Abstract: A server rack includes an optical patch panel and a plurality of processing units. The optical patch panel includes a plurality of L1 connections, a plurality of L2 connections, and a removable connectivity cartridge optically connecting the plurality of L1 connections and the plurality of L2 connections. The plurality of processing units is connected to an L2 domain of a datacenter through the removable connectivity cartridge of the optical patch panel.

Abstract: A server rack is provided that includes a plurality of tray support structures each configured to support a tray. Each support structure accommodates a selected one of air cooling or liquid cooling of the tray supported by the support structure.

Abstract: According to an aspect of the present disclosure, a tray is provided that includes a plurality of groups of processing units. Each group of processing units includes one or more processing units in communication with one another. In certain embodiments, communication between pairs of groups of processing units is enabled by an on-tray communication bridge. Each of the one or more processing units are one of a CPU, GPU, DPU, QPU, PPU and/or the like.

Abstract: A mobile communication device having a plurality of mobile devices coupled to one another. The mobile communication device includes a first mobile device that has a screen display portion and a user input portion. The mobile communication device also includes at least one second mobile device detachably coupled to the first mobile device. The first mobile device is configured to function as a first standalone mobile communication device, and the second mobile device is configured to function as a second standalone mobile communication device when detached from the first mobile device. The second mobile device is detachably coupled to the first mobile device in such a way that the first mobile device continues to include the display screen portion and the user input portion when the second mobile device is detached from the first mobile device.

Abstract: Methods and systems are described for predicting the likely causes of service outages using only time information, and for predicting and the likely costs of service outages. The likely causes are found by defining a narrow likely cause window around an outage based on service quality and/or service usage data, and correlating service events to the likely cause window in the time domain to find a probability distribution for the events. The likely costs are found by measuring usage loss and duration for a given point during an outage and using cost component functions of the time and usage to extrapolate over the outage. These cause and cost predictions supply service administrators with tools for making more informed decisions about allocation of resources in preventing and correcting service outages.

Abstract: Methods and systems are described for predicting the likely causes of service outages using only time information, and for predicting and the likely costs of service outages. The likely causes are found by defining a narrow likely cause window around an outage based on service quality and/or service usage data, and correlating service events to the likely cause window in the time domain to find a probability distribution for the events. The likely costs are found by measuring usage loss and duration for a given point during an outage and using cost component functions of the time and usage to extrapolate over the outage. These cause and cost predictions supply service administrators with tools for making more informed decisions about allocation of resources in preventing and correcting service outages.

Abstract: Methods and systems are described for predicting the likely causes of service outages using only time information, and for predicting and the likely costs of service outages. The likely causes are found by defining a narrow likely cause window around an outage based on service quality and/or service usage data, and correlating service events to the likely cause window in the time domain to find a probability distribution for the events. The likely costs are found by measuring usage loss and duration for a given point during an outage and using cost component functions of the time and usage to extrapolate over the outage. These cause and cost predictions supply service administrators with tools for making more informed decisions about allocation of resources in preventing and correcting service outages.