Integration of an Assisted P2P Live Streaming Service in Community Network Clouds

Abstract

Abstract-Wireless community networks (CNs) are large-scale, self-organized and decentralized communication infrastructures built and operated by citizens for citizens. Community network cloud infrastructures have been recently introduced to run services inside the network, without the need to consume them from the Internet. We have developed a Linux-based distribution code-named Cloudy, which fosters the service deployment and automation in community network clouds. In this paper we present two ways provisioned by Cloudy to integrate the services and improve the users QoS in these clouds. First, we present a distributed service discovery mechanism that helps users with service quality metrics to choose the best service from a pool of instances. Second, we experiment with a live video streaming service deployed in CN environments, using more than 50 real CN nodes across Europe for the evaluation. Our analysis shows that tuning the vital parameters of this service as neighborhood peer selection strategies, and source node dispersion strategy, improves the video streaming QoS in the CNs. Our results indicate that both ways help the user to experience improved service performance. Automated service selection, needed once the number of micro service providers becomes larger, is the next step that can be built upon our results.

Gossip-based service monitoring platform for wireless edge cloud computing

“…The nodes average latencies can be as high as 800 milliseconds. Also noting that the simulated network shows as a worst case scenario, in fact other experiments done on CNs [11] tend to experience, on average, lower latencies and higher bandwidth between nodes, on normal usage of the network. However, our evaluation comprises the worst cases, to infer on the monitoring activity when high latencies are dominant in the network.…”

Practical Service Placement Approach for Microservices Architecture

“…Services that require intensive inter-component communication (e.g streaming service), can perform better if the replicas (service components) are placed close to each other in high capacity links [3]. On other side, bandwidthintensive services (e.g., distributed storage, video-on-demand) can perform much better if their replicas are as close as possible to their final users (e.g., overall reduction of bandwidth for service provisioning) [7].…”

“…Among other issues, this question spurred the invention of "alternative" service deployment models to cater for users in Services can be of any type, ranging from personal storage to video streaming and P2P-TV [3]. Observe that this model is different from Fog computing, which extends cloud computing by introducing an intermediate layer between devices and datacenters.…”

Bandwidth-Aware Service Placement in Community Network Micro-Clouds

“…Furthermore, the services deployed have different QoS requirements. Services that require intensive inter-component communication (e.g streaming service), can perform better if the replicas (service components) are placed close to each other in high capacity links [14]. On other side, bandwidth-intensive services (e.g., distributed storage, video-on-demand) can perform much better if their replicas are as close as possible to their final users (e.g., overall reduction of bandwidth for service provisioning).…”