Amazon Technologies, Inc.

Timothy David Gasser

Thomas A Volpe

Mark Bradley Davis

Robert Michael Johnson

Ratnakar Choudhary

Vithal Dattatraya Shirodkar

Chao Wang

Programmatically defined fields of metadata for a network packet may be generated. Instructions indicating different portions of data from different headers of a network packet may be stored at a packet processor. When a network packet is received, the different portions of the data may be extracted from the different headers of the packet according to the instructions and provided to other stages of the packet processor for processing. Different portions of the same programmatically defined field may be utilized at different stages in the packet processor. The programmatically defined field may be used to generate a hash value that selects an entry in a lookup table describing a forwarding decision for a network packet.

Generating programmatically defined fields of metadata for network packets

Techniques for monitoring and optimizing Monte Carlo simulations within a provider network are described. A metric representing a similarity between a first data distribution associated with a Monte Carlo simulation template and a second data distribution associated with a data source is generated and evaluated against a condition based on a threshold. A new Monte Carlo simulation template is generated based on the Monte Carlo simulation template. A Monte Carlo simulation is run based on the new Monte Carlo simulation template using a plurality of virtual machines (VMs).

Monte Carlo simulation monitoring and optimization

Techniques for performing Monte Carlo simulations within a provider network are described. A request to perform a Monte Carlo simulation is received that specifies a template identifying input parameters to the simulation. The simulation is performed in one or more virtual machines (VMs) by sampling input values from an input data distribution stored at a first network location specified in the template and by processing the sampled input values using an input parameter specified in the template to form an output distribution that is stored at a second network location specified in the template. A statistic of the output distribution is generated and sent to an electronic device.

Configuring and deploying Monte Carlo simulation pipelines

Techniques for optimizing background tasks based on forecast data are described. Customer workloads may be monitored by a local monitor in a first time period. Future customer workloads in a second time period following the first time period may be forecast based at least on the customer workloads using a local model. A background availability may be determined based at least on the future customer workloads. Execution of at least one background workload may be scheduled to use the background availability during the second time period. The local monitor may then cause the execution of the at least one background workload.

Optimizing background tasks based on forecast data

Intelligent compression of data storage volumes in a service provider system. For example, in one embodiment of a computer-implemented method, attachment metrics are compiled for block storage volumes coupled to a storage server. The attachment metrics may include temporal data related to block storage volume detachments and attachments in relation to a plurality of compute instances; and prioritizing compression of the block storage volumes based on the attachment metrics; and compressing the block storage volumes in accordance with the prioritization.

System and method for storage volume compression

Methods, systems, and computer-readable media for dynamic and selective hardware acceleration are disclosed. An indirection layer receives a plurality of computation calls generated by an application. The application and the indirection layer are executed by at least one processor of a computing device, and the application links to the indirection layer. The indirection layer determines whether to execute individual computation calls using the at least one processor or using the hardware accelerator. The indirection layer sends one or more of the computation calls and associated data to the hardware accelerator. The one or more of the computation calls are executed using the hardware accelerator, and one or more results are returned from the hardware accelerator to the indirection layer.

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Seattle, WA, United States of America

Timothy David Gasser