Virtualization techniques help edge environments separate the role of the traditional edge providers into two: edge infrastructure providers (EIPs), who manage the physical edge infrastructure, and edge service providers (ESPs), who aggregate resources (especially, compute resources) from multiple EIPs to place service entities and offer value-added services to end users (EUs). In such an environment, end users submit their data analysis jobs to ESPs; ESPs process the data analysis jobs using their service entities. One fundamental and critical problem for an ESP is to decide how much compute resources to rent from each edge server under the constraint that the total amount of rental resources is no more than a specified budget threshold, so that the average makespan of the data analysis jobs submitted to it is minimized. This Edge Resource Allocation (ERA) problem is proven to be NP-complete by reducing the set cover problem to a special case of it. To design an approximation algorithm for ERA, we perform two transformations on ERA: first, we transform ERA into mERA by replacing minimization with maximization; second, we transform mERA into dmERA by limiting the possible amounts of rental resources to a finite set of values. We find that dmERA has several tractable properties that allow us to design Hermes, a provably efficient algorithm that approximates the optimal allocation. We demonstrate that the gap between Hermes and the optimum in simulations and Android-based testbed experiments are no larger than 4.78% and 12.43%, respectively. Hermes can also output a curve showing the trade-off between the average makespan and the budget threshold, so that an ESP can choose the right balance.

中文翻译：

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使用Hermes为边缘服务实体提供有效的资源分配

虚拟化技术可帮助边缘环境将传统边缘提供程序的角色分为两部分：管理物理边缘基础结构的边缘基础结构提供程序（EIP）和从多个EIP聚合资源（尤其是计算资源）的边缘服务提供程序（ESP）放置服务实体并向最终用户（EU）提供增值服务。在这种环境下，最终用户将其数据分析作业提交给ESP。ESP使用其服务实体处理数据分析作业。对于ESP来说，一个基本且关键的问题是，在租赁资源总量不超过指定预算阈值的约束下，决定从每个边缘服务器租赁多少计算资源，以便数据分析作业的平均有效期提交给它是最小化。通过将集合覆盖问题简化为特殊情况，可以证明此边缘资源分配（ERA）问题是NP完全的。为了设计ERA的近似算法，我们对ERA进行了两次转换：首先，通过将最大化替换为最小化，将ERA转换为mERA。其次，通过将租赁资源的可能数量限制为有限的一组值，我们将mERA转换为dmERA。我们发现dmERA具有几个易于处理的属性，这些属性使我们能够设计Hermes，这是一种可证明有效的算法，可以近似最佳分配。我们证明，爱马仕与最佳仿真和基于Android的测试平台实验之间的差距分别不大于4.78％和12.43％。爱马仕还可以输出一条曲线，显示平均有效期与预算阈值之间的权衡，