One embodiment includes data communication apparatus including a storage sub-system to be connected to storage devices, and processing circuitry to manage transfer of content with the storage devices over the storage sub-system responsively to content transfer requests, while pacing commencement of serving of respective ones of the content transfer requests responsively to availability of spare data capacity of the storage sub-system, find a malfunctioning storage device currently assigned a given data capacity of the storage sub-system and currently assigned to serve at least one content transfer request, and reallocate the given data capacity of the storage sub-system currently assigned to the malfunctioning storage device for use by at least another one of the storage devices while the at least one content transfer request assigned to be served by the malfunctioning storage device is still awaiting completion by the malfunctioning storage device.

A system includes a plurality of watchdog components. Each watchdog component is configured to receive a kick signal from its monitored function to determine whether the monitored function is active. Each watchdog component is further configured to receive a respective token from all watchdog components that the each watchdog component is connected to. The respective token determines whether its respective watchdog component has timed out. Each watchdog component is further configured to generate a token responsive to the kick signal and further responsive to the respective token from all watchdog component that the each watchdog component is connected to. Each watchdog component is further configured to transmit the generated token to the all watchdog components that the each watchdog component is connected to.

In one embodiment, a method for margin determination for a computing system with a real time operating system and priority preemptive scheduling comprises: scheduling a set of tasks to be executed in one or more partitions, wherein each is assigned a priority, wherein the tasks comprise periodic and/or aperiodic tasks; executing the set of tasks on the computing system within the scheduled periodic time window; introducing an overhead task executed for an execution duration controlled either by the real time operating system or by the overhead task; controlling the overhead task to converge on a point of failure at which a length of the execution duration of the overhead task causes either: 1) a periodic task to fail to execute within a deadline, or 2) time available for the aperiodic tasks to execute to fall below a threshold; and defining a partition margin corresponding to the point of failure.

Technologies are provided to ensure integrity of erasure coded data that is subject to read and write access from distributed processes. Multiple processes that access erasure coded data can be coordinated in an efficient, scalable and fault-tolerant manner so that integrity of the original data is maintained. The Technologies include a fault-tolerant access coordination protocol that ensures exclusive write access by a client. The coordination protocol achieves scalability by not relying on centralized components, and achieves efficiency and performance by piggy-packing access coordination messages on operations of the underlying erasure coding protocol.

Methods, systems, and devices for reporting control information errors are described. A state of a memory array may be monitored during operation. After detecting an error (e.g., in received control information), the memory device may enter a first state (e.g., a locked state) and may indicate to a host device that an error was detected, the state of the memory array before the error was detected, and/or at least a portion of a control signal carrying the received control information. The host device may diagnose a cause of the error based on receiving the indication of the error and/or the copy of the control signal. After identifying and/or resolving the cause of the error, the host device may transmit one or more commands (e.g., unlocking the memory device and returning the memory array to the original state) based on receiving the original state from the memory device.

An analytics server receives from client computing devices end-user events. Each client computing device is operated by an end-user to access an application at a web server based on the end-user events resulting in calls being passed through a proxy to the web server. The analytics server receives from the proxy the calls being made to the web server, and receives return responses from the web server being passed through the proxy. The return responses correspond to activities being performed within the application. The end-user events are correlated with the corresponding calls and return responses from the proxy. Respective correlated end-user events, calls and return responses are translated into respective event vectors. The respective event vectors are processed to determine similarities among the client computing devices. The similar activities are associated with a quality indicator to identify anomalies within the application for corrective action to be taken.

Described is an improved approach to implement fast detection of node death. Instead of just relying on multiple heart beats to fail in order to determine whether a node is dead, the present approach performs an on demand validation using RDMA to determine whether the node is reachable, where the approach of using RDMA is significantly faster than the heartbeat approach.

A lock is applied automatically without any user involvement, to each of one or more data objects stored by the data processing system. Each of the one or more locks prevent deletion, by any user, of the one or more data objects within a predetermined time frame. A cloud garbage collector marks the one or more data objects for a) deletion, or b) for lock extension, the garbage collector scheduled to run periodically at a period that is less than the predetermined time frame. The deletion or the lock extension is executed for the one or more data objects, based on marks by the garbage collector. Other embodiments are described and claimed.

Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

A signal tuning method for a peripheral component interconnect express (PCIe) includes assigning a first signal setting to the PCIe to generate a first PCIe signal, and tuning a link by the first PCIe signal, and determining whether to assign a second signal setting to the PCIe according to a signal status of the link, for generating a second PCIe signal to tune the link; wherein the PCIe is connected to a plurality of electronic devices via the link.