A study on the evaluation of HPC microservices in containerized environment

Abstract

Summary Containers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment.

Performance comparison of multi-container deployment schemes for HPC workloads: an empirical study

“…Maliszewski et al [24] investigated the performance of scientific workloads with single or multi-tenant instances in a single node, where each tenant held its independent application among other tenants. Jha et al have studied HPC microservices in different container environments [17,18]. Their work includes flexible deployments for HPC applications on a single node, from running a single or multiple applications in a single container, to running multiple containers each holding a single application.…”

“…However, there is a lack of research on multi-container deployment solutions for a singletenant multi-process HPC workload. Unlike the multi-container deployments holding workloads for multiple tenants [16][17][18]24], using multiple containers to package a single-tenant multi-process/thread HPC workload refers to partitioning the processes or threads belonging to each application into different containers, obtaining in that way a finer-grained deployment. Whereas few works include experiments with different container granularity [8,34,35], none of them provide a deep understanding of the impact of such multi-container deployments on the performance of HPC workloads, which considers different containerization technologies, container grain sizes, and hardware platforms.…”

On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes

“…Because Spark executor exchanges task results with each other for fault tolerance, the inter-container communication latency of containers on the same host would be negligible. In order to constrain the performance degradation caused by the resource competition of these containers [ 21 ], we propose a task scheduling mechanism (TSM) to change the containers’ affinities with the hosts based on a weighted average policy. The TSM adjusts the pod affinities to a host after referencing the ratio between the number of completed and failed tasks at the runtime.…”

rAck: Proposta de Configuração para Garantia de Entrega de Mensagens no Apache Kafka

“…de configuração para ack no Kafka apresentam dificuldades em conciliar confiabilidade e desempenho [2] [3].…”