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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm
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Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm

机译：通过调用基于学习自动机的两次规模分离范式来实现公平负载平衡

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摘要

In this article, we consider the problem of load balancing (LB), but, unlike the approaches that have been proposed earlier, we attempt to resolve the problem in a fair manner (or rather, it would probably be more appropriate to describe it as an epsilon-fair manner because, although the LB can, probably, never be totally fair, we achieve this by being "as close to fair as possible"). The solution that we propose invokes a novel stochastic learning automaton (LA) scheme, so as to attain a distribution of the load to a number of nodes, where the performance level at the different nodes is approximately equal and each user experiences approximately the same Quality of the Service (QoS) irrespective of which node that he/she is connected to. Since the load is dynamically varying, static resource allocation schemes are doomed to underperform. This is further relevant in cloud environments, where we need dynamic approaches because the available resources are unpredictable (or rather, uncertain) by virtue of the shared nature of the resource pool. Furthermore, we prove here that there is a coupling involving LA's probabilities and the dynamics of the rewards themselves, which renders the environments to be nonstationary. This leads to the emergence of the so-called property of "stochastic diminishing rewards." Our newly proposed novel LA algorithm epsilon-optimally solves the problem, and this is done by resorting to a two-time-scale-based stochastic learning paradigm. As far as we know, the results presented here are of a pioneering sort, and we are unaware of any comparable results.
机译：在本文中，我们考虑负载平衡（LB）的问题，但是，与早些时候提出的方法不同，我们试图以公平的方式解决问题（或者，或者，将其描述为可能更合适埃塞尔顿公平的方式，因为，虽然LB可以，可能永远不会完全公平，但我们通过“尽可能接近”）来实现这一目标。我们提出的解决方案调用了一种新型的随机学习自动化（LA）方案，以便达到负载的分布到多个节点，其中不同节点处的性能等级大致相等，并且每个用户频繁地遇到大致相同的质量服务（QoS）无论他/她都连接到哪个节点。由于负载动态变化，因此静态资源分配方案注定要低于uport。这在云环境中进一步相关，我们需要动态方法，因为借助于资源池的共享性质，可用资源是不可预测的（或者而不是不确定）。此外，我们在此证明，涉及La的概率和奖励本身的动态的耦合，这使得环境成为不存在的环境。这导致出现“随机减少奖励”所谓的财产。我们的新提出的新颖LA算法epsilon - 最佳地解决了这个问题，这是通过诉诸基于两次规模的随机学习范式来完成的。据我们所知，这里呈现的结果是开创性的排序，我们不知道任何可比结果。

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