A microservice-based application is composed of a set of small services that run within their own processes and communicate with a lightweight mechanism. Processing the microservices efficiently with minimum processing time and cost, while utilizing the computing resources efficiently, is a challenging task in a cloud environment. To address this challenge, in this paper, we propose a new container-aware application scheduling strategy with an auto-scaling policy. The proposed strategy deploys the requested applications on the best-fit lightweight containers, with minimum deployment time, based on the resource requirements. Another important issue of the container-aware cloud environment is the cold start effect, which is solved using a rule-based policy in the proposed work for minimizing deployment time and cost of the applications. Furthermore, a dynamic bin-packing strategy is designed for deploying the applications to the minimum number of physical machines (PMs) with efficient utilization of the computing resources. Finally, a heuristic-based auto-scaling policy has been designed for minimizing the wastage of the computing resources in the cloud data center. Through numerical evaluation, we have shown the superiority of the proposed method over the existing state-of-the-art algorithms in terms of processing time, processing cost, resource utilization, and required numbers of PMs.

基于微服务的应用程序由一组小型服务组成，这些小型服务在自己的进程中运行并与轻量级机制进行通信。在云环境中，以最小的处理时间和成本有效地处理微服务，同时有效地利用计算资源，是一项艰巨的任务。为了解决这一挑战，在本文中，我们提出了一种具有自动扩展策略的新的容器感知应用程序调度策略。提议的策略根据资源需求，以最短的部署时间将请求的应用程序部署在最适合的轻量级容器上。容器感知云环境的另一个重要问题是冷启动效应，在建议的工作中使用基于规则的策略可以解决该问题，以最大程度地减少应用程序的部署时间和成本。此外，动态垃圾箱打包策略旨在通过高效利用计算资源将应用程序部署到最少数量的物理机（PM）。最后，已经设计了一种基于启发式的自动扩展策略，以最大程度地减少云数据中心中计算资源的浪费。通过数值评估，我们在处理时间，处理成本，资源利用率和所需的PM数量方面显示了所提出方法相对于现有技术的优越性。设计了基于启发式的自动扩展策略，以最大程度地减少云数据中心中计算资源的浪费。通过数值评估，我们在处理时间，处理成本，资源利用率和所需的PM数量方面显示了所提出方法相对于现有技术的优越性。设计了基于启发式的自动扩展策略，以最大程度地减少云数据中心中计算资源的浪费。通过数值评估，我们在处理时间，处理成本，资源利用率和所需的PM数量方面显示了所提出方法相对于现有技术的优越性。