Amazon Technologies, Inc.

Valentin Gabriel Priescu

Danny Wei

Marc Stephen Olson

James Michael Thompson

James T Pinkerton

Colin Harrison Brace

Benjamin Arthur Hawks

Dragos Diaconescu

Anandh Mahalingam

Rajiv Satish Nair

Memory is identified for a first set of histogram buckets for a metric with bucket value ranges distributed according to a first function, and a second set of histogram buckets with bucket value ranges distributed according to a second function. The second set of buckets overlaps with a metric value range targeted for enhanced granularity analysis. After the histogram is updated in response to obtaining metric values, a representation of at least some of the buckets is sent to a destination.

Tunable-granularity multi-level histograms for efficient computer system metric analysis

Systems and methods for an incast mitigation approach that first modifies network responses for content requests above a threshold size to be delayed according a response latency selected from an established latency range. Additionally, as incast characteristics are analyzed from network traffic, the volumes can selectively modify the individual established latency range to increase the latency range during periods of higher incast characteristics and to decrease the latency range when incast characteristics appear to be decreasing.

Adaptive recovery based on incast

Data storage services may provide customers with access to computing resources of data storage server and throttling methods may be used manage the computing resources of the data storage service in order to ensure that the customers do not overload the data storage servers. Tokens may represent I/O operations executed by the customer of the data storage service. When I/O request are received the data storage service may determine if the I/O request is a member of a sequence and remove a reduced number for tokens from the work token bucket as a result. The data storage server may determine the I/O request is a member of a sequence based at least in part on a logical chunk of a storage volume the I/O request maps to.

Approximating sequential workloads on resource constrained systems

The current document describes a storage system that provides a storage volume with a block-data interface to one or more clients. The storage system provides a key-value store that is associated with the storage volume. The clients access the key-value store via a key-value interface that provides a number of key-value operations to read, write, list, and delete key-value records from the key-value store. In some implementations, the key-value interface provides additional key-value operations that support the use of the key-value store by more than one client at a time. An environment in which the key-value store is used by the clients to retain I/O throttling parameters is described.

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Seattle, WA, United States of America

Valentin Gabriel Priescu