Microservice Systems Research Articles

ML and DL 4 Security Discussions in Microservices Systems

ML and DL 4 Security Discussions in Microservices Systems

Building orchestrated microservice systems using declarative business processes

The microservices architecture style proposes a solution for efficiently scaling computational resources and solving other problems presented in the traditional monolithic architecture. Despite providing benefits, microservices bring challenges when there is a need to manage business processes that expand across the boundaries of an individual microservice. Traditional approaches such as workflows are not suitable to manage business processes in microservice systems, and existing solutions have limitations in dealing with the dynamic location of microservices. To fill that gap, this work proposes the use of declarative business processes to facilitate the modeling and execution of microservice orchestration from the perspective of data flow processes. To this end, we use Beethoven, a platform composed of a reference architecture, and a domain-specific language for expressing microservice communication flows. Finally, we demonstrate the feasibility of Beethoven in orchestrating an existing microservice application and assess its impact on the performance of the orchestrated application.

Design and Implementation of Microservices System Based on Domain-Driven Design

Design and Implementation of Microservices System Based on Domain-Driven Design

An Intelligent Anomaly Detection Scheme for Micro-Services Architectures With Temporal and Spatial Data Analysis

Service-oriented 5G mobile systems are commonly believed to reshape the landscape of the Internet with ubiquitous services and infrastructures. The micro-services architecture has attracted significant interests from both academia and industry, offering the capabilities of agile development and scale capacity. The emerging mobile edge computing is able to firmly maintain efficient resource utility of 5G systems, which can be empowered by micro-services. However, such capabilities impose significant challenges on micro-services system management. Although substantial data are produced for system maintenance, the interleaved temporal-spatial information has not been fully exploited. Additionally, the flooding data impose heavy pressures on automatic analysis tools. Automated digestion of data is in an urgent need for system maintenance. In this paper, we propose a new learning-based anomaly detection framework for service-provision systems with micro-services architectures using service execution logs (temporally) and query traces (spatially). It includes two major parts: logging and tracing representation, and two-stage identification via a sequential model and temporal-spatial analysis. The experimental results show that the temporal-spatial features can accurately capture the nature of operational data. The proposed framework performs well on anomaly detection, and helps gain in-depth insights of large-scale systems.

ML 4 Security Points in OSS Microservices Systems: Dataset and Script

ML 4 Security Points in OSS Microservices Systems: Dataset and Script

Microscaler: Cost-Effective Scaling for Microservice Applications in the Cloud With an Online Learning Approach

Recently, the microservice becomes a popular architecture to construct cloud native systems due to its agility. In cloud native systems, autoscaling is a key enabling technique to adapt to workload changes by acquiring or releasing the right amount of computing resources. However, it becomes a challenging problem in microservice applications, since such an application usually comprises a large number of different microservices with complex interactions. When the performance decreases due to an unpredictable workload peak, it is difficult to pinpoint the scaling-needed services which need to scale out and evaluate how many resources they need. In this article, we present a novel system named <i>Microscaler</i> to automatically identify the scaling-needed services and scale them to meet the Service Level Agreement (SLA) with an optimal cost for microservice applications. <i>Microscaler</i> first collects the quality of service (QoS) metrics in the service mesh enabled microservice infrastructure. Then, it determines under-provisioning or over-provisioning service instances along the service dependency graph with a novel scaling-needed service criterion named <i>service power</i>. The service dependency graph could be obtained by correlating each request flow in the service mesh. By combining an online learning approach and a step-by-step heuristic approach, <i>Microscaler</i> can precisely reach the optimal service scale meeting the SLA requirements. The experimental evaluations in a microservice benchmark show that <i>Microscaler</i> achieves an average 93 percent precision in scaling-needed service determination and converges to the optimal service scale faster than several state-of-the-art methods. Moreover, <i>Microscaler</i> is lightweight and flexible enough to work in a large-scale microservice system.

Automatic Analysis of Complex Interactions in Microservice Systems

Interactions in microservice systems are complex due to three dimensions: numerous asynchronous interactions, the diversity of asynchronous communication, and unbounded buffers. Analyzing such complex interactions is challenging. In this paper, we propose an approach for interaction analysis using model checking techniques, which is supported by the Process Analysis Toolkit (PAT) tool. First, we use Labeled Transition Systems (LTSs) to model interaction behaviors in microservice systems as sequences of send actions under synchronous and asynchronous communications. Second, we introduce a notion of correctness called “interaction soundness” which is considered as a minimal requirement for microservice systems. Third, we propose an encoding of LTSs into the CSP# process algebra for automatic verification of the property interaction soundness. The experimental results show that our approach can automatically and effectively identify interaction faults in microservice systems.

Delta Debugging Microservice Systems with Parallel Optimization

Microservice systems are complicated due to their runtime environments and service communications. Debugging a failure involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimalize failure-inducing deltas of circumstances (e.g., deployment, environmental configurations). Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas during delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimalize, our approach defines a set of circumstance dimensions that can affect the execution of microservice systems. To automate the testing of deltas, our approach includes the design of an infrastructure layer for automating deployment and manipulation of microservice systems. To optimize the delta debugging process, our approach includes the design of parallel execution for delta testing tasks. Our evaluation shows that our approach is scalable and efficient with the provided infrastructure resources and the designed parallel execution for optimization. Our experimental study on a medium-size microservice benchmark system shows that our approach can effectively identify failure-inducing deltas that help diagnose the root causes.

Fault Analysis and Debugging of Microservice Systems: Industrial Survey, Benchmark System, and Empirical Study

The complexity and dynamism of microservice systems pose unique challenges to a variety of software engineering tasks such as fault analysis and debugging. In spite of the prevalence and importance of microservices in industry, there is limited research on the fault analysis and debugging of microservice systems. To fill this gap, we conduct an industrial survey to learn typical faults of microservice systems, current practice of debugging, and the challenges faced by developers in practice. We then develop a medium-size benchmark microservice system (being the largest and most complex open source microservice system within our knowledge) and replicate 22 industrial fault cases on it. Based on the benchmark system and the replicated fault cases, we conduct an empirical study to investigate the effectiveness of existing industrial debugging practices and whether they can be further improved by introducing the state-of-the-art tracing and visualization techniques for distributed systems. The results show that the current industrial practices of microservice debugging can be improved by employing proper tracing and visualization techniques and strategies. Our findings also suggest that there is a strong need for more intelligent trace analysis and visualization, e.g., by combining trace visualization and improved fault localization, and employing data-driven and learning-based recommendation for guided visual exploration and comparison of traces.

A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems

Recently, microservice has been a popular architecture to construct cloud-native systems. This novel architecture brings agility and accelerates the software development process significantly. However, it is not easy to manage and operate microservice systems due to their scale and complexity. Many approaches are proposed to automatically operate microservice systems such as anomaly detection. Nevertheless, those methods cannot be sufficiently validated and compared due to a lack of real microservice systems, which leads to the slow process of intelligent operation. These challenges inspire us to build a system named “VWR”, a framework of Virtual War Room for operating microservice applications which allows users to simulate their microservice architectures with low overhead and inject multiple types of faults into the microservice system with chaos engineering. VWR can mimic user requests and record the end-to-end tracing data (i.e., service call chains) for each request in a way consistent with OpenTracing. With easily designed tests and the produced streaming tracing data, the users can validate the performance of their intelligent operation algorithms and improve the algorithms as needed. Besides, based on the streaming tracing data generated by VWR, we introduce a novel unsupervised anomaly detection algorithm based on Matrix Sketch and set it as a default intelligent operation algorithm in VWR. This algorithm can detect anomalies by analyzing high-dimensional performance data collected from a microservice system in a streaming manner. The experimental result in VWR shows that the matrix sketch based method can precisely detect anomalies in microservice systems and outperform some widely used anomaly detection methods such as isolation forest in some scenario. We believe more approaches on the intelligent operation of microservice systems can be constructed based on VWR.

Design and Implement of Microservice System for Edge Computing

Abstract In the Internet of Things (IoT) scenario, the hardware resources in the edge computing paradigm are limited, so the applications in the cloud computing paradigm are not suitable for edge computing. New system architecture and key technologies are needed for application implement and deployment in edge computing. Based on the hardware performance constraints and scene functional requirements under edge computing, this paper designs and develops a microservice system for edge computing. So it introduces the design of microservice system architecture for edge computing. And it analyzes the key technologies of the system. It selects the PostgreSQL database to achieve data storage, the Flask framework to build back-end services, and the Vue.js framework to build front-end services. Meanwhile, docker containers are used for system deployment and testing. The test results prove that the system has perfect functions and stable operation, which is very suitable for edge devices with limited resources. According to the start-up time and resource consumption of the project, the distributed deployment of applications has better performance than the unified deployment.

QoS-Constrained Service Selection for Networked Microservices

In the microservice system, microservices are generally deployed as many microservice instances connected by a network system, and there are several composite services (CSs) in the system, each of which consists of several tasks that need to be executed on instances and has some Quality of Service (QoS) constraints to represent the user requirement for QoS. At runtime, it is necessary to select the microservice instance for each task under constraints and form an execution path of the CS. However, traditional service selection methods focus on the QoS optimization of a single CS, while ignoring sharing and competition of instances. To address this problem, this paper presents a Microservice Service Selection algorithm (MSS) based on list scheduling. Firstly, a workflow model is employed to describe the CS and to analyze the processing speed of instances, the transferring speed of the network and the degree of task concurrency, to calculate the sub-deadline of each task. Then, according to the sub-deadline and other information, the scheduling urgency of each task is calculated and updated in real-time. Finally, two service selection strategies based on the sub-deadline and urgency are proposed to complete the microservice instances selection and constitute the service path. Experiments have been carried out using the standard workflow examples of real-world applications. The results show that the proposed selection strategies can effectively improve the performance of service selection in network systems.

A SCADA platform architecture for cloud-based micro-service system with real time data process

This paper proposes a SCADA platform architecture for cloud-based micro-service system with real time data process. Firstly, the main functions of the “collection, processing, storage, interaction and display” of the SCADA system are analyzed. Then, based on the micro-service formation principle, the technical solutions of cloud computing and micro-service integration are illustrated. The functions of cloud-based SCADA micro-service system, front collection subsystem and front collection service are analyzed. Finally, the application scenario of the architecture is illustrated.

Joint optimization of service request routing and instance placement in the microservice system

Microservice architecture is a promising architectural style. It decomposes monolithic software into a set of loosely coupled containerized microservices and associates them into multiple microservice chains to serve service requests. The new architecture creates flexibility for service provisioning but also introduces increased energy consumption and low service performance. Efficient resource allocation is critical. Unfortunately, existing solutions are designed at a coarse level for virtual machine (VM)-based clouds and not optimized for such chain-oriented service provisioning. In this paper, we study the resource allocation optimization problem for service request routing and microservice instance placement, so as to jointly reduce both resource usage and chains’ end-to-end response time for saving energy and guaranteeing Quality of Service (QoS). We design detailed workload models for microservices and chains and formulate the optimization problem as a bi-criteria optimization problem. To address it, a three-stage scheme is proposed to search and optimize the trade-off decisions, route service requests into instances and deploy instances to servers in a balanced manner. Through numerical evaluations, we show that while assuring the same QoS, our scheme performs significantly better than and faster than benchmarking algorithms on reducing energy consumption and balancing load.

Research on Microservice Anomaly Detection Technology Based on Conditional Random Field

Recently, the existing microservice anomaly detection methods which mainly monitor the system metrics and analyze the system logs have low efficiency and consume much time. This paper proposes a microservice anomaly detection method based on Conditional Random Field(CRF). The method takes the collected system parameters such as CPU utilization, memory utilization, bandwidth occupancy as the characteristic values of the observation sequence, and labels the abnormal types of the sequences corresponding to the feature values, and then creates the microservice fault matrix. Operators can quickly retrieve the specific anomaly information of the microservice error according to the microservice fault matrix. Compared with the traditional Hidden Markov Model-based detection method, this method can find the abnormal sequence corresponding to the observation sequence in the global scope. The experimental results show that the method proposed by this paper can accurately find the faults in the microservice system, and the accuracy and recall rate are relatively high.

Dynamic Priority based Weighted Scheduling Algorithm in Microservice System

With the development of cloud computing and container virtualization, the microservice architecture has received more and more attention. While microservices bring many benefits, they also bring some challenges. A user’s request may require multiple microservices to collaborate, and chained invocations between microservices result in higher latency than a monolithic architecture. In this paper, we propose a dynamic priority based weighted scheduling algorithm for the problem that the latency of chained-service requests is too long in the case of high concurrency. Experiments show that the algorithm can reduce the latency of chained-service requests effectively in the case of little impact on the latency of leaf-service requests.

Practical Defense-in-depth Solution for Microservice Systems

Microservices are a widely deployed pattern for implementing large-scale distributed systems. However, in order to harden the overall system and when crossing datacenter boundaries, the authenticity and confidentiality of microservice calls have to be secured even for internal calls. In practice, however, in many cases no internal security mechanisms are employed mainly due to the increased complexity on backend side. This complexity arises as result of standard security mechanisms like TLS requiring secrets for each involved microservice. Building on previous work [19], in this paper we present a novel communication architecture based on roles that on the one hand guarantees a high level of security and on the other hand remains easy to manage. The approach provides encryption, forward secrecy and protection against replay attacks even for out-of-order communication.

Dynamic Microservices to Create Scalable and Fault Tolerance Architecture

One of the industry’s most important trends in enterprise architecture is related to the use of microservices, to the detriment of monolithic architectures, which are beginning to no longer be used. Due the cloud-native architectures the deployment of microservices systems is more productive, flexible and cost effective. Anyway, a lot of companies started to migrate from one type of architecture to another, but it is still in the early phase. In this paper we address the challenges raised by the need to develop a scalable and fault tolerance system based on microservices. In our experiments we consider two types of microservices, simple and extended and the proposed solution proves to be an innovative one especially based on its dynamic behavior.

Availability optimization of consistency and availability-based micro-service systems through elastic scheduling of container resources

Availability optimization of consistency and availability-based micro-service systems through elastic scheduling of container resources

Migrating Enterprise Legacy Source Code to Microservices: On Multitenancy, Statefulness, and Data Consistency

Microservice migration is a promising technique to incrementally modernize monolithic legacy enterprise applications and enable them to exploit the benefits of cloud-computing environments. This article elaborates on three challenges of microservice migration: multitenancy, statefulness, and data consistency. The authors show how to identify each of these challenges in legacy code and explain refactoring and architectural pattern-based migration techniques relevant to microservice architectures. They explain how multitenancy enables microservices to be utilized by different organizations with distinctive requirements, why statefulness affects both the availability and reliability of a microservice system, and why data consistency challenges are encountered when migrating legacy code that operates on a centralized data repository to microservices operating on decentralized data repositories. They also explain the interdependencies between multitenancy, statefulness, and data consistency.