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Geo-distributed Edge sites are expected to cater to the stringent demands of situation-aware applications like collaborative autonomous vehicles and drone swarms. While clients of such applications benefit from having network-proximal compute resources, an Edge site has limited resources compared to the traditional Cloud. Moreover, the load experienced by an Edge site depends on a client's mobility pattern, which may often be unpredictable. The Function-as-a-Service (FaaS) paradigm is poised aptly to handle the ephemeral nature of workload demand at Edge sites. In FaaS, applications are decomposed into containerized functions enabling fine-grained resource management. However, spatio-temporal variations in client mobility can still lead to rapid saturation of resources beyond the capacity of an Edge site.To address this challenge, we develop FEO (Federated Edge Orchestrator), a resource allocation scheme across the geodistributed Edge infrastructure for FaaS. FEO employs a novel federated policy to offload function invocations to peer sites with spare resource capacity without the need to frequently share knowledge about available capacities among participating sites. Detailed experiments show that FEO's approach can reduce a site's P99 latency by almost 3x, while maintaining application service level objectives at all other sites. 

Spatiotemporal variation in cellular bandwidth availability is well-known and could affect a mobile user's quality of experience (QoE), especially while using bandwidth intensive streaming applications such as movies, podcasts, and music videos during commute. If such variations are made available to a streaming service in advance it could perhaps plan better to avoid sub-optimal performance while the user travels through regions of low bandwidth availability. The intuition is that such future knowledge could be used to buffer additional content in regions of higher bandwidth availability to tide over the deficits in regions of low bandwidth availability. Foresight is a service designed to provide this future knowledge for client apps running on a mobile device. It comprises three components: (a) a crowd-sourced bandwidth estimate reporting facility, (b) an on-cloud bandwidth service that records the spatiotemporal variations in bandwidth and serves queries for bandwidth availability from mobile users, and (c) an on-device bandwidth manager that caters to the bandwidth requirements from client apps by providing them with bandwidth allocation schedules. Foresight is implemented in the Android framework. As a proof of concept for using this service, we have modified an open-source video player---Exoplayer---to use the results of Foresight in its video buffer management. Our performance evaluation shows Foresight's scalability. We also showcase the opportunity that Foresight offers to ExoPlayer to enhance video quality of experience (QoE) despite spatiotemporal bandwidth variations for metrics such as overall higher bitrate of playback, reduction in number of bitrate switches, and reduction in the number of stalls during video playback.