The present disclosure relates to a method and system for optimizing a parameter configuration of a distributed computing job. The method includes: obtaining job programs of different distributed computing jobs, and determining a key parameter configuration set; obtaining a cluster status during execution of the distributed computing job, randomly generating a sample data set based on the key parameter configuration set and the cluster status, and establishing a performance prediction model; correcting the performance prediction model by using a multi-objective genetic algorithm and an optimization module configured with an optimal configuration selection strategy; obtaining a job program of a to-be-optimized distributed computing job and a cluster status during execution of the to-be-optimized distributed computing job, and determining a to-be-optimized key parameter configuration item combination; and inputting, to the performance prediction model, the to-be-optimized key parameter configuration item combination and the cluster status during execution of the to-be-optimized distributed computing job, and outputting a key parameter configuration item combination with a shortest execution time. The present disclosure can rapidly and effectively optimize the key parameter configuration.

Systems, apparatuses and methods may provide for technology that initializes an integrated memory of a processor during a boot sequence and conducts a runtime initialization of an external system memory associated with the processor. The technology may also bypass the runtime initialization of the external system memory during the boot sequence.

An example method of placing a virtual machine (VM) in a cluster of hosts is described. Each of the hosts having a hypervisor managed by a virtualization management server for the cluster, the hosts separated into a plurality of nonuniform memory access (NUMA) domains. The method including: comparing a virtual central processing unit (vCPU) and memory configuration of the VM with physical NUMA topologies of the hosts; selecting a set of the hosts spanning at least one of the NUMA domains, each host in the set of hosts having a physical NUMA topology that maximizes locality for vCPU and memory resources of the VM as specified in the vCPU and memory configuration; and providing the set of hosts to a distributed resource scheduler (DRS) executing in the virtualization management server, the DRS configured to place the VM in a host selected from the set of hosts.

An access control system controls access to a shared resource for various functional circuits. The access control system can include a comparison circuit, a processing circuit, and a selection circuit. The comparison circuit receives identification data associated with a functional circuit based on a transaction initiated by the functional circuit, and compares the identification data and reference data to generate a select signal. The processing circuit receives error data and response data outputted by the shared resource based on an execution of the transaction, and generates another response data. The selection circuit selects and outputs, based on the select signal, one of the response data outputted by the shared resource and the response data generated by the processing circuit as a transaction response that is to be provided to the functional circuit.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

In an approach for generating differentiated workload telemetry data, a processor corresponds one or more services with a workload related telemetry generating an event emitter. A processor performs a correlation analysis of corresponding relationship and connection among connected resources and current traffic into and out of the one or more services. A processor labels domain context for each telemetry event. A processor communicates each telemetry event to a global event handler. A processor performs a cross-correlation in real-time of telemetry data with the global event handler. A processor updates a real-time differentiated workload report.

An apparatus comprises at least one processing device that includes a processor coupled to a memory. The processing device is configured to identify a plurality of resource objects associated with a processing device, to group correlated resource objects according to processing device utilization of the resource objects, to assign a first weight to a first resource object grouping, wherein the first weight is associated with a performance impact of the first resource object grouping on the processing device, and to release at least some of the first resource object grouping to provide additional resources to a second resource object grouping, the additional resources resulting from the releasing, wherein the first object grouping is selected for the releasing based on a comparison between the first weight and a second weight associated with the second resource object grouping, wherein the releasing is performed to improve performance of the processing device.

A compute instance is instrumented to detect certain kernel memory allocation functions, in particular functions that allocate heap memory and/or make allocated memory executable. Dynamic shell code exploits can then be detected when code executing from heap memory allocates additional heap memory and makes that additional heap memory executable.

In some examples, a computing system receives an indication of an increased workload portion to be added to a workload of a storage system, the workload comprising buckets of operations of different characteristics. The computing system computes, based on quantities of operations of the different characteristics in the workload, factor values that indicate distribution of operations of the increased workload portion to the buckets of operations of the different characteristics, and distributes, according to the factor values, the operations of the increased workload portion into the buckets of operations of the different characteristics.

In an application execution system having a plurality of application servers, each application server stores a plurality of applications, and has computational resources for executing applications in response to received requests. Each application server also includes instructions for loading a respective application into volatile storage and executing the application in response to a request from a client, and for returning a result. A generic application instance may be cloned, creating a pool of generic application instance clones that can be loaded with code for a requested application to produce an application instance. The application instance can then be stored in a cache to be used for a future application request.