Serverless edge computing promises autonomous function management across the heterogeneous edge-cloud continuum. Specifically, autoscaling of functions increases resource efficiency by creating and destroying instances on demand. However, reactive function instance creation can cause high end-to-end latency due to requests waiting for processing. Researchers and practitioners use proactive autoscaling mechanisms to overcome this by predicting the required number of function instances in advance. Proactive approaches pose challenges because prediction models must be fine-tuned for new environments and updated over time. Therefore, we propose a simulation-driven framework, EdgeCloudForge, that generates datasets to train prediction models for autoscaling in edge-cloud environments ahead of their deployment, thus preparing them in advance for new environments. We introduce the concept of Edge-Cloud Domain Space to describe different infrastructure attributes. EdgeCloudForge uses a two-step method based on initial sampling and Bayesian optimization to navigate this domain space. Simulation results show that our approach can reduce SLO violations by up to 79% across different request patterns compared to the reactive approach and is able to outperform approaches from related work by up to 91%. EdgeCloudForge also shows the capability of training models that can be used across different infrastructures.

This paper presents AirMochi, a tool that provides remote access and control of apps by leveraging a mobile platform's publicly exported accessibility features. While AirMochi is designed to be platform-independent, we discuss its iOS implementation. We show that AirMochi places no restrictions on apps, is able to handle a variety of scenarios, and imposes a negligible performance overhead. https://youtu.be/rhPz2Hs4Ius https://github.com/nkllkc/air_mochi

Online sports betting is a $50B industry that is heavily driven by software. The domain imposes significant demands on developers: the resulting solutions are large, complex, distributed, concurrent software systems with strict availability, real-time performance, scalability, reliability, and security requirements. This paper describes our experience with EmpireBet, a family of online sports betting platforms built and deployed over the past 15 years. The initial solution, implemented by four developers in a start-up, catered to users who connected to the system intermittently, for limited periods, via dial-up connections. Today’s system, engineered and maintained in 27 programming and markup languages by a team of 20 developers, is deployed in over 30 countries, integrated with over 50 third-party systems, and processes tens of millions daily transactions by over 680,000 players who are continuously using the system. This was accomplished via an an explicit focus on EmpireBet’s critical non-functional requirements; a modular, extensible architecture; a set of novel abstractions we introduced into the system; and several reusable libraries developed in the process.