论文标题
分布式CPU计划受非线性约束
Distributed CPU Scheduling Subject to Nonlinear Constraints
论文作者
论文摘要
本文考虑了一个由非线性模型约束约束的本地资源分配的协作代理网络。在许多应用程序中，要求解决方案在满足总和的全局约束方面可行。本文介绍了以此为激励的，以任何时间可行性（或非征服可行性）的足够条件。对于提出的两种分布式解决方案，一种收敛于定向的重量平衡网络，另一个收敛于无向网络。特别是，我们详细阐述了均匀的量化并讨论ε精制解决方案的概念，这估算了我们可以获得不同量化水平的确切优化器的近距离。此外，这项工作还可以通过分布式解决方案来处理一般（可能是非二次）的严格凸出目标功能，并应用于数据中心云之间的CPU分配。结果可以用作协调机制，以最佳平衡一组网络服务器之间的任务和CPU资源，同时解决量化或有限的服务器容量。 索引术语：多代理系统，保存总和资源分配，分布式优化，任何时间可行性
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on the nonlinear mapping for anytime feasibility (or non-asymptotic feasibility) are addressed in this paper. For the two proposed distributed solutions, one converges over directed weight-balanced networks and the other one over undirected networks. In particular, we elaborate on uniform quantization and discuss the notion of ε-accurate solution, which gives an estimate of how close we can get to the exact optimizer subject to different quantization levels. This work, further, handles general (possibly non-quadratic) strictly convex objective functions with application to CPU allocation among a cloud of data centers via distributed solutions. The results can be used as a coordination mechanism to optimally balance the tasks and CPU resources among a group of networked servers while addressing quantization or limited server capacity. Index Terms: multi-agent systems, sum-preserving resource allocation, distributed optimization, anytime feasibility
