A method for dynamic management of a first level of verbosity of a component defined in a data network, the data network having a control centre, each component allowing the execution of tasks and including a security management configuration associating a status message, forming a log, with the execution of a task of the component, each log associated with a task indicating an event and having a local level, the control centre allowing the collection of a set of logs to supervise the network, the method including: detecting a stimulus by a control centre; determining a component associated with the stimulus detected by the control centre; modifying the first level of verbosity of the component to a second level of verbosity during a first given period, the modification being launched by generating a supervision command by the control centre, the second level of verbosity being defined according to the stimulus.

In one embodiment, a software agent profiler process attaches to an application and a primary instrumentation interface for the application, and discovers one or more software agents associated with the application. The software agent profiler process may then launch the one or more software agents within an encapsulated container environment of the software agent profiler process by configuring each of the one or more software agents, respectively, to point to a proxy instrumentation interface of the software agent profiler process instead of the primary instrumentation interface for the application. As such, the software agent profiler process may receive calls from the one or more software agents on the proxy instrumentation interface of the software agent profiler process, and can manage the calls from the one or more application agents prior to the calls being passed to the primary instrumentation interface for the application.

A system can effectively compress instrumentation data related to a page view on a client-side application thread (such as a web browser thread) and communicate the compressed data over the Internet to a server (such as an analytics server). Leading up to the compression, the system can include data scanning, code building, code encoding, and code serialization. The compression can run on a background thread of the client-side application, so that it does not interfere with other processes run by the client-side application. For example, a web worker can implement the code compression described herein.

A computer-implemented method of reducing impact of performance data gathering on execution of instrumented code comprises gathering respective performance data for each of a plurality of routines in the instrumented code over a plurality of execution instances of the instrumented code. The method also comprises determining that the gathered performance data for the at least one selected routine follows an identified pattern for at least one selected routine from the plurality of routines and, in response to determining that the at least one selected routine follows an identified pattern, removing instrumentation associated with the at least one selected routine.

An information processing device (1) includes: a Syscall instruction monitoring part (313) configured to monitor at least an instruction to pass processing to a kernel (35) of an OS among instructions issued to a CPU (11) ; and an exclusive loader (201) configured to load a monitoring software (31) functioning as the Syscall instruction monitoring part (313) at region A in a RAM (30), the monitoring software set at ring 0 that is higher than ring (2) set for the kernel (35) of the OS. Even when an access is tried to a resource by executing a malicious program, the access can be detected and intrusion of the malicious program to the kernel can be blocked.

Provided is a technique of compensating time degradation of a CPU and maintaining performance of an electronic device without disturbing a normal operation of the electronic device. A maintenance apparatus includes: a degradation information acquisition unit that acquires degradation information from a sensor circuit integrated in a CPU when the CPU performs a normal operation, the degradation information varying according to degradation of the CPU; a degradation level determination unit that determines a degradation level based on the degradation information, the degradation level indicating a degree of progression of degradation of the CPU; and a power supply control unit that controls a power supply to increase a power supply voltage applied to the CPU with increasing the degradation level.

A setting support program related to monitoring of a system causes a computer to execute a process including (1) receiving an input of first system information including a first configuration element group of a first system, (2) referring to a storage unit that stores a second configuration element group of a second system and setting information related to monitoring of the second system for a plurality of the second systems to identify setting information related to the monitoring of the second system corresponding to the second configuration element group having predetermined similarity to the first configuration element group included in the first system information, and (3) outputting the identified setting information as setting information of the first system.

Expressly turning tracing on and off at each juncture between code that a developer wants to have traced and other code may reduce trace file size but adds computational cost. Described technologies support selectively tracing a process's execution, with some extra tracing done beyond the code the developer wanted traced, but with significantly reduced computational cost, by reducing the number of trace enablement and disablement operations. A trace controller uses a tracing disablement distance variable whose values indicate the computational distance from trace disablement. A distance variable modifier automatically moves the distance variable closer to a stop-tracing value as the process executes. The amount of extra tracing is balanced against the reduction in trace enablement/disablement operations by tuning thresholds, based on information about routine size and computational cost. Operation of the trace controller is illustrated by sample APIs, a tracing state diagram, and a side-by-side comparison, among other items.

According to one embodiment, a system includes probes operable to monitor information associated with a host device and includes a controller operable to control the probes. A first probe instance is associated with a plurality of monitoring modules. Each monitoring module is operable to monitor information associated with the host device. The first probe instance is operable to determine a resource usage associated with the first probe instance and determine whether the resource usage exceeds a threshold. The first probe instance is operable to divide the plurality of monitoring modules into a first subset of monitoring modules and a second subset of monitoring modules. The first probe instance is operable to spawn a second probe instance, wherein the second probe instance is associated with the second subset of monitoring modules. The first probe module is operable to associate the first probe instance with the first subset of monitoring modules.

A power consumption amount estimating apparatus for estimating a power consumption amount of a resource allocated to multiple virtual machines operating on a physical machine, the estimation being made for each virtual machine, includes a power consumption amount measuring unit configured to measure the power consumption amount of the resource; a time identifying unit configured to identify a start and an end time of a duration during which a processor is continuously allocated for one of the virtual machines; a power consumption amount obtaining unit configured to obtain the power consumption amount of the resource during the duration using the power consumption amounts at the start and end times; and a power consumption amount accumulating unit configured to accumulate multiple power consumption amounts of the resource for each virtual machine, the multiple power consumption amounts being obtained during multiple durations, respectively.