Abstract: A method for performing preemptive scaling is disclosed. The method includes monitoring of incoming data volume outside of a cloud network at a node upstream from the cloud network, and identifying micro services required for processing of the incoming data volume. The method further includes determining a number of instances for each of the identified micro services, and contemporaneously creating the determined number of instances for each of the identified micro services in the cloud for processing of the incoming data volume.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for verifying an incoming communication. A recipient client device receives an incoming communication including an identifier identifying a second client device as having initiated the incoming communication. A verification service installed on the recipient client device queries a call placement service directory based on the first identifier. The call placement service directory maintains a listing of identifiers for client devices and corresponding communication providers that manage the identifiers. The call placement service directory returns information identifying the communication provider that manages the identifier. In turn, the verification service transmitting a request to the communication provider to confirm whether the second client device initiated the incoming communication.

Abstract: A device is provided, which includes one or more antennas and a circuit. The circuit is configured to receive a packet from a transmitter device through the antennas, and obtain channel-state information from the received packet. The circuit includes one or more filters to filter out or recognize the received packet that complies with one or more preset conditions. The circuit uses the channel-state information of the received packet, which complies with the one or more preset conditions, to estimate a status of a channel between the transmitter device and the device.

Abstract: Techniques are disclosed in which a server computer system manages a database cache for multiple different tenants. The system may compare a key having an unidentified tenant with key segments having a number of occurrences within a database cache satisfying a threshold count, where the key is included in a key-value entry of the cache storing data for multiple tenants. Key segments for keys of the database cache and corresponding occurrence counts are stored in a central database. Based on the comparing, the system determines whether the unidentified key matches one of the key segments satisfying the threshold count. In response to the system determining a match, a tenant corresponding to the unidentified key is identified based on the matching key segment. The disclosed techniques may advantageously allow for analysis of cache metrics for tenants and more efficient use of the cache (e.g., by altering cache parameters for individual tenants).

Abstract: Storage processes are described. For instance, dumping of cache data in a memory table at a source directory table partition in a source container group is stopped in response to receiving a close signal. A notification for creating a target directory table partition is sent to a target container group, the notification including a checkpoint at the source directory table partition. The cache data in the memory table is sent to the target container group, and the source container group is closed in response to a determination that an ownership of the target directory table partition is updated to the target container group. A rescheduling and/or upgrading solution is thus proposed for a source container group, so that the source container group is still in a service state during the rescheduling and/or upgrading solution, which improves the performance of storage services and reduces the data unavailable time.

Abstract: An apparatus includes a processor and a memory that stores code executable by the processor. The code is executable by the processor to receive a request at a first device to execute a machine learning workload for the first device, dynamically determine at least one characteristic of the first device that is related to execution of the machine learning workload, dynamically determine at least one characteristic of a second device that is related to execution of the machine learning workload, and select one of the first and second devices to execute the machine learning workload in response to the at least one characteristic of the selected one of the first and second devices being more suitable for execution of the machine learning workload than another of the first and second devices.

Abstract: Techniques are disclosed for providing a number of user interfaces. A computing system may execute a declarative infrastructure provisioner. The computing system may provide declarative instructions and instruct the declarative infrastructure provision to deploy a plurality of infrastructure resources and a plurality of artifacts. One example user interface may provide a global view of the plurality of infrastructure components and artifacts. Another example user interface may provide corresponding states and change activity of the plurality of infrastructure components and artifacts. Yet another user interface may be provided that presents similarities and/or differences between a locally-generated safety plan indicating first changes for a computing environment and a remotely-generated safety plan indicating second changes for the computing environment.

Abstract: The method includes: acquiring, by a computing scheduling module, first sub-account information sent by a blockchain computing distribution module, wherein the blockchain computing distribution module includes a plurality of sub-accounts; scheduling, by the computing scheduling module, the blockchain server to provide computing capability for a first sub-account according to the first sub-account information: determining an up-to-standard result of the computing capability of the first sub-account, and feeding back the up-to-standard result to the computing scheduling module by the blockchain computing distribution module: scheduling the blockchain server to provide computing capability for a second sub-account when the computing scheduling module determines that the computing capability of the first sub-account is up to standard according to the up-to-standard result: or scheduling, by the computing scheduling module, and repeating the above steps until the computing capability of all sub-accounts is up to s

Abstract: Presented herein are methods, non-transitory computer readable media, and devices for optimizing thread assignment to schedulers, avoid starvation of individual data partitions, and maximize parallelism in the presence of hierarchical data partitioning are disclosed, which include: partitioning, by a network storage server, a scheduler servicing a data partitioned system into a plurality of autonomous schedulers; determining what fraction of thread resources in the data partitioned system at least one of the plurality of autonomous schedulers is to receive; and determining, with minimal synchronization, when it is time to allow the at least one of the plurality of autonomous schedulers servicing a coarse hierarchy to run.

Abstract: A system for shutting down a process of a database is provided. In some aspects, the system performs operations including tracking, during startup of a process, code locations of a process in the at least one memory. The operations may further include tracking, during runtime of the process and in response to the tracking the code locations, memory segments of the at least one memory allocated to the process. The operations may further include receiving an indication for a shutdown of a process. The operations may further include waking, in response to the indication, at least one processing thread of a plurality of processing threads allocated to a database system. The operations may further include allocating a list of memory mappings to the plurality of processing threads. The operations may further include freeing, by the first processing thread, the physical memory assigned to the processing thread by the memory mappings.

Abstract: A system includes a subsystem, a database, a memory, and a processor. The subsystem includes a computational resource associated with a resource usage and having a capacity corresponding to a maximum resource usage value. The database stores training data that includes historical resource usages and historical events. The memory stores a machine learning algorithm that is trained, based on the training data, to predict, based on the occurrence of an event, that a future value of the resource usage at a future time will be greater than the maximum value. The processor detects that the event has occurred. In response, the processor applies the machine learning algorithm to predict that the future value of the resource usage will be greater than the maximum value. Prior to the future time, the processor increases the capacity of the computational resource to accommodate the future value of the resource usage.

Abstract: A memory system, a method of assembling the memory system, and a computer system. The memory system includes a global memory device coupled to a plurality of processing elements. The global memory device is positioned external to a chip on which the plurality of processing devices reside. The memory system also includes at least one main scratchpad coupled to the at least one processing element of the plurality of processing devices and the global memory device. The memory system further includes a plurality of auxiliary scratchpads coupled to the plurality of processing elements and the global memory device. The one or more auxiliary scratchpads are configured to store static tensors. At least a portion of the plurality of auxiliary scratchpads are configured as a unitary multichannel device.

Abstract: A method and an apparatus for handling a memory failure is provided. The method includes: in response to detecting a failure occurring in memory of a host machine, acquiring a failure parameter of the memory, determining a crash probability of the host machine based on the failure parameter, and transferring all virtual machines on the host machine to a target host machine when the crash probability is greater than or equal to a first predetermined threshold. A crash probability of the target host machine is less than a second predetermined threshold. The second predetermined threshold is less than the first predetermined threshold. Further, an electronic device and storage medium are provided.

Abstract: An emulation and simulation acceleration system includes a plurality of processors, wherein each of the plurality of processors is configured to run a separate simulation task in a parallel manner. Each of the plurality of processors includes a local memory for instructions and data, a memory space for saving received data from other processors of the plurality of processors. The system includes a plurality of connection channels between each processor of the plurality of processors, wherein each of the plurality of connection channels is either virtual or physical, and each of the plurality of connection channels extends through computer hardware or memory mapping. The system includes a plurality of system interfaces configured to interface with other systems for expansion to a larger system or to connect with external target system. Each of the plurality of system interfaces contains a virtual interface and a physical interface for connecting with other simulators.

Abstract: Aspects described herein relate to a flexible order fulfillment management system that utilizes predefined fulfillment workflows including common business processes. By utilizing predefined workflows including common business processes, the order fulfillment management system is able to drive store-based fulfillment programs efficiently as multiple fulfillment workflows may utilize the same common business processes and a single fulfillment management system may operate workflows in parallel. In addition, such an order fulfillment management system may also be more easily adapted for different retail store environments as the system allows a designer to easily create or modify a fulfillment workflow by selecting predefined common business processes or creating any additional custom business processes.

Abstract: A method may include receiving, via a secure deployment management (SDM) system, data associated with operations of an industrial device from a SDM node associated with the industrial device. The data is received via a secure communication channel established by the SDM system with the SDM node and security protocols. The SDM node is communicatively coupled with a machine learning system for sending and receiving data. The machine learning system may generate an updated machine learning model based on the data and a machine learning model representative of expected outputs associated with the operations of the industrial device and generate updated configuration data based on the updated machine learning model. The method may then include receiving the updated configuration data from the SDM node via the secure communication channel and sending the updated configuration data to the industrial device without performing security operations on the updated configuration data.

Abstract: In one embodiment, a method includes empirically analyzing, by a computer cluster comprising a plurality of computers, a set of active reservations and a current set of consumable resources belonging to a class of consumable resources. Each active reservation is of a managed task type and comprises a group of one or more tasks task requiring access to a consumable resource of the class. The method further includes, based on the empirically analyzing, clocking the set of active reservations each clocking cycle. The method also includes, responsive to the clocking, sorting, by the computer cluster, a priority queue of the set of active reservations.

Abstract: The technology disclosed relates to resource activity management in a cloud environment. A computer-implemented method includes detecting a plurality of virtual networks in the cloud environment and deploying a plurality of sensors in the plurality of virtual networks using an orchestration engine of the cloud environment. Each sensor, of the plurality of sensors, includes an executable package configured to execute in a respective virtual network, of the plurality of virtual networks, independent of other sensors, of the plurality of sensors, to manage activities in the respective virtual network. The method includes identifying an activity management task to be performed in a particular virtual network of the plurality of virtual networks, sending a task command representing the activity management task to the sensor deployed in the particular virtual network, and receiving an execution result representing execution of the activity management task by the sensor deployed in the particular virtual network.

Abstract: A computer-implemented method, according to one embodiment, includes: determining, for each pair of HCI systems where each pair includes a first HCI system coupled to another HCI system, a federation relationship setting that corresponds to each pair. The federation relationship settings are used to control a flow of data, as well as to control a flow of workload scheduling, between the first HCI system and the other HCI systems in the respective pairs. Moreover, determining a federation relationship setting that corresponds to a pair includes: determining whether a risk score which corresponds to the pair is outside a predetermined range. In response to determining that the risk score is outside the predetermined range, a restrictive federation relationship setting is assigned to the pair, and in response to determining that the risk score is not outside the predetermined range, a permissive federation relationship setting is assigned to the pair.

Abstract: A method includes obtaining feature data including one or more features and obtaining priority information corresponding to the one or more features of the feature data. The method includes, based on the priority information, dividing the one or more features into first features and second features, compressing the second features, and combining the first features with the compressed second features. The method includes obtaining preprocessed features by applying a combination of the first features and the compressed second features to a plurality of first machine learning models. The method includes obtaining, by applying the preprocessed features to a second machine learning model, probability values of a plurality of applications that are predicted to be executed on the electronic device. The method includes, based on the probability values, executing one or more applications from among the plurality of applications in the background process of the electronic device.

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: This disclosure describes techniques that include provisioning compute nodes within a data center out of available pools of hardware. In one example, this disclosure describes a method that includes monitoring, by a computing system, a first workload executing on a first compute node, wherein the first compute node includes processing circuitry and first node secondary storage; monitoring, by the computing system, a second workload executing on a second cluster of compute nodes; expanding, by the computing system, the second cluster of compute nodes to include a second compute node that includes second node secondary storage; redeploying the processing circuitry included within the first compute node to the second compute node; and enabling, by the computing system, the second workload to continue executing on the second cluster of compute nodes including the second compute node.

Abstract: Systems, apparatuses and methods may provide for technology that automatically determines a first proposed change to an existing resource allocation associated with a first application in a first node, wherein the first proposed change is determined at least partially based on a requested resource allocation associated with a pending application and a first tolerance associated with the first application. The technology may also issue the first proposed change to the first application and automatically conduct the first proposed change if the first application accepts the first proposed change.

Abstract: A computer-implemented method to estimate computing cost of database queries. The method includes receiving a query. The method also includes generating one or more access plans for the query. The method further includes dynamically generating a learning model based on each of the generated access plans. The method includes estimating, by the learning model and for each of the one or more access plans, a predicted cost to execute the query. The method includes selecting, based on the predicted cost, a first access path associated with a lowest cost of the predicted costs and executing the query according the selected access path.

Abstract: In a synchronization method, a first processor creates a first synchronization object for a first synchronization event. The first synchronization object includes an identifier of a first synchronization register. A value of the first synchronization register includes a first value or a second value. The first value is used to indicate that the first synchronization event does not occur, and the second value is used to indicate that the first synchronization event occurs. The first processor includes a first CPU. The second processor determines, based on the value of the first synchronization register, whether the first synchronization event occurs. The second processor includes a first NPU.

Abstract: A neural processing device processing circuitry comprising and method for controlling the same are provided. The neural processing device comprises at least one processing engine group each of which includes at least one processing engines, a first memory shared by the at least one processing engine group, and an interconnection configured to exchange data between the at least one processing engine group and the first memory. The processing circuitry is configured to monitor the at least one processing engine to check performance related to the at least one processing engine, and provide hardware resources to at least one of the first memory, the interconnection or the at least one processing engine, according to the performance.

Abstract: An information handling system includes a memory, and a basic input/output system (BIOS). The BIOS receives a request to map multiple processor cores to multiple integrated memory controllers of a multiple core processor. In response to the reception of the request, the BIOS calculates a different latency for each of the processor cores. Based on the calculated different latency for each of the processor cores, the BIOS assigns mapping priority levels to the processor cores of the multiple core processor. Based on the mapping priority levels, the BIOS maps each of the processor cores to an associated one of the integrated memory controllers. The BIOS stores the map of the processor cores in the memory.

Abstract: Various embodiments include systems and methods pertaining to a network sensor host configured to implement a receive side scaling (RSS) configuration component in a security environment. The RSS configuration component may be used to automatically generate an RSS configuration comprising one or more settings customized for the network sensor host based at least in part on hardware information of the network sensor host. In some embodiments, the RSS configuration may be applied to change settings of a network interface driver of the network sensor host, e.g., to implement RSS and multithreading for network sensor tasks.

Abstract: Techniques are disclosed relating to automated operations management. In various embodiments, a computer system accesses operational information that defines commands for an operational scenario and accesses blueprints that describe operational entities in a target computer environment related to the operational scenario. The computer system implements the operational scenario for the target computer environment. The implementing may include executing a hierarchy of controller modules that include an orchestrator controller module at top level of the hierarchy that is executable to carry out the commands by issuing instructions to controller modules at a next level. The controller modules may be executable to manage the operational entities according to the blueprints to complete the operational scenario.

Abstract: A scheduling method performed by a computing apparatus includes: generating an input vector including a resource status and metadata of each of tasks for parallel execution; determining an action for the input vector by executing an actor network based on the input vector; performing first resource scheduling for each of the tasks based on the determined action; performing second resource scheduling for each of the tasks based on the input vector; evaluating performance of first resource scheduling results of the first resource scheduling and second resource scheduling results of the second resource scheduling, for each of the tasks, using a critic network; selecting one of the first and second resource scheduling results for each of the tasks based on a result of the evaluating; and allocating resources to each of the tasks based on a resource scheduling result selected for each of the tasks.

Abstract: A virtual function (VF) of a virtual machine is enabled to directly reset a processing portion of a processing unit. The VF initiates the reset of the processing portion directly and a host driver associated with the processing unit is bypassed during the reset process. By allowing for a direct reset of the processing portion, a processing system reduces the overhead associated with the reset process, enhances system security, and improves overall VM and hardware isolation at the processing system.

Abstract: Techniques for a cloud-based workload optimization service to identify customer workloads that are optimized to run on burstable instance types. The techniques include identifying workloads that are successfully running on burstable instance types, and using historical-utilization data for those workloads to train classification models. The optimization service can extract feature data from the historical-utilization data, where the feature data represents utilization characteristics that are indicative of burstable workloads. The feature data is then used to train classification models to receive utilization data for candidate workloads, and determine whether the candidate workloads would be optimized for burstable instance types. The optimization service can then migrate suitable workloads to burstable instance types, and/or provide users with recommendations that their workloads are optimized or suitable for burstable instance types.

Abstract: Apparatuses, systems, and techniques to collect performance data for one or more computations tasks executed by a plurality of nodes of a computational pipeline and enable optimization of distribution of task execution among the plurality of nodes.

Abstract: This application provides a data read method. The data read method includes: A resource management server receives a data read request from a client. The data read request is used to request a plurality of files. The resource management server reads a replica of target data from a first data center. The target data includes data of different files among the plurality of files, the first data center is a data center with highest data locality among a plurality of data centers that store replicas of the target data, and data locality is used to indicate a degree of proximity between a replica of the target data stored in a data center and the target data. The resource management server sends, to the client, the replica of the target data read from the first data center.

Abstract: Embodiments of the disclosure discloses a method and system of a virtualization environment for a data processing (DP) accelerator. In one embodiment, a data processing (DP) accelerator includes a resource management unit and one or more dynamically isolated resources managed by the resource management unit. The DP accelerator includes one or more virtual functions (VFs) each associated with one of the one or more dynamically isolated resources, where a virtual machine (VM) of a host is assigned one of the one or more VFs to access the dynamically isolated resources associated with the assigned VF, and where the VM has no access to the rest of the one or more dynamically isolated resources.

Abstract: A method, system and computer program product are presented for reducing memory consumption in depth first search (DFS)-based cycle detection processes to detect an n-length cycle on a Pregel model. The method includes generating a graph including a plurality of vertices and edges connecting the plurality of vertices, commencing a cycle from a first vertex of the plurality of vertices, setting an ID of the first vertex to a path and the first vertex as a target vertex, and iterating the following n?1 times: send the path to out-edges starting from the first vertex and transferring the path, via out-edges, n?1 times by subsequent received vertices, find one vertex before returning to the target vertex, add an ID of a found vertex to the path, and set the found vertex as the target vertex such that n=n?1, where is a number of iterations.

Abstract: The access method includes: implementing a resident virtual CPU to which a physical CPU is always assigned and a non-resident virtual CPU to which a physical CPU is not always assigned, on the virtual machine in the virtual environment; and taking over the process of accessing the virtual device by the non-resident virtual CPU when accessing from the resident virtual CPU to the virtual device corresponding to the occupancy type physical device.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to control processing of telemetry data at an edge platform. An example apparatus includes an orchestrator interface to, responsive to an amount of resources allocated to an orchestrator to orchestrate a workload at the edge platform meeting a first threshold, transmit telemetry data associated with the orchestrator to a computer to obtain a first orchestration result at a first granularity; a resource management controller to determine a second orchestration result at a second granularity to orchestrate the workload at the edge platform, the second granularity finer than the first granularity; and a scheduler to schedule a workload assigned to the edge platform based on the second orchestration result.

Abstract: The present disclosure is related to methods, systems, and machine-readable media for force provisioning using available resources. A request can be received to provision a virtual computing instance by a software defined data center according to a storage policy specified as part of the request. An insufficiency of storage policy resources to satisfy the storage policy specified for the VCI can be determined. A best-match storage policy can be determined for the VCI based on available storage policy resources, and the VCI can be force provisioned according to the best-match storage policy using the available storage policy resources.

Abstract: In one embodiment, a method includes generating an application stack. The application stack includes an application logic module. The method also includes embedding a service mesh module into the application stack. The method further includes managing, by the service mesh module, security of a network packet while maintaining separation of memory regions between the application logic module and the service mesh module.

Abstract: An audio video navigation (AVN) system for a vehicle includes a communication module configured to communicate with media sources providing media, a display module configured to display a playlist of the media, an output module configured to output the media, and a processor configured to selectively play any one of the same media based on a predetermined play preference mode when the same media are present in the playlist when the media in the playlist are played.

Abstract: Some embodiments provide a method for configuring logical routers of a logical network. The logical routers are implemented in a Kubernetes cluster as a first set of Pods that each perform logical forwarding operations for the logical routers and a second set of Pods that each perform L7 service operations for a respective logical router. From a Kubernetes control plane component, the method receives a notification that the first set requires scaling to include an additional Pod. The first-set Pods process data messages between the logical network and external networks. Within the network management system, the method defines at least one new interface for processing data messages between the logical network and external networks. The method configures the at least one interface on the additional Pod to communicate with external physical routers to receive traffic from the external networks and send traffic to the external networks.

Abstract: A game reward system is described herein that pays players to play a video game. The system does this by utilizing the unused resources of the player's computing device to perform cryptographic hashes and mine one or more cryptocurrencies. The game reward system generates a jackpot based on the cryptocurrency that has been mined, allocates some amount to the system operator for profit of running the system (e.g., 10%), and divides the remaining amount among the players of enough rank as a reward. Instead of charging people to buy a game, the game maker using the game reward system can make the game free for players to play. Thus, the game reward system completely changes the business model of games, makes games more fun, and rewards players in a new way for playing games.

Abstract: An image processing apparatus includes a memory, a user interface, and a processor. The memory stores a plurality of user applications and a plurality of background applications related to image processing. The user interface receives a user input. The processor controls an image processing unit (e.g., a printer, a scanner, etc.) to execute image processing functions (e.g., printing, scanning, copying, etc.). The processor controls activation and deactivation of the plurality of user applications and the plurality of background applications. The processor activates a subset of the plurality of background applications in a number equal to or less than an activation limit number and based on execution history of the plurality of background applications.

Abstract: A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.

Abstract: The present disclosure describes a queued message service (QMS) that provides a multi-region, low-latency and reliable service for managing a large number of durable message queues for async targeted communication. This general-purpose, internal service QMS may be implemented in print queues as well as command queues for sensors. One example implementation of QMS is a print service. Such implementation introduces status indicators for printers internally, wherein such status indicators may identify whether a printer is blocked, offline, suspected of failure, properly functioning, etc.

Abstract: Methods and apparatus for hierarchical reinforcement learning (RL) algorithm for network function virtualization (NFV) server power management. A first RL model at a first layer is trained by adjusting a frequency of the core of processor while performing a workload to obtain a first trained RL model. The trained RL model is operated in an inference mode while training a second RL model at a second level in the RL hierarchy by adjusting a frequency of the core and a frequency of processor circuitry external to the core to obtain a second trained RL model. Training may be performed online or offline. The first and second RL models are operated in inference modes during online operations to adjust the frequency of the core and the frequency of the circuitry external to the core while executing software on the plurality of cores of to perform a workload, such as an NFV workload.

Abstract: Systems and methods are disclosed including a memory and a processing device operatively coupled to the memory. The processing device can perform operations including receiving log data related to a first order of a set of error-handling operations performed on data residing in a segment of a memory device; applying an optimization model to the log data, wherein the optimization model is based on probability data of error recovery and latency data of the set of error-handling operations; and responsive to applying the optimization model to the log data, obtaining, as an output of the optimization model, a second order of the set of error-handling operations, wherein the second order adjusts an order of one or more error-handling operations of the set of error-handling operations in the first order.

Abstract: The present invention provides a vehicle control device capable of avoiding a problem due to insufficient energy remaining in a vehicle in a parking lot. The vehicle control device 100 includes a remaining energy amount calculation unit 110, a parking lot information acquisition unit 120, a prediction unit 130, a required energy amount calculation unit 140, and a parking determination unit 150. The remaining energy amount calculation unit 110 calculates a remaining amount of energy of a vehicle V. The parking lot information acquisition unit 120 acquires map information and other vehicles information of a parking lot. The prediction unit 130 predicts a traveling speed and a movement time of the vehicle V at the time of entry and exit. The required energy amount calculation unit 140 calculates a required amount of energy. The parking determination unit 150 determines whether or not the vehicle V can be parked based on the remaining amount of energy and the required amount of energy.

Abstract: A database system is operable to determine a query for execution that requires access to a set of records stored by the database system. A first proper subset of the set of records are accessed in conjunction with executing the query by reading exactly one of a set of multiple replicas of each record of the first proper subset of the set of records from the replication-based storage system. A second proper subset of the set of records are accessed in conjunction with executing the query by reading at least one redundancy-coded segment from the redundancy-coding based storage system. A final resultant for the query is generated by performing at least one query operation on the first proper subset of the set of records and the second proper subset of the set of records in conjunction with executing the query.