Dynamic Resource Allocation and Pricing for Fog-Cloud Networking as a Service

雾云网络即服务的动态资源分配和定价

• 批准号: RGPIN-2020-06520
• 负责人: Dziong, Zbigniew
• 金额: $ 2.4万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754000
• 关键词: Dynamic; Resource; Allocation; Pricing; Fog

The Fog computing (comprising the Edge computing) concepts are gaining a significant momentum since they can provide low service latency combined with geographically distributed computing, independent of the user locations. These ingredients can significantly empower Tactile Internet, IoT and multi-user applications. To achieve these objectives, a well-developed and economically viable infrastructure for the Fog-Cloud computing is needed. In this proposal, we address some important issues related to this goal. In particular, we believe that the applications should have access to multi-node and multi-layer Fog-Cloud computation resources, enabling distributed computations, where the networking aspect is of great importance. The considered computation nodes can belong to different owners and can include edge nodes, local data centers, and distant data centers. Managing resource allocation in such a distributed set of computing and networking resources can be too complicated for users and applications. To address this issue we propose a service that we name Fog-Cloud Networking as a Service (FCNaaS) where the FCNaaS operator (FCNO) combines the role of an overlay network and a broker. The overlay network part manages the networking and computing resource allocation to a given demand while the broker part facilitates long-time-scale interaction between the users, applications and networking-computing resource owners that involves resource reservations for FCNO and pricing. The research will be focused on three areas related to FCNaaS: 1) Resource management architecture for Fog-Cloud Networking, 2) Dynamic resource allocation and related economic framework, and 3) Pricing and resource reservations. In particular, we will develop AI based resource allocation models within an economic framework that takes into account heterogeneous environment with several resource and application providers. Also, using a game theoretical framework we will develop models for pricing that give incentives to each player to participate in the market in an economically efficient and balanced way that provides benefits to all players. The research results will be useful to Canadian application providers, network operators, and computing node owners for optimizing their operations and to grow their market shares. The developed knowledge and models will be also useful to the government regulatory bodies for providing social wellness of the offered services. Also, the training of HQP is an integral part of the proposed research program and the students will acquire important skills in the domain that is becoming of high importance to several Canadian industries.

雾计算（包括边缘计算）概念正在获得巨大的发展势头，因为它们可以提供低服务延迟与地理分布式计算相结合，而与用户位置无关。这些成分可以显着增强触觉互联网、物联网和多用户应用程序的能力。为了实现这些目标，需要一个成熟且经济可行的雾云计算基础设施。在本提案中，我们解决了与此目标相关的一些重要问题。特别是，我们认为应用程序应该能够访问多节点和多层雾云计算资源，从而实现分布式计算，其中网络方面非常重要。所考虑的计算节点可以属于不同的所有者，并且可以包括边缘节点、本地数据中心和远程数据中心。对于用户和应用程序来说，管理此类分布式计算和网络资源集中的资源分配可能过于复杂。为了解决这个问题，我们提出了一项称为雾云网络即服务（FCNaaS）的服务，其中 FCNaaS 运营商（FCNO）结合了覆盖网络和代理的角色。覆盖网络部分根据给定需求管理网络和计算资源分配，而代理部分促进用户、应用程序和网络计算资源所有者之间的长期交互，其中涉及 FCNO 的资源预留和定价。该研究将重点关注与 FCNaaS 相关的三个领域：1）雾云网络的资源管理架构，2）动态资源分配和相关经济框架，3）定价和资源预留。特别是，我们将在考虑到具有多个资源和应用程序提供商的异构环境的经济框架内开发基于人工智能的资源分配模型。此外，我们将使用博弈论框架开发定价模型，激励每个参与者以经济有效和平衡的方式参与市场，为所有参与者提供利益。研究结果将有助于加拿大应用程序提供商、网络运营商和计算节点所有者优化其运营并增加其市场份额。开发的知识和模型也将有助于政府监管机构提供所提供服务的社会健康。此外，HQP 培训是拟议研究计划的一个组成部分，学生将获得对加拿大多个行业变得非常重要的领域的重要技能。