International Business Machines Corporation

Michael Masin

Segev Eliezer Wasserkrug

Lev Greenberg

Henry Broodney

David Breitgand

Sergey Zeltyn

Alexander Zadorojniy

Dany Moshkovich

Evgeny Shindin

Michael Katz

Dolev Dotan

Vladimir Lipets

Asser Nasreldin Tantawi

Malgorzata Steinder

Ian N Whalley

Eran Raichstein

Nir Naaman

Yishai Abraham Feldman

Lior Limonad

Diana Jeanne Arroyo

Ofer Biran

Amir Kantor

Nili Guy

Ilyas Mohamed Iyoob

Eliezer Segev Wasserkrug

Dean Lorenz

Mike A Marin

Amit Fisher

Ira Rosen

Zohar Feldman

Aviad Sela

Dagan Gilat

Segev Shlomov

Lior Turgeman

Adar Amir

Rotem Dror

Shmuel Kalner

Avi Weit

A method comprising using at least one hardware processor for receiving sensory data from at least one physical or virtual sensor. The hardware processor(s) are used for computing a plurality of decision options for configuration of the at least one physical or virtual sensor. The hardware processor(s) are used for computing a plurality of utility functions, and for each utility function: (a) computing a utility value for each decision option, and (b) identifying a first subset of decision options that substantially maximize the computed utility values. The hardware processor(s) are used for selecting at least one cross-function decision option from of the first subsets, wherein the at least one cross-function decision option is included in a substantially maximum number of the first subsets. The hardware processor(s) are used for applying at least one of the at least one cross-function decision options, to at least one physical or virtual sensor.

Optimizing combined sensory data by utilitarian selection of a subgroup of available sensors

Techniques for generating a set of Deep Learning (DL) models are described. An example method includes training an initial set of DL models using the training data, wherein a topology of each of the DL models is determined based on the parameters vector. The method also includes generating a set of estimate performance functions for each of the DL models in the initial set based on the set of edge-related metrics, and generating a plurality of objective functions based on the set of estimated performance functions. The method also includes generating a final DL model set based on the objective functions, receiving a user selection of a selected DL model from the final DL model set, and deploying the selected DL model to an edge device.

Adaptation of deep learning models to resource constrained edge devices

A method, apparatus and a product for generating a dataset for a reinforcement model. The method comprises obtaining a plurality of different subsets of the set of features; for each subset of features, determining a policy using a Markov Decision Process; obtaining a state comprises a valuation of each feature of the set of features; applying the plurality of policies on the state, whereby obtaining a plurality of suggested actions for the state, based on different projections of the state onto different subsets of features; determining, for the state, one or more actions and corresponding scores thereof based on the plurality of suggested actions; and training a reinforcement learning model using the state and the one or more actions and corresponding scores thereof.

States simulator for reinforcement learning models

Constructing a MARS prediction model using predictor variables at a first point in time within a time horizon, including directly-controllable variables of first physical characteristics of a system and that are associated with adjustable operational control settings for directly controlling the first physical characteristic, and including controllable variables of second physical characteristics that are affected by the first physical characteristics, recursively using the prediction model to define an optimization problem for later point in time within the time horizon, transforming the optimization problem into a MILP problem, and solving the MILP problem using an optimization engine to determine, for any given one of the directly-controllable variables and corresponding to at least one of the points in time, for adjusting, using the optimized value, the adjustable operational control setting corresponding to the given directly-controllable variable and thereby control the physical characteristic associated with the directly-controllable variable.

Control system with optimized periodic adjustments of system control settings using MARS-based MILP optimization

A heterogeneous resource reservation (HRR) manager configured to classify historical application requests from a past time interval for a first workload to generate labeled historical application requests. The HRR manager further configured to generate a forecast based on the labeled historical application requests and for predicting future application requests for the first workload for a future time interval and calculate a joint plan based on the forecast. The joint plan including a set of virtual resources, a set of billing contracts, and a set of load balancer weights. The HRR manager further configured to implement the joint plan for a distributed computing workload during the future time interval.

Heterogeneous resource reservation

An example method includes receiving a nominal equivalent resource usage data, an infrastructure usage data, an effective production capacity, a demand elasticity curve, and workload scheduling constraints across a plurality of accounts. The method includes calculating an equivalent resource utilization based on the nominal equivalent resource usage data, the infrastructure usage data, and the effective production capacity. The method includes calculating a potential value increase for a service based on the workload scheduling constraints, the nominal equivalent resource usage data, the effective production capacity, and the demand elasticity curve. The method includes calculating a value increase scheme for the service based on the potential value increase and sending the value increase scheme to a user workload device. The method includes receiving a workload constraint from the user workload device and scheduling a workload service operation based on the infrastructure usage data, the value increase scheme, and the workload constraint.

Scheduling workload service operations using value increase scheme

A method comprising obtaining a scheduling problem comprising: a set of agents and a set of tasks to be performed by the set of agents, wherein solving the scheduling problem using an automated solver is not feasible using available predetermined resources. The method comprises automatically generating a plurality of alternative scheduling problems, wherein a solution to each such alternative scheduling problem defines a solution to the scheduling problem and determining a solution to the scheduling problem by applying the automated solver to solve, while using the available predetermined resources, an alternative problem of the plurality of alternative scheduling problems to determine a solution to the alternative problem and by mapping the solution to the alternative problem to the scheduling problem, whereby determining the solution.

Automatic solution to a scheduling problem

Method, system and product for decomposing a simulation model. The method comprising automatically decomposing the simulation model into a predetermined number of co-simulation components, wherein each co-simulation component is allocated to a different simulation platform, wherein said automatically decomposing comprises: defining a target optimization function, wherein the target optimization function computes an estimated run time of the simulation model, wherein the target optimization function is based on a communication time within each co-simulation component and a communication time between each pair of co-simulation components; and determining a decomposition of the simulation model that optimizes a value of the target optimization function. The method further comprises executing the decomposed simulation model by executing in parallel each co-simulation component on a different simulation platform, whereby the simulation model is executed in a distributed manner.

Automatic decomposition of simulation model

A method, system, and product for simulation of Internet of Things (IoT) environment. The method performed by a simulation node in the IoT environment, which comprises the simulation node and a cloud server connected by a computerized network. The method comprises selecting a simulated IoT device to simulate from a plurality of simulated IoT devices that are being simulated by the simulation node; invoking a real-world model to obtain real-world simulated values; determining a simulated behavior of the selected simulated IoT device by invoking a device model and providing the real-world simulated values thereto, o wherein the simulated behavior comprises transmitting a message to the cloud server; setting a next simulated action of the simulation node to occur at a designated time, wherein the next simulated action is the simulated behavior; and performing the next simulated action at the designated time.

Simulation of internet of things environment

A computer program product comprising a non-transitory computer readable storage medium retaining program instructions configured to cause a processor to perform actions, which program instructions implement: a framework for creating a model of an NP-hard problem, the model comprising at least one entity selected from the group comprising: an objective, a variable, an equation and a constraint, wherein the framework provides methods for automatically transforming the model, comprising: one or more methods for manipulating or changing a status of the entity of the model, the methods comprising a method for imposing or ignoring the constraint; and one or more methods related to operations to be applied to the entity of the model.

Reusable modeling for solving problems

A method comprising: obtaining a first viewpoint representing a partial depiction of a system, wherein the first viewpoint comprises a property base, an instance base and a class base; obtaining a second viewpoint representing a partial depiction of the system, wherein the second viewpoint comprises a property base, an instance base and a class base, wherein the instance base of the second viewpoint is different than the instance base of the first viewpoint; creating a third viewpoint based on the first and second viewpoints, wherein the third viewpoint representing the system, wherein the third viewpoint comprises a property base, an instance base and a class base, wherein the instance base comprises instances defined by the instance base of the first viewpoint and instances defined by the instance base of the second viewpoint.

Representing a system using viewpoints

A method for concise modeling including receiving a first model mapping a plurality of prototype connections between a plurality of prototype objects, receiving at least one dataset having a plurality of optional objects, each object matching one of the prototype objects, and automatically constructing a second model having at least one of the optional objects mapped according to the first model.

Concise modeling and architecture optimization

Systems and methods for admission control to a physical host system are provided herein. One aspect provides for receiving at least one resource request at an admission control component of a distributed computing system, the at least one resource request comprised of at least one system type; processing the at least one resource request utilizing at least one physical host accessible to the distributed computing system; specifying a number of resource request slots to be reserved for at least one system type based on at least one future reservation threshold accessible to the admission control component; and blocking resource requests from entering the system through the admission control component based on a number of available resource request slots and the at least one future reservation threshold. Other embodiments and aspects are also described herein.

Host system admission control

A novel and useful system and method of decentralized decision-making for real-time scheduling in a multi-process environment. For each process step and/or resource capable of processing a particular step, a service index is calculated. The calculation takes into account several measures, such as business level measures, operational measures and employee level measure. The decision of which process step a resource should next work on or what step to assign to a resource is based on the service index calculation and, optionally, other production factors. In one embodiment, the resource is assigned the process step with the maximal service index. Alternatively, when a resource becomes available, all process steps the resource is capable of processing are presented in order of descending service index. The resource then selects which process step to work on next.

Resource optimization for real-time task assignment in multi-process environments

A method of interactively achieving a Pareto-optimal design of systems is provided herein. The method includes the following stages: generating a system model containing elements that are associated with design parameters and design alternatives, as well as directives for calculating the system's measures of effectiveness (being the optimization objectives); exploring a plurality of design configurations all satisfying specified system constraints, meeting a Pareto optimal condition over the objective space, and representing different areas of the design and objective spaces; presenting a user with the determined set of design configurations; allowing the user to choose a range of interest, and repeating the exploring and presenting stages, wherein in each repetition, the exploring is carried out within a smaller sub-range, until the user makes a final selection of a design configuration.

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Haifa, Israel

Michael Masin