Algorithms and Techniques for Dynamic Resource Management across Cloud-Edge Resource Spectrum
Shekhar, Shashank
:
2018-06-13
Abstract
An increasing number of Internet of Things (IoT) and other
latency-sensitive applications are cloud-hosted. However, limitations
in performance assurances from the cloud, and the longer and often
unpredictable end-to-end network latencies between the end user and
the cloud can be detrimental to the response time requirements of the
applications, specifically those that have stringent Quality of
Service (QoS) requirements. Although fog/edge resources, such as
cloudlets, may alleviate some of the latency concerns, there is a
general lack of mechanisms that can dynamically manage resources
across the cloud-edge spectrum. The problem becomes even more
challenging when performance interference on multi-tenant fog servers
along with workload variations, and user mobility are considered. To
address these concerns, this dissertation presents the design and
implementation of the Dynamic Data Driven Cloud and Edge Systems
(D3CES) framework. It defines approaches to utilize the performance
metrics collected from adaptively instrumenting the cloud and edge
resources to learn and enhance performance interference-aware models
of the distributed resource pool. In turn, the framework optimizes
resource provision in a way that satisfies service level objectives
(SLOs) while minimizing cost to the service providers. This
dissertation evaluates the approach on a variety of real world
scenarios.