Ke-Jou Hsu

School of Computer Science

College of Computing

Georgia Institute of Technology

https://sites.cc.gatech.edu/~khsu38/

Date: Monday April 8th 2023

Time: 2:00 PM (EST)

Location: Klaus 3100 and https://gatech.zoom.us/j/4645200533

Committee:

Dr. Ada Gavrilovska (Advisor, School of Computer Science, Georgia Institute of Technology)

Dr. Ahmed Saeed    (School of Computer Science, Georgia Institute of Technology) 

Dr. Ketan Bhardwaj  (School of Computer Science, Georgia Institute of Technology)

Dr. Mostafa Ammar  (School of Computer Science, Georgia Institute of Technology)

Dr. Umakishore Ramachandra   (School of Computer Science, Georgia Institute of Technology)

Dr. Karthikeyan Sundaresan  (School of Electrical and Computer Engineering, Georgia Institute of Technology)

Abstract: 

Multi-access edge computing (MEC) systems, similar to cloud systems, offer advantages such as multi-tenancy, fast delivery, and pay-as-you-go models. However, the limited capacity at each edge site, the collocated workloads’ stringent latency-centric performance requirements, and the heterogeneous nature of the edge, present limitations for cloud-native resource management solutions. This thesis demonstrates these limitations and addresses them via new systems support for faster and more cost-effective resource management for MEC.

One limitation is the mismatch between the resource requirements for certain edge applications and the resources available at an edge site. To address this, in this thesis we first develop Couper – systems support for decomposing resource-intensive video analytics applications based on Deep Neural Networks (DNN) into finer-granular components, allowing resource management to balance the DNN inference load between the edge and the cloud, and to improve end-to-end performance. In addition, we demonstrate the importance of careful placement of components across the edge-cloud continuum. For a concrete example of a Content Delivery Network (CDN), we show that by managing the placement and collocation of components in MEC-CDN can lead to average latency reduction of 75% compared to existing solutions. We generalize the methodology used to establish this observation and develop Anitya – lightweight systems support for capturing cross-component dependencies that enables effective management of componentized microservice-based MEC application deployments.

A second limitation is the mismatch among the resource allocation granularity of current MEC platforms vs. what is needed for emerging MEC workloads. We show that this gap can completely eliminate any expected edge benefits in multi-tenant settings. To address this, we develop ShapeShifter – systems support for fine-grained software-level traffic controls that augment the underlying platform capabilities to specialize the resource allocations on workload granularity. This prevents hidden congestion problems and provides 4x improvements in application performance.

A third limitation is related to the mismatch among the time granularities at which cloud-native resource management operates vs. what is needed in MEC. Naive adjustments of cloud-native systems lead to prohibitive resource overheads for resource-limited MEC environments. As part of Colibri – a new observability tool for MEC, we develop new systems support for dynamically controlling and specializing the execution of control plane functionality needed for resource management, focused on resource monitoring in this case.

The evaluations of the different systems developed as part of this thesis, performed using new experimental testbeds and MEC benchmarks, demonstrate that the new systems support enables improvements in the effectiveness of different resource management tasks which span the entire lifecycle of MEC application and service deployments, and results in improvements in end-to-end application performance and infrastructure efficiency.