Abstract: Computer-implemented techniques for executing jobs on parallel processors using dynamic storage-aware job scheduling are disclosed. A network storage system is accessed along with a scheduling queue of pending job processes. The networked storage system is polled to determine the status of members of the storage system. These members comprise storage devices and storage shares. A database is created of metrics describing the status of the members of the networked storage system. Job processes are then dispatched to the networked storage system based on this database of metrics.

Abstract: Computer-implemented techniques for executing jobs on parallel processors using dynamic storage-aware job scheduling are disclosed. A network storage system is accessed along with a scheduling queue of pending job processes. The networked storage system is polled to determine the status of members of the storage system. These members comprise storage devices and storage shares. A database is created of metrics describing the status of the members of the networked storage system. Job processes are then dispatched to the networked storage system based on this database of metrics.

Abstract: Electronic design automation (EDA) libraries are delivered using a geographically distributed private cloud including EDA design centers and EDA library stores. EDA projects associated with an EDA library are determined by matching information describing the EDA library with information describing the projects. A set of design centers hosting the projects is determined. A data delivery model is determined for transmitting the EDA library to the design centers. The EDA library is scheduled for delivery to the design centers based on a deadline associated with a project stage that requires the EDA library. Network links with specialized hardware for transmitting data are determined in the private cloud by measuring their deterioration in performance on increase of data transmission load. These links are used for delivering EDA libraries expected to be used urgently for a stage of an EDA project.

Abstract: Electronic design automation (EDA) libraries are delivered using a geographically distributed private cloud including EDA design centers and EDA library stores. EDA projects associated with an EDA library are determined by matching information describing the EDA library with information describing the projects. A set of design centers hosting the projects is determined. A data delivery model is determined for transmitting the EDA library to the design centers. The EDA library is scheduled for delivery to the design centers based on a deadline associated with a project stage that requires the EDA library. Network links with specialized hardware for transmitting data are determined in the private cloud by measuring their deterioration in performance on increase of data transmission load. These links are used for delivering EDA libraries expected to be used urgently for a stage of an EDA project.

Abstract: Provisioning resources in public cloud infrastructure to perform at least part of electronic design automation (EDA) tasks on the public cloud infrastructure. Performance metrics of servers in the public cloud infrastructure and performance history of a user's past EDA tasks are maintained to estimate operation parameters such as runtime of a new EDA task. Based on the estimation, a user can provision appropriate types and amounts of resources in the public cloud infrastructure in a cost-efficient manner. Also, a plurality of EDA tasks are assigned to computing resources in a manner that minimizes the overall cost for performing the EDA tasks.

Abstract: Provisioning resources in public cloud infrastructure to perform at least part of electronic design automation (EDA) tasks on the public cloud infrastructure. Performance metrics of servers in the public cloud infrastructure and performance history of a user's past EDA tasks are maintained to estimate operation parameters such as runtime of a new EDA task. Based on the estimation, a user can provision appropriate types and amounts of resources in the public cloud infrastructure in a cost-efficient manner. Also, a plurality of EDA tasks are assigned to computing resources in a manner that minimizes the overall cost for performing the EDA tasks.

Abstract: One embodiment of the present invention provides a system that enforces a resource-usage policy in a compute farm. During operation, the system can receive etiquette rules which include resource-usage rules and corrective-action rules. Resource-usage rules can specify situations in which the resource-usage policy is violated, and corrective-action rules can specify situations in which a corrective action needs to be performed. Next, the system can receive resource-usage information which includes job monitoring data and process monitoring data. The system can then determine a resource-usage violation by applying the resource-usage rules to the resource-usage information. Next, the system can store the resource-usage violation in a violation database. The system can then determine a corrective action by applying corrective-action rules to a series of violations stored in the violation database. Next, the system can perform the corrective action, thereby enforcing the resource-usage policy in the compute farm.