实现基于意图的云管理的资源设计框架

Chaofeng Wu, Shingo Horiuchi, Kenichi Tayama
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引用次数: 8

摘要

越来越多的企业和组织选择云来适应他们的工作负载,例如,企业的年报部门使用公共/私有云来部署数据分析功能,电信运营商在云上部署虚拟演进分组核心(vEPC)等虚拟网络功能(nfv)来提供电信服务。在利用云的时候,云用户关心的是基于云的功能的功能性、可靠性、性能等,我们称之为意图或服务需求。然而,为了满足这个意图,用户需要告诉云资源协调器/控制器需要什么类型的虚拟机(VM),以及分配给每个VM所需的计算资源的数量,我们称之为资源需求。在前期工作[1]中,我们解决了这个问题,提出了一个基于意图的云管理(IBCM)框架,将云用户的服务需求“转化”为资源需求。在这项工作中,我们专注于实现IBCM的资源设计框架(RDF),该框架决定所需的计算资源量以满足云用户的意图/服务需求。本工作的主要贡献如下:(1)系统地分析了资源设计时需要考虑的因素。(2)在此基础上,提出了适用于各种基于云的应用和场景的RDF体系结构。据我所知,这是第一个解决这个问题的工作。(3)我们在一个基于云的机器学习实验场景中验证了提出的RDF。实验结果表明,我们的RDF能够以90%以上的准确率推断出性能和运行状态,从而能够根据性能需求和运行策略精确地设计资源。(4)在进行精确资源设计的基础上,提出了RDF的服务水平协议(SLA)违反预防机制,并验证了其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Resource Design Framework to Realize Intent-Based Cloud Management
More businesses and organizations are choosing clouds to accommodate their workload, e.g., annalistic departments of enterprises use public/private cloud to deploy the analysis function of data, and telecommunication operators deploy Virtual Network Functions (NFVs) such as virtual Evolved Packet Core (vEPC) on clouds to provide telecommunication services. When utilizing the cloud, the cloud user is concerned about the functionality, reliability, performance, etc. of the cloud-based function, which we call the intent or the service requirements. However, to meet the intent, the user needs to tell the cloud resource orchestrator/controller what kinds of Virtual Machines (VMs) are needed and the amount of computing resources necessary to allocate to each VM, which we call resource requirements. In our preliminary work [1], we addressed this problem and proposed an Intent-Based Cloud Management (IBCM) framework to "translate" the cloud user's service requirements into resource requirements. In this work, we have focused on realizing a Resource Design Framework (RDF) for IBCM that decides the needed computing resource amount to fulfill the cloud user' intent/ service requirements. The main contributions of this work are as follows: (1) We have systematically analyzed factors that need to be taken into consideration when designing the resources. (2) On the basis of the analysis, we propose the architecture of RDF that is applicable for various cloud-based applications and scenarios. To the best of knowledge, this is the first work to address this issue. (3) We have validated the proposed RDF in an experimental cloud-based machine learning scenario. The experiment results show our RDF is able to infer the performance and operation state with over 90% precision, thus enabling resources to be precisely designed in accordance with performance requirements and operation policy. (4) In addition to precise resource design, we also proposed a service level agreement (SLA) violation prevention mechanism for RDF and verified its effectiveness.
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