Amadeus S.a.s.

Jacques Bonaud

Michel Demazeau

Vincent Boulineau

Celine Soubra

Jean-Philippe Perret

Jean Michel Collomb

Marco Salibba

David Renaudie

Thierry Sabran

Ahmed Oulabas

Guillaume Deacken Owanssango

Mohand Arezki Kessaci

Pierrick Francois

Céline Soubra

A device, system and method for changing a topology of a geographically distributed system is provided. One or more computing devices determine resource usage of an initial topology of a geographically distributed system that includes data stored at one or more initial locations and applications, that consume the data, being implemented at the one or more of initial locations. The computing device(s) determine projected resource usage of additional topologies of the geographically distributed system, the additional topologies defining respective locations, different from the one or more initial locations, where respective portions of the data and/or the applications are located. In response to an additional topology having a respective projected resource usage that is less than the resource usage of the initial topology, the computing device(s) control a respective portion of the data and/or the applications to move to the respective locations defined by the additional topology.

Devices, system and method for changing a topology of a geographically distributed system

A method of determining a primary storage location for a data record in a distributed system comprising a plurality of data stores physically located in corresponding geographic locations, includes initialising a machine learning mapping model using topology information of the distributed system, and determining a set of training feature vectors derived from metadata values associated with prior location requests. The model is trained using the training feature vectors and a corresponding set of target primary storage locations. A location request that includes a plurality of metadata values and is associated with a data record is received, and the metadata values are processed to determine a prediction feature vector comprising a plurality of prediction feature values. The model is executed using the prediction feature vector to identify one data store of the plurality of data stores as the primary storage location for the data record associated with the location request.

Machine learning systems and methods for data placement in distributed storage

The present invention describes a system and a method for dynamically optimising the computing resources allocated to a client application in different data regions of one or more service providers. A number of monitoring modules are provided in each data regions, which are configured to collect operational information from each data region, which is communicated to the other data regions. As such, all data regions are aware of the operational environment of the other data regions.

Method and a system for managing the computing resources in data regions

Systems and methods for determining a set of routes using a graph having nodes and links connecting the nodes. The routes are determined for one or more tokens, and each token is associated with an origin node and a destination node included in the graph and with a token weight. Token constraints, which include at least one constraint relative to the token weight, are received for each token. Candidate routes satisfying at least some of the token constraints are received for each token. Each candidate route includes at least one route link included in the graph and the each route link is associated with a link weight. Shared route links are those route links that share at least two tokens. The candidate routes are filtered based on a condition related to the link weight of each shared route link and to the weights of the tokens sharing the link.

System and method for determining a set of routes, in a computerized environment

A method and a system are provided for optimising Virtual Machine (VM) instances () of a Virtual Machine (VM) cluster () in a cloud computing platform () to avoid the use of VM instances () with degraded performance. The optimisation process of the VM instances () comprises the steps of identifying and optimising VM instances () with degraded performance in VM clusters (). The performance of the deficient VM instances may be optimised by requesting for each deficient VM instance () a corresponding replacement VM instance () to be created by the IaaS (), and accordingly maintain the best performing VM instance from each pair of deficient and replacement VM instances ().

Method and a system for optimising virtual machine clusters of a cloud computing platform

Methods, caches, database systems and computer program products for processing database requests and retrieving results from the cache of a database system. The cache stores a representation of a data space of one or more databases of the database system, including one or more results for any database request for which the data space is able to output a result. In response to receiving a database request from a client, the cache determines by utilizing the stored representation whether the data space is able to output one or more results for the database request. If affirmative, one or more results are returned to the client from the representation of the cache. Otherwise, the cache returns an invalidity notification.

Database caching

Methods, apparatus, and computer program products for handling or managing delayed answers to shopping requests directed to travel content provider platforms. A query processing server of a travel reservation system receives a multi-provider shopping request from an inquiring entity, such as a travel agency, and directs it to a content provider platform, such a platform of a rental car agency. If the content provider platform responds within a given time limit, the query processing server forwards the response to the inquiring entity and updates cache data based upon the response. If the content provider platform does not respond within the given time limit, the query processing server forwards a cache-based response to the inquiring entity. If the content provider platform responds after the given time limit has exceeded, the query processing server updates the cached data to reflect to the belated response from the content provider platform.

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Cagnes sur Mer, France

Jacques Bonaud