相关论文: Resilient Auto-Scaling of Microservice Architectur…
Microservice architectures have gained prominence in both academia and industry, offering enhanced agility, reusability, and scalability. To simplify scaling operations in microservice architectures, container orchestration platforms such…
Microservices architecture offers various benefits, including granularity, flexibility, and scalability. A crucial feature of this architecture is the ability to autoscale microservices, i.e., adjust the number of replicas and/or manage…
In cloud-native systems, Kubernetes clusters with interdependent services often face challenges to their operational resilience due to poor workload management issues such as resource blocking, bottlenecks, or continuous pod crashes. These…
An effective auto-scaling framework is essential for microservices to ensure performance stability and resource efficiency under dynamic workloads. As revealed by many prior studies, the key to efficient auto-scaling lies in accurately…
The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the…
Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human…
Existing state-of-the-art vertical autoscalers for containerized environments are traditionally built for cloud applications, which might behave differently than HPC workloads with their dynamic resource consumption. In these environments,…
Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely…
While cloud environments and auto-scaling solutions have been widely applied to traditional monolithic applications, they face significant limitations when it comes to microservices-based architectures. Microservices introduce additional…
Distributed Stream Processing (DSP) systems are capable of processing large streams of unbounded data, offering high throughput and low latencies. To maintain a stable Quality of Service (QoS), these systems require a sufficient allocation…
Serverless platforms such as Kubernetes are increasingly adopted in high-performance computing, yet autoscaling remains challenging under highly dynamic and heterogeneous workloads. Existing approaches often rely on uniform reactive…
Microservice architecture has become a dominant paradigm in application development due to its advantages of being lightweight, flexible, and resilient. Deploying microservice applications in the container-based cloud enables fine-grained…
Resource autoscaling mechanisms in cloud environments depend on accurate performance metrics to make optimal provisioning decisions. When infrastructure faults including hardware malfunctions, network disruptions, and software anomalies…
Autoscaling in cloud-native platforms like Kubernetes is reactive and metric-driven, leading to a strategic void problem. This comes from the decoupling of higher-level business policies from lower-level resource provisioning. The strategic…
Kubernetes offers two default paths for scaling Node\.js workloads, and both have structural limitations. The Horizontal Pod Autoscaler scales on CPU utilization, which does not directly measure event loop saturation: a Node.js pod can…
Achieving high availability and robust security in Kubernetes requires more than reactive scaling and standard perimeter firewalls. Traditional autoscalers, such as HPA, often fail to react quickly to traffic spikes and cannot distinguish…
Autoscaling is a critical component for efficient resource utilization with satisfactory quality of service (QoS) in cloud computing. This paper investigates proactive autoscaling for widely-used scaling-per-query applications where scaling…
Microservice applications are created as loosely coupled application components and they leverage cloud elasticity to reduce costs and increase development speed. However, microservice applications exhibit complex interactions among…
Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators. As cloud applications progressively adopt microservices, resource managers are faced with two distinct levels of system…
Autoscaling is critical for ensuring optimal performance and resource utilization in cloud applications with dynamic workloads. However, traditional autoscaling technologies are typically no longer applicable in microservice-based…