Orchestrating honeypot deployment in lightweight container platforms to improve security

In modern software engineering, it has gained much attention as an effective paradigm to traditional monolithic architectures. Microservices provide an application development approach in a module-based way, where larger systems are divided into a number of small, independently deployable, and loosely coupled services. It performs pre-defined functions, with loose coupling among these services via APIs, which interact with other services; thus, modifications or failures in one will not bring down the whole application. This architecture thus gives faster development cycles, continuous delivery, and scaling of individual components depending upon demand, making it quite useful in dynamic and large-scale cloud environments. The wide adoption of cloud computing platforms such as AWS, Microsoft Azure, and Google Cloud has accelerated further the growth of microservices-based systems. Cloud infrastructure provides the necessary tools that are vital for the seamless deployment and management of microservices at scale, such as automated provisioning, scaling, monitoring, and orchestration. This enables the use of cloud-based containerization technologies such as Docker and Kubernetes to easily isolate services and efficiently orchestrate and manage distributed applications. These cloud environments also provide robust service discovery mechanisms, load balancing, and fault-tolerance features necessary for maintaining system reliability and availability in production environments. However, migration toward a microservices-based architecture introduces a different set of challenges that requires careful consideration of various aspects of software engineering. At the design level, a key aspect that would affect this architecture’s success pertains to clearly establishing the proper service boundaries, data consistency, and assurance that services align well with the business capabilities. Additional concerns in the flexibility provided by microservices come in the complexity of their communications, management of their data, and keeping this across a distributed system in a consistent manner. Ensuring seamless communication between services, in particular when dealing with eventual consistency models and distributed transactions, is one of the important design challenges in microservices systems. The deployment phase involves the adoption of best practices for continuous integration and continuous delivery, the utilization of automated build pipelines, and the deployment of services in containers to ensure smooth and efficient releases. The elasticity of the cloud allows organizations to scale services up or down, depending on demand. Additionally, orchestrators like Kubernetes can automate deployment and the management of containers across a set of nodes. However, all these advantages require a very effective monitoring strategy that will guarantee performance, identify bottlenecks, and handle failures. Latency, throughput, and error rates are some metrics that need to be monitored relentlessly in order to keep a system healthy, and it’s important to set automated alerts for proactive issue resolution. Managing microservices at a production level is actually far beyond just deployment; there should be continuous monitoring of the performance of the system, interaction management of services, fault processing, and security ensuring of the application. Since microservices architectures can have hundreds or thousands of services, the adoption of centralized logging, distributed tracing, and performance management tools becomes critical for holistic views of system health. In addition, the organization should put in place strong fault tolerance mechanisms like circuit breakers and retries to ensure that the system stays up even when some of its constituent services fail. Security in microservices-based architectures is another critical consideration, as the distributed nature of microservices increases the attack surface, necessitating effective authentication, authorization, and encryption strategies to protect sensitive data. This research explores microservices architecture from a software engineering perspective, with a focus on its design, deployment, and management within cloud computing environments. This paper presents the evolution of practices and strategies for adopting microservices in cloud-native environments through an extensive review of existing literature, industry case studies, and expert interviews. The paper aims to highlight best practices for designing microservices that are scalable, resilient, and maintainable. Furthermore, it provides a detailed examination of deployment strategies, particularly those involving CI/CD pipelines, containerization, and orchestration tools. It finally goes into the operational challenges of managing microservices at scale, including how to monitor, tolerate faults, and keep microservices secure in cloud environments. Apart from the theoretical view, this paper provides practical recommendations that will enable software engineers and organizations to manage or adopt microservices architecture without getting lost in the way. The work contributes to the growing literature on cloud-based microservices by addressing a number of challenges related to service decomposition, interservice communication, data consistency, deployment pipelines, and system management. The findings presented in this study aim to provide both academics and industry professionals with an in-depth understanding of how to effectively implement and manage microservices architectures, thereby making software solutions more efficient, reliable, and scalable in cloud computing environments.

Context. Since its proclamation in 2012, microservices-based architecture has gained widespread popularity due to its advantages, such as improved availability, fault tolerance, and horizontal scalability, as well as greater software development agility. Motivation. Yet, refactoring a monolith to microservices by smaller businesses and expecting that the migration will bring benefits similar to those reported by top global companies, such as Netflix, Amazon, eBay, and Uber, might be an illusion. Indeed, for systems that do not have thousands of concurrent users and can be scaled vertically, the benefits of such migration have not been sufficiently investigated, while the existing evidence is inconsistent. Objective. The purpose of this paper is to compare the performance and scalability of monolithic and microservice architectures on a reference web application. Method. The application was implemented in four different versions, covering not only two different architectural styles (monolith vs. microservices) but also two different implementation technologies (Java vs. C#.NET). Next, we conducted a series of controlled experiments in three different deployment environments (local, Azure Spring Cloud, and Azure App Service). Findings. The key lessons learned are as follows: (1) on a single machine, a monolith performs better than its microservice-based counterpart; (2) The Java platform makes better use of powerful machines in case of computation-intensive services when compared to.NET; the technology platform effect is reversed when non-computationally intensive services are run on machines with low computational capacity; (3) vertical scaling is more cost-effective than horizontal scaling in the Azure cloud; (4) scaling out beyond a certain number of instances degrades the application performance; (5) implementation technology (either Java or C#.NET) does not have a noticeable impact on the scalability performance.

Cloud computing has become a popular choice for deploying web applications, but the selection of the appropriate architecture for different application types remains a challenge for businesses. In this study, we conducted performance and cost analyses of three architectures: Monolith, Microservice, and Serverless (specifically Lambda and Fargate) to provide businesses with valuable insights for decision-making. To ensure the relevance of our findings, we conducted tests on an application designed for static, database, and batch job services, deployed on AWS. Using JMeter, we simulated traffic with varying levels of high, medium, and low intensity, distributing 500 requests across six test scenarios. The results and subsequent analysis revealed that Lambda outperformed the other architectures significantly, while Fargate and Microservice architectures exhibited lower performance in comparison. However, cost played a crucial role in architecture selection. Fargate proved to be exceptionally expensive, with costs escalating further when batch jobs were involved. On the other hand, Lambda demonstrated lower overall costs compared to the other architectures, especially when batch jobs were running under low traffic conditions. However, the cost advantage diminished when running batch jobs in higher traffic scenarios, surpassing the expenses of Monolith and Microservice architectures. The findings highlight the superior performance of Lambda, but also emphasize the importance of considering costs and specific workload requirements when selecting an architecture. Lambda outperformed other architectures by reducing average response time by 25% and cost by 15% under low-traffic scenarios compared to Monolithic and Microservice architectures.

The Microservice Architecture (MSA) is an advanced architecture with flexible technology selection, independent on-demand expansion, and high availability. It is one of the best solution for enterprise software systems to cope with cloud deployment. Moreover, Spring Cloud provides comprehensive technical support for microservice architecture; also, it is the best technical framework for implementing microservice architecture. The container technology represented by Dockers provides an independent and undisturbed deployment environment for microservice architecture. Based on the microservice architecture and lightweight container technology, this article propose an implementation idea for service composition.

The ever-metamorphosing nature of modern businesses demand changes to be rapidly deployed to the production environments without causing any major outages. Cloud computing in conjunction with containerization and orchestration technologies has undoubtedly contributed to the development and proliferation of quicker and light weight deployment strategies. Of late, Micro service architecture (MSA) has become the de-facto standard for building such lightweight containers, which made the deployments even quicker. Though public cloud technologies and MSA based containers helped in rapid deployments, continuous deployment with zero downtime is not something which comes out of the box. It needs the implementation of Blue Green (BG) deployment techniques to achieve the zero downtime with no disruption to end users while applying the patches. But there are multiple ways to implement the BG deployment pattern. This research paper aims to address three things - (i) Explore Blue Green, Canary and Rolling deployment techniques possible in the cloud platforms (ii) Perform a research project, to simulate the BG deployment strategies using DNS routing swap versus Load Balancer swap versus newer image switch techniques(iii) Draw the comparisons and elucidate which deployment strategy is suitable for which scenario.

Context: Re-architecting monolithic systems with Microservices-based architecture is a common trend. Various companies are migrating to Microservices for different reasons. However, making such an important decision like re-architecting an entire system must be based on real facts and not only on gut feelings.\nObjective: The goal of this work is to propose an evidence-based decision support framework for companies that need to migrate to Microservices, based on the analysis of a set of characteristics and metrics they should collect before re-architecting their monolithic system.\nMethod: We conducted a survey done in the form of interviews with professionals to derive the assessment framework based on Grounded Theory.\nResults: We identified a set consisting of information and metrics that companies can use to decide whether to migrate to Microservices or not. The proposed assessment framework, based on the aforementioned metrics, could be useful for companies if they need to migrate to Microservices and do not want to run the risk of failing to consider some important information.

The emergence of microservice architecture has revolutionized software development practices by decentralizing components, facilitating scalability, and enabling agility in system design and deployment. There are some benefits of incorporating microservices instead of a single server, however, distributed components introduce extra constraints and complexities in maintaining data consistency as well. As microservices interact independently, coordinating data updates across multiple services becomes challenging, particularly in scenarios where transactional integrity is required. Distributed transactions are one of the solutions for ensuring data consistency across services. Regardless of effectiveness distributed transactions entail different trade-offs and performance implications. Those trade-offs are not always justified. This study highlights the need for a nuanced understanding of distributed transactions in microservices by revisiting challenges in managing distributed transactions within data storage systems. It also represents existing solutions to the different distributed transaction methods. In this paper, through experiments comparing microservices and monolithic systems, the impact of distributed transactions on system performance is evaluated, giving intuition about consequences when a single data source transaction is migrated to the distributed environment. This research also contributes to enhancing understanding and decision-making regarding the utilization of distributed transactions in a microservices architecture. Ultimately, this paper presents an optimized decision-framework for the application of distributed transactions in microservices architecture, aiming to simplify and expedite processes of software architecture for software engineers, solution architects, and developers.

Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be independently developed, tested, deployed, scaled, operated, and upgraded. However, aside from gaining agility, independent development, and scalability, how microservices affect the infrastructure costs is a major evaluation topic for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison with standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices, such as AWS Lambda, reduces infrastructure costs by 70% or more, and unlike microservices operated by cloud customers, these specialized services help to guarantee the same performance and response times as the number of users increases. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications, and include a discussion on how to replicate the results on other cloud providers.

Changes in the software development process were brought about by Containerization technologies that help organizations build and manage applications from separate standalone environments. The paper examines critical containerization solutions starting with Amazon Elastic Container Registry (ECR) and Docker. Containers and microservices architecture become enabled by the joint implementation of solutions that create loosely connected application services. This research aims to demonstrate Docker and ECR's value to contemporary development practices by explaining their strength in enhancing operational efficiency while simplifying expansion capabilities and running processes more efficiently. Dockeris an open-source platform that runs automatic application deployment through lightweight containers. Developers can merge all application files and essential components within one unit through its Docker system, ensuring performance consistency across multiple operating systems. Independent service operation remains supported by this system because it allows individual components to function during separate development and deployment phases. The Docker system requires minimal host resources because different containers work from a single node while eliminating traditional virtual machine resource needs. The advanced capability of Docker allows it to work effectively with Kubernetes orchestration tools for handling distributed container deployments. The core Kubernetes capabilities offer businesses tools to distribute workloads and locate services with automatic system scaling functions enabling thorough microservices control systems. The Docker extension of Amazon ECR enables systematic storage deployment and management of container images by running Docker container registry services. The platform allows developers to use Amazon Web Services (AWS) through ECR for fast application creation and deployment of containerized programs through an intuitive connection. Quick delivery of new versions depends on CI/CD pipelines that result from tight integration between development tools. Container image storage protection in ECR depends on security protocols with built-in encryption features and secure data access control systems. By adopting ECR, developers eliminate the need to handle infrastructures because they can focus on application development to achieve faster development times while attaining greater productivity. The combination of Docker and ECR provides organizations with enhanced solutions for managing microservices deployment and their CI/CD pipelines. Local application development with Docker enables developers to make identical production-ready applications. Amazon ECR operates as the primary storage facility for image containers, allowing simplified application deployment throughout multiple environments. This combination supports team collaboration because development groups can rapidly share container images with configuration files that ensure consistent versions for all members. Docker and Amazon ECR create substantial architectural changes for software systems that impact everysoftware architecture level. Through these tools, organizations achieve better innovation since they can successfully implement microservices architecture to enhance their scaling capabilities while operating efficiently and with agility. Cloud-native applications will elevate container significance because they provide developers with more efficient ways to deploy applications and better reliability. Organizations adopt Docker together with ECR to build successful software development and maintain competitive advantages within the current fast-evolving technological landscape. This paper emphasizes the critical nature of understanding and implementing these technologies because they shape essential components required in next-generation application creation and deployment arrangements.

Most developers still use the monolithic architecture, where all components of an application are combined into one integrated system, so each part depends on other components. The monolithic architecture has weaknesses, such as when a failure occurs in one component, all parts cannot be executed because each component relies on one other component. Microservices can be a solution to this, considering that in the microservices architecture, each element or service is created and put separately, so when a failure occurs in one component, other components will not be affected and can still run normally. This research aims to determine the implementation and performance comparison between monolithic architecture and microservices Architecture in the Agreeculture Market web app. Agreeculture Market is a web application that aims to facilitate the transaction process of agricultural commodities and make it easier for agricultural commodity producers to market their products. The measurement method used to measure the performance of both architectures is load testing using JMeter and performance tools from task manager and comparing the response time, throughput, disk usage, CPU usage, and memory usage of both used architectures. With two measurement schemes with Docker and without Docker, the result of this research is a performance comparison between the two architectures, where the backend application Agreeculture Market, which uses microservices architecture with Docker and API gateway, performs better than the monolithic architecture version. Conversely, the monolithic architecture performs better than the microservices architecture in the scheme without Docker and API gateway.

The article explores the fundamental requirements for microservice architecture in information systems and justifies its suitability for e-commerce information systems. The concept of micro-service architecture is based on developing software applications as a collection of independent modular services aligned with business capabilities and priorities. The characteristics of service interaction and factors influencing architectural decisions are discussed. Microservice architec-ture's flexibility to employ different technologies and platforms for individual services is high-lighted, allowing the adaptation of technical solutions to the unique needs of each service. Func-tional, non-functional, and business requirements for e-commerce systems are defined, and an architectural approach for web services is proposed. This approach is based on a client-server and microservice architecture, enabling both horizontal and vertical scaling of the server-side components. The architecture involves creating separate services to correspond to distinct busi-ness functions, facilitating further scalability and expansion since each microservice can be de-ployed independently. A list of business requirements for microservices in an e-commerce in-formation system, along with the specifications for their implementation, has been formed. An app architecture for the e-commerce system is presented, comprising a collection of micro-services that collectively constitute a comprehensive e-commerce system, and the tools for practical implementation are justified. This architecture effectively embodies the advantages of the microservices development approach, allowing the system to dynamically adapt to changing business needs. It provides scalability and simplifies system maintenance, rendering it more agile and prepared to meet the challenges of modern business and technology.

Microservices are fast becoming the predominant architectural style for orchestrating online services due to the advantages they can bestow over monolithic systems. However, as microservice architectures grow in size they quickly become complicated to understand and manage. Their characteristics raise the question as to whether they may behave like complex systems. In this paper, we use tools from graph theory to analyse the static and temporal dependency structure of a large-scale microservice architecture. We find that the dependency structure can fluctuate significantly at run time and further, that it can be clustered into distinct and persistent states with recognisable characteristics. Importantly, we show that these states can have functional implications for the performance of the microservice architecture. These early findings suggest that microservices may indeed behave like complex systems and, as such, would benefit from complex systems thinking when approaching their management and development.

Applications developed to fulfil distributed systems needs have been growing rapidly. Major evolutions have happened beginning with basic architecture relying on initiated request by a client to a processing side referred to as the server. Such architectures were not enough to cope up with the fast ever-increasing number of requests and need to utilize network bandwidth. Mobile agents attempted to overcome such drawbacks but did cope up for so long with the growing technology platforms. Service Oriented Architecture (SOA) then evolved to be one of the most successful representations of the client-server architecture with an added business value that provides reusable and loosely coupled services. SOA did not meet customers and business expectations as it was still relying on monolithic systems. Resilience, scalability, fast software delivery and the use of fewer resources are highly desirable features. Microservices architecture came to fulfil those expectations of system development, yet it comes with many challenges. This paper illustrates how distributed systems evolved from the traditional client-server model to the recently proposed microservices architecture. All architectures are reviewed containing brief definitions, some related work and reasoning of why they had to evolve. A feature comparison of all architectures is also provided.

This review paper explores the scalability of healthcare applications by adopting microservices architecture in cloud environments. As healthcare systems face increasing demands for efficient and flexible application performance, traditional monolithic architectures often struggle to accommodate fluctuating workloads and rapidly evolving technological landscapes. This paper highlights the fundamental characteristics of microservices, emphasizing their advantages over monolithic systems, particularly in enhancing agility and resource optimization. It further examines the role of cloud computing in providing scalable solutions, enabling healthcare organizations to allocate resources based on real-time demands dynamically. The paper discusses essential strategies and best practices for implementing microservices, addressing common challenges and offering solutions to facilitate a smooth transition. Ultimately, the findings underscore the transformative potential of microservices architecture in improving operational efficiency and patient care within the healthcare sector.

Context: microservices architecture enables organizations to develop an application as a suite of loosely coupled small and independent services that can be developed, tested, and deployed independently. Various organizations are re-architecting their existing monolithic systems with microservices architecture. However, re-architecting the entire system can bring some indefinite challenges. Objective: the goal of this research project is to investigate (i) the need for migration from monolithic to microservices architecture, (ii) architectural description for adopting microservices, (iii) refactoring tools and methods (iv) potential challenges while transforming to microservices, and (v) effective patterns and strategies to adopt microservice successfully. Methodology: the industrial empirical (interviews, case study, and questionnaire survey) approach will be used to meet the research objective. Possible outcomes: the expected outcomes would be (i) an evidence-based decision-making framework for transforming monolithic architecture (ii) strategies to refactor the monolithic architecture (iii) prioritization-based taxonomy of challenges while transforming to microservices. Finally, (iv) the development of a decision model for selecting patterns and strategies for successful implementation of the microservices system.