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Resource Allocationfor Mobile Edge Clouds

Research Area:  Edge Computing


   Recent advances in Internet technologies have led to the proliferation of new distributed applications in the transportation, healthcare, mining, security, and entertainment sectors. The emerging applications have characteristics such as being bandwidth-hungry, latency-critical, and applications with a user population contained within a limited geographical area, and require high availability, low jitter and security.
   This thesis addresses the above challenges by proposing several models, algorithms,and simulation and software frameworks. In the first part, we investigate methods for early detection of short-lived and significant increase in demand for computing resources (also called spikes) which may cause significant degradation in the performance of a distributed application. We make use of adaptive signal processing techniques for early detection of spikes. We then consider trade-offs between parameters such as the time taken to detect a spike and the number of false spikes that are detected. In thesecond part, we study the resource planning problem where we study the cost benefitsof adding new compute resources based on performance requirements for emergingapplications. In the third part, we study the problem of allocating resources to appli-cations by formulating as an optimization problem, where the objective is to minimizeoverall operational cost while meeting the performance targets of applications. We also propose a hierarchical scheduling framework and policies for allocating resources to applications based on performance metrics of both applications and compute resources.In the last part, we propose a framework,Calvin Constrained, for resource-constrained devices, which is an extension of the Calvin reference framework and supports a limited but essential subset of the features of the reference framework taking into account the limited memory and processing power of the resource-constrained IoT devices.

Name of the Researcher:  Amardeep Mehta

Name of the Supervisor(s):  Erik Elmroth

Year of Completion:  2018

University:  Umea University

Thesis Link:   Home Page Url