Nec Corporation

Nec Europe Ltd.

Felipe Huici

Saverio Niccolini

Simon Kuenzer

Andrea Di Pietro

Mathias Niepert

Mohamed Ahmed

Florian Schmidt

Roberto Bifulco

Jan Seedorf

Takahide Sugita

Nicolas Weber

Sofia Nikitaki

Diego Costantini

Filipe Manco

Sharan Santhanam

A method searches and tests for performance optima in an operating system (OS) configuration space. The method includes generating a plurality of OS configurations. For at least a first OS configuration, of the generated OS configurations, the method further includes: fetching a plurality of OS modules based on the first OS configuration; building a first OS image from the fetched OS modules; and testing the first OS image to determine a first value of a performance metric.

Fast operating system configuration option space exploration via cross-OS grafting

Systems and methods for automatically generating a secure image with a reduced or minimal set of system calls (syscalls) required by an application to run. A method includes the steps of receiving as input a configuration file specifying one or more image parameters to vary; generating a set of one or more unikernel images, or experiment images, each unikernel image including a specification of how to build the image and how to run the image, each unikernel image based on one of the one or more image parameters; populating a run queue with the one or more unikernel images; and iteratively: executing each of the one or more unikernel images in a host virtual machine; and monitoring, at run-time, a usage of syscalls in the executing image to identify syscalls actually used at any point in time during the executing.

Automated and dynamic system call sealing

A method for accelerating a neural network includes identifying neural network layers that meet a locality constraint. Code is generated to implement depth-first processing for different hardware based on the identified neural network layers. The generated code is used to perform the depth-first processing on the neural network based on the generated code.

Acceleration of neural networks using depth-first processing

Methods and systems for building an optimized image for an application are provided. An operating system is decomposed into granular modules. An initial configuration file to a build system is provided. The build system builds an initial image including initial modules for the application based on the initial configuration file. A monitoring system monitors performance indicators for the initial image. Using a machine learning algorithm, a subsequent configuration file based on the performance indicators is derived. The build system, builds a subsequent image for the application.

Method and system for automated building of specialized operating systems and virtual machine images based on reinforcement learning

A method for operating virtual machines on a virtualization platform includes: embedding control information in a predetermined memory area of a front-end virtual machine where at least one virtual device is to be initialized, the control information being required for initiating a communication with a back-end virtual machine where at least one back-end driver runs; retrieving, by the front-end virtual machine, the control information from the predetermined memory area of the front-end virtual machine; and performing the communication between the front-end virtual machine and the back-end virtual machine via a direct communication channel to exchange information for initializing the at least one virtual device of the front-end virtual machine, by communicating with the at least one back-end driver via the direct communication channel. The direct communication channel is established based on the control information embedded in the predetermined memory area of the front-end virtual machine.

Method for operating virtual machines on a virtualization platform and corresponding virtualization platform

A method for monitoring resources in a computing system having system information includes transforming, via representation learning, variable-size information into fixed-size information, and creating a machine learning neural network model and training it the machine learning model to predict future resource usage of an application. The method further includes providing the prediction of further resources usage of the application as an input to an action component, wherein the action component is one of an anomaly detector or a reinforcement learner that drives a scheduler. The method additionally includes performing, by the action component, at least one of scheduling resources within the computing system or detecting a resources usage anomaly.

Methods and systems for machine-learning-based resource prediction for resource allocation and anomaly detection

A method for caching objects at one or more cache servers of a content delivery network (CDN) includes: determining, by a processor, attributes of objects of a set of objects; calculating, by the processor, an efficiency metric for each object of the set of objects based on the attributes of each object, wherein the attributes of each object include an expected future popularity associated with the object; selecting, by the processor, a subset of objects from the set of objects for caching based on calculated efficiency metrics; and caching the subset of objects at the one or more cache servers.

Efficient caching in content delivery networks based on popularity predictions

For providing a simple monitoring mechanism with reduced resource and performance requirements a method for monitoring traffic in a network is claimed, wherein a monitoring activity of at least two monitoring probes of the network is coordinated by a coordinating element, wherein at least two nodes of the network are able to operate as coordinating elements and wherein the responsibility for coordinating the monitoring activity of the monitoring probes is split between the nodes according to a compressed representation of flow parameter keys. Further, an according network is described, preferably for carrying out the above mentioned method.

Method for monitoring traffic in a network and a network

A method for probabilistic processing of data, wherein the data is provided in form of a data set S composed of multidimensional n-tuples of the form (x, . . . , x), is characterized in that an n-dimensional data structure is generated by way of providing a bit matrix, providing a number K of independent hash functions Hthat are employed in order to address the bits in the matrix, and inserting the n-tuples (x, . . . , x) into the bit matrix by computing the hash values H(x) for all values x of the n-tuple for each of the number K of independent hash functions H, and by setting the resulting bits [H(x), . . . , H(x)] of the matrix. Furthermore, a respective system is disclosed.

Method and system for probabilistic processing of data using a bit matrix, tuples, and hash values

For allowing a very flexible scheduling of data flows within an OpenFlow (OF) switch a method for operating an OpenFlow switch within a network includes using the OpenFlow switch to direct arriving data flows out of different ports of the OpenFlow switch. The method is characterized in that a scheduling mechanism for performing at least one scheduling task is assigned to the OpenFlow switch, wherein a metric will be used to assign weights to the arriving data flows and wherein the data flows will then be scheduled based on the assigned weights and based on a scheduling policy. Further, a corresponding OpenFlow switch and a corresponding network are described, preferably for carrying out the above mentioned method.

Method for operating an openflow switch within a network, an openflow switch and a network

A method for monitoring a network, wherein the network has a connected graph topology, in particular a tree structure, including a plurality of monitoring nodes that collect network measurement data, a plurality of mediator nodes each performing at least the task of aggregating network measurement data received from different monitoring nodes and/or other mediator nodes, and at least one root entity that receives network measurement data and/or aggregated network measurement data from the mediator nodes, is characterized in that the aggregation of network measurement data is performed by condensing network measurement data into a summarized probabilistic data structure. Furthermore, a network including a monitoring functionality is disclosed.

Method for monitoring a network and network including a monitoring functionality

A network distribution monitoring method is executed by a computer which configures each of a plurality of nodes, in a network which comprises the plurality of nodes which are connected with a management network. The network distribution monitoring method includes: generating a first detection message by detecting a first traffic on an object network of a corresponding one of a plurality of first predetermined applications, in each of first probe nodes of the plurality of nodes; transmitting the first detection message to a node determined from a first hash value to only a first function identifier showing a kind of the corresponding one or the first function identifier and address data of the first traffic; generating a first mediation processing result by performing first mediation processing on the first detection message to the plurality of first applications, in a first mediation node of the plurality of nodes; and storing a result of the first mediation processing in a first collector node of the plurality of nodes. In this way, the distribution monitoring system is provided in which it is possible to monitor the advanced function.

Growing community of inventors

Dossenheim, Germany

Felipe Huici