Abstract: Systems described herein operate to improve network performance in a multi-tenant cloud computing environment. Systems can include communication circuitry and processing circuitry to generate a phase sequence matrix that indicates the identity and number of phases of a workload by measuring resources of the compute node during execution of the workload throughout a lifetime of the workload. The processing circuitry can generate a workload fingerprint that includes the phase sequence matrix and a phrase residency matrix. The phase residency matrix can indicate the fraction of execution time of the workload spent in each phase identified in the phase sequence matrix. A cloud controller can access the workload fingerprint for multiple workloads operating on multiple compute nodes in the cloud cluster to adjust workload allocations based at least on these workload fingerprints and on whether service level objectives (SLOs) are being met.

Abstract: Technologies for providing dynamically managed quality of service in a distributed storage system include an apparatus having a processor. The processor is to determine capabilities of one or more compute devices of the distributed storage system. The processor is also to obtain an indicator of a target quality of service to be provided by the distributed storage system, determine target performance metrics associated with the target quality of service, determine target configuration settings for the one or more compute devices of the distributed storage system to provide the target quality of service, configure the one or more compute devices with the target configuration settings, determine whether a present performance of the distributed storage system satisfies the target quality of service, and reconfigure the one or more compute devices in response to a determination that the target quality of service is not satisfied. Other embodiments are described and claimed.

Abstract: Technologies for deploying dynamic underlay networks in a cloud computing infrastructure include a network controller of the cloud computing infrastructure communicatively coupled via disaggregated switches to one or more compute nodes of the cloud computing infrastructure. The network controller is configured to receive tenant network creation requests from a cloud operating system (OS) of the cloud computing infrastructure indicating that a tenant network is to be created in the cloud computing infrastructure (e.g., for a new tenant of the cloud computing infrastructure). The network controller is configured to provision an underlay network to support the tenant network based on identified physical resources using criteria specified by the cloud OS and transmit information of the provisioned underlay network to the cloud OS that is usable to create a cloud visible overlay network associated with the underlay network. Other embodiments are described herein.

Abstract: Technologies for contention-aware cloud compute scheduling include a number of compute nodes in a cloud computing cluster and a cloud controller. Each compute node collects performance data indicative of cache contention on the compute node, for example, cache misses per thousand instructions. Each compute node determines a contention score as a function of the performance data and stores the contention score in a cloud state database. In response to a request for a new virtual machine, the cloud controller receives contention scores for the compute nodes and selects a compute node based on the contention score. The cloud controller schedules the new virtual machine on the selected compute node. The contention score may include a contention metric and a contention score level indicative of the contention metric. The contention score level may be determined by comparing the contention metric to a number of thresholds. Other embodiments are described and claimed.

Abstract: Technologies for contention-aware cloud compute scheduling include a number of compute nodes in a cloud computing cluster and a cloud controller. Each compute node collects performance data indicative of cache contention on the compute node, for example, cache misses per thousand instructions. Each compute node determines a contention score as a function of the performance data and stores the contention score in a cloud state database. In response to a request for a new virtual machine, the cloud controller receives contention scores for the compute nodes and selects a compute node based on the contention score. The cloud controller schedules the new virtual machine on the selected compute node. The contention score may include a contention metric and a contention score level indicative of the contention metric. The contention score level may be determined by comparing the contention metric to a number of thresholds. Other embodiments are described and claimed.

Abstract: Technologies for adjusting server power consumption include a computing device to receive resource utilization data from a plurality of servers. The computing device identifies a source server and a destination server of the plurality of servers based on the resource utilization data and calculates the difference in resource utilization between the identified source server and the identified destination server based on the resource utilization data. If the computing device determines that the difference exceeds a threshold value, the computing device increases processor utilization of a processor of the source server by a power consumption change amount and decreases processor utilization of a processor of the destination server by a corresponding power consumption change amount to balance the servers' resources and create more head-room to place new workloads on the servers.

Abstract: The present disclosure is directed to capability determination for computing resource allocation. A device may comprise a management engine (ME) to determine device information for use in generating an enhanced universally unique identifier (UUID) based on a UUID corresponding to the device. The ME may interact with equipment in the device to obtain the device information, and may augment the UUID using at least part of the device information. Device information may include a device media access control (MAC) address, a central processing unit (CPU) identification (ID) for at least one CPU in the device and a device capability ID. The capability ID may be generated utilizing capability information obtained from the equipment, and may be encoded into the capability ID based on tables that describe different capabilities. The device may provide the enhanced UUID to a group agent that may group the device with other devices comprising similar capabilities.

Abstract: A method comprising is described.

Abstract: Technologies for adjusting server power consumption include a computing device to receive resource utilization data from a plurality of servers. The computing device identifies a source server and a destination server of the plurality of servers based on the resource utilization data and calculates the difference in resource utilization between the identified source server and the identified destination server based on the resource utilization data. If the computing device determines that the difference exceeds a threshold value, the computing device increases processor utilization of a processor of the source server by a power consumption change amount and decreases processor utilization of a processor of the destination server by a corresponding power consumption change amount to balance the servers' resources and create more head-room to place new workloads on the servers.

Abstract: A method, system, and device for managing hardware resources in a cloud scheduling environment includes a zone controller. The zone controller can manage groups of node servers in a cloud datacenter using a checkin service. The checkin service allows server groups to be created automatically based on one or more hardware characteristics of the node servers, server health information, workload scheduling or facilities management parameters, and/or other criteria.

Abstract: A method, system, and device for managing hardware resources in a cloud scheduling environment includes a zone controller. The zone controller can manage groups of node servers in a cloud datacenter using a checkin service. The checkin service allows server groups to be created automatically based on one or more hardware characteristics of the node servers, server health information, workload scheduling or facilities management parameters, and/or other criteria.

Abstract: In an embodiment, a processor includes a plurality of counters each to provide a count of a performance metric of at least one core of the processor, a plurality of threshold registers each to store a threshold value with respect to a corresponding one of the plurality of counters, and an event logic to generate an event digest packet including a plurality of indicators each to indicate whether an event occurred based on a corresponding threshold value and a corresponding count value. Other embodiments are described and claimed.

Abstract: In accordance with some embodiments, a cloud service provider may operate a data center in a way that dynamically reallocates resources across nodes within the data center based on both utilization and service level agreements. In other words, the allocation of resources may be adjusted dynamically based on current conditions. The current conditions in the data center may be a function of the nature of all the current workloads. Instead of simply managing the workloads in a way to increase overall execution efficiency, the data center instead may manage the workload to achieve quality of service requirements for particular workloads according to service level agreements.

Abstract: A method, system, and device for managing hardware resources in a cloud scheduling environment includes a zone controller. The zone controller can manage groups of node servers in a cloud datacenter using a checkin service. The checkin service allows server groups to be created automatically based on one or more hardware characteristics of the node servers, server health information, workload scheduling or facilities management parameters, and/or other criteria.

Abstract: A system and method provide for receiving data corresponding to a computing node and identifying a processor usage, a memory usage and an input/output (IO) usage based at least in part on the data corresponding to the computing node. In addition, a compute usage value may be determined for the computing node based at least in part on the processor usage, the memory usage and the IP usage.