Abstract: In some examples, for process-to-process communication, such as in function linking, a virtual channel can be provisioned to provide virtual machine to virtual machine communications. In response to a transmit request from a source virtual machine, the virtual channel can cause a data copy from a source buffer associated with the source virtual machine without decryption or encryption. The virtual channel provisions a key identifier for the copied data. The destination virtual machine can receive an indication data is available and can cause the data to be decrypted using a key accessed using the key identifier and source address of the copied data. In addition, the data can be encrypted using a second, different key for storage in a destination buffer associated with the destination virtual machine. In some examples, the key identifier and source address is managed by the virtual channel and is not visible to virtual machine or hypervisor.

Abstract: Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution.

Abstract: Various approaches to efficiently allocating and utilizing hardware resources in data centers while maintaining compliance with a service level objective (SLO) specified for a computational workload is translated into a hardware-level SLO to facilitate direct enforcement by the hardware processor, e.g., using a feedback control loop or model-based mapping of the hardware-level SLO to allocations of microarchitecture resources of the processor. In some embodiments, a computational model of the hardware behavior under resource contention is used to predict the application performance (e.g., as measured in terms of the hardware-level SLO) to be expected under certain contention scenarios. Scheduling of workloads among the compute nodes within the data center may be based on such predictions. In further embodiments, configurations of microservices are optimized to minimize hardware resources while meeting a specified performance goal.

Abstract: Method and system for performing data movement operations is described herein. One embodiment of a method includes: storing data for a first memory address in a cache line of a memory of a first processing unit, the cache line associated with a coherency state indicating that the memory has sole ownership of the cache line; decoding an instruction for execution by a second processing unit, the instruction comprising a source data operand specifying the first memory address and a destination operand specifying a memory location in the second processing unit; and responsive to executing the decoded instruction, copying data from the cache line of the memory of the first processing unit as identified by the first memory address, to the memory location of the second processing unit, wherein responsive to the copy, the cache line is to remain in the memory and the coherency state is to remain unchanged.

Abstract: Embodiments for allocating shared resources are disclosed. In an embodiment, an apparatus includes a core and a hardware rate selector. The hardware rate selector is to, in response to a first indication that demand for memory bandwidth from the core has reached a threshold value, determine a delay value to be used to limit allocation of memory bandwidth to the core. The hardware rate selector includes a controller having a first counter to count a second indication of demand for memory bandwidth from the first core and a second counter to count expirations of time windows. The first indication is based on a difference between the first counter value and the second counter value.

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: Examples include a computing system for receiving memory class of service parameters; setting performance monitoring configuration parameters, based at least in part on the memory class of service parameters, for use by a performance monitor of a memory controller to generate performance monitoring statistics by monitoring performance of one or more workloads by a plurality of processor cores based at least in part on the performance monitoring configuration parameters; receiving the performance monitoring statistics from the performance monitor; and generating, based at least in part on the performance monitoring statistics, a plurality of memory bandwidth settings to be applied by a memory bandwidth allocator to the plurality of processor cores to dynamically adjust priorities of memory bandwidth allocated for the one or more workloads to be processed by the plurality of processor cores.

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 central processing unit can offload table lookup or tree traversal to an offload engine. The offload engine can provide hardware accelerated operations such as instruction queueing, bit masking, hashing functions, data comparisons, a results queue, and a progress tracking. The offload engine can be associated with a last level cache. In the case of a hash table lookup, the offload engine can apply a hashing function to a key to generate a signature, apply a comparator to compare signatures against the generated signature, retrieve a key associated with the signature, and apply the comparator to compare the key against the retrieved key. Accordingly, a data pointer associated with the key can be provided in the result queue. Acceleration of operations in tree traversal and tuple search can also occur.

Abstract: Technologies for analyzing and optimizing workloads (e.g., virtual network functions) executing on edge resources are disclosed. According to one embodiment disclosed herein, a compute device launches a virtualized system including a virtual network function and a performance manager, the performance manager to monitor a current resource usage of the virtual network function as a function of a performance profile. The compute device determines, in response to a determination that one or more quality-of-service (QoS) requirements is not satisfied, whether one or more resources from the platform are available for satisfying the QoS requirements. The compute device receives, in response to a determination that the one or more resources are available for satisfying the QoS requirements, the one or more resources and updates the performance profile as a function of the received resources.

Abstract: A processor of an aspect includes a decode unit to decode an aperture access instruction, and an execution unit coupled with the decode unit. The execution unit, in response to the aperture access instruction, is to read a host physical memory address, which is to be associated with an aperture that is to be in system memory, from an access protected structure, and access data within the aperture at a host physical memory address that is not to be obtained through address translation. Other processors are also disclosed, as are methods, systems, and machine-readable medium storing aperture access instructions.

Abstract: An architecture to perform resource management among multiple network nodes and associated resources is disclosed. Example resource management techniques include those relating to: proactive reservation of edge computing resources; deadline-driven resource allocation; speculative edge QoS pre-allocation; and automatic QoS migration across edge computing nodes.

Abstract: Methods and apparatus implementing Hardware/Software co-optimization to improve performance and energy for inter-VM communication for NFVs and other producer-consumer workloads. The apparatus include multi-core processors with multi-level cache hierarchies including and L1 and L2 cache for each core and a shared last-level cache (LLC). One or more machine-level instructions are provided for proactively demoting cachelines from lower cache levels to higher cache levels, including demoting cachelines from L1/L2 caches to an LLC. Techniques are also provided for implementing hardware/software co-optimization in multi-socket NUMA architecture system, wherein cachelines may be selectively demoted and pushed to an LLC in a remote socket. In addition, techniques are disclosure for implementing early snooping in multi-socket systems to reduce latency when accessing cachelines on remote sockets.

Abstract: A compute device includes one or more processors, one or more resources capable of being utilized by the one or more processors, and a platform interconnect to facilitate communication of messages between the one or more processors and the one or more resources. The compute device is to obtain class of service data for one or more workloads to be executed by the compute device. The class of service data is indicative of a capacity of one or more of the resources to be utilized in the execution of each corresponding workload. The compute device is also to execute the one or more workloads and manage the amount of traffic transmitted through the platform interconnect for each corresponding workload as a function of the class of service data as the one or more workloads are executed.

Abstract: Examples described herein relate to a executing a service mesh in a trust domain in a network interface device and executing one or more services in a second trust domain in one or more devices. In some examples, the network interface device is configured to determine trust domain capabilities of the network interface device and provide the trust domain capabilities based on a query.

Abstract: Apparatuses, methods, and systems for dynamic resource allocation based on quality-of-service prediction are disclosed. In embodiments, an apparatus includes quality-of-service prediction circuitry and a resource controller. The quality-of-service prediction circuitry is to make quality-of-service predictions using a model based at least in part on at least one performance counter measurements and at least one quality-of-service measurement. The resource controller is to allocate one or more shared resources based on the quality-of-service predictions and architectural performance counter measurements.

Abstract: Technologies for providing multi-tenant support in edge resources using edge channels include a device that includes circuitry to obtain a message associated with a service provided at the edge of a network. Additionally, the circuitry is to identify an edge channel based on metadata associated with the message. The edge channel has a predefined amount of resource capacity allocated to the edge channel to process the message. Further, the circuitry is to determine the predefined amount of resource capacity allocated to the edge channel and process the message using the allocated resource capacity for the identified edge channel.

Abstract: A processor of an aspect includes a decode unit to decode an aperture access instruction, and an execution unit coupled with the decode unit. The execution unit, in response to the aperture access instruction, is to read a host physical memory address, which is to be associated with an aperture that is to be in system memory, from an access protected structure, and access data within the aperture at a host physical memory address that is not to be obtained through address translation. Other processors are also disclosed, as are methods, systems, and machine-readable medium storing aperture access instructions.

Abstract: Examples described herein relate to a network interface device. In some examples, the network interface device is to receive a request to transmit data, based on a first reliable transport protocol, and cause the data to be transmitted in at least one packet, based on a second reliable transport protocol, to a destination device and receive at least one packet, from a sender device, based on the second reliable transport protocol and indicate receipt of the at least one packet, based on the first reliable transport protocol, wherein the first reliable transport protocol is different than the second reliable transport protocol.

Abstract: Method and system for performing data movement operations is described herein. One embodiment of a method includes: storing data for a first memory address in a cache line of a memory of a first processing unit, the cache line associated with a coherency state indicating that the memory has sole ownership of the cache line; decoding an instruction for execution by a second processing unit, the instruction comprising a source data operand specifying the first memory address and a destination operand specifying a memory location in the second processing unit; and responsive to executing the decoded instruction, copying data from the cache line of the memory of the first processing unit as identified by the first memory address, to the memory location of the second processing unit, wherein responsive to the copy, the cache line is to remain in the memory and the coherency state is to remain unchanged.