Intel Corporation

Andrzej Kuriata

Rameshkumar G Illikkal

Teck Joo Goh

Francesc Guim Bernat

Kshitij Arun Doshi

Ravi L Sahita

Qian Li

Andrew J Herdrich

Susanne M Balle

Alexander Bachmutsky

Mona Vij

Slawomir Putyrski

Nagabhushan Chitlur

Sundar Nadathur

Samuel Ortiz

Anjo Lucas Vahldiek-Oberwagner

Haidong Xia

Wenhui Shu

Long Cui

Mihai Daniel Dodan

Dharmisha Ketankumar Doshi

Dayeol Lee

Jinshi Chen

Mourad Cherfaoui

Daniel Richins

Anna Drewek-Ossowicka

Mihai-Daniel Dodan

Methods, apparatus, and software for remote storage of hardware microservices hosted on other processing units (XPUs) and SOC-XPU Platforms. The apparatus may be a platform including a System on Chip (SOC) and an XPU, such as a Field Programmable Gate Array (FPGA). Software, via execution on the SOC, enables the platform to pre-provision storage space on a remote storage node and assign the storage space to the platform, wherein the pre-provisioned storage space includes one or more container images to be implemented as one or more hardware (HW) microservice front-ends. The XPU/FPGA is configured to implement one or more accelerator functions used to accelerate HW microservice backend operations that are offloaded from the one or more HW microservice front-ends. The platform is also configured to pre-provision a remote storage volume containing worker node components and access and persistently store worker node components.

Remote storage for hardware microservices hosted on XPUs and SOC-XPU platforms

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.

Efficient resource allocation for service level compliance

In function-as-a-service (FaaS) environments, a client makes use of a function executing within a trusted execution environment (TEE) on a FaaS server. Multiple tenants of the FaaS platform may provide functions to be executed by the FaaS platform via a gateway. Each tenant may provide code and data for any number of functions to be executed within any number of TEEs on the FaaS platform and accessed via the gateway. Additionally, each tenant may provide code and data for a single surrogate attester TEE. The client devices of the tenant use the surrogate attester TEE to attest each of the other TEEs of the tenant and establish trust with the functions in those TEEs. Once the functions have been attested, the client devices have confidence that the other TEEs of the tenant are running on the same platform as the gateway.

Scalable attestation for trusted execution environments

Growing community of inventors

Gdansk, Poland

Andrzej Kuriata