Abstract: A benchmark generating system is presented. The system monitors an execution of a computer program to collect performance traces for one or more load metrics. The system generates a program model based on the collected performance traces. Each state of the program model corresponds to a workload level at one load metric or a combination of workload levels at the two or more load metrics. The system also generates one or more workload models based on the collected performance traces. Each workload model is configured to generate simulated workload for one load metric of the one or more load metrics at a workload level that is determined based on a state of the program model. The system provides the generated program model and the generated workload models as a benchmark of the computer program.

Abstract: A system for generating a minimum working example (MWE) of the source code of a computer program is provided. The system receives the source code and executes the computer program. The system encounters a runtime error and obtains a stack trace from the execution of the computer program. The stack trace identifies runtime errors and the code segments that were executed that led to the runtime error. The system uses the stack trace to identify one or more variables that affect a most recently executed segment of the source code when the runtime error occurred. The system identifies one or more segments of the source code that do not affect the identified relevant variables. The system generates an MWE of the source code by pruning the identified one or more segments from the source code.

Abstract: Aspects of the invention include a computer-implemented method that includes detecting, by a processor, a performance of a step of an installation protocol for a sensor-based device. The processor performs a negative outcome analysis comprising determining a likelihood that the performance of the step will cause a negative outcome. Based at least in part on a result of the negative outcome analysis, an interruption alarm is issued.

Abstract: Resource usage is monitored for a hybrid cloud computing environment having on-premises computing resources and off-premises computing resources, for at least a first time period. For the time period, the amount of the on-premises computing resources and off-premises computing resources are related to the demand for computing services from the environment. Future demand for computing services from the environment is dynamically predicted based on the relating step and a learned function of the demand and/or a projection of future business growth for a business utilizing the on-premises and off-premises computing resources. Based on the predicted future demand, the amount of the on-premises computing resources is dynamically optimized for a future time period, so as to minimize costs for the future time period; and new amounts of on-premises computing resources and off-premises computing resources are provisioned for the future time period, in accordance with the optimizing step.

Abstract: A method includes confirming when a vehicle accesses a region of interest, obtaining data associated within the region of interest, and determining, based on analytics performed on the data obtained of the region of interest, whether one or more anomalies are present at the region of interest. Obtaining data includes at least one of collecting, with an imaging sensor associated with the vehicle, image data of a given point of interest within the region of interest or collecting, with a motion sensor associated with the vehicle, motion data of the vehicle in a given fragment of interest within the region of interest. The confirming, obtaining and determining steps are performed by at least one processing device comprising a processor operatively coupled to a memory.

Abstract: A computer-implemented method, a computer program product, and a computer system for dynamically streaming social media live content and displaying advertisements. A server identifies characteristics of crowd members who log in wireless network access points in an environment having displays and consume social media live content produced by digital influencers. The server extracts features of the social media live content. The server matches the social media live content to the crowd members, based on the features of the social media live content and the characteristics of the crowd members. The server determines selected social media live content. The server matches the selected social media live content to advertisements, based on the features of the social media live content and information of the advertisements. The server determines selected advertisements. The server streams the selected social media live content and displays the selected advertisements on the displays.

Abstract: A method, a computer program product, and a computer system manage tree risk. The method includes receiving images corresponding to a geographic area. The method includes determining whether a first tree captured in at least one of the images has a condition exhibiting a tree risk that poses a hazard above an acceptable threshold. As a result of the first tree having the condition above the acceptable threshold, the method includes generating a notification identifying the first tree and a location of the first tree. The method includes transmitting the notification to a team equipped to remediate the condition of the first tree.

Abstract: A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.

Abstract: A mobile device may generate a plurality of signals based on sensor data received from a plurality of sensors associated with the mobile device. Based on a logic combination of the plurality of signals, the mobile device may detect its status indicating one of different scenarios. Based on the detected mobile device status and a configured policy, the mobile device may determine a type of deletion action for deleting data on the mobile device. The mobile device may perform the determined type of deletion action.

Abstract: A system for initiating web containers and isolating network traffic to web containers is described. The system initiates and uses web containers for associated web-based resources, such as domain addresses owned by a single entity. When a web container for a domain address has not been initiated, the system determines an owner of the domain address and any domain addresses associated with the owner and initiates a web container for the domain address and the associated domains. When a user navigates to the domain addresses associated with the determined owner, the system isolates the network activity to the initiated web container.

Abstract: A microservice and the recipe for that microservice are automatically created by modifying a local environment in an integrated development environment executing on a computing system to construct a desired microservice, recording commands entered while modifying the local environment, computing a list of changes from the recorded commands that change the local environment and compiling the list of changes into a recipe comprising commands and dependencies sufficient to assemble an operating system and software files that are sufficient to instantiate the desired microservice.

Abstract: In response to identifying one or more applications on a computing device, terms and conditions associated with the one or more applications are identified. A knowledge base based on the identified terms and conditions is generated. The knowledge base includes a determination of sensitive information of a user required by each of the identified one or more applications. Secondary associations of the identified one or more applications are identified from a plurality of external data sources with information related to the identified one or more applications. In response to identifying the secondary associations, it is determined whether sensitive information about the user is found in one or more of the identified secondary associations. In response to determining that sensitive information about the user is found, an advisory is transmitted to the user.

Abstract: A computer-implemented method, a computer program product, and a computer system for infrastructure resource provisioning using trace-based workload temporal analysis. An infrastructure provider receives, from an on-premises infrastructure, scheduler traces of the on-premises infrastructure, one or more scenarios, one or more goals, and one or more constraints of resource deployment on the on-premises infrastructure. The infrastructure provider performs trace-based workload temporal analysis, based on the scheduler traces, the one or more scenarios, the one or more goals, and the one or more constraints. The infrastructure provider generates one or more options of provisioning infrastructure resources, based on the trace-based workload temporal analysis.

Abstract: A method of conducting computing experiments includes executing a set of jobs based on user-selected parameters, learning a user strategy by checking the user-selected parameters during the executing of the set of jobs, and refining the user strategy by refining the set of jobs.

Abstract: An illustrative embodiment includes a method for improving performance of a computer. The method includes: automatically identifying an algorithm supplied by a user for execution on the computer; searching a database of algorithms for at least one algorithm similar to the user-supplied algorithm; determining whether the at least one similar algorithm will improve performance of the computer relative to the user-supplied algorithm; and if the at least one similar algorithm will improve performance of the computer relative to the user-supplied algorithm, modifying the user-supplied algorithm to incorporate at least in part the at least one similar algorithm.

Abstract: A method includes: scheduling a job submitted by a user to be executed on at least a portion of a computing infrastructure; determining a level of sanity check to be performed on the job; performing the sanity check on the job while waiting for the job to be executed; when the sanity check shows that the job will not successfully execute, estimating at least one time required to repair the job such that the job will successfully execute; finding whether the estimated time to repair the job exceeds an estimated time remaining before the job is scheduled to be executed; and when the estimated time to repair the job is found to exceed the estimated time remaining before the job is scheduled to be executed, rescheduling the job such that the estimated time remaining before the job is scheduled to be executed exceeds the estimated time to repair the job.

Abstract: A method of conducting computing experiments, includes executing a set of jobs, performing a comparison of a result of the executed set of jobs with templates of previously-executed experiments which are stored in a knowledge base, and identifying a prunable job of the set of jobs based on the comparison and a user constraint.

Abstract: A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.

Abstract: A job executing on a computer system according to a scheduler configuration may be monitored. Responsive to determining that the job completed executing, a difference between an allocated resource allocated to the job and the job's actual resource usage may be computed. Responsive to determining that the difference meets a criterion, the scheduler configuration may be reconfigured to reduce the allocated resource for a submitted job associated with a given user.

Abstract: A method includes: scheduling a job submitted by a user to be executed on at least a portion of a computing infrastructure; determining a level of sanity check to be performed on the job; performing the sanity check on the job while waiting for the job to be executed; when the sanity check shows that the job will not successfully execute, estimating at least one time required to repair the job such that the job will successfully execute; finding whether the estimated time to repair the job exceeds an estimated time remaining before the job is scheduled to be executed; and when the estimated time to repair the job is found to exceed the estimated time remaining before the job is scheduled to be executed, rescheduling the job such that the estimated time remaining before the job is scheduled to be executed exceeds the estimated time to repair the job.