Abstract: A locally optimized plan for executing a command using a sequence of steps can be determined for a single computing node. However, the locally optimized sequence of steps may not be optimized for a combined system comprising multiple computing nodes, any one of which may be tasked with executing the command. A plan that is optimized for the combined system may be determined by comparing the predicted cost of locally optimized plans for computing nodes in the combined system.

Abstract: A locally optimized plan for executing a command using a sequence of steps can be determined for a single computing node. However, the locally optimized sequence of steps may not be optimized for a combined system comprising multiple computing nodes, any one of which may be tasked with executing the command. A plan that is optimized for the combined system may be determined by comparing the predicted cost of locally optimized plans for computing nodes in the combined system.

Abstract: Burst throttling methods may be used to manage computing resources of a data storage service. Tokens may represent I/O operations executed by a customer of the data storage service. A first token bucket may contain a set of tokens representing the overall I/O operation capacity of the data storage service. Additionally, a second token bucket may contain a set of tokens for a given logical volume maintained by the data storage service. When I/O requests are received tokens may be charged the first token bucket and the second token bucket. Furthermore, if there is sufficient capacity, the data storage service may charge a reduced number of tokens to the third token bucket.

Abstract: Burst throttling methods may be used to manage computing resources of a data storage service. Tokens may represent I/O operations executed by a customer of the data storage service. A first token bucket may contain a set of tokens representing the overall I/O operation capacity of the data storage service. Additionally, a second token bucket may contain a set of tokens for a given logical volume maintained by the data storage service. When I/O requests are received tokens may be charged the first token bucket and the second token bucket. Furthermore, if there is sufficient capacity, the data storage service may charge a reduced number of tokens to the third token bucket.

Abstract: Burst throttling methods may be used to manage computing resources of a data storage service. Tokens may represent I/O operations executed by a customer of the data storage service. A first token bucket may contain a set of tokens representing the overall I/O operation capacity of the data storage service. Additionally, a second token bucket may contain a set of tokens for a given logical volume maintained by the data storage service. When I/O requests are received tokens may be charged the first token bucket and the second token bucket. Furthermore, if there is sufficient capacity, the data storage service may charge a reduced number of tokens to the third token bucket.

Abstract: Systems and methods are described providing detailed input/output (I/O) metric information that is collected and gathered by an agent of the storage volume. An I/O request is received by a storage volume, and the agent associates primary and secondary identifiers with that I/O request. For example, a trace may be associated with that I/O request. The agent may store this I/O metric information in a ring buffer. Further, after collection and aggregation, statistics may be published by an I/O metric service that further processes the data provided by the agent. Advantageously, interdependent relationships associated with the I/O request or I/O operations of that request may be included in those statistics. This may allow an operator to evaluate the performance of I/O requests for a network.

Abstract: Burst throttling methods may be used to manage computing resources of a data storage service. Tokens may represent I/O operations executed by a customer of the data storage service. A first token bucket may contain a set of tokens representing the overall I/O operation capacity of the data storage service. Additionally, a second token bucket may contain a set of tokens for a given logical volume maintained by the data storage service. When I/O requests are received tokens may be charged the first token bucket and the second token bucket. Furthermore, if there is sufficient capacity, the data storage service may charge a reduced number of tokens to the third token bucket.

Abstract: Systems and methods are described for dynamically detecting outliers in a set of input/output (I/O) metrics collected and aggregated by a storage volume network. An I/O request is received by a storage volume network, and an agent of the storage volume network associates primary and secondary identifiers with that I/O request. For example, a trace may be associated with a request to write data to a storage volume network, and spans may be associated with the individual operations required to fulfill that request. Once gathered, I/O metrics may be aggregated based on the associated identifiers. I/O metric information regarding outliers may be received from the storage volume network, processed, and published by an I/O metrics service to identify the outliers among the primary and secondary identifiers. These outliers may then be stored for further analysis, and may be utilized to determine improvements to the performance of a storage volume network.

Abstract: Burst throttling methods may be used manage computing resources of a data storage service. Tokens may represent I/O operations executed by a customer of the data storage service. A global token bucket may contain a set of tokens representing the overall I/O operation capacity of the data storage service. Additionally, a work token bucket may contain a set of tokens for a given logical volume maintained by the data storage service. When I/O request are received tokens may be charged the global token bucket and the work token bucket. Furthermore, if there is sufficient capacity the data storage service may charge a reduced number of tokens to the work token bucket.

Abstract: Burst throttling methods may be used manage computing resources of a data storage service. Tokens may represent I/O operations executed by a customer of the data storage service. A global token bucket may contain a set of tokens representing the overall I/O operation capacity of the data storage service. Additionally, a work token bucket may contain a set of tokens for a given logical volume maintained by 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 removed a reduced number for tokens from the work token bucket as a result. Furthermore, if there is sufficient capacity the data storage service may charge a reduced number of tokens to the work token bucket.

Abstract: Burst throttling methods may be used manage computing resources of a data storage service. Tokens may represent I/O operations executed by a customer of the data storage service. A global token bucket may contain a set of tokens representing the overall I/O operation capacity of the data storage service. Additionally, a work token bucket may contain a set of tokens for a given logical volume maintained by the data storage service. When I/O requests are received the data storage service may dynamically determine an amount of tokens to be removed from the global token bucket and/or work token bucket for each received request. Furthermore, if there is sufficient capacity the data storage service may charge a reduced number of tokens to the work token bucket.

Abstract: A locally optimized plan for executing a command using a sequence of steps can be determined for a single computing node. However, the locally optimized sequence of steps may not be optimized for a combined system comprising multiple computing nodes, any one of which may be tasked with executing the command. A plan that is optimized for the combined system may be determined by comparing the predicted cost of locally optimized plans for computing nodes in the combined system.