Abstract: Systems and methods are described for implementing an “arc jump” technique in conjunction with bounded loads in consistent hashing. In general, bounded loads refers to limiting the ability of a single device within a distributed system to store data objects, such that when a request to store a new data object would otherwise be directed to that device, it is instead redirected to an alternative device. Redirecting all requests to a single alternative device can lead to cascading failures, as the alternative device must maintain its own load and that which has been redirected to it. Embodiments of the present disclosure address this by determining an alternative device on a per-object basis, such as by again hashing the object with an additional seed value. This distributes request from an overloaded device among all other devices of the distributed system, avoiding cascading failures.

Abstract: Systems and methods are described for load balancing requests in a distributed system using consistent hashing. Specifically, systems and methods are described for using “the power of k choices” when placing new servers on a consistent hash ring used to load balance requests. Rather than placing each new server at a fixed point determined by a hashing algorithm, a load balancer can identify multiple potential points on the hash ring for the new server. The load balancer can then compare these points to determine a preferred location, and place the server at the preferred location. Techniques described herein can substantially improve placement of servers, which in turn results in better load balancing.

Abstract: A device, system, and methods are described to perform machine-learning camera-based indoor mobile positioning. The indoor mobile positioning may utilize inexact computing, wherein a small decrease in accuracy is used to obtain significant computational efficiency. Hence, the positioning may be performed using a smaller memory overhead at a faster rate and with lower energy cost than previous implementations. The positioning may not involve any communication (or data transfer) with any other device or the cloud, providing privacy and security to the device. A hashing-based image matching algorithm may be used which is cheaper, both in energy and computation cost, over existing state-of-the-art matching techniques. This significant reduction allows end-to-end computation to be performed locally on the mobile device.

Abstract: A device, system, and methods are described to perform machine-learning camera-based indoor mobile positioning. The indoor mobile positioning may utilize inexact computing, wherein a small decrease in accuracy is used to obtain significant computational efficiency. Hence, the positioning may be performed using a smaller memory overhead at a faster rate and with lower energy cost than previous implementations. The positioning may not involve any communication (or data transfer) with any other device or the cloud, providing privacy and security to the device. A hashing-based image matching algorithm may be used which is cheaper, both in energy and computation cost, over existing state-of-the-art matching techniques. This significant reduction allows end-to-end computation to be performed locally on the mobile device.

Abstract: A device, system, and methods are described to perform machine-learning camera-based indoor mobile positioning. The indoor mobile positioning may utilize inexact computing, wherein a small decrease in accuracy is used to obtain significant computational efficiency. Hence, the positioning may be performed using a smaller memory overhead at a faster rate and with lower energy cost than previous implementations. The positioning may not involve any communication (or data transfer) with any other device or the cloud, providing privacy and security to the device. A hashing-based image matching algorithm may be used which is cheaper, both in energy and computation cost, over existing state-of-the-art matching techniques. This significant reduction allows end-to-end computation to be performed locally on the mobile device.