论文标题
具有能量收集的可扩展雾计算系统的动态网络切片
Dynamic Network Slicing for Scalable Fog Computing Systems with Energy Harvesting
论文作者
论文摘要
本文研究了雾计算系统，其中云数据中心可以由在广泛的地理区域中部署的大量雾节补充。每个节点都依靠周围环境的收获能量为当地用户提供计算服务。我们提出了动态网络切片的概念，在该概念中，区域协调器协调本地雾节点之间的工作负载分布，提供能源和计算资源的分区/切片，以支持特定类型的服务，并提供某些服务质量（QOS）的保证。分配给每个切片的资源可以根据服务需求和能源可用性进行动态调整。开发了一个随机重叠的联盟形成游戏，以调查在随机波动的能量收集和工作量到达过程中雾节之间的分布式合作和联合网络切片。我们观察到，通过允许雾节点保持有关未知状态和其他节点的私人信息的信念功能，可以改善雾计算网络的总体处理能力。提出了一种基于信念状态的马尔可夫决策过程（B-POMDP）的算法，以实现所有雾节点之间的最佳资源切片结构。我们描述了如何在3GPP网络共享体系结构中实施我们提出的动态网络切片，并使用真正的BS位置数据在都柏林市部署了200多个BSS的真实BS位置数据。我们的数值结果表明，我们的框架可以显着提高雾计算网络的工作负载处理能力。特别是，即使每个雾节点只能与其最接近的邻居进行协调，在某些情况下，雾节点处理的工作负载总量几乎可以加倍。
This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area. Each node relies on harvested energy from the surrounding environment to provide computational services to local users. We propose the concept of dynamic network slicing in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain quality-of-service (QoS) guarantees. The resources allocated to each slice can be dynamically adjusted according to service demands and energy availability. A stochastic overlapping coalition-formation game is developed to investigate distributed cooperation and joint network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes. We observe that the overall processing capacity of the fog computing network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes. An algorithm based on a belief-state partially observable Markov decision process (B-POMDP) is proposed to achieve the optimal resource slicing structure among all fog nodes. We describe how to implement our proposed dynamic network slicing within the 3GPP network sharing architecture, and evaluate the performance of our proposed framework using the real BS location data of a real cellular system with over 200 BSs deployed in the city of Dublin. Our numerical results show that our framework can significantly improve the workload processing capability of fog computing networks. In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios.
