Abstract: Technologies for load balancing a storage network include a system. The system includes circuitry to adjust routing rules in a network interface controller to deliver a packet from one of multiple uplinks to one of any physical functions, circuitry to remap, in response to a failure of a switch, a port from one physical function to another physical function, and circuitry to communicate control data between a software defined network controller and one or more agents in one or more host endpoints with a hierarchical distributed hashing table.

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: 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 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: 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: Apparatus and method to dynamically compose network resources are disclosed herein. In some embodiments, a network management fabric controller may include a module, in response to a request for a network service, that is to identify a child pool included in a particular network service pool, from among a plurality of network service pools associated with respective network services, that is capable of providing the network service, the child pool comprising identification of one or more particular ports of a particular compute node switch within the network; and another module that is to establish a connection between a compute component and the one or more particular ports of the particular compute node switch and between the one or more particular ports of the particular compute node switch and one or more particular ports of the main network switch in accordance with the particular network service pool.

Abstract: Techniques and apparatus for managing a distributed computing environment using event digests are described. In one embodiment, for example, an apparatus may include at least one memory, and logic for a system manager, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to determine a workload to schedule, access an event digest associated with a plurality of compute hosts, the event digest comprising event digest values determined using out-of-band information, determine metrics from the event digest, generate at least one host weight for at least a portion of the plurality of compute hosts based on the metrics, identify at least one candidate host from the portion of the plurality of compute hosts based on the at least one host weight, and schedule the workload on the at least one candidate host. Other embodiments are described and claimed.

Abstract: A method comprising is described. The method includes receiving a virtual machine and a service level objective compute consumption metrics (SLO) parameters from a cloud controller, mapping the SLO parameters to resources at the compute node, monitoring utilization and performance of the virtual machine and dynamically adjusting performance of the virtual machine based on monitored performance data.

Abstract: Technologies for determining and storing workload characteristics include an orchestrator server to identify a workload to be executed by a managed node, obtain a profile associated with the workload, wherein the profile includes a model that relates an input parameter set indicative of one of more characteristics of the workload with an output parameter set indicative of one or more aspects of resources to be allocated for execution of the workload, determine, as a function of the input parameter set and the model, resources to allocate to the managed node to execute the workload, and allocate the determined resources to the managed node to execute the workload. Other embodiments are also described and claimed.

Abstract: Technologies for performing orchestration with online analytics of telemetry data include an orchestrator server to assign workloads to each of a set of managed nodes, receive telemetry data indicative of resource utilization from the managed nodes as the workloads are performed, generate data analytics as a function of the telemetry data as the workloads are performed, determine, as a function of the data analytics, adjustments to the workload assignments to increase resource utilization among the managed nodes as the workloads are performed, and apply the determined adjustments to the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Abstract: Technologies for dynamically allocating resources within a self-managed node include a self-managed node to receive quality of service objective data indicative of a performance objective of one or more workloads assigned to the self-managed node. Each workload includes one or more tasks. The self-managed node is also to execute the one or more tasks to perform the one or more workloads, obtain telemetry data as the workloads are performed, determine, as a function of the telemetry data, an adjustment to the allocation of resources among the workloads to satisfy the performance objective, and apply the determined adjustment as the workloads are performed by the self-managed node. Other embodiments are also described and claimed.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads based on resource utilization phase residencies include an orchestrator server to receive resource allocation objective data and determine an assignment of a set of workloads among the managed nodes. The orchestrator server is further to receive telemetry data from the managed nodes, determine, as a function of the telemetry data, phase residency data, determine, as a function of at least the phase residency data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing the achievement of any of the other resource allocation objectives, and apply the adjustment to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: Technologies for dynamically allocating resources among a set of managed nodes include an orchestrator server to receive telemetry data from the managed nodes indicative of resource utilization and workload performance by the managed nodes as the workloads are executed, generate a resource allocation map indicative of allocations of resources among the managed nodes, determine, as a function of the telemetry data and the resource allocation map, a dynamic adjustment to allocation of resources to at least one of the managed nodes to improve performance of at least one of the workloads executed on the at least one of the managed nodes, and apply the adjustment to the allocation of the resources among the managed nodes as the workloads are executed. Other embodiments are also described and claimed.

Abstract: Technologies for assigning workloads based on resource utilization phases include an orchestrator server to assign a set of workloads to the managed nodes. The orchestrator server is also to receive telemetry data from the managed nodes and identify, as a function of the telemetry data, historical resource utilization phases of the workloads. Further, the orchestrator server is to determine, as a function of the historical resource utilization phases and as the workloads are performed, predicted resource utilization phases for the workloads, and apply, as a function of the predicted resources utilization phases, adjustments to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Abstract: Technologies for identifying managed nodes available for workload assignments include an orchestrator server to assign workloads to the managed nodes and receive availability data from the managed nodes, indicative of a determination by each of the managed nodes as to an availability of the managed node to receive an additional workload. The orchestrator server is also to receive telemetry data from the managed nodes, indicative of resource utilization by each of the managed nodes as the workloads are performed. The orchestrator server is also to determine, as a function of the availability data, a reduced set of available managed nodes for analysis, determine, as a function of the telemetry data, adjustments to the workload assignments to increase the resource utilization among the reduced set of managed nodes, and apply the determined adjustments to the reduced set of managed nodes as the workloads are performed.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads to manage heat generation include an orchestrator server to receive resource allocation objective data including a target temperature for one or more of the managed nodes. The orchestrator server is also to determine an initial assignment of a set of workloads among the managed nodes, receive telemetry data from the managed nodes indicative of resource utilization by each of the managed nodes and one or more temperatures and fan speeds of the managed nodes as the workloads are performed, predict future heat generation of the workloads as a function of the telemetry data, determine, as a function of the predicted future heat generation, an adjustment to the assignment of the workloads to achieve the target temperature, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: Technologies for managing the efficiency of workload execution in a managed node include a managed node that includes one or more processors that each include multiple cores. The managed nodes is to execute threads of workloads assigned to the managed node, generate telemetry data indicative of an efficiency of execution of the threads, determine, as a function of the telemetry data, an adjustment to a configuration of the threads among the cores to increase the efficiency of the execution of the threads, and apply the determined adjustment. Other embodiments are also described and claimed.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads with a hierarchical model include an orchestrator server to receive resource allocation objective data.

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.