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Title page for ETD etd-06112018-163601


Type of Document Dissertation
Author Shekhar, Shashank
Author's Email Address shashank.shekhar@vanderbilt.edu
URN etd-06112018-163601
Title Algorithms and Techniques for Dynamic Resource Management across Cloud-Edge Resource Spectrum
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Aniruddha S. Gokhale Committee Chair
Douglas C. Schmidt Committee Member
Gautam Biswas Committee Member
Janos Sztipanovits Committee Member
Xenofon Koutsoukos Committee Member
Keywords
  • Cloud computing
  • Optimization
  • Performance interference
  • Resource management
  • Edge computing
  • Fog computing
Date of Defense 2018-05-31
Availability unrestricted
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.

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