Abstract: A system for learning admission policy for optimizing quality of service of computer resources networks is provided herein. The system includes a statistical data extractor configured to extract historical data of deployment requests issued to an admission unit of a computer resources network. The system further includes a Markov decision process simulator configured to generate a simulation model based on the extracted historical data and resources specifications of the computer resources network, in terms of a Markov decision process. The system further includes a value function generator configured to determine a value function for deployment requests admissions. The system further includes a machine learning unit configured to train a classifier based on the simulation model and the value function, to yield an admission policy usable for processing incoming deployment requests.

Abstract: 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.

Abstract: Systems and methods for scheduling events in a virtualized computing environment are provided. In one embodiment, the method comprises scheduling one or more events in a first event queue implemented in a computing environment, in response to determining that number of events in the first event queue is greater than a first threshold value, wherein the first event queue comprises a first set of events received for purpose of scheduling, wherein said first set of events remain unscheduled; mapping the one or more events in the first event queue to one or more server resources in a virtualized computing environment; receiving a second set of events included in a second event queue, wherein one more events in the second set of event are defined as having a higher priority than one or more events in the first event queue that have or have not yet been scheduled.

Abstract: 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.