Transforming Digital Experiences: The Evolution of Digital Experience Platforms (DXPs) from Monoliths to Microservices: A Practical Guide

The research aims to explore the transition from monolithic Digital Experience Platforms (DXPs) to Microservices-based DXPs, addressing scalability challenges. The study systematically decomposes monolithic structures into Microservices, emphasizing business capability and subdomain decomposition. Concrete insights, challenges, and solutions encountered during this transformation process are presented. The research contributes valuable insights into the challenges and benefits of adopting Microservices in DXPs. Results highlight the importance of architectural patterns and strategic scaling dimensions for improved performance and scalability. The case study on Backbase’s Engagement Banking Platform showcases successful implementation, providing flexibility, integration, and efficient development in the evolving DXP landscape.

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex systems.Microservices Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed independently.This methodology brings numerous benefits like scalability,resilience,flexibility in development,faster time to market,etc.and the advantages;Microservices bring some challenges too.Multiple microservices need to be invoked one by one as a chain.In most applications,more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s request.It results in competition for resources and the need for more inter-service communication among the services,which increases the overall latency of the application.A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests.A machine learning technique is followed to predict the weighting time of different types of requests.The communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting time.Experiments were done for both interactive as well as non interactive workloads to test the effectiveness of the solution.The approach has been proved to be very effective in reducing latency in the case of long service chains.

Towards a Framework for Evaluating Technologies for Implementing Microservices Architectures

Microservice architecture is an architectural style, which allows structuring software as a suite of fine-grained services, each running in its process and deployed independently. Knowing the strengths and limitations of this architectural style, the development team is responsible to select the appropriate technologies which guarantee the consistency between the implementation and the design. This study proposes an evaluation framework which consists of a set of evaluation criteria that are architectural patterns recognized by the community and covering all the implementation aspects of software;and an evaluation function which combines these criteria for a given technology to determine its compatibility score with the microservice style, while taking into account the specific requirements of the software under development. Applying this approach to Spring Boot and JAVA EE technologies, we found that Spring Boot scores 96.3% while JAVA EE scores 44.4%. These scores reflect the effort required to conform software with the principles of this development style.

Accelerator network monitoring system based on microservices architecture

Purpose The BEPCII(Upgrade project of Beijing Electron Positron Collider)is a high-performance accelerator,composed of a vast array of front-end devices.When the accelerator is operational,high-speed,real-time data communication,and information exchange are necessary among its various subsystems.The network system of BEPCII is a critical infrastructure that ensures the normal operation and data transmission of the accelerator.Switches,as the core devices for constructing the accelerator’s control network and realizing data transmission and communication,play a vital role in the reliability and stability of the accelerator’s functioning.This study aims to design an accelerator network monitoring system based on a microservice architecture to monitor the data information of network devices such as switches.Methods and Results Remote management of switch configurations and monitoring of switch port statuses are implemented via the simple network management protocol.The working status data of the switches obtained are saved to a MySQL database to facilitate statistical queries and data analysis when called upon.Utilizing the Nameko microservice framework,different services such as storage,querying,and analysis are managed and invoked separately to prevent process waiting due to excessive data volumes or too many services being called simultaneously.Additionally,a variety of functional web front-end interfaces has been designed based on the Flask framework to improve human–machine interaction.By testing two switches,the system achieved monitoring of the switches’CPU usage,temperature,port status,and port traffic.Storage and querying of historical data were also enabled through the database,making data analysis more accessible.This study has fulfilled the monitoring and management requirements for the control network’s switch information through testing.The system will subsequently be deployed in the control network system of BEPCII.

A search-based identification of variable microservices for enterprise SaaS

Recently,SaaS applications are developed as a composition of microservices that serve diverse tenants having similar but different requirements,and hence,can be developed as variability-intensive microservices.Manual identification of these microservices is difficult,time-consuming,and costly,since,they have to satisfy a set of quality metrics for several SaaS architecture configurations at the same time.In this paper,we tackle the multi-objective optimization problem of identifying variable microservices aiming optimal granularity(new metric proposed),commonality,and data convergence,with a search-based approach employing the MOEA/D algorithm.We empirically and experimentally evaluated the proposed method following the Goal-Question-Metric approach.The results show that the method is promising in identifying fully consistent,highly reusable,variable microservices with an acceptable multi-tenancy degree.Moreover,the identified microservices,although not structurally very similar to those identified by the expert architects,provide design quality measures(granularity,etc.)close to(and even better than)the experts.

MPDP: A Probabilistic Architecture for Microservice Performance Diagnosis and Prediction

In recent years,container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources.However,there is a noticeable absence of techniques for predicting microservice performance in current research,which impacts cloud service users’ability to determine when to provision or de-provision microservices.Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention,which potentially leads to user confusion.In this paper,we propose,develop,and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction(MPDP).MPDP considers various factors such as response time,throughput,CPU usage,and othermetrics to dynamicallymodel interactions betweenmicroservice performance indicators for diagnosis and prediction.Using experimental data fromourmonitoring tool,stakeholders can build various networks for probabilistic analysis ofmicroservice performance diagnosis and prediction and estimate the best microservice resource combination for a given Quality of Service(QoS)level.We generated a dataset of microservices with 2726 records across four benchmarks including CPU,memory,response time,and throughput to demonstrate the efficacy of the proposed MPDP architecture.We validate MPDP and demonstrate its capability to predict microservice performance.We compared various Bayesian networks such as the Noisy-OR Network(NOR),Naive Bayes Network(NBN),and Complex Bayesian Network(CBN),achieving an overall accuracy rate of 89.98%when using CBN.

TCLBM:A Task Chain-Based Load Balancing Algorithm for Microservices

The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture.Scalability is one of the most attractive features of microservices.Scaling in the microservices architecture requires the scaling of specified services only,rather than the entire application.Scaling services can be achieved by deploying the same service multiple times on different physical machines.However,problems with load balancing may arise.Most existing solutions of microservices load balancing focus on individual tasks and ignore dependencies between these tasks.In the present paper,we propose TCLBM,a task chainbased load balancing algorithm for microservices.When an Application Programming Interface(API)request is received,TCLBM chooses target services for all tasks of this API call and achieves load balancing by evaluating the system resource usage of each service instance.TCLBM reduces the API response time by reducing data transmissions between physical machines.We use three heuristic algorithms,namely,Particle Swarm Optimization(PSO),Simulated Annealing(SA),and Genetic Algorithm(GA),to implement TCLBM,and comparison results reveal that GA performs best.Our findings show that TCLBM achieves load balancing among service instances and reduces API response times by up to 10%compared with existing methods.

Informer:Irregular traffic detection for containerized microservices RPC in the real world

Containerized microservices have been widely deployed in the industry.Meanwhile,security issues also arise.Many security enhancement mechanisms for containerized microservices require predefined rules and policies.However,it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data.Hence,automatic policy generation becomes indispensable.In this paper,we focus on the automatic solution for the security problem:irregular traffic detection for RPCs.We propose Informer,a two-phase machine learning framework to track the traffic of each RPC and automatically report anomalous points.We first identify RPC chain patterns using density-based clustering techniques and build a graph for each critical pattern.Next,we solve the irregular RPC traffic detection problem as a prediction problem for attributed graphs with time series by leveraging spatial-temporal graph convolution networks.Since the framework builds multiple models and makes individual predictions for each RPC chain pattern,it can be efficiently updated upon legitimate changes in any graphs.In evaluations,we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from a large Kubernetes system for two weeks.We provide two case studies of detected real-world threats.As a result,our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario,which demonstrates the effectiveness of Informer.Furthermore,we extensively evaluated the risk of adversarial attacks for our prediction model under different reality constraints and showed that the model is robust to such attacks in most real-world scenarios.

Current Trends in the Management of Distributed Transactions in Micro-Services Architectures: A Systematic Literature Review

In the evolving landscape of software engineering, Microservice Architecture (MSA) has emerged as a transformative approach, facilitating enhanced scalability, agility, and independent service deployment. This systematic literature review (SLR) explores the current state of distributed transaction management within MSA, focusing on the unique challenges, strategies, and technologies utilized in this domain. By synthesizing findings from 16 primary studies selected based on rigorous criteria, the review identifies key trends and best practices for maintaining data consistency and integrity across microservices. This SLR provides a comprehensive understanding of the complexities associated with distributed transactions in MSA, offering actionable insights and potential research directions for software architects, developers, and researchers.

A Dynamic Leader Election Algorithm for Decentralized Networks

Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after the leader has failed and has a high overhead in performance and state recovery. Further, these algorithms are not generally applicable to cloud-based native microservices-based applications where the resources available to the group and resources participating in a group continuously change and the current leader may exit the system with prior knowledge of the exit. Our proposed algorithm, the dynamic leader selection algorithm, provides several benefits through selection (not, election) of a set of future leaders which are then alerted prior to the failure of the current leadership and handed over the leadership. A specific illustration of this algorithm is provided with reference to a peer-to-peer distribution of autonomous cars in a 5G architecture for transportation networks. The proposed algorithm increases the efficiencies of applications that use the leader election algorithm and finds broad applicability in microservices-based applications.

Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.

Automatic data archiving and visualization at HLS-Ⅱ

The control system of Hefei Light Source II(HLS-Ⅱ) is a distributed system based on the experimental physics and industrial control system(EPICS). It is necessary to maintain the central configuration files for the existing archiving system. When the process variables in the control system are added, removed, or updated, the configuration files must be manually modified to maintain consistency with the control system. This paper presents a new method for data archiving, which realizes the automatic configuration of the archiving parameters. The system uses microservice architecture to integrate the EPICS Archiver Appliance and Rec Sync. In this way, the system can collect all the archived meta-configuration from the distributed input/output controllers and enter them into the EPICS Archiver Appliance automatically. Furthermore, we also developed a web-based GUI to provide automatic visualization of real-time and historical data. At present,this system is under commissioning at HLS-Ⅱ. The results indicate that the new archiving system is reliable and convenient to operate. The operation mode without maintenance is valuable for large-scale scientific facilities.

An Approach for Enabling Intelligent Edge Gateway Based on Microservice Architecture in Cloud Manufacturing

Cloud manufacturing has become a reality. It requires sensing and capturing heterogeneous manufacturing resources and extensive data analysis through the industrial internet. However,the cloud computing and serviceoriented architecture are slightly inadequate in dynamic manufacturing resource management. This paper integrates the technology of edge computing and microservice and develops an intelligent edge gateway for internet of thing(IoT)-based manufacturing. Distributed manufacturing resources can be accessed through the edge gateway,and cloud-edge collaboration can be realized. The intelligent edge gateway provides a solution for complex resource ubiquitous perception in current manufacturing scenarios. Finally,a prototype system is developed to verify the effectiveness of the intelligent edge gateway.

Intelligent Microservice Based on Blockchain for Healthcare Applications

Nowadays,the blockchain,Internet of Things,and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing,and analytics approaches,including improved service platforms.Nevertheless,one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment.Improved data analytics model not only provides support insights in IoT data but also fosters process productivity.Designing a robust IoT-based secure analytic model is challenging for several purposes,such as data from diverse sources,increasing data size,and monolithic service designing techniques.This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment.The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT.To demonstrate the proposed model effectiveness,we have used the IoT fitness application as a case study.Based on the designed predictive analytic model,a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness.Moreover,the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan.Finally,the proposed model is evaluated using Hyperledger Caliper in terms of latency,throughput,and resource utilization with varying peers and orderer nodes.The experimental result shows that the proposed model is applicable for diverse resourceconstrained blockchain-enabled IoT applications and extensible for several IoT scenarios.

微服务架构在SWIM信息交换服务中的应用研究

广域信息管理SWIM(System Wide Information Management)是下一代空中交通管理系统NGATM中的核心技术之一,国际民航组织定义的SWIM全球互操作架构包括网络连接、SWIM基础设施、信息交换模型、信息交换服务和基于SWIM运行的业务系统等5层结构。微服务是一种“细粒度”的SOA(Service-Oriented Architecture面向服务架构),它提倡将单一应用程序划分成一组小的服务,服务之间相互协调、互相配合,为用户提供最终价值。论文重点讨论微服务在SWIM信息交换服务中的应用。

Approach to Anomaly Detection in Microservice System with Multi-Source Data Streams

Microservices have become popular in enterprises because of their excellent scalability and timely update capabilities.However,while fine-grained modularity and service-orientation decrease the complexity of system development,the complexity of system operation and maintenance has been greatly increased,on the contrary.Multiple types of system failures occur frequently,and it is hard to detect and diagnose failures in time.Furthermore,microservices are updated frequently.Existing anomaly detection models depend on offline training and cannot adapt to the frequent updates of microservices.This paper proposes an anomaly detection approach for microservice systems with multi-source data streams.This approach realizes online model construction and online anomaly detection,and is capable of self-updating and self-adapting.Experimental results show that this approach can correctly identify 78.85%of faults of different types.

Vertical Handover Decision for Mobile IoT Edge Gateway Using Multi-Criteria and Fuzzy Logic Techniques

Internet of Things (IoT) is ubiquitous, including objects or devices communicating through heterogenous wireless networks. One of the major challenges in mobile IoT is an efficient vertical handover decision (VHD) technique between heterogenous networks for seamless connectivity with constrained resources. The conventional VHD approach is mainly based on received signal strength (RSS). The approach is inefficient for vertical handover, since it always selects the target network with the strongest signal without taking into consideration of factors such as quality of service (QoS), cost, delay, etc. In this paper, we present a hybrid approach by integrating the multi-cri- teria based VHD (MCVHD) technique and an algorithm based on fuzzy logic for efficient VHD among Wi-Fi, Radio and Satellite networks. The MCVHD provides a lightweight solution that aims to achieving seamless connectivity for mobile IoT Edge Gateway over a set of heterogeneous networks. The proposed solution is evaluated in real time using a testbed containing real IoT devices. Further, the testbed is integrated with lightweight and efficient software techniques, e.g., microservices, containers, broker, and Edge/Cloud techniques. The experimental results show that the proposed approach is suitable for an IoT environment and it outperforms the conventional RSS Quality based VHD by minimizing handover failures, unnecessary handovers, handover time and cost of service.

Plotinus:A Satellite Internet Digital Twin System

The development of space-air-ground integrated networks (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response, and scalability. This paper proposes a digital twin system based on microservices for satellite Internet emulation,namely Plotinus,which aims to solve these problems. Plotinus features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables replacing of path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic,enhancing practical network models. The evaluation result shows Plotinus’s effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus’s role as a vital tool for developing and analyzing SAGIN systems, offering a cross-layer,real-time,and scalable digital twin system.

Resource scheduling techniques in cloud from a view of coordination:a holistic survey

Nowadays,the management of resource contention in shared cloud remains a pending problem.The evolution and deployment of new application paradigms(e.g.,deep learning training and microservices)and custom hardware(e.g.,graphics processing unit(GPU)and tensor processing unit(TPU))have posed new challenges in resource management system design.Current solutions tend to trade cluster efficiency for guaranteed application performance,e.g.,resource over-allocation,leaving a lot of resources underutilized.Overcoming this dilemma is not easy,because different components across the software stack are involved.Nevertheless,massive efforts have been devoted to seeking effective performance isolation and highly efficient resource scheduling.The goal of this paper is to systematically cover related aspects to deliver the techniques from the coordination perspective,and to identify the corresponding trends they indicate.Briefly,four topics are involved.First,isolation mechanisms deployed at different levels(micro-architecture,system,and virtualization levels)are reviewed,including GPU multitasking methods.Second,resource scheduling techniques within an individual machine and at the cluster level are investigated,respectively.Particularly,GPU scheduling for deep learning applications is described in detail.Third,adaptive resource management including the latest microservice-related research is thoroughly explored.Finally,future research directions are discussed in the light of advanced work.We hope that this review paper will help researchers establish a global view of the landscape of resource management techniques in shared cloud,and see technology trends more clearly.

Performance optimization for cloud computing systems in the microservice era:state-of-the-art and research opportunities

With the demand of agile development and management,cloud applications today are moving towards a more fine-grained microservice paradigm,where smaller and simpler functioning parts are combined for providing end-to-end services.In recent years,we have witnessed many research efforts that strive to optimize the performance of cloud computing system in this new era.This paper provides an overview of existing works on recent system performance optimization techniques and classify them based on their design focuses.We also identify open issues and challenges in this important research direction.