Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

Two Time-Scale Caching Placement and User Association in Dynamic Cellular Networks

With the rapid growth of data traffic in cellular networks, edge caching has become an emerging technology for traffic offloading. We investigate the caching placement and content delivery in cache-enabling cellular networks. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay which considers both content transmission delay and content update delay. To solve this challenging problem, we decompose the optimization problem into two sub-problems, the user association sub-problem in a short time scale and the caching placement in a long time scale. Specifically, we propose a low complexity user association algorithm for a given caching placement in the short time scale. Then we develop a deep deterministic policy gradient based caching placement algorithm which involves the short time-scale user association decisions in the long time scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We illustrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively

Edge Computing for Extreme Reliability and Scalability

The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud