Abstract: Methods and systems for profiling requests include generating request units based on collected kernel events that include complete request units and half-open request units. The generated request units are sequenced based on a causality relationship set that describes causality relationships between kernel events.

Abstract: Methods and systems for network management include performing path regression to determine an end-to-end path across physical links for each data flow in a network. A per-flow utilization of each physical link in the network is estimated based on the determined end-to-end paths. A management action is performed in the network based on the estimated per-flow utilization.

Abstract: A computer implemented method for network monitoring includes providing network packet event characterization and analysis for network monitoring that includes supporting summarization and characterization of network packet traces collected across multiple processing elements of different types in a virtual network, including a trace slicing to organize individual packet events into path-based trace slices, a trace characterization to extract at least 2 types of feature matrix describing those trace slices, and a trace analysis to cluster, rank and query packet traces based on metrics of the feature matrix.

Abstract: Methods and systems for process constraint include collecting system call information for a process. It is detected whether the process is idle based on the system call information and then whether the process is repeating using autocorrelation to determine whether the process issues system calls in a periodic fashion. The process is constrained if it is idle or repeating to limit an attack surface presented by the process.

Abstract: Systems and methods are provided for optimizing system output in production systems, comprising. The method includes separating, by a processor, one or more initial input variables into a plurality of output variables, the output variables including environmental variables and system response variables. The method also includes building, using the processor, a nonparametric estimation that determines a relationship between one or more initial control variables and the system response variables, and estimating a global input-output mapping function, using the determined relationship, and a range of the environmental variables. The method further includes generating one or more optimal control variables from the initial control variables by maximizing the input-output mapping function and the range of the environmental variables. The method additionally includes incorporating one or more of the optimal control variables into a production system to increase production output of the production system.

Abstract: The present invention enables capturing API level calls using a combination of dynamic instrumentation and library overriding. The invention allows event level tracing of API function calls and returns, and is able to generate an execution trace. The instrumentation is lightweight and relies on dynamic library/shared library linking mechanisms in most operating systems. Hence we need no source code modification or binary injection. The tool can be used to capture parameter values, and return values, which can be used to correlate traces across API function calls to generate transaction flow logic.

Abstract: Systems and methods are provided for acquiring data from an input signal using multitask regression. The method includes: receiving the input signal, the input signal including data that includes a plurality of features; determining at least two computational tasks to analyze within the input signal; regularizing all of the at least two tasks using shared adaptive weights; performing a multitask regression on the input signal to create a solution path for all of the at least two tasks, wherein the multitask regression includes updating a model coefficient and a regularization weight together under an equality norm constraint until convergence is reached, and updating the model coefficient and regularization weight together under an updated equality norm constraint that has a greater l1-penalty than the previous equality norm constraint until convergence is reached; selecting a sparse model from the solution path; constructing an image using the sparse model; and displaying the image.

Abstract: Methods and systems for detecting malicious processes include modeling system data as a graph comprising vertices that represent system entities and edges that represent events between respective system entities. Each edge has one or more timestamps corresponding respective events between two system entities. A set of valid path patterns that relate to potential attacks is generated. One or more event sequences in the system are determined to be suspicious based on the graph and the valid path patterns using a random walk on the graph.

Abstract: This invention provides a new mechanism for “Hot-Tracing” using a novel placeholder mechanism and binary rewriting techniques, which leverages existing compiler flags in order to enable light-weight and highly flexible dynamic instrumentation. Broadly, I-Probe can be divided in 2 distinct workflows—1. Pre-processing (ColdPatch), and 2. Hot Tracing. The first phase is a pre-processing mechanism to prepare the binary for phase 2. The second phase is the actual hot-tracing mechanism, which allows users to dynamically instrument functions (more specifically symbols) of their choice.

Abstract: Methods and systems for detecting anomalous communications include simulating a network graph based on community and role labels of each node in the network graph based on one or more linking rules. The community and role labels of each node are adjusted based on differences between the simulated network graph and a true network graph. The simulation and adjustment are repeated until the simulated network graph converges to the true network graph to determine a final set of community and role labels. It is determined whether a network communication is anomalous based on the final set of community and role labels.

Abstract: A computer implemented method for maintaining a program's calling context correct even when a monitoring of the program goes out of a scope of a program analysis by validating function call transitions and recovering partial paths before and after the violation of the program's control flow. The method includes detecting a violation of control flow invariants in the software system including validating a source and destination of a function call in the software system, interpreting a pre-violation partial path responsive to a failure of the validating, and interpreting a post violation path after a violation of program flow.

Abstract: A data stream system includes one or more monitored machines generating real-time data stream that describes system activities of the monitored machines; a data stream management module receiving the real-time data stream; and a data stream archiving module coupled to the data stream management module, the data stream archiving module including a data stream receiver and a data stream inserter.

Abstract: Systems and methods for managing one or more physical systems, including determining system behavior switching based on time series data from one or more sensors in the system. Time series is divided into a plurality of segments, and each of the segments represents a system behavior. A fitness model is generated for each of the segments to determine whether to select each of the segments as invariants, and an ensemble of local relationship models are built for each of the time series for each invariant to identify local behavior switching points over time. The identified local behavior switching points of each invariant are aggregated by aligning the local switching points of all invariant segments, computing a density distribution of the aligned switching points, and extracting local maximas of the density distribution to determine the global switching points. System operations are controlled based on the determined system behavior switching.

Abstract: Method and systems for controlling a hybrid network having software-defined network (SDN) switches and legacy switches include initializing a hybrid network topology by retrieving information on a physical and virtual infrastructure of the hybrid network; generating a path between two nodes on the hybrid network based on the physical and virtual infrastructure of the hybrid network; generating a virtual local area network by issuing remote procedure call instructions to legacy switches in accordance with a network configuration request; and generating an SDN network slice by issuing SDN commands to SDN switches in accordance with the network configuration request.

Abstract: A process to control a machine by receiving data captured from one or more sensors in the machine generating high-dimensional time series sets in a machine; performing structure precomputing to obtain structures of different sets and time series in each set; performing supervised distance learning by imposing label information to the obtained structures, learning a transformation matrix; transforming the data to shrink a distance between sets with the same label and to stretch the distance between sets with different labels; and applying the transformed data to control the machine responsive to the time series data.

Abstract: Systems and methods for controlling legacy switch routing in one or more hybrid networks of interconnected computers and switches, including generating a network underlay for the one or more hybrid networks by generating a minimum spanning tree (MST) and a forwarding graph (FWG) over a physical network topology of the one or more hybrid networks, determining an optimal path between hosts on the FWG by optimizing an initial path with a minimum cost mapping, and adjusting the initial path to enforce the optimal path by generating and installing special packets in one or more programmable switches to trigger installation of forwarding rules for one or more legacy switches.

Abstract: Systems and methods are disclosed for detecting error in a cloud infrastructure by running a plurality of training tasks on the cloud infrastructure and generating training execution logs; generating a model miner with the training execution logs to represent one or more correct task executions in the cloud infrastructure; after training, running a plurality of tasks on the cloud infrastructure and capturing live execution logs; and from the live execution logs, if a current task deviates from the correct task execution, indicating an execution error for correction in real-time.

Abstract: A system and method for profiling a request in a service system with kernel events including a pre-processing module configured to obtain kernel event traces from the service system and determine starting and ending communication pairs of a request path for a request. A learning module is configured to learn pairwise relationships between the starting and ending communication pairs of training traces of sequential requests. A generation module is configured to generate communication paths for the request path from the starting and ending communication pairs of testing traces of concurrent requests using a heuristic procedure that is guided by the learned pairwise relationships and generate the request path for the request from the communication paths. The system and method precisely determine request paths for applications in a distributed system from kernel event traces even when there are numerous concurrent requests.

Abstract: Methods and systems for performance inference include inferring an internal application status based on a unified call stack trace that includes both user and kernel information by inferring user function instances. A calling context encoding is generated that includes information regarding function calling paths. Application performance is analyzed based on the encoded calling contexts. The analysis includes performing a top-down latency breakdown and ranking calling contexts according to how costly each function calling path is.

Abstract: Systems and methods for managing components of physical systems, including decomposing raw time series by extracting an aging trend and a fluctuation term from the time series using an objective function of an optimization problem, the objective function minimizing reconstruction error and ensuring flatness of the fluctuation term over time. The optimization problem is transformed into a Quadratic Programming (QP) formulation including a monotonicity constraint and a non-negativity constraint, the constraints being merged together to reduce computational costs. An aging score and a confidence score are generated for the extracted aging trend to determine a severeness of aging for one or more components of the physical system, and the aging score and confidence score are fused to provide a fused ranking for the extracted aging trend for predicting future failures of the components.