Abstract: In response to a request to duplicate a network, the network is duplicated. The duplicate network includes one or more virtual devices that correspond to one or more devices in the network being duplicated. The devices of the duplicate network are communicatively arranged in a manner consistent with a topology of the network being duplicated. Once the duplicate network is created, access to the duplicate network is provided.

Abstract: Systems, methods and related processes for securely erasing and/or rendering permanently inaccessible data stored on storage systems securely erased is described. Such storage systems may, in some aspects, integrate hardware capable of secure erasure. In some aspects, a cryptographically-based system is utilized.

Abstract: A data storage device is modified or adapted to support the storage and retrieval of versioned data sets. In some embodiments, the modified or adapted data storage device is capable of writing and tracking multiple data sets per logical location, and is capable of retrieving a specific versioned data set upon receiving a read request for that version of the data set.

Abstract: As exterior routing protocols generally do not provide information about the internal routing paths of an autonomous system, a particular autonomous system has limited information about the internal health of other autonomous systems. However, if a monitoring system has access to routing data and/or other network data from multiple points of an autonomous system, the monitoring system can estimate, with some accuracy, the health of the autonomous system. In turn, by monitoring at least some of autonomous systems forming a larger internetwork, such as the Internet, the monitoring system can estimate the overall health of at least portions of the internetwork.

Abstract: Virtual data stores may be sparsely provisioned by virtual data storage services in a manner that controls risk of implementation resource shortages. Relationships between requested data storage space size, data storage server capacity, allocated data storage space size and/or allocated data storage space utilization may be tracked on a per data store, per customer, per data storage server, and/or a per virtual data storage service basis. For each such basis, a set of constraints may be specified to control the relationships. The set of constraints may be enforced during implementation resource allocation, and by migration of data storage space portions to different implementation resources as part of a sparse provisioning load balancing. Sparse provisioning details may be made explicit to virtual data storage service customers to varying degrees including explicit, aggregate on a per customer basis, and aggregate on a per virtual data storage service basis.

Abstract: A system and method for optimizing the performance of one or more disks in a storage array in response to access requests by other computer devices and processes across a network. For requests to access to the storage disk from the network, such as a read/write request, a response monitor notes the service time for each access requests by the storage disk, and if the performance in handling the access requests fails to meet, at least, a predetermined time threshold, actions can be taken to migrate data, modify accesses permitted to the disk, or otherwise indicate underperformance.

Abstract: With the advent of virtualization technologies, networks and routing for those networks can now be simulated using commodity hardware rather than actual routers. For example, virtualization technologies such as those provided by VMWare, XEN, or User-Mode Linux can be adapted to allow a single physical computing machine to be shared among multiple virtual networks by providing each virtual network user with one or more virtual machines hosted by the single physical computing machine, with each such virtual machine being a software simulation acting as a distinct logical computing system that provides users with the illusion that they are the sole operators and administrators of a given hardware computing resource. In addition, routing can be accomplished through software, providing additional routing flexibility to the virtual network in comparison with traditional routing. As a result, in some implementations, supplemental information other than packet information can be used to determine network routing.

Abstract: A set of virtualized computing services may include multiple types of virtualized data store differentiated by characteristics such as latency, throughput, durability and cost. A sequence of captures of a data set from one data store to another may be scheduled to achieve a variety of virtualized computing service user and provider goals such as lowering a probability of data loss, lowering costs, and computing resource load leveling. Data set captures may be scheduled according to policies specifying fixed and flexible schedules and conditions including flexible scheduling windows, target capture frequencies, probability of loss targets and/or cost targets. Capture lifetimes may also be managed with capture retention policies, which may specify fixed and flexible lifetimes and conditions including cost targets. Such data set capture policies may be specified with a Web-based administrative interface to a control plane of the virtualized computing services.

Abstract: As exterior routing protocols generally do not provide information about the internal routing paths of an autonomous system, a particular autonomous system has limited information about the internal health of other autonomous systems. However, if a monitoring system has access to routing data and/or other network data from multiple points of an autonomous system, the monitoring system can estimate, with some accuracy, the health of the autonomous system. In turn, by monitoring at least some of autonomous systems forming a larger internetwork, such as the Internet, the monitoring system can estimate the overall health of at least portions of the internetwork.

Abstract: Techniques, including systems and methods, for capturing data sets include performing a client-side two-phase commit to ensure one or more data consistency conditions. A logical volume may represent a data set that is distributed among a plurality of physical storage devices. One or more client devices are instructed to block at least acknowledgment of write operations. When the one or more client devices have blocked at least acknowledgment of write operations, one or more servers in communication with the physical storage devices are instructed to capture corresponding portions of the data set. When the servers have been instructed to capture corresponding portions of the data set, the client devices are instructed to resume at least acknowledgment of write operations.

Abstract: Virtual data stores may be sparsely provisioned by virtual data storage services in a manner that controls risk of implementation resource shortages. Relationships between requested data storage space size, data storage server capacity, allocated data storage space size and/or allocated data storage space utilization may be tracked on a per data store, per customer, per data storage server, and/or a per virtual data storage service basis. For each such basis, a set of constraints may be specified to control the relationships. The set of constraints may be enforced during implementation resource allocation, and by migration of data storage space portions to different implementation resources as part of a sparse provisioning load balancing. Sparse provisioning details may be made explicit to virtual data storage service customers to varying degrees including explicit, aggregate on a per customer basis, and aggregate on a per virtual data storage service basis.

Abstract: Systems and methods dynamically manage replicas of data sets. A collection of data stores is used to redundantly store one or more replicas of one or more data sets. The replicas may be used to respond to read requests from multiple sources. Upon identification of a need to allocate storage space to a data object, space used by one or more of the replicas is allocated to the data object. Various parameters may be utilized in the selection of one or more replicas whose storage space can be allocated to the data object. The parameters may be based at least in part on characteristics of the one or more data sets relative to one another.

Abstract: A storage controller is implemented for controlling a storage system. The storage controller may be implemented using a distributed computer system and may include components for servicing client data requests based on the characteristics of the distributed computer system, the client, or the data requests. The storage controller is scalable independently of the storage system it controls. All components of the storage controller, as well as the client, may be virtual or hardware-based instances of a distributed computer system.

Abstract: A storage controller is implemented for controlling a storage system. The storage controller may be implemented using a distributed computer system and may include components for servicing client data requests based on the characteristics of the distributed computer system, the client, or the data requests. The storage controller is scalable independently of the storage system it controls. All components of the storage controller, as well as the client, may be virtual or hardware-based instances of a distributed computer system.

Abstract: A set of virtualized computing services may include multiple types of virtualized data store differentiated by characteristics such as latency, throughput, durability and cost. A sequence of captures of a data set from one data store to another may be scheduled to achieve a variety of virtualized computing service user and provider goals such as lowering a probability of data loss, lowering costs, and computing resource load leveling. Data set captures may be scheduled according to policies specifying fixed and flexible schedules and conditions including flexible scheduling windows, target capture frequencies, probability of loss targets and/or cost targets. Capture lifetimes may also be managed with capture retention policies, which may specify fixed and flexible lifetimes and conditions including cost targets. Such data set capture policies may be specified with a Web-based administrative interface to a control plane of the virtualized computing services.

Abstract: A storage controller is implemented for controlling a storage system. The storage controller may be implemented using a distributed computer system and may include components for servicing client data requests based on the characteristics of the distributed computer system, the client, or the data requests. The storage controller is scalable independently of the storage system it controls. All components of the storage controller, as well as the client, may be virtual or hardware-based instances of a distributed computer system.

Abstract: Patterns of access and/or behavior can be analyzed and persisted for use in pre-fetching data from a physical storage device. In at least some embodiments, data can be aggregated across volumes, instances, users, applications, or other such entities, and that data can be analyzed to attempt to determine patterns for any of those entities. The patterns and/or analysis can be persisted such that the information is not lost in the event of a reboot or other such occurrence. Further, aspects such as load and availability across the network can be analyzed to determine where to send and/or store data that is pre-fetched from disk or other such storage in order to reduce latency while preventing bottlenecks or other such issues with resource availability.

Abstract: Techniques are described for managing access of executing programs to non-local block data storage. In some situations, a block data storage service uses multiple server storage systems to reliably store network-accessible block data storage volumes that may be used by programs executing on other physical computing systems. A group of multiple server block data storage systems that store block data volumes may in some situations be co-located at a data center, and programs that use volumes stored there may execute on other physical computing systems at that data center. If a program using a volume becomes unavailable, another program (e.g., another copy of the same program) may in some situations obtain access to and continue to use the same volume, such as in an automatic manner in some such situations.

Abstract: Virtual data stores may be sparsely provisioned by virtual data storage services in a manner that controls risk of implementation resource shortages. Relationships between requested data storage space size, data storage server capacity, allocated data storage space size and/or allocated data storage space utilization may be tracked on a per data store, per customer, per data storage server, and/or a per virtual data storage service basis. For each such basis, a set of constraints may be specified to control the relationships. The set of constraints may be enforced during implementation resource allocation, and by migration of data storage space portions to different implementation resources as part of a sparse provisioning load balancing. Sparse provisioning details may be made explicit to virtual data storage service customers to varying degrees including explicit, aggregate on a per customer basis, and aggregate on a per virtual data storage service basis.

Abstract: Systems and methods dynamically manage replicas of data sets. A collection of data stores is used to redundantly store one or more replicas of one or more data sets. The replicas may be used to respond to read requests from multiple sources. Upon identification of a need to allocate storage space to a data object, space used by one or more of the replicas is allocated to the data object. Various parameters may be utilized in the selection of one or more replicas whose storage space can be allocated to the data object. The parameters may be based at least in part on characteristics of the one or more data sets relative to one another.