Microsoft Technology Licensing, LLC

Hewlett-packard Development Company, L.p.

John Philip MacCormick

Chandramohan Amyangot Thekkath

Lidong Zhou

Marcus Jon Jager

Marcos Kawazoe Aguilera

Moises Goldszmidt

Paul Barham

William Raymond Hoffman

Kristof Roomp

Mark David Lillibridge

Michael Harville

Richard John Black

Frank David McSherry

Donald Ovila Tanguay, Jr

Rebecca Isaacs

Richard Mortier

Adrian E Broadhurst

Douglas J McCulloch

Nicholas Charles Murphy

A method and system for estimating the three dimensional position of an object in a three dimensional physical space. Specifically, the method discloses capturing a plurality of images of a human form within the three dimensional (3D) physical space. Each of the plurality of images is captured from a different viewpoint location of the human form. At least one image capturing device calibrated within the 3D physical space is used to capture the images. A plurality of silhouettes of the human form is extracted from the plurality of images. A plurality of contours of an object of the human form is obtained from the plurality of silhouettes. A location of the object within the 3D physical space is determined from an object model of the object based on the plurality of contours.

Method and system for estimating the three dimensional position of an object in a three dimensional physical space

Techniques are provided for moving an item from one item server to another item server in a distributed storage system, using a location service, APIs, and item states. There are three parties that transact with each other: a source item server, a destination item server, and a location service. Each item stored at the source has a move status, e.g., 'stationary', 'source attempting transfer', &#x201c;destination accepting transfer&#x201d;, or &#x201c;destination attempting transfer&#x201d;. Similarly, each item stored at the destination also has a move status, e.g., &#x201c;stationary&#x201d;, &#x201c;source attempting transfer&#x201d;, &#x201c;destination accepting transfer&#x201d;, or &#x201c;destination attempting transfer&#x201d;. The location service desirably has an entry for every item stored by the storage system. An item's entry may comprise the item's current location (e.g., an identifier of an item server) together with a state, such as &#x201c;stable&#x201d; or &#x201c;attempting transfer&#x201d;.

Relocating item in distributed storage system

An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing data in the computer. The collected data is analyzed to form a graph that describes and predicts what output is generated in response to received input. Later, a window of input and output data is collected from the computer. This collected window of data is used to query the activity model. The graph in the activity model is then used to give the probability that the collected window of data was collected from the computer used to generate the activity model. A high probability indicates that the computer is performing normally, while a low probability indicates that the computer may behaving erratically and there may be a problem with the computer.

Dynamic activity model of network services

Preventing replay attacks on servers. At least one Bloom filter may be set up in a server for tracking requests received from clients. Identifying data may be generated for each request. The identifying data may be checked against the Bloom filter array. If a match is found, the message may be a replay and may be rejected. If a match is not found, the request identifying data may be added to the Bloom filter and the request may be processed.

System and method for preventing replay attacks

An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing channels for packets for a predetermined window of time. To generate an activity model, an input and an output channel are selected. A probability distribution function describing the observed waiting time between packet arrivals on the selected input channel and the selected output channel is generated by mining the data collected during the selected window of time. A probability distribution function describing the observed waiting time between a randomly chosen instant and receiving a packet on the selected input channel is also generated. The distance between the two generated probability distribution functions is computed. If the computed distance is greater than a predefined confidence level, then the two selected channels are deemed to be related. Otherwise, the selected channels are deemed to be unrelated. The activity model is further generated by comparing each input and output channel pair entering or leaving a particular computer.

Automatic discovery of service/host dependencies in computer networks

Methods and apparatuses are provided for recovering one or more replicated datasets. In accordance to some embodiments of the invention, this is accomplished by determining whether a log contains one or more entries indicating a corruption in a replicated data item; and recovering the replicated data item if the log contains any such entries. The log, however, is maintained at a higher layer in a software hierarchy than the layer at which the replicated data item is recovered. As a result, there is no need to maintain a separate replication log at the layer where the replicated data item is recovered. In accordance to some embodiments of the invention, this is accomplished by recording an entry indicating that an operation was performed on a replicated record in a log at a higher layer in a software hierarchy, determining whether a failure occurred during the operation, and reconciling the replicated record at a lower layer in the software hierarchy if it is determined that a failure occurred during the operation. As a result, the present invention eliminates the need for additional disk writes required by conventional techniques that use a replication log to at the same layer where the data recovery is performed.

Efficient recovery of replicated data items

A searchable storage system indexes data, such as a blob (binary large object) as it is stored. The indexes are stored simultaneously with the data by the same storage mechanism. Data cannot be stored without indexing it. Any indexing technique may be used. The indexes are then used to access the data. This ensures that any inconsistencies can handled at the local level, and are not visible to clients of the system. Data can be put in to, or taken out from, the searchable storage system. Additionally, a query on the data in the searchable storage system may be performed.

Searchable storage system

A deterministic technique is used for assigning locations to object replicas in a data center, while taking the various constraints into account that guarantee high availability and fast recovery. Example constraints include not putting the replicas on the same machine and not on the same rack, in case the rack or the network switch goes down. Also, because power distribution units (PDUs) can fail, the two replicas are not put on the same PDU. A solution is guaranteed to be found, if one exists.

Network flow for constrained replica placement

While consulting indexes to conduct a search, a determination is made from time to time as to whether it is more efficient to consult individual indexes in a set or to merge the indexes and consult the merged index. The cost of merging indexes is compared with the cost of individually querying indexes. In accordance with the result of this comparison, the indexes are merged and the merged index is consulted, or the indexes are individually consulted. A cost-balance invariant in the form of an inequality is used to equate the cost of merging indexes to a weighted cost of individually querying indexes. As query events are received, the costs are updated. As long as the cost-balance invariant is not violated, indexes are merged and the merged index is queried. If the cost-balance invariant is violated, indexes are not merged, and the indexes are individually queried.

Scheduling of index merges

A global state management service manages replication in a distributed system. A distributed system can have several replica groups. The global state management service manages state associated with each replica group. Data replication is implemented without implementing an instance of a state machine on each device in a replica group. A replica group comprises a primary device and at least one secondary device. Each device in a replica group contains a replica of data of interest. The global state management service manages the allocation of primary and secondary devices. In the absence of a failure in either a primary device or a secondary device, read and write operations are performed without consulting the global state management service. When a failure is detected, the global state management service manages failover.

Data replication in a distributed system

Balanced prefetching automatically balances the benefits of prefetching data that has not been accessed recently against the benefits of caching recently accessed data, and can be applied to most types of structured data without needing application-specific details or hints. Balanced prefetching is performed in applications in a computer system, such as storage-centric applications, including file systems and databases. Balanced prefetching exploits the structure of the data being prefetched, providing superior application throughput. For a fixed amount of memory, it is automatically and dynamically determined how much memory should be devoted to prefetching.

Balanced prefetching exploiting structured data

An efficient method for renaming consecutive keys in a B-tree representing a hierarchical namespace, such as a file system, has an estimated time efficiency of O(logN), where N is the number of nodes in the B-tree. All the consecutive keys to be renamed are first excised from the original B-tree to form a trimmed B-tree, and the excised nodes are stored in a separate temporary extracted B-tree. The nodes in extracted B-tree are then renamed, and the renamed extracted B-tree is inserted into the trimmed B-tree to form a final B-tree that contains the renamed keys.

Method and system for renaming consecutive keys in a B-tree

A method for calculating a probability distribution that represents probabilities of occurrence of time differences between causally related incoming messages and outgoing messages of a node in a system of communicating nodes is described. The method comprises obtaining a set of time differences between times at which incoming messages arrive at a node and times at which outgoing messages leave the node, the set of time differences including time differences between causally related incoming messages and outgoing messages and time differences between incoming messages and outgoing messages that are not causally related. The method also comprises processing the set of time differences using a statistical estimation algorithm to calculate the probability distribution that represents probabilities of occurrence of time differences between causally related incoming messages and outgoing messages. An apparatus configured to execute the methods is also described.

Growing community of inventors

Mountain View, CA, United States of America

John Philip MacCormick