ABSTRACT We develop a generic game platform that can be used to model various real-world systems with multiple intelligent cloud-computing pools and parallel-queues for resources-competing users. Inside the platform, the software structure is modelled as Blockchain. All the users are associated with Big Data arrival streams whose random dynamics is modelled by triply stochastic renewal reward processes (TSRRPs). Each user may be served simultaneously by multiple pools while each pool with parallel-servers may also serve multi-users at the same time via smart policies in the Blockchain, e.g. a Nash equilibrium point myopically at each fixed time to a game-theoretic scheduling problem. To illustrate the effectiveness of our game platform, we model the performance measures of its internal data flow dynamics (queue length and workload processes) as reflecting diffusion with regime-switchings (RDRSs) under our scheduling policies. By RDRS models, we can prove our myopic game-theoretic policy to be an asymptotic Pareto minimal-dual-cost Nash equilibrium one globally over the whole time horizon to a randomly evolving dynamic game problem. Iterative schemes for simulating our multi-dimensional RDRS models are also developed with the support of numerical comparisons.

中文翻译：

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具有多个智能云计算池的大数据平台建模与调度博弈

摘要 我们开发了一个通用游戏平台，可用于为具有多个智能云计算池和资源竞争用户的并行队列的各种现实世界系统建模。在平台内部，软件结构被建模为区块链。所有用户都与大数据到达流相关联，其随机动态由三重随机更新奖励过程 (TSRRP) 建模。每个用户可以由多个池同时服务，而每个具有并行服务器的池也可以通过区块链中的智能策略同时为多个用户提供服务，例如在每个固定时间短视的纳什均衡点到博弈论调度问题. 为了说明我们游戏平台的有效性，我们对其内部数据流动态（队列长度和工作负载过程）的性能度量进行建模，以反映在我们的调度策略下的状态切换（RDRS）的扩散。通过 RDRS 模型，我们可以证明我们的短视博弈论策略是一个渐近的帕累托最小双成本纳什均衡，在整个时间范围内全局适用于随机演化的动态博弈问题。在数值比较的支持下，还开发了用于模拟我们的多维 RDRS 模型的迭代方案。