Mobile device clusters as edge compute resources: Design, deployment, and role in the computing ecosystem
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Edge computing offers an alternative to centralized, in-the-cloud compute services. Among the potential advantages of edge-computing are lower latency that improves responsiveness, reduced wide-area network congestion, and possibly greater privacy by keeping data more local. However, widely deploying the needed edge-compute resources requires (1) provisioning the load introduced at various locations, (2) huge initial deployment cost and management expenses, and (3) continuous upgrades to keep up with the increase in demand. The availability of under-utilized mobile and personal computing devices at the edge provides a potential solution to these deployment challenges. In this thesis, we propose taking advantage of clusters of co-located mobile devices to offer an edge computing platform. Scenarios with co-located devices include, but are not limited to, passengers with mobile devices using public transit services, students in classrooms and groups of people sitting in a coffee shop. We propose, design, implement and evaluate the Femtocloud system which provides a dynamic, self-configuring and multi-device mobile cloud out of a cluster of mobile devices. Within the Femtocloud system, we develop a variety of adaptive mechanisms and algorithms to manage the workload on the edge-resources and effectively mask their churn. These mechanisms enable building a reliable and efficient edge computing service on top of unreliable, voluntary resources. Our work also includes building a system that enable mobile devices to accurately and efficiently acquire knowledge of the existing compute service providers, their compute capacities, and the network parameters while communicating with each of these providers. Such data is acquired through measurements that involve a set of voluntary mobile devices and is be used to allow allow mobile devices to select the compute service provider that matches their demand and meets their target level of quality of experience. The data acquired by our system can also be used by compute service providers to identify potential locations for service deployment and discover any shortcomings in their existing deployments.
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