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A Quest for Optimizing the Data Processing Decision for Cloud-Fog Hybrid Environments

机译：优化云雾混合环境数据处理决策的任务

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In the Cloud, computing loads found a new virtualized and dynamically scaling home. Cloud data centers offered a safe haven from risky capital investments and high dependence on internal resources for enterprises and all of that was achieved by going back to the central computing concept. The pros are ideal for tried and tested scenarios of predictable or at least bounded enterprise loads. However, because of the pay-as-you-go business concept that is dominant in the Cloud, scenarios where a big percentage of requests turn up with low-value data (as in incomplete, meaningless or out-of-sync) can be financially detrimental to the Cloud tenant. The Internet of Things offerings, for example, expand the horizons of data sources for a certain Cloud based service but it also jumps to a new dimension in terms of the data filtering and preprocessing required of the same service to get to the core value-returning requests. This contributed to the emergence of Fog (edge) computing where some of the processing is done on the edge of the network with the aim of distributing the load and minimizing the network congestion caused by low-value data. The availability of Fog nodes poses a challenge to Cloud service designers regarding how to optimize the process. The decision as to where to perform each step of the data management can make the difference for Cloud providers in mitigating both the risk of pushing high loads to the Cloud servers and network and the risk of almost localizing the whole process and losing the benefits from Cloud services. To tackle this challenge, we consider the question of optimizing the decision process for a data-intensive highly distributed Cloud service. A novel optimization model is presented exploring the factors in effect with the objective of maximizing the Cloud provider value and initial experimental results are presented. Shown results offer some insight into the contradicting factors in play (cost, network capacity, node and Cloud capacity). This work builds towards a comprehensive technique that helps Cloud providers decide their data processing strategy in any mixed Cloud-fog Cloud service platform.
机译：在云中，计算负载找到了一个新的虚拟化和动态缩放的家庭。云数据中心从风险资本投资和对企业内部资源的高依赖提供了一个安全的避风港，并通过回到中央计算概念来实现的所有这些。优点是可预测或至少有界企业负载的尝试和测试场景的理想选择。但是，由于云中占据主导地位的代价，所以请求大量请求的方案可以使用低价值数据（如不完整，毫无意义或不同步）所示对云租户的财务不利。例如，事物互联网提供了一定的基于云服务的数据源的视野，但它也跳到了一个新的维度，就可以在相同服务的数据过滤和预处理到达核心值返回方面跳转到新的维度要求。这导致了雾（边缘）计算的出现，其中一些处理是在网络的边缘上完成的，目的是分发负载并最小化由低值数据引起的网络拥塞。 FOG节点的可用性对云服务设计师对如何优化该过程构成挑战。关于执行数据管理的每个步骤的决定可以对云提供商来说差异，以减轻将高负载推向云服务器和网络的风险以及几乎本地化整个过程的风险并失去云中的好处服务。为了解决这一挑战，我们考虑了优化数据密集型高度分布式云服务的决策过程的问题。提出了一种新颖的优化模型，探讨了对云提供商值最大化的目标，并提出了初始实验结果的目标。所示结果对播放（成本，网络容量，节点和云容量）的相对因素提供了一些洞察力。这项工作建立了一种全面的技术，帮助云提供商在任何混合云云服务平台中决定其数据处理策略。

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