Dynamic resource allocation for FaaS applications in the fog

Fog computing is an extension of the traditional cloud computing model in which compute, storage, and network capabilities are distributed closer to users along a cloud-to-thing continuum [1]. The main motivation for fog computing is supporting latency-sensitive and bandwidth-intensive applications, such as Internet of Things (IoT) applications (e.g., smart cities). A promising programming model for developing fog applications is the Function-as-a-Service (FaaS) model [2]. FaaS enables developers to write and deploy functions without being concerned with managing servers, shifting the burden of resource management entirely to FaaS platforms.

Resource management in emerging fog environments, however, poses significant challenges. First, fog resources are geo-distributed, heterogeneous (e.g., sensors, mobile devices, micro data centres), and subject to unpredictable changes (e.g., fog nodes joining, leaving, failing), which makes it difficult to make effective resource allocation decisions. Second, fog resources are consumed and provided by multiple, independent economic actors [3]. These actors include service providers that create and operate applications, and various types of infrastructure providers (e.g., individuals, small companies, traditional cloud providers) that own the resources used by these applications. Dynamically coordinating the interactions between these actors to enable them to jointly serve their interests is a challenging problem.

The goal of the thesis is to propose mechanisms for dynamically coordinating fog actors in allocating resources and for continuously managing the execution of FaaS applications on these resources. A well-known coordination approach is applying economic and pricing mechanisms (e.g., auctions), which specify the rules and incentives that govern interactions among self-interested agents. The PhD student will first select an appropriate economic mechanism for fog environments, drawing on the extensive literature on applying such mechanisms in cloud resource management [4, 5, 6], and taking into account the novel challenges introduced by the fog (e.g., heterogeneity, dynamism). The PhD student will then propose automated management tools that implement this economic mechanism and manage function execution to satisfy user requirements (e.g., performance, availability). The tools will be integrated into an existing resource orchestration system, such as Kubernetes, and an existing open source FaaS platform, such as Fission.

Qualifications

• Excellent communication and writing skills
• Knowledge and experience in one or more of the following areas is an advantage: distributed systems, adaptive systems, economic mechanisms, cloud, fog, and IoT technologies