Amazon Technologies, Inc.

Apptio, Inc.

Eric Yves Theriault

Jason George McHugh

Seth William Markle

Paul Damien McLachlan

Supin Ko

Alyssa H Henry

Michael A Uhlar

Embodiments are directed towards for managing data models, including resource allocation forecasting. A main data model may be provided. A delta ratio value based on a difference between modified cloned resource values and their corresponding original resource values in the main data model may be provided. Line items from the cloned data model associated with the one or more modified cloned resource values may be provided. Each of the cloned line items may be modified based on the delta ratio value. The modified cloned line items may be stored in the cloned data model. Reports including report information based on the cloned data model may be provided. The report information may indicate changes that were made to one or more other cloned resource values based on the modifications to the one or more cloned resource values.

Resource allocation forecasting

Various embodiments are directed towards allocating costs for a plurality of cost objects. In at least one of the various embodiments, a source object and a target object in a data model may be determined such that an allocation rule is used to define one or more cost allocations where the costs flows from the source object to the target object. Allocation rules that are part of a recursive allocation rule may be executed on the source object and the target object until a terminal condition is met. The cost value that corresponds to the source object may be modified based on the allocation rule and the generated cost value. This process may continue until a terminal condition is met. After the terminal condition has been met final costs value corresponding to the target object and the source object may be generated and displayed.

Recursive processing of object allocation rules

Embodiments may include a consistency measurement component that utilizes memory-efficient sets (e.g., Bloom filters) assigned to different time periods for tracking when different write operations are performed on replicated data objects within a distributed data store. The consistency measurement component may evaluate whether read operations directed to the distributed data store are inconsistent. To do so, the consistency measurement component may determine, for a given read operation, the age of the value read from a given replicated data object (e.g., by evaluating a 'last-modified' timestamp). The consistency measurement component may identify a memory-efficient set that includes the key of that replicated data object in order to determine when the replicated data object was last written to. If the age of the value read is older than the time at which the replicated data object was last written to, the consistency measurement component may determine that the read operation was inconsistent.

System and method for measuring consistency within a distributed storage system

Embodiments may include a consistency measurement component that utilizes memory-efficient sets (e.g., Bloom filters) to generate consistency metrics for read operations performed on different replicated data objects of distributed storage system. Based on the consistency metrics, the consistency measurement component may identify a subset of replicated data objects associated with low levels of consistency. The consistency measurement component may target this subset for consistency improvement by generating instructions to improve the consistency of the subset. In other cases, the consistency measurement component may notify a consistency improvement component about the targeted subset. In response, the consistency improvement component may generate instructions to improve the consistency of the targeted subset.

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Seattle, WA, United States of America

Eric Yves Theriault