towards infrastructure with a microservice architecture. Therefore, microservices should not be seen as a silver bullet. Rather, the type of architecture is highly dependent on the scope of the project and the size of the organization. / Med stora framstegen inom molntjänster har microservice arkitekturen kommit att bli en lämplig kandidat för utveckling av företagsprogramvara. Denna typ av systemarkitektur har föreslagits att lösa de problem som den traditionella monolitiska arkitekturen medför; långsamma lanseringar, begränsad skalbarhet och låg produktivitet. Således fokuserar denna avhandling på att utforska de möjligheter samt utmaningar som följer vid adoptering av microservices samt skillnaden i prestanda jämfört med den monolitiska arkitekturen. Detta undersöktes på en av Sveriges största banker, SEB, den Skandinaviska Enskilda Banken. Utredningen bestod av en litteraturstudie av vetenskapliga artiklar samt officiell dokumentation för microservices. Dessutom utvecklades och lanserades två applikationer byggt med två olika typer av systemarkitektur - en som monolitisk arkitektur och den andra som en microservice arkitektur. Prestandatest utfördes sedan på båda systemen för att samla kvantitativ data för analys. De två nyckelvardena som undersöktes i denna studie var responstid och felfrekvens. Resultaten indikerar att microservice arkitekturen har en signifikant högre felfrekvens men en långsammare responstid än den monolitiska arkitekturen, vilket stärker resultaten av Ueda et. al. [47] och Villamizar et. al. [48]. Forskningsresultaten har diskuterats med hänsyn till den komplexitet och de utmaningar som följer vid implementering av distribuerade system. Från denna studie blir det tydligt att komplexiteten i en microservice arkitektur skiftar från inuti applikationen ut till infrastrukturen. Således borde microservices inte ses som en silverkula. Istället är valet av systemarkitektur strikt beroende på omfattningen av projektet samt storleken på organisationen i fråga.

Containers have been gaining popularity in recent years due to their ability to provide higher flexibility, higher reliability and dynamic scalability to enterprise software systems. In order to fully utilize containers, software developers aim to build their software using microservice architecture, meaning that instead of working on a single large codebase for the whole project, the software is split into smaller units. These microservices can be deployed in their own container instead of the traditional virtual machine setup where a server has to configured with all necessary dependencies. Moving away from the monolithic software architecture to containerized microservices is bound to bring performance penalties due to increased network calls between services and container overhead. The integration must therefor be carefully planned in order to fully utilize the container setup while minimizing the overhead. The purpose of this thesis project was to measure how much overhead can be expected due to containers in an enterprise environment. By using a combination of virtual machines and Docker containers, a microservice system was deployed with four different deployment strategies and the system’s performance was measured by analyzing request response times under various loads. The services were made to run on a single server and on multiple servers, with and without Docker. The performance measurements showed that the system performed worse in every case when Docker was used. Furthermore, the results showed that Docker can have significant negative impact on performance when there is a heavy load on the system. / Containers har blivit populärare under de senaste åren tack vare deras förmåga att ge högre flexibilitet, högre tillförlitlighet och dynamisk skalbarhet för företagsprogramvarusystem.  För att fullt ut kunna använda containers har programutvecklarna för avsikt att bygga sin programvara med hjälp av mikroservicearkitekturen, vilket innebär att programvaran delas upp i mindre enheter istället för att arbeta på en enda stor kodbas för hela projektet. Dessa mikroservices kan distribueras i sina egna containers istället för den traditionella virtuella maskininstallationen, där en server måste konfigureras med alla nödvändiga beroenden.  Att flytta sig från monolitisk mjukvaruarkitektur till containeriserade microservices kommer att få prestandaförsämringar på grund av ökade nätverksanrop mellan tjänster och container-overhead. Integrationen måste därför noggrant planeras för att fullt ut utnyttja containeruppsättningen och minimera overhead. Syftet med detta avhandlingsprojekt var att mäta hur mycket overhead kan förväntas på grund av containers i en företagsmiljö. Genom att använda en kombination av virtuella maskiner och Dockercontainers, implementerades ett microservices-system med fyra olika implementeringsstrategier och systemets prestanda mättes genom att analysera anropens svarstid under olika belastningar. Tjänsterna gjordes för att köras på en enda server och på flera servrar, med och utan Docker.  Prestandamätningarna visade att systemet var sämre i alla fall när Docker användes. Dessutom, visade resultaten att Docker kan ha signifikant negativ inverkan på prestanda när det är tung belastning på systemet.

Large-Scale Digital Twins Notification System (LSDTNS) monitors a Digital Twin (DT) cluster for a predefined critical state, and once it detects such a state, it sends a Notification Event (NE) to a predefined recipient. Additionally, the time from producing the DT's Complex Event (CE) to sending an alarm has to be less than a predefined deadline. However, addressing scalability and multi-objectives, such as deployment cost, resource utilization, and meeting the deadline, on top of process scheduling, presents a complex challenge. Therefore, this thesis presents a complex methodology consisting of three contributions that address system scalability, multi-objectivity and scheduling of CE processes using Reinforcement Learning (RL). The first contribution proposes the IoT Notification System Architecture based on a micro-service-based notification methodology that allows for running and seamlessly switching between various CE reasoning algorithms. Our proposed IoT Notification System architecture addresses the scalability issue in state-of-the-art CE Recognition systems. The second contribution proposes a novel methodology for multi-objective optimization for cloud provisioning (MOOP). MOOP is the first work dealing with multi-optimization objectives for microservice notification applications, where the notification load is variable and depends on the results of previous microservices subtasks. MOOP provides a multi-objective mathematical cloud resource deployment model and demonstrates effectiveness through the case study. Finally, the thesis presents a Scheduler for large-scale Critical Notification applications based on a Deep Reinforcement Learning (SCN-DRL) scheduling approach for LSDTNS using RL. SCN-DRL is the first work dealing with multi-objective optimization for critical microservice notification applications using RL. During the performance evaluation, SCN-DRL demonstrates better performance than state-of-the-art heuristics. SCN-DRL shows steady performance when the notification workload increases from 10% to 90%. In addition, SCN-DRL, tested with three neural networks, shows that it is resilient to sudden container resources drop by 10%. Such resilience to resource container failures is an important attribute of a distributed system.

In today’s Data Centers, a combination of factors leads to the static allocation of physical servers and switches into dedicated clusters such that it is difficult to add or remove hardware from these clusters for short periods of time. This silofication of the hardware leads to inefficient use of clusters. This dissertation proposes a novel architecture for improving the efficiency of clusters by enabling them to add or remove bare-metal servers for short periods of time. We demonstrate by implementing a working prototype of the architecture that such silos can be broken and it is possible to share servers between clusters that are managed by different tools, have different security requirements, and are operated by tenants of the Data Center, which may not trust each other. Physical servers and switches in a Data Center are grouped for a combination of reasons. They are used for different purposes (staging, production, research, etc); host applications required for servicing specific workloads (HPC, Cloud, Big Data, etc); and/or configured to meet stringent security and compliance requirements. Additionally, different provisioning systems and tools such as Openstack-Ironic, MaaS, Foreman, etc that are used to manage these clusters take control of the servers making it difficult to add or remove the hardware from their control. Moreover, these clusters are typically stood up with sufficient capacity to meet anticipated peak workload. This leads to inefficient usage of the clusters. They are under-utilized during off-peak hours and in the cases where the demand exceeds capacity the clusters suffer from degraded quality of service (QoS) or may violate service level objectives (SLOs). Although today’s clouds offer huge benefits in terms of on-demand elasticity, economies of scale, and a pay-as-you-go model yet many organizations are reluctant to move their workloads to the cloud. Organizations that (i) needs total control of their hardware (ii) has custom deployment practices (iii) needs to match stringent security and compliance requirements or (iv) do not want to pay high costs incurred from running workloads in the cloud prefers to own its hardware and host it in a data center. This includes a large section of the economy including financial companies, medical institutions, and government agencies that continue to host their own clusters outside of the public cloud. Considering that all the clusters may not undergo peak demand at the same time provides an opportunity to improve the efficiency of clusters by sharing resources between them. The dissertation describes the design and implementation of the Market Driven Elastic Secure Infrastructure (MESI) as an alternative to the public cloud and as an architecture for the lowest layer of the public cloud to improve its efficiency. It allows mutually non-trusting physically deployed services to share the physical servers of a data center efficiently. The approach proposed here is to build a system composed of a set of services each fulfilling a specific functionality. A tenant of the MESI has to trust only a minimal functionality of the tenant that offers the hardware resources. The rest of the services can be deployed by each tenant themselves MESI is based on the idea of enabling tenants to share hardware they own with tenants they may not trust and between clusters with different security requirements. The architecture provides control and freedom of choice to the tenants whether they wish to deploy and manage these services themselves or use them from a trusted third party. MESI services fit into three layers that build on each other to provide: 1) Elastic Infrastructure, 2) Elastic Secure Infrastructure, and 3) Market-driven Elastic Secure Infrastructure. 1) Hardware Isolation Layer (HIL) – the bottommost layer of MESI is designed for moving nodes between multiple tools and schedulers used for managing the clusters. It defines HIL to control the layer 2 switches and bare-metal servers such that tenants can elastically adjust the size of the clusters in response to the changing demand of the workload. It enables the movement of nodes between clusters with minimal to no modifications required to the tools and workflow used for managing these clusters. (2) Elastic Secure Infrastructure (ESI) builds on HIL to enable sharing of servers between clusters with different security requirements and mutually non-trusting tenants of the Data Center. ESI enables the borrowing tenant to minimize its trust in the node provider and take control of trade-offs between cost, performance, and security. This enables sharing of nodes between tenants that are not only part of the same organization by can be organization tenants in a co-located Data Center. (3) The Bare-metal Marketplace is an incentive-based system that uses economic principles of the marketplace to encourage the tenants to share their servers with others not just when they do not need them but also when others need them more. It provides tenants the ability to define their own cluster objectives and sharing constraints and the freedom to decide the number of nodes they wish to share with others. MESI is evaluated using prototype implementations at each layer of the architecture. (i) The HIL prototype implemented with only 3000 Lines of Code (LOC) is able to support many provisioning tools and schedulers with little to no modification; adds no overhead to the performance of the clusters and is in active production use at MOC managing over 150 servers and 11 switches. (ii) The ESI prototype builds on the HIL prototype and adds to it an attestation service, a provisioning service, and a deterministically built open-source firmware. Results demonstrate that it is possible to build a cluster that is secure, elastic, and fairly quick to set up. The tenant requires only minimum trust in the provider for the availability of the node. (iii) The MESI prototype demonstrates the feasibility of having a one-of-kind multi-provider marketplace for trading bare-metal servers where providers also use the nodes. The evaluation of the MESI prototype shows that all the clusters benefit from participating in the marketplace. It uses agents to trade bare-metal servers in a marketplace to meet the requirements of their clusters. Results show that compared to operating as silos individual clusters see a 50% improvement in the total work done; up to 75% improvement (reduction) in waiting for queues and up to 60% improvement in the aggregate utilization of the test bed. This dissertation makes the following contributions: (i) It defines the architecture of MESI allows mutually non-trusting tenants of the data center to share resources between clusters with different security requirements. (ii) Demonstrates that it is possible to design a service that breaks the silos of static allocation of clusters yet has a small Trusted Computing Base (TCB) and no overhead to the performance of the clusters. (iii) Provides a unique architecture that puts the tenant in control of its own security and minimizes the trust needed in the provider for sharing nodes. (iv) A working prototype of a multi-provider marketplace for bare-metal servers which is a first proof-of-concept that demonstrates that it is possible to trade real bare-metal nodes at practical time scales such that moving nodes between clusters is sufficiently fast to be able to get some useful work done. (v) Finally results show that it is possible to encourage even mutually non-trusting tenants to share their nodes with each other without any central authority making allocation decisions. Many smart, dedicated engineers and researchers have contributed to this work over the years. I have jointly led the efforts to design the HIL and the ESI layer; led the design and implementation of the bare-metal marketplace and the overall MESI architecture.