In recent years, an important new class of applications has emerged that is characterized by its tight latency requirements. Latency-sensitive applications pose new challenges to cloud providers as cloud data centers are often geographically distant from users. As a result, the computing industry has proposed edge computing as a solution to the challenges presented by latency-sensitive applications. Edge computing promises lower response time by bringing server clusters closer to end users and devices.
However, from the perspective of applications, the end-to-end latency of a request includes three parts: network latency, service time, and queueing delay. While edge data centers have the advantage of lower network latency, applications deployed at the edge are often vulnerable to longer queue delays. As a result, proper resource allocation techniques are needed to ensure that latency-sensitive applications deployed in edge environments can achieve optimal performance.
In this thesis, I present model-driven resource allocation algorithms for latency-sensitive applications deployed at the edge in various contexts. First, I design and implement a framework for running latency-sensitive serverless functions on edge resources. My approach can allocate the appropriate number of containers for each function to meet service-level objectives (SLO) in the absence of resource pressure while also providing fairness guarantees during resource overload. Second, I develop inversion-aware resource allocation and workload scheduling algorithms for latency-sensitive applications deployed in distributed edge-cloud environments. Finally, I investigate the problem of container allocation for serverless applications in the form of directed acyclic graphs (DAGs) while ensuring SLO compliance.
Advisor: Prashant Shenoy