On the Impact of Clustering for IoT Analytics and Message Broker Placement across Cloud and Edge

With edge computing emerging as a promising solution to cope with the challenges of Internet of Things (IoT) systems, there is an increasing need to automate the deployment of large-scale applications along with the publish/subscribe brokers they communicate over. Such a placement must adjust to the resource requirements of both applications and brokers in the heterogeneous environment of edge, fog, and cloud. In contrast to prior work focusing only on the placement of applications, this paper addresses the problem of jointly placing IoT applications and the pub/sub brokers on a set of network nodes, considering an application provider who aims at minimizing total end-to-end delays of all its subscribers. More specifically, we devise two heuristics for joint deployment of brokers and applications and analyze their performance in comparison to the current cloud-based IoT solutions wherein both the IoT applications and the brokers are located solely in the cloud. As an application provider should consider not only the location of the application users but also how they are distributed across different network components, we use von Mises distributions to model the degree of clustering of the users of an IoT application. Our simulations show that superior performance of our heuristics in comparison to cloud-based IoT operation is most pronounced under a high degree of clustering. When users of an IoT application are in close network proximity of the IoT sensors, cloud-based IoT unnecessarily introduces latency to move the data from the edge to the cloud and vice versa while processing could be performed at the edge or the fog layers.</p

EdgeDASH: Exploiting Network-Assisted Adaptive Video Streaming for Edge Caching

While edge video caching has great potential to decrease the core network traffic as well as the users' experienced latency, it is often challenging to exploit the caches in current client-driven video streaming solutions due to two key reasons. First, even those clients interested in the same content might request different quality levels as a video content is encoded into multiple qualities to match a wide range of network conditions and device capabilities. Second, the clients, who select the quality of the next chunk to request, are unaware of the cached content at the network edge. Hence, it becomes imperative to develop network-side solutions to exploit caching. This can also mitigate some performance issues, in particular for the scenarios in which multiple video clients compete for some bottleneck capacity. In this paper, we propose a network-side control logic running at a WiFi AP to facilitate the use of cached video content. In particular, an AP can assign a client station a different video quality than its request, in case the alternative quality provides a better utility. We formulate the quality assignment problem as an optimization problem and develop several heuristics with polynomial complexity. Compared to the baseline where the clients determine the quality adaptation, our proposals, referred to as EdgeDASH, offer higher video quality, higher cache hits, and lower stalling ratio which are essential for user's satisfaction. Our simulations show that EdgeDASH facilitates significant cache hits and decreases the buffer stalls only by changing the client's request by one quality level. Moreover, from our analysis, we conclude that the network assistance provides significant performance improvement, especially when the clients with identical interests compete for a bottleneck link's capacity