Amazon Technologies, Inc.

Christopher Kakovitch

Arijit Ganguly

Rajesh Kumar Pandey

Luben Karavelov

Nils Anders Palumbo

Systems and methods are described for the chained execution of a set of code in an on-demand network code execution system. A user may provide a set of code for execution in the on-demand network code execution system and the system may determine that the set of code comprises multiple chained tasks. The system may provide the set of code to a first virtual machine instance for execution of a first task. The system may obtain an indication that the first task has been executed. The results of the execution of the first task may be sent to a second virtual machine instance, via a push or pull, for execution of a second task. Based on identifying that the first task has been executed, the system may instruct the second virtual machine instance to execute the second task.

Chained tasks for execution in an on-demand network code execution system

Systems and methods are described for execution of multiple tasks associated with a set of code in an on-demand network code execution system. A user may provide a set of code that is associated with the multiple tasks. The system may generate a first virtual machine instance for execution of a first task. The system may determine that a second task is associated with the first task and may identify a location of the first virtual machine instance. The system may further identify a second virtual machine instance for execution of the second task based on the location of the first virtual machine instance. For example, the system may identify the first virtual machine instance from a plurality of pre-generated virtual machine instances and/or may generate the first virtual machine instance.

Collocated virtual machine instances in an on-demand network code execution system

Systems and methods are described for allocating requests to implement new workloads within a heterogenous fleet. The fleet can include various sub-fleets, each corresponding to a set of computing devices having a given configuration of computing resources. A routing device can calculate n-dimensional decision surfaces that map expected resource usage associated with an incoming request to probabilities to route the request to each sub-fleet. The decision surfaces can be calculated to maximize cost-weighted headroom across the sub-fleets, with headroom on each sub-fleet reflecting a geometrical dissimilarity in a shape of load on the sub-fleet and a shape of resources available on the sub-fleet. By comparing the expected resource usage associated with the incoming request to the decision surfaces, the device can determine a sub-fleet to which to route the requests.

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Seattle, WA, United States of America

Christopher Kakovitch