| Patent application number | Description | Published |
|---|
| 20080228459 | Method and Apparatus for Performing Capacity Planning and Resource Optimization in a Distributed System - Disclosed is a method and apparatus for performing capacity planning and resource optimization in a distributed system. In particular, the capacity needs of individual components (e.g., server, operating system, CPU, application software, memory, networking device, storage device, etc.) in a distributed system can be analyzed using relationships between measurements collected from the distributed system. These relationships, called invariants, do not change over time. From these measurements, a network of invariants are determined. The network of invariants characterize the relationships between the measurements. The capacity need of at least one component in the distributed system can be determined from the network of invariants. | 09-18-2008 |
| 20090112780 | DISCOVERING OPTIMAL SYSTEM CONFIGURATIONS USING DECENTRALIZED PROBABILITY BASED ACTIVE SAMPLING - A system and method for optimizing system performance includes applying sampling based optimization to identify optimal configurations of a computing system by selecting a number of configuration samples and evaluating system performance based on the samples. Based on feedback of evaluated samples, a location of an optimal configuration is inferred. Additional samples are generated towards the location of the inferred optimal configuration to further optimize a system configuration. | 04-30-2009 |
| 20090292954 | RANKING THE IMPORTANCE OF ALERTS FOR PROBLEM DETERMINATION IN LARGE SYSTEMS - A system and method for prioritizing alerts includes extracting invariants to determine a stable set of models for determining relationships among monitored system data. Equivalent thresholds for a plurality of rules are computed using an invariant network developed by extracting the invariants. For a given time window, a set of alerts are received from a system being monitored. A measurement value of the alerts is compared with a vector of equivalent thresholds, and the set of alerts is ranked. | 11-26-2009 |
| 20090310783 | Controlled Dissemination of Information in Mobile Networks - The present invention discloses systems and methods for controlled dissemination of information in mobile networks using encrypted broadcasts that are decrypted at the device. An encryption key is generated corresponding to a particular category or granularity of information. The information is encrypted before it is broadcast to the sector. A user within the sector sends a key request across the network, in response to which the encryption key is sent to the user. The user can decrypt the encrypted information received in the broadcast. Additionally, a credit-checking mechanism may be employed to ensure that the user has sufficient credit to purchase the key. In one embodiment, the information to be disseminated is divided into a plurality of categories, wherein each category corresponds to a granularity of information. The encryption key is one in a set of encryption keys, each of said set of encryption keys being assigned to a particular hierarchical level corresponding to a particular granularity of information. | 12-17-2009 |
| 20100058475 | FEEDBACK-GUIDED FUZZ TESTING FOR LEARNING INPUTS OF COMA - Embodiments of the present invention combine static analysis, source code instrumentation and feedback-guided fuzz testing to automatically detect resource exhaustion denial of service attacks in software and generate inputs of coma for vulnerable code segments. The static analysis of the code highlights portions that are potentially vulnerable, such as loops and recursions whose exit conditions are dependent on user input. The code segments are dynamically instrumented to provide a feedback value at the end of each execution. Evolutionary techniques are then employed to search among the possible inputs to find inputs that maximize the feedback score. | 03-04-2010 |
| 20100131440 | EXPERIENCE TRANSFER FOR THE CONFIGURATION TUNING OF LARGE SCALE COMPUTING SYSTEMS - A computer implemented method employing experience transfer to improve the efficiencies of an exemplary configuration tuning in computing systems. The method employs a Bayesian network guided tuning algorithm to discover the optimal configuration setting. After the tuning has been completed, a Bayesian network is obtained that records the parameter dependencies in the original system. Such parameter dependency knowledge has been successfully embedded to accelerate the configuration searches in other systems. Experimental results have demonstrated that with the help of transferred experiences we can achieve significant time savings for the configuration tuning task. | 05-27-2010 |
| 20100262858 | Invariants-Based Learning Method and System for Failure Diagnosis in Large Scale Computing Systems - A method system for diagnosing a detected failure in a computer system, compares a failure signature of the detected failure to an archived failure signature contained in a database to determine if the archived failure signature matches the failure signature of the detected failure. If the archived failure signature matches the failure signature of the detected failure, an archived solution is applied to the computer system that resolves the detected failure, the archived solution corresponding to a solution used to resolve a previously detected computer system failure corresponding to the archived failure signature in the database that matches the detected failure. | 10-14-2010 |
| 20110058499 | METHOD FOR INFERRING PHYSICAL NETWORK TOPOLOGY FROM END-TO-END MEASUREMENT - A method for inferring end-to-end network topology and to accurately determine a layer-3 routing tree between one sender and a set of receivers in the presence of anonymous routers in a network. | 03-10-2011 |
| 20110072130 | Extracting Overlay Invariants Network for Capacity Planning and Resource Optimization - A method and system determines capacity needs of components in a distributed computer system. In the method and system, a pair-wise invariant network is determined from collected flow intensity measurements. The network includes at least two separate and unconnected pair-wise invariant subnetworks, each of the subnetworks including two of the flow intensity measurements connected by a pairwise invariant, each of the pair-wise invariants characterizing a constant relationship between their two connected flow intensity measurements. At least one overlay invariant is determined from the pair-wise invariant network and from the collected flow intensity measurements using a minimal redundancy least regression process. The capacity needs of the components are determined using the pair-wise and overlay invariants. | 03-24-2011 |
