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: Technologies for managing disaggregated resources in a data center includes a compute device configured to determine that a service related task has been generated and create one or more microservices to perform the created service related task using at least one of a plurality of services managed by the microservice resource controller circuitry. The compute device is further configuration to generate one or more microtasks to compose at least one service based on the one or more microservices. Other embodiments are described herein.

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: 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 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 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 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 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: 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: Technologies for storage cluster quality of service (QoS) management include multiple compute devices in communication via a storage network. A controller node monitors network traffic of the storage cluster. The network traffic includes a replication traffic class and a rebuild traffic class. The controller node determines whether burst bandwidth is required by the storage cluster and, if so, applies a group policy indicative of burst bandwidth to the storage cluster. The group policy may be applied to an end to end path of the storage cluster. Applying the group policy may include setting one or more bits or fields of an overlay network header of network traffic of the storage cluster. Other embodiments are described and claimed.

Abstract: Technologies for switch link and ply management for variable oversubscription ratios include powering up and down links of one or more network plys according to bandwidth demand, desired oversubscription ratio and/or other parameters. Telemetry data representing one or more network traffic metrics of one or more switch plies is monitored to determine respective power states of the plurality of links associated with the one or more switch plies as a function of a desired oversubscription ratio calculated based on the telemetry data. The respective power state of the plurality of links is set accordingly.

Abstract: A switch or network interface can detect congestion caused by a flow of packets. The switch or network interface can generate a congestion hint packet and send the congestion hint packet directly to a source transmitter of the flow of packets that caused the congestion. The congestion hint packet can include information that the source transmitter can use to determine a remedial action to attempt to alleviate or stop congestion at the switch or network interface. For example, the transmitter can reduce a transmit rate of the flow of packets and/or select another route for the flow of packets. Some or all switches or network interfaces between the source transmitter and a destination endpoint can employ flow differentiation whereby a queue is selected to accommodate for a flow's sensitivity to latency.

Abstract: Technologies for latency based service level agreement (SLA) management in remote direct memory access (RDMA) networks include multiple compute devices in communication via a network switch. A compute device determines a service level objective (SLO) indicative of a guaranteed maximum latency for a percentage of RDMA requests of an RDMA session. The compute device receives latency data indicative of latency of an RDMA request from a host device. The compute device determines a priority associated with the RDMA request as a function of the SLO and the latency data. The compute device schedules the RDMA request based on the priority. The network switch may allocate queue resources to the RDMA request based on the priority, reclaim the queue resources after the RDMA request is scheduled, and then return the queue resources to a free pool. Other embodiments are described and claimed.

Abstract: Technologies for network interface controllers (NICs) include a compute sled and an accelerator sled in communication over a network. The accelerator sled configures a virtual switch endpoint associated with a remote direct memory access (RDMA) server instance that is associated with a field-programmable gate array (FPGA) of the accelerator sled. The accelerator sled updates local software defined networking (SDN) tables with a virtual tunnel associated with the virtual switch endpoint and a remote compute sled. A virtual switch of the accelerator sled switches virtual tunnel traffic from the remote compute sled to the RDMA server instance, which transfers data to or from the FPGA. The compute sled also updates a local SDN table with the virtual tunnel, and a virtual switch of the compute sled switches virtual tunnel traffic to or from the accelerator sled. Other embodiments are described and claimed.

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: Technologies for remote direct memory access (RDMA) queue pair quality of service (QoS) management are disclosed. In the illustrative embodiment, several queue pairs associated with a virtual machine on a compute sled may be created in a network interface controller of the compute sled. A QoS parameter such as a class of service identifier or a weighting may be assigned to each queue pair such that each queue pair has a different available bandwidth. The compute sled may also predict future RDMA queue pair bandwidth usage and adjust RDMA queue pair bandwidth allocation based on the prediction.

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 providing latency-aware consensus management in a disaggregated system include a compute device. The compute device includes circuitry to determine latencies associated with subsystems of the disaggregated system. Additionally, the circuitry is to determine, as a function of the determined latencies, a time period in which a configuration change to the disaggregated system is to reach a consistent state in the subsystems.

Abstract: Technologies for managing burst bandwidth requirements are disclosed. In the illustrative embodiment, a software-defined network (SDN) controller monitors storage devices in a data center. If a storage device fails, the SDN controller manages the bandwidth used to replicate the data that was stored on the failed storage device. The SDN controller may allocate an initial amount of bandwidth based on one or more parameters of the storage device, and the SDN controller may increase the bandwidth in a series of discrete steps. In another embodiment, the SDN controller may predict a bandwidth burst based on sequential writes at a storage sled from several compute devices, and allocate bandwidth accordingly in a tiered manner.

Abstract: Technologies for providing in-processor workload phase detection include a sled having a compute engine, which itself includes a performance monitor unit. The compute engine obtains telemetry data from the performance monitor unit. The performance monitor unit produces telemetry data indicative of performance metrics of the sled during execution of one or more workloads. The telemetry data is indicative of a resource utilization and workload performance by the sled as the workloads are executed. The compute engine determines, from a lookup table indicative of resource utilization phases, a resource utilization phase based on the obtained telemetry data. A workload fingerprint is updated based on the determined resource utilization phase, and the workload fingerprint is output. Other embodiments are also described and claimed.