Abstract: An embodiment for resource management is provided. The embodiment may include receiving created text of an assigned activity to a proposed assignee. The embodiment may also include identifying information about the assigned activity. The embodiment may further include predicting resources and capabilities required to complete the assigned activity. The embodiment may also include identifying the proposed assignee. The embodiment may further include analyzing the resources and capabilities available on one or more devices of the proposed assignee. The embodiment may also include in response to determining the proposed assignee is able to complete the assigned activity, displaying to an assignor a predicted start time and time of completion of the assigned activity and in response to determining the proposed assignee is unable to complete the assigned activity, recommending to the assignor another assignee that is able to complete the assigned activity.

Abstract: Embodiments of the present disclosure may provide dynamic and fair assignment techniques for allocating resources on a demand basis. Assignment control may be separated into at least two components: a local component and a global component. Each component may have an active dialog with each other; the dialog may include two aspects: 1) a demand for computing resources, and 2) a total allowed number of computing resources. The global component may allocate resources from a pool of resources to different local components, and the local components in turn may assign their allocated resources to local competing requests. The allocation may also be throttled or limited at various levels.

Abstract: A method of scheduling a plurality of tasks in an autonomous vehicle system (AVS) includes, by a processor, prior to runtime of an autonomous vehicle, identifying a plurality of tasks to be implemented by the AVS of the autonomous vehicle, for each of the tasks, identifying at least one fixed parameter and at least one variable, and developing a schedule for each of the tasks. The schedule includes an event loop that minimizes an overall time for execution of the tasks. The method includes compiling the schedule into an execution plan, and saving the execution plan to a memory of the autonomous vehicle. During runtime of the autonomous vehicle, the processor receives data corresponding to the variables of the tasks, and uses the variables to implement the execution plan on the autonomous vehicle.

Abstract: Central processing units (CPUs) are configured to support host access instruction(s) that are associated with accessing solid state storage. A resource management module, implemented independently of the CPUs, receives a resource allocation request that includes a usage type identifier and requested amount of a resource, where the usage type identifier is associated with a group identifier. Adjustable resource configuration information is accessed to obtain: (1) a maximum associated with the usage type identifier, (2) a minimum associated with the usage type identifier, and (3) a group limit associated with the group identifier. Resource state information is accessed and it is determine whether to grant the request based at least in part on the maximum, minimum, group limit, and resource state information. The resource allocation request is then granted or denied based on the determination.

Abstract: An apparatus can include a control board operatively coupled to a modular compute boards and to a resource boards by (1) a first connection associated with control information and not data, and (2) a second connection associated with data and not control information. The control board can determine a computation load and a physical resource requirement for a time period. The control board can send, to the modular compute board and via the first connection, a signal indicating an allocation of that modular compute board during the time period. The control board can send, from the control board to the resource board, a signal indicating an allocation of that resource board to the modular compute board such that that resource board allocates at least a portion of its resources during the time period based on at least one of the computation load or the physical resource requirement.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for adjusting a computing load. The method in an illustrative embodiment includes: determining a total computing power demand of at least one user device that will be switched, due to movement, to being provided a computing service by a computing node; determining an available computing power of the computing node; and if the available computing power is unable to meet the total computing power demand, by adjusting a computing load of the computing node, adjusting the available computing power before the at least one user device is switched to being provided the computing service by the computing node, so as to meet the total computing power demand.

Abstract: A system and method for scheduling tasks associated with changing a personality of a ticketing interface. One or more processors generate interaction scores for each of the plurality of user devices based on receiving interactions between the ticketing engine and a plurality of user devices. The system further generate interaction patterns for each of the plurality of user devices that include a relation between the interaction scores generated for each of the plurality of user devices with the interactions from the plurality of user devices. The system further classify each of the plurality of user devices based on the generated interaction patterns to identify whether a user device from the plurality of user devices is a fraudulent or a non-fraudulent user device and modify interface of the ticketing engine based on the classification of each of the plurality of user devices.

Abstract: Based on historic job data, a computer processor can predict a configuration of a computer node for running a future computer job. The computer processor can pre-configure the computer node based on the predicted configuration. Responsive to receiving a submission of a job, the computer processor can launch the job on the pre-configured computer node.

Abstract: A method and apparatus using machine learning for scheduling server maintenance. In one embodiment of the method, load values for a server are recorded over a period of time, wherein each of the load values is time stamped with a date and time. A first plurality of the load values are classified. The classified first plurality of values are then processed to create a model for predicting a load value of the server. The model is used to generate a first predicted load value of the server for a first date and a first time.

Abstract: Various embodiments are generally directed to techniques for supporting the distributed execution of a task routine among multiple secure controllers incorporated into multiple computing devices. An apparatus includes a first processor component and first secure controller of a first computing device, where the first secure controller includes: a selection component to select the first secure controller or a second secure controller of a second computing device to compile a task routine based on a comparison of required resources to compile the task routine and available resources of the first secure controller; and a compiling component to compile the task routine into a first version of compiled routine for execution within the first secure controller by the first processor component and a second version for execution within the second secure controller by a second processor component in response to selection of the first secure controller. Other embodiments are described and claimed.

Abstract: Systems and methods for inter-cluster deployment of compute services using federated operator components are generally described. In some examples, a first request to deploy a compute service may be received by a federated operator component. In various examples, the federated operator component may send a second request to provision a first compute resource for the compute service to a first cluster of compute nodes. In various examples, the first cluster of compute nodes may be associated with a first hierarchical level of a computing network. In some examples, the federated operator component may send a third request to provision a second compute resource for the compute service to a second cluster of compute nodes. The second cluster of compute nodes may be associated with a second hierarchical level of the computing network that is different from the first hierarchical level.

Abstract: Example methods and computer systems for cloud environment configuration based on task parallelization. One example method may comprise: obtaining a task data structure specifying execution dependency information associated with a set of multiple configuration tasks that are executable to perform cloud environment configuration. The method may also comprise: In response to identifying a first configuration task and a second configuration task that are ready for execution based on the task data structure, triggering execution of the first configuration task and the second configuration task. The method may further comprise: in response to determination that the first configuration task has been completed, identifying third configuration task(s) that are ready for execution based on the task data structure; and triggering execution of the third configuration task(s) by respective third compute node(s).

Abstract: Disclosed are systems, methods, and computer readable media for automatically assessing and allocating virtualized resources (such as CPU and GPU resources). In some embodiments, this method involves a computing infrastructure receiving a request to perform a workload, determining one or more workflows for performing the workload, selecting a virtualized resource, from a plurality of virtualized resources, wherein the virtualized resource is associated with a hardware configuration, and wherein selecting the virtualized resources is based on a suitability score determined based on benchmark scores of the one or more workflows on the hardware configuration, scheduling performance of at least part of the workload on the selected virtualized resource, and outputting results of the at least part of the workload.

Abstract: The present disclosure provides a computer-implemented method, computer system and computer program product for managing a task flow. According to the computer-implemented method, a definer module may receive a request for executing a task flow. The definer module may determine a cluster of edge devices to execute the task flow from a set of edge devices. The definer module may retrieve metadata information for the task flow and edge devices in the cluster, wherein the metadata information is used to schedule the task flow in the cluster. Then the edge devices in the cluster may execute the task flow according to the metadata information.

Abstract: In an example, a method is performed by a computing system that is one of a group of computing systems involved in facilitating a manufacturing of an aircraft. The method comprises generating a plurality of manufacturing task work statements (MTWSs), each MTWS being associated with a task involved in the manufacturing and comprising smart contract data and computer code. The method also comprises receiving system state information indicating (i) a schedule according to which the aircraft is to be manufactured, (ii) resources available for use in executing the MTWSs, and (iii) one or more aircraft certification requirements with which the tasks involved in the manufacturing of the aircraft are to comply. The method also comprises executing the MTWSs based on the system state information and storing, in a blockchain-based distributed ledger accessible by the group of computing systems, an end state result of the execution of each MTWS.

Abstract: Embodiments of the present disclosure relate to a memory system and an operating method thereof. According to the embodiments of the present disclosure, the memory system may include a plurality of cores, a control core, and a shared memory. When processing the event, the control core may select a first core executing a target job requiring distributed execution among the plurality of cores, and the first core may run a first firmware among the plurality of firmwares to execute the target job. The control core may select a second core to execute the target job together with the first core, and may control the second core to run the first firmware. The control core may control the first core and the second core to perform distributed execution of the target job.

Abstract: Based on a predetermined number of available processor sockets, a plurality of candidate matrix decompositions are identified, which correspond to a multiplication of matrices. Based on a first comparative relationship of a variation of first sizes of the plurality of candidate matrix decompositions along a first dimension and a second comparative relationship of a variation of second sizes of the plurality of candidate matrix decomposition sizes along a second dimension, a given candidate matrix decomposition is selected. Processing of the multiplication among the processor sockets is distributed based on the given candidate matrix decomposition.

Abstract: A method and apparatus for carrying out a database select, or query, on a data storage device, upon data stored on that device. Data is received from a host and compressed on the data storage device using a compression code developed on the data storage device for the data. When the host issues a database select request on the compressed data, the compression code is distributed to processing cores of the data storage device and compiled, including the select request, into machine code. The machine code is used to decompress the compressed data while filtering the data with the select request. The filtering result is returned to the host.

Abstract: A method and system for allocating tasks among processing devices in a data center. The method may include receiving a request to allocate a task to one or more processing devices, the request indicating a required bandwidth for performing the task, a list of predefined processing device groups connected to a host server and indicating availability of the processing device groups included therein for allocation of tasks and available bandwidth for each available processing device group, assigning the task to a processing device group having an available bandwidth greater than or equal to the required bandwidth for performing the task, and updating the list to indicate that each of the processing device group to which the task is assigned and other processing device group sharing at least one processing device is unavailable. The task may be assigned to an available processing device group having a lowest amount of power needed.

Abstract: A system and method have been devised for optimization and load balancing for computer clusters, comprising a distributed computational graph, a server architecture using multi-dimensional time-series databases for continuous load simulation and forecasting, a server architecture using traditional databases for discrete load simulation and forecasting, and using a combination of real-time data and records of previous activity for continuous and precise load forecasting for computer clusters, datacenters, or servers.

Abstract: Technologies for composing a managed node with multiple processors on multiple compute sleds to cooperatively execute a workload include a memory, one or more processors connected to the memory, and an accelerator. The accelerator further includes a coherence logic unit that is configured to receive a node configuration request to execute a workload. The node configuration request identifies the compute sled and a second compute sled to be included in a managed node. The coherence logic unit is further configured to modify a portion of local working data associated with the workload on the compute sled in the memory with the one or more processors of the compute sled, determine coherence data indicative of the modification made by the one or more processors of the compute sled to the local working data in the memory, and send the coherence data to the second compute sled of the managed node.

Abstract: Image subunit based guest scheduling is disclosed. For example, a memory stores an image registry, which stores a plurality of reference entries each associated with subunits hosted on each node of a plurality of nodes. A scheduler executing on a processor manages deployment of guests to the plurality of nodes including a first node and a second node, where a first guest is associated with an image file that includes a first subunit and a second subunit. The image registry is queried for at least one node of the plurality of nodes hosting the first subunit and/or the second subunit and the first node is determined to host the first subunit. The first guest is scheduled to the first node based on the first node hosting the first subunit.

Abstract: Systems and techniques for adaptive limited-duration edge resource management are described herein. Available capacity may be calculated for a resource for a node of the edge computing network based on workloads executing on the node. Available set-aside resources may be determined based on the available capacity. A service request may be received from an application executing on the edge computing node. A priority category may be determined for the service request. Set-aside resources from the available set-aside resources may be assigned to a workload associated with the service request based on the priority category.

Abstract: Implementations described and claimed herein provide systems and methods for tuning and sizing one or more storage appliances in a storage system with respect to an application load and for optimizing a storage system based on a configuration of a client network and/or a storage appliance in a storage network. In one implementation, data corresponding to an application load configured to be applied to a storage appliance in the storage system is obtained. The application load is characterized in the context of a configuration of the storage system. One or more recommendations for optimizing performance of the storage system based on the characterized application load are generated.

Abstract: Described herein are systems, methods, and software to manage resources in a gateway shared by multiple tenants. In one example, a system may monitor usage of resources by a tenant of the gateway and compare the usage with usage limits associated with the resources. The system may further determine when the usage of a resource exceeds a usage limit associated with the resource and, when the usage of the resource exceeds the usage limit, identify an operation associated with causing the usage limit to be exceeded and blocking the operation.

Abstract: Provided is a non-transitory computer-readable recording medium storing a service management program that causes a computer to execute a process, the process including acquiring a first input load indicating an amount of inputs received by a service at a first point in time, the service being implemented by containers, identifying first numbers of the containers corresponding to the first input load by referring to a storage unit that stores information where a second input load is associated with second numbers of the containers, the second input load indicating an amount of inputs received by the service when a response time of the service is reduced by increasing numbers of the containers to the second numbers of the containers in each of second points in time prior to the first point in time, and increasing the numbers of containers to the first numbers of the containers.

Abstract: Systems and methods for monitoring utilization rates of a plurality of network-connected databases; receiving a first data read request from a first user device for a data element stored in the plurality of network-connected databases; selecting a first target database among the plurality of network-connected databases based on the utilization rates and a load sharing ratios; generating a first data query for a copy of the data element stored in the first target database; and forwarding the copy of the data element from the first target database to the first user device in response to the first data read request.

Abstract: A data management process determines, from user-implemented provisional reservations (400) for data processing resources, a projected total capacity requirement for each said data processing resource, by maintaining a record (9, 90, 91) recording previous such reservations made by each user and comparing each reservations with records (87, 88, 89) of the actual resources used, to provide an estimate of resources required to meet the projected capacity requirement, and to provide data for a demand management processor (2), which control associated configurable data processing equipment (1) to provide the resources required to meet the estimated capacity required. The process takes account of over- and under-ordering of capacity by comparing each reservation (400) with the use actually made (600), and includes a record (10) of ad-hoc (unreserved) usage.

Abstract: A method for configuring at least one OPC UA PubSub subscriber in an in particular industrial network, in which a) a virtual address space is provided for the at least one subscriber on a configuration module that is separate from the at least one subscriber, b) a configuration for the at least one subscriber is performed and/or a configuration already existing for the at least one subscriber is changed in the virtual address space of the at least one subscriber, c) the configuration module converts the configuration and/or configuration change into at least one PubSub message, d) the at least one PubSub message is transmitted to the at least one subscriber, and e) the at least one subscriber is configured according to the at least one PubSub message. In addition, the invention relates to an automation system, a computer program and a computer-readable medium.

Abstract: A method for managing a storage system includes monitoring the storage system to obtain a set of input/output (I/O) telemetry entries, determine a workload signature based on the set of I/O telemetry entries, obtaining, based on the workload signature, a set of performance metrics, performing a parameter analysis to determine a set of alternative storage system parameterizations, based on the set of alternative storage system parameterization, generating a set of alternative performance metrics, updating a performance index structure based on the performance metrics, and the set of alternative performance metrics to obtain an updated performance index structure, and initiating a storage system update based on the updated performance index structure.

Abstract: Techniques are described for distributing network device tasks across virtual machines executing in a computing cloud. A network device includes a network interface to send and receive messages, a routing unit comprising one or more processors configured to execute a version of a network operating system, and a virtual machine agent. The virtual machine agent is configured to identify a virtual machine executing at a computing cloud communicatively coupled to the network device, wherein the identified virtual machine executes an instance of the version of the network operating system, to send, using the at least one network interface and to the virtual machine, a request to perform a task, and to receive, using the at least one network interface and from the virtual machine, a task response that includes a result of performing the task. The routing unit is configured to update the network device based on the result.

Abstract: A method and system for distributing a compute model and data to process to heterogeneous and distributed compute devices. The compute model and a portion of the data is processed on a benchmark system and the timing used to make a job execution speed estimate for each compute device. Compute devices are selected and assigned data chunks based on the estimate so distributed processing is completed within a predefined time period. The compute model and data chunks can be sent to the respective compute devices using separate processes, such as a payload manager configured to transfer compute jobs to remote devices and a messaging engine configured to transfer data messages, and where the payload manager and messaging engine communicate with corresponding software engines on the compute devices.

Abstract: A predefined hierarchical service tree can be stored that includes a top at a service category definition level and a bottom at a level of a number of devices, each of the number of devices selected to perform a specific service function. A sequential progression can be enforced through the predefined hierarchical service tree to perform a service.

Abstract: An operating device for a bicycle is disclosed. The operating device includes a display ring and a coaxially arranged operating ring arranged to rotate relative to the display ring. The display ring includes a display for displaying information and a first magnetic element that cooperates with a second magnetic element arranged on or in the operating ring. In a first position of the operating ring relative to the display ring, the first magnetic element is proximate to the second magnetic element, and if the operating ring is rotated away from the first position, the first magnetic element and the second element magnetically cooperate to bias the operating ring back to the first position. The display ring further includes a sensor that receives a signal when in proximity of a signal providing element.

Abstract: An apparatus, related devices and methods, having a memory element operable to store instructions; and a processor operable to execute the instructions, such that the apparatus is configured to determine, based on operating system workload demands, whether a high-demand application is running and, based on a determination that a high-demand application is running, apply an optimization policy that modifies a security application, wherein the optimization policy modification includes reducing a protection applied by the security application.

Abstract: A cloud service provider network may receive, from a cloud subscriber device, a request to access an application, wherein the cloud service provider network includes a split interface associated with the cloud subscriber device. The cloud service provider network may provide, to the cloud operator device, the request to access the application, wherein the cloud operator device stores the application. The cloud service provider network may receive, from the cloud operator device, the application, based on the request to access the application. The cloud service provider network may provide the application to the cloud subscriber device via the application interface of the split interface, wherein the connectivity interface connects the cloud subscriber device and the cloud operator device so that the application is provided to the cloud subscriber device via the application interface.

Abstract: A method, an improvement node, a system and a computer program for computing an improvement result for a runtime environment of at least one application, on a device in a medical context. An embodiment of the method includes detecting a state of the runtime environment on the device; accessing a database with the state detected, to retrieve a corresponding at least one candidate improvement result; using the at least one candidate improvement result retrieved, for test-wise execution on a test infrastructure in which the state of the runtime environment detected is provided identically; measuring improvement parameters of the test-wise execution; and adding, upon the improvement parameters measured meeting defined requirements, candidate improvement results, of the at least one corresponding candidate improvement result retrieved, for which the improvement parameters measured meet defined requirements.

Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Abstract: A cluster view method of a database to perform compaction and clustering of database objects, such as database materialized view is shown. The database can comprise a cache to store changes to storage units of tables of the database objects. The cluster view method can implement clustering to remove data based on the cache and clustering to group the data of the materialized view.

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

Abstract: Optimizing a performance of a software function withing a content distribution network, such as a software-implemented virtual cable modem termination system (CMTS) network, a virtualized Radio Access Network (vRAN), a Passive Optical Network (PON), or a Wi-Fi network. The performance may be optimized by dynamically changing a deployment location within the content distribution network for the software function from an original location to an updated location using an instance management platform. The deployment location may be dynamically changing in response to a variety of trigger conditions or concerns, such as but not limited to a difference in compute resources, responding to latency needs or tolerances, and a desired cohabitation of data or other software.

Abstract: Certain embodiments of the present disclosure provide techniques for dynamically and reliably scaling a data processing pipeline in a computing environment. The method generally includes receiving a definition of a data pipeline to be instantiated on a set of resources in a computing environment. The data pipeline is converted into a plurality of steps, each step being defined as one or more workers. The one or more workers are instantiated. Each worker generally includes a user process and a processing coordinator to coordinate termination of the user process. Communications are orchestrated between one or more data sources and the one or more workers. The one or more workers are terminated by invoking a termination coordination process exposed by the user process and the processing coordinator associated with each worker of the one or more workers.

Abstract: Disclosed is a system and method for cloud workload provisioning. In one implementation, the present invention provides a system enabling an automated guidance to the user for the workload to be provisioned. The present invention matches the user's workload profile based on a wide variety of historical data set and makes easy for users to choose the cloud provisioning for various kinds of workloads. The system can automatically readjust a workload profile for cloud provisioning. The system can provide a manual selection option for cloud provisioning. In one embodiment, the present invention provides a system and method that derives a workload provision scaling factor mechanism using historic data set. Furthermore, the system and method can automatically or manually readjust the provision scaling factor based on a workload profile for cloud provisioning.

Abstract: A software switch and a method performed by the software switch are disclosed. The software switch receives, from a node deploying a virtual machine, a request for a virtual port to be polled by the virtual machine. The request includes a Central Processing Unit “CPU” identity identifying a CPU on which the virtual machine executes. The request includes an indication of a clock frequency at which the CPU is set to operate. The software switch determines a number of packets in a queue associated with the virtual port. The software switch adjusts the clock frequency of the CPU based on the number of packets in the queue. A corresponding computer program and a computer program carrier are also disclosed.

Abstract: A hierarchical resource management system for a building includes one or more processors. The processors implement a plurality of agents that each monitor sensed values, and generate operating scenarios based on the sensed values for corresponding resources. The processors also implement a coordinator that filters the operating scenarios to remove the operating scenarios that violate internal laws of the agents to form an aggregate validated set of operating scenarios. The processors further implement a supervisor that, responsive to receipt of target conditions for the zones and the aggregate validated set of operating scenarios from the coordinator, selects a combination of the operating scenarios from the aggregate validated set of operating scenarios that achieves target conditions and minimizes overall energy consumption by the resources such that some of the operating scenarios of the combination do not minimize energy consumption of the resources corresponding to the some of the operating scenarios.

Abstract: Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.

Abstract: A resource management system includes a resource management device that manages a usage of a resource, a reservation management device that manages a reservation for the resource, and a communication relay device that communicates with the resource management device. The resource management device receives reservation information transmitted from the reservation management device. The reservation information is information on the reservation for the resource. The resource management device transmits, to the communication relay device, a request for identification information identifying the communication relay device. The resource management device receives the identification information, which is transmitted from the communication relay device in response to the request. The identification information is used to execute an event associated with the reservation information.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: Systems and methods for redirecting one or more printers from a virtual computing device to a physical computing device. The method includes initiating a printing application on a virtual computing device and exporting at least one printer configuration. The at least one printer configuration corresponds to one or more printers communicatively coupled to a physical computing device. The method also includes storing the at least one printer configuration to a data file on the virtual computing device and copying the data file from the virtual computing device to the physical computing device. The method further includes invoking the data file on the physical computing device such that the at least one printer configuration is imported on the physical computing device. The method also includes generating for display on the virtual computing device at least one indication of the one or more printers communicatively coupled to the physical computing device.

Abstract: A combinatorial solution determination system includes a candidate solution generation unit that generates candidate combinatorial solutions, a simulation unit, an evaluation index calculation unit, a solution determination unit, and a necessary-series-data extraction unit. The simulation unit calculates simulation data using information related to the candidate combinatorial solutions, and series data to evaluate a combinatorial solution. The evaluation index calculation unit calculates evaluation indices based on the simulation data. The solution determination unit determines a combinatorial solution having high evaluation from the candidate combinatorial solutions based on the evaluation indices each calculated by the evaluation index calculation unit from a corresponding one of the candidate combinatorial solutions.