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
摘要：
本文研究了雾计算系统，在该系统中，云数据中心可以通过部署在广阔地理区域的大量雾节点来补充。每个节点依靠从周围环境中收集的能量为本地用户提供计算服务。我们提出了动态网络切片的概念，其中区域协调器协调本地雾节点之间的工作负载分布，提供分区/切片的能量和计算资源，以支持特定类型的服务，具有一定的服务质量（QoS）保证。分配给每个切片的资源可以根据服务需求和能量可用性进行动态调整。提出了一种随机重叠联盟形成博弈模型，研究了随机波动的能量收集和负载到达过程下fog节点之间的分布式合作和联合网络切片.我们观察到，雾计算网络的整体处理能力，可以提高通过允许雾节点，以保持一个信念函数的未知状态和其他节点的私人信息。提出了一种基于信念-状态部分可观测马尔可夫决策过程（B-POMDP）的最优资源切片算法。我们描述了如何实现我们提出的动态网络切片内的3GPP网络共享架构，并评估我们提出的框架的性能，使用一个真正的蜂窝系统的实际BS位置数据，超过200个BS部署在都柏林市。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近的邻居协调，在某些情况下，雾节点处理的工作负载总量几乎可以增加一倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
