具有细粒度带宽控制的基于模型的应用程序自治管理器

2017 13th International Conference on Network and Service Management (CNSM) Pub Date : 2017-11-01 DOI:10.23919/CNSM.2017.8255994

Nasim Beigi Mohammadi, Mark Shtern, Marin Litoiu

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引用次数: 3

摘要

在本文中，我们提出并实现了一个基于机器学习的应用程序自治管理系统，该系统控制分配给web应用程序的每个场景的带宽速率，以尽可能长时间地推迟扩展。通过在亚马逊AWS云上的实验，我们证明了自治管理器能够快速满足服务水平协议(SLA)，并且与之前基于启发式的方法相比，减少了56%的SLA违规。

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A model-based application autonomic manager with fine granular bandwidth control

In this paper, we propose and implement a machine learning based application autonomic management system that controls the bandwidth rates allocated to each scenario of a web application to postpone scaling out for as long as possible. Through experiments on Amazon AWS cloud, we demonstrate that the autonomic manager is able to quickly meet Service level Agreement (SLA) and reduce the SLA violations by 56% compared to a previous heuristic-based approach.

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2017 13th International Conference on Network and Service Management (CNSM)

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