Abstract: Methods, systems, and processor-readable media for adaptive cycle-level traffic signal control are described. An adaptive cycle-level traffic signal controller and control method that operate within a continuous action space. A reinforcement learning algorithm called Proximal Policy Optimization (PPO), which is a type of actor-critic model for reinforcement learning, may be used to generate signal cycle phase durations selected from a continuous range of values. The controller thus does not treat the action space as discrete, but instead produces continuous values as output. The generated phase durations may define a full traffic signal cycle. The inputs to the controller may indicate current and past states of the traffic environment. The average duration of delay of vehicles in the traffic environment may be used to calculate the reward for the reinforcement learning model that drives the behavior of the controller.

Abstract: Methods, systems, and processor-readable media for generating a temporal detector scan image for traffic signal control are described. An intelligent adaptive cycle-level traffic signal controller uses a deep learning module for traffic signal control, applying image processing techniques to traffic environment data formatted as image data, called “temporal detector scan image” data. A temporal detector scan image is generated by formatting point detector data collected by point detectors (e.g. inductive-loop traffic detectors) over time into two-dimensional matrices representing the traffic environment state in a plurality of lanes over a plurality of points in time, combined with traffic signal data indicating the state of a traffic signal of each lane. The deep learning module may be trained using temporal detector scan image data collected from a traffic environment, and then may be deployed to control the traffic signal for the traffic environment once trained.

Abstract: Methods, systems, and processor-readable media for adaptive cycle-level traffic signal control are described. An adaptive cycle-level traffic signal controller and control method that operate within a continuous action space. A reinforcement learning algorithm called Proximal Policy Optimization (PPO), which is a type of actor-critic model for reinforcement learning, may be used to generate signal cycle phase durations selected from a continuous range of values. The controller thus does not treat the action space as discrete, but instead produces continuous values as output. The generated phase durations may define a full traffic signal cycle. The inputs to the controller may indicate current and past states of the traffic environment. The average duration of delay of vehicles in the traffic environment may be used to calculate the reward for the reinforcement learning model that drives the behavior of the controller.

Abstract: Resource scheduling methods and a resource manager define leaf buckets, each of which have coordinates in corresponding dimensions in a coordinate space. Nodes having resource values in the corresponding dimensions are mapped on the leaf buckets. A task request identifies candidate nodes and requested resources that relate to the coordinate space dimensions. The leaf buckets are scanned to find a fittable node among the list of candidates. A result of the scan is stored in a positive or a negative cache entry. When a next task request of a same task group is received, scanning of the leaf buckets is performed using information from the positive or negative cache entry. For a positive cache entry, the scanning is initiated from a point where the previous scanning has ended. For a negative cache entry, the scanning ignores nodes that were found not to contain the requested resources in the previous scanning.

Abstract: The disclosed apparatuses and methods are directed to resource scheduling of resource nodes of a computer cluster or a cloud computing platform. The disclosed method comprises receiving node identifiers of nodes of a node set and receiving values of node attributes for each one of node identifiers; receiving a sequence of tasks, each specifying values of task parameters; generating a node graph structure having at least one graph structure vertex mapped to a coordinate space; mapping each task to the coordinate space; determining a first node identifier of a first node by analyzing the at least node graph structure vertex located within a fittable area for each task; and mapping the first node identifier to each task to generate a scheduling scheme.

Abstract: A shuffler receives information associated with partition segments of map task outputs and a pipeline policy for a job running on a computing device. The shuffler transmits to an operating system of the computing device a request to lock partition segments of the map task outputs and transmits an advisement to keep or load partition segments of map task outputs in the memory of the computing device. The shuffler creates a pipeline based on the pipeline policy, wherein the pipeline includes partition segments locked in the memory and partition segments advised to keep or load in the memory, of the computing device for the job, and the shuffler selects the partition segments locked in the memory, followed by partition segments advised to keep or load in the memory, as a preferential order of partition segments to shuffle.

Abstract: A shuffler receives information associated with partition segments of map task outputs and a pipeline policy for a job running on a computing device. The shuffler transmits to an operating system of the computing device a request to lock partition segments of the map task outputs and transmits an advisement to keep or load partition segments of map task outputs in the memory of the computing device. The shuffler creates a pipeline based on the pipeline policy, wherein the pipeline includes partition segments locked in the memory and partition segments advised to keep or load in the memory, of the computing device for the job, and the shuffler selects the partition segments locked in the memory, followed by partition segments advised to keep or load in the memory, as a preferential order of partition segments to shuffle.

Abstract: A shuffler receives information associated with partition segments of map task outputs and a pipeline policy for a job running on a computing device. The shuffler transmits to an operating system of the computing device a request to lock partition segments of the map task outputs and transmits an advisement to keep or load partition segments of map task outputs in the memory of the computing device. The shuffler creates a pipeline based on the pipeline policy, wherein the pipeline includes partition segments locked in the memory and partition segments advised to keep or load in the memory, of the computing device for the job, and the shuffler selects the partition segments locked in the memory, followed by partition segments advised to keep or load in the memory, as a preferential order of partition segments to shuffle.

Abstract: A shuffler receives information associated with partition segments of map task outputs and a pipeline policy for a job running on a computing device. The shuffler transmits to an operating system of the computing device a request to lock partition segments of the map task outputs and transmits an advisement to keep or load partition segments of map task outputs in the memory of the computing device. The shuffler creates a pipeline based on the pipeline policy, wherein the pipeline includes partition segments locked in the memory and partition segments advised to keep or load in the memory, of the computing device for the job, and the shuffler selects the partition segments locked in the memory, followed by partition segments advised to keep or load in the memory, as a preferential order of partition segments to shuffle.

Abstract: A shuffler receives information associated with partition segments of map task outputs and a pipeline policy for a job running on a computing device. The shuffler transmits to an operating system of the computing device a request to lock partition segments of the map task outputs and transmits an advisement to keep or load partition segments of map task outputs in the memory of the computing device. The shuffler creates a pipeline based on the pipeline policy, wherein the pipeline includes partition segments locked in the memory and partition segments advised to keep or load in the memory, of the computing device for the job, and the shuffler selects the partition segments locked in the memory, followed by partition segments advised to keep or load in the memory, as a preferential order of partition segments to shuffle.

Abstract: A shuffler receives information associated with partition segments of map task outputs and a pipeline policy for a job running on a computing device. The shuffler transmits to an operating system of the computing device a request to lock partition segments of the map task outputs and transmits an advisement to keep or load partition segments of map task outputs in the memory of the computing device. The shuffler creates a pipeline based on the pipeline policy, wherein the pipeline includes partition segments locked in the memory and partition segments advised to keep or load in the memory, of the computing device for the job, and the shuffler selects the partition segments locked in the memory, followed by partition segments advised to keep or load in the memory, as a preferential order of partition segments to shuffle.