Leveraging Resources on Anonymous Mobile Edge Nodes

LEVERAGING RESOURCES ON ANONYMOUS MOBILE EDGE NODES Ahmed Salem Old Dominion University, 2018 Director: Dr. Tamer Nadeem Co-Director: Dr. Ravi Mukkamala Smart devices have become an essential component in our lives. The rapid rise of smartphones, IoTs, and wearable devices has enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing has laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices has granted access to huge amounts of raw data. Researchers have seized the moment with complex algorithms and data models used to process the data over the cloud in order to extract as much information as possible. However, with the pace and amount of data generation, in addition to the networking protocols transmitting data to cloud servers only 20% of what has been generated on the edge of the network was processed. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, and these resources sit idle most of the time. Studies have shown that, for much of the time, resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in close proximity. In this dissertation, we propose harvesting underutilized edge resources then using them in processing the huge amount of data generated, and currently wasted, through applications running at the edge of the network. We propose to flip the concept of cloud computing; instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users’ smart devices. We envision that this approach will enhance the network’s bandwidth, grant access to larger datasets, provide low latency responses, and most importantly include user’s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources about what to do and when to do it? How to orchestrate applications’ execution on multiple devices? and How to communicate between devices at the edge? Communication between devices at the edge uses different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, they do not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, a decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community; however, its low data rate and unicast communication limits its use on the edge. Despite its being the most suitable communication protocol for edge computing and despite the fact that it is unlike other protocols, Bluetooth uses a closed source code that blocks its lower layer from all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then we customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN ), a three-tier framework in which edge devices are clustered by proximity. On finding an application to execute, LAMEN clusters discover and allocate resources, share the application’s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e. the mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices, each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker; this can be extended to any combination of resources. Then, the mediator handles applications’ distribution within a cluster, as needed. Also, we provide a communication protocol that is customizable to the edge environment and the application’s need. Pushing lightweight applications that end devices can execute over their locally generated data has the following benefits: First, it avoids sharing user data with a cloud server, which is a privacy concern for many users; Second, it introduces mediators as local cloud controllers, closer to the edge; Third, it hides the user’s identity behind the mediators; and finally, it enhances bandwidth utilization by keeping the raw data at the edge and by transmitting processed information. Our evaluation shows both an optimized resource lookup and application assignment schemes, in addition to scalability in handling networks with a large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications; however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network and use them in processing data generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not previously feasible.