Abstract: Aspects related to reducing application carbon footprints using predictive load balancing and adaptive process scaling are provided. An adaptive scaling platform may train a delegation model to output event pools based on input of event processing traffic information. The platform may receive event processing traffic information. The platform may generate an event pool using the delegation model. The event pool may comprise tasks required to fulfill event processing requests corresponding to the event processing traffic information and indicators of applications corresponding to the plurality of tasks. The platform may receive event processing information from a server. The platform may cause, based on the event processing information, an application to identify thread requirements. The computing platform may allocate resources based on the thread requirements. The computing platform may delegate tasks to the threads based on the allocating.

Abstract: Embodiments of the present disclosure include techniques for moving data. In one embodiment, computer systems import and/or export data. The computer systems may be application servers coupled to a content management system accessible over a plurality of tenant frontends. The data movement may use a plurality of jobs executing in parallel to move packages of content between the systems. In one embodiment, before a job is executed, a check is made of other jobs to ensure any overlap does not cause data conflicts.

Abstract: A system and method monitor a communication directed to a computer-based system. A memory stores predetermined keywords and predetermined scores each corresponding to a predetermined keyword. A communications monitoring module monitors data string corresponding to the communications and detects a first keyword in the data string. An event code generating module generates an event code corresponding to the data string. A management module detects the generated event code. A keyword matching module searches the predetermined keywords for the detected first keyword and matches the detected first keyword with the generated event code. A score generating module generates a determined score from the detected first keyword and from the predetermined scores. A message generating module compares the determined score with a predetermined threshold and generates a message based on the comparison. The output device outputs the message corresponding to the communication. The method implements the system.

Abstract: A resource dynamic allocation device includes: a unit thread ID acquisitor that acquires identification information of an application process initialized at a time of activation, identification information of a thread which is included in the application process and to which a possible physical resource on a physical server is allocated, and identification information of the physical resource allocated to the thread; and a resource recorder/allocator that freezes all threads of the initialized application process and freezes the thread or unfreezes the thread in accordance with any one of transfer traffic, a resource load, and a load of the application process.

Abstract: A method, apparatus, device and medium for executing tasks in a computing system are provided in the present disclosure. The computing system comprises a first computing node and a plurality of second computing nodes, available duration of the first computing node is known, and the available duration of the plurality of second computing nodes is unknown. A task manager is started at the first computing node, the task manager being used to manage a plurality of tasks to be executed in the computing system. The task manager requests a set of second computing nodes in the plurality of second computing nodes to the computing system. The task manager distributes a target task in the plurality of tasks to the set of second computing nodes so as to execute the target task by using the set of second computing nodes.

Abstract: The present disclosure relates generally to the generation and deployment of a machine learning-enabled decision engine (MLDE). The MLDE includes decision options that are composed of a discrete list of selectable options. Further, the MLDE includes data inputs that can be used to influence decisions made by the machine learning models of the MLDE. Controls are applied to the MLDE to overlay and bound the decisioning within guidelines established by an operator of the MLDE. Once the MLDE is established, the MLDE is validated and deployed for use by software applications to make decisions.

Abstract: A system may include a memory having computer-readable instructions stored thereon, and a processor that executes the computer-readable instructions to determine a plurality of scheduled events to be executed in a time period, determine a number of buckets in the time period based on a predetermined permissible interval, generate a plurality of buckets equal to the number of buckets in the time period, wherein each of the plurality of buckets is associated with a time interval, and schedule each of the plurality of scheduled events in one of the plurality of buckets, wherein execution of each of the plurality of scheduled events is delayed or advanced from an original scheduled time based on the predetermined permissible interval.

Abstract: Systems include data processors to process a set of image data in parallel, and thread schedulers coupled to the data processors. Each of the thread schedulers provides a respective task start signal for a respective data processor. Such systems also include a bandwidth controller coupled to one or more data processors. The bandwidth controller is configured to, for each of the data processor(s): maintain a respective token count, and determine whether to stall or propagate the respective task start signal from the respective thread scheduler to the data processor based on the respective token count. Other aspects include pattern adaptors respectively provided in the schedulers to allow mixing of multiple data patterns across blocks of data, transaction aggregators that allow re-using the same image data by multiple threads of execution while the image data remains in a given data buffer, and timers to detect failure and hang events.

Abstract: In one embodiment, a system includes a processor to control a resource according to policies selected by a multi-armed bandit machine learning agent in exploration phases and in exploitation phases, and execute the multi-armed bandit machine learning agent to select from the policies to control the resource in the exploration phases according to probabilities to explore corresponding one of the policies, wherein the probabilities include different probabilities, perform measurements on the system during execution of the multi-armed bandit machine learning agent, and execute the multi-armed bandit machine learning agent to select from the policies to maximize potential rewards from controlling the resource in exploitation phases based on the performed measurements, and a memory to store data used by the processor.

Abstract: Apparatus and method for dynamic and persistent data sharing between cloud services. To address limitations in existing systems, resource provisioning and application deployment pipelines are configured with calls to share data using a configuration service. In some implementations, the configuration service includes an API, which is called by one or more stages of the resource provisioning pipeline to write configuration data in key-value pairs (e.g., data related to resource provisioning). One or more stages of the application deployment pipeline are configured with calls to access the configuration data. The stages then use the configuration data for application deployment. The configuration service manages the key-value store using versioning, repeatability, and immutability, to ensures that a configuration generated by any pipeline execution can be automatically re-used by another pipeline, at execution time.

Abstract: In an approach for optimizing job preemption using a scheduling mechanism that considers dynamic and static information, a processor, responsive to a pending job being selected to be run, determines that no computing hosts within a computing cluster have available resources capable of running the pending job and non-preemptive measures will not provide required resources for the pending job. A processor identifies candidate jobs for preemption and dynamic information and static information for each candidate job. A processor ranks the candidate jobs for preemption based on the dynamic information and the static information for each candidate job. A processor attempts to preempt a top N candidate jobs whose released resources in combination with the available resources of the computing cluster satisfy the required resources of the pending job. A processor, responsive to successfully preempting the top N candidate jobs, initiates the pending job.

Abstract: A cluster is provided to execute at least one containerized service in a cloud data platform. One or more monitor agents deployed on a plurality of worker nodes in the cluster collect a set of metrics associated with the at least one containerized service. A controller in the cluster aggregates the set of metrics according to at least one aggregation type defined in one or more autoscaling policies associated with the at least one containerized service and evaluates the aggregated set of metrics against criteria based on the one or more autoscaling policies to generate one or more autoscaling decisions.

Abstract: A method for identifying an Android system drawing thread, for a client based on an Android operating system, including following operations: creating a plurality of threads, wherein the threads include one or more of a main thread of an application process, a hardware rendering thread of an Android system, and a customized thread; performing cross-process communication through the threads, applying for a graphics buffer area, and calling a QueueBuffer function of BufferQueue to submit a graphic content drawn by the client to a system graphics buffer area; identifying a thread task according to a state of a process state switching, identifying a current running thread, marking a current task_struct of the thread as ui_thread, and obtaining a drawing thread state; and identifying the thread task according to the state of the process state switching, if the thread is not the current running thread, resetting the drawing thread state.

Abstract: Described is improved resource utilization for multithreading. A first run-time stack is constructed for a first tasklet by allocating a contiguous block of memory between starting and ending memory addresses in a stack pool. The first tasklet executes using a first portion of the contiguous block of memory, up to an intermediate memory address, prior to being preempted by a second tasklet. A second run-time stack is constructed in the stack pool by allocating a second starting memory address, before the ending memory address of the first run-time stack. The second tasklet executes from the second starting memory address, without overlapping the first portion of memory used to execute the first tasklet prior to preemption. Memory addresses associated with the second run-time stack are freed after the second tasklet terminates. Execution of the first tasklet resumes, using memory addresses previously associated with the first run-time stack prior to being preempted.

Abstract: A unified management framework for mediating access to an entity that interacts with a reconfigurable processor is presented. Furthermore, a system is presented that includes a reconfigurable processor configured to execute a dataflow graph, an entity that provisions and deprovisions the dataflow graph on the reconfigurable processor and that controls execution of the dataflow graph on the reconfigurable processor, and a unified management framework for mediating access to the entity and to the reconfigurable processor. Moreover, a method of operating a unified management framework for mediating access to an entity that interacts with a reconfigurable processor is presented.

Abstract: Some embodiments of the invention provide a method of migrating a VM from a first host computer to a second host computer, the first host computer having a first PNIC that performs at least one of network forwarding operations and middlebox service operations for the VM. At an RDMA client executing on a set of one or more processors of the first host computer, the method directs an RDMA server executing on the first PNIC to provide networking state data associated with at least one of network forwarding operations and middlebox service operations that the first PNIC performs for the VM. The provided networking state data resides in a memory of the first PNIC that is accessible to the RDMA server. At the RDMA client, the method provides the obtained networking state data to the second host computer as part of a data migration that is performed to migrate the VM from the first host computer to the second host computer.

Abstract: An energy-efficient assignment of a task set T to a group of M processing devices models the process of deciding the assignment as a combinatorial optimization problem having an objective function optimizing the power consumption of the devices when executing subsets of the tasks, under a constraint that the total utilization of the task subset assigned to each respective processing device is lower than a threshold depending on the number of its processor cores. The objective function may be: min ? ? i = 1 M P i ( ? i ) , where ?i denotes the subset of tasks allocated to the ith device, and Pi(?i) represents power consumption of the ith device when executing ?i, and the constraint U?i?Mi/4 for all the devices, where U?i is the total utilization of ?i executing on the ith device, and Mi denotes the number of cores of the ith device. Solving the problem using a MaxMin or genetic algorithm gives good energy efficiency.

Abstract: Disclosed embodiments relate to systems and methods for analysis of data associated with software instances. Techniques include obtaining data associated with a software instance; archiving delta data associated with software instance; analyzing one more previous states of the software instance based on the archived delta data; and performing a security action based on the analysis of the one or more previous states of the software instance based on the archived delta data.

Abstract: A method for processing data with a data processing network including a plurality of data processing modules which each include at least one data processing component, each data processing component being designed for a defined data processing task for processing the data, each data processing module receiving, as input data, data from a data source and/or output data from further data processing modules and generating output data which is network output data of the data processing network and/or input data of further data processing modules. The method for at least one data processing module including: a) receiving a set of input data for performing the data processing tasks; b) receiving a stimulus for activating the at least one data processing component; c) performing the data processing task with the corresponding input data to generate output data; and d) providing the output data.

Abstract: Performing container layer translation across different types of processor architectures is provided. Dynamic mapping of respective source image layers of an original source container image built for a first type of processor architecture to corresponding target image layers for a second type of processor architecture that is different from the first type of processor architecture is performed. Computational resource ratios needed to run dynamically mapped image layers of a target container image using the second type of processor architecture is determined based, at least in part, on a technical stack type of an application associated with the respective source image layers of the original source container image. The target container image having the dynamically mapped image layers along with the computational resource ratios is served to a target host computer node that includes the second type of processor architecture via a network.