Container-based Microservices Research Articles

A Survey on Observability of Distributed Edge & Container-Based Microservices

Edge computing is proposed as a technical enabler for meeting emerging network technologies (such as 5G and Industrial Internet of Things), stringent application requirements and key performance indicators (KPIs). It aims to alleviate the problems associated with centralized cloud computing systems by placing computational resources to the network’s edge, closer to the users. However, the complexity of distributed edge infrastructures grows when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Observability aims to support operators in managing and operating complex distributed infrastructures and microservices architectures by instrumenting end-to-end runtime performance. To the best of our knowledge, no survey article has been recently proposed for distributed edge and containerized microservices observability. Thus, this article surveys and classifies state-of-the-art solutions from various communities. Besides surveying state-of-the-art, this article also discusses the observability concept, requirements, and design considerations. Finally, we discuss open research issues as well as future research directions that will inspire additional research in this area.

Zeus: Improving Resource Efficiency via Workload Colocation for Massive Kubernetes Clusters

With the popularity of container-based microservices and cloud-native architectures, Kubernetes has established itself as the de facto standard for container orchestration. Kubernetes is known for its advantage in easy deployment and operations for applications; however, it suffers from low resource utilization, incurring high server provisioning and operational costs, due to following reasons. First, it is common practice for latency-sensitive services to be over-provisioned for the peak load: Kubernetes might consider such peak-load provisioning as constant, even though the actual resource utilization is low. Second, the isolation between different containers is poor and cannot prevent performance degradation when best-effort jobs and latency-sensitive services run together. Users may have to run these two classes of workloads on separate machines to avoid interference, resulting in higher provisioning costs. This paper presents a highly scalable cluster scheduling system named Zeus, which is designed based on Kubernetes extension mechanisms. Zeus achieves safe colocation of best-effort jobs and latency-sensitive services. Furthermore, Zeus schedules best-effort jobs based on the real server utilization and can adaptively allocate resources between the two classes of workloads. In addition, Zeus enhances container isolation by coordinating software and hardware isolation features. As a result, Zeus can effectively improve the resource utilization of Kubernetes clusters. We discuss the design and implementation of Zeus and evaluate its effectiveness using latency-sensitive services and best-effort jobs in a massive production environment. The results show that by colocating latency-sensitive services with best-effort jobs, Zeus can increase the average CPU utilization from 15% to 60% without SLO violations.

An Approach for Reconstructing Applications to Develop Container-Based Microservices

Microservices are small-scale services that can operate independently. An application consisting of microservice units can be developed independently as a service unit, and it can handle individual logic without being affected by other services. In addition, it is possible to rapidly distribute the configured microservices by a container, and a container orchestration technology that manages the distributed multiple containers can be realized; thus, it is possible to update and distribute the microservices separately. Therefore, many companies are moving away from existing monolithic structures and attempting to switch to microservices. In this paper, we present a method for reconstructing a monolithic application into a container-based microservice unit. The microservices of data units are derived through the collection and analysis of monolithic design data. Furthermore, we propose a method to generate a template script based on deployment design data so that the derived microservice and support distribution can be implemented in a container environment. The results of a case study conducted verified that the container-based microservices deployed in this study work properly. In addition, for the development of monolithic applications and the development of container-based microservices presented in this paper, we confirmed that developing on the basis of microservices is efficient by conducting execution time performance evaluation for API calls at various iterations. Finally, we show that microservices constructed using the proposed method have higher reusability than those constructed using existing methods.

Towards Cost-Efficient Edge Intelligent Computing With Elastic Deployment of Container-Based Microservices

With the tremendous growth of the Internet of Things (IoT), big data, and artificial intelligence (AI), the edge computing-based service paradigm has been introduced to meet the increasing demand of applications. To provide efficient computing services at the network edge, the algorithms and applications are generally deployed based on the container-based microservice strategy, which significantly impacts the system efficiency and QoS. Considering the fundamental system uncertainties, including the dynamic workload and service rate, we investigate how to minimize the long-term system cost through the elastic microservice deployment in this paper. To this end, we formulate the container-based microservice deployment as a stochastic optimization problem to minimize the system cost while maintaining the system QoS and stability. We develop a cost-aware elastic microservice deployment algorithm to solve the formulated problem, which balances the tradeoff between system cost and QoS. Our algorithm makes the real-time decisions based on current queue backlogs and system states without predicting the future knowledge. Finally, we conduct the theoretical analysis and extensive simulations based on data traces from the ResNet-50 model-based visual recognition application. The results demonstrate that our algorithm outperforms the baseline strategies with respect to the system cost, queue backlogs, and the number of Pod replicas.

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

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

Micro-Id-Gym: Identity Management Workouts with Container-Based Microservices

Identity Management (IdM) solutions are increasingly important for building trust in current and future digital ecosystems. Unfortunately, not only their secure deployment but even their usage are non-trivial activities that require a good level of security awareness. For this, we introduce Micro-Id-Gym, an easy to configure training environment in which users can develop hands-on experiences on how IdM solutions work and better understand the underlying security issues.