A Self-Distributing System Framework for the Computing Continuum

Abstract

Applications such as autonomous vehicles, virtual reality, augmented reality, and heavy machine learning-based applications are becoming popular and demanding more flexible deployment environments. The computing continuum, a hierarchical hybrid infrastructure comprehending user devices (smartphones, sensors, laptops, etc.), edge data centers, and cloud platforms, offers a wide range of deployment possibilities with a full range of varying computing resources. To take full advantage of such infrastructure, application development is faced with many challenges, the most important being the implementation of a transparent and generalized mechanism for code offloading and mobility throughout the continuum. To tackle such issues, this paper presents the Self-Distributing Systems (SDS) framework, a self-distribution framework that supports generalized code-offloading capabilities at the application level with a machine learning agent for deciding where to place components and a component-based model to enable seamless distribution of an application's components at runtime. We describe the framework, show its applicability in different application scenarios, and report our preliminary results. We conclude the paper with a list of challenges and invite the systems community to join the effort to further investigate them.