Abstract: A system for managing allocation of resources based on service level agreements between application owners and cloud operators. Under some service level agreements, the cloud operator may have responsibility for managing allocation of resources to the software application and may manage the allocation such that the software application executes within an agreed performance level. Operating a cloud computing platform according to such a service level agreement may alleviate for the application owners the complexities of managing allocation of resources and may provide greater flexibility to cloud operators in managing their cloud computing platforms.

Abstract: Various technologies pertaining to scheduling network traffic in a network are described. A request to transfer data from a first computing device to a second computing device includes data that identifies a volume of the data to be transferred and a deadline, where the data is to be transferred prior to the deadline. A long-term schedule is computed based upon the request, wherein the long-term schedule defines flow of traffic through the network over a relatively long time horizon. A short-term schedule is computed based upon the long-term schedule, where devices in the network are configured based upon the short-term schedule.

Abstract: This document describes techniques for dynamically placing computing jobs. These techniques enable reduced financial and/or energy costs to perform computing jobs at data centers.

Abstract: A data center system is described which includes multiple data centers powered by multiple power sources, including any combination of renewable power sources and on-grid utility power sources. The data center system also includes a management system for managing execution of computational tasks by moving data components associated with the computational tasks within the data center system, in lieu of, or in addition to, moving power itself. The movement of data components can involve performing pre-computation or delayed computation on data components within any data center, as well as moving data components between data centers. The management system also includes a price determination module for determining prices for performing the computational tasks based on different pricing models. The data center system also includes a “stripped down” architecture to complement its use in the above-summarized data-centric environment.

Abstract: A data center system is described which includes multiple data centers powered by multiple power sources, including any combination of renewable power sources and on-grid utility power sources. The data center system also includes a management system for managing execution of computational tasks by moving data components associated with the computational tasks within the data center system, in lieu of, or in addition to, moving power itself. The movement of data components can involve performing pre-computation or delayed computation on data components within any data center, as well as moving data components between data centers. The management system also includes a price determination module for determining prices for performing the computational tasks based on different pricing models. The data center system also includes a “stripped down” architecture to complement its use in the above-summarized data-centric environment.

Abstract: Described are performance-based pricing models for pricing execution of a client job in a cloud service. Client-provided performance-related parameters are used to determine a price. The price may be a minimum bid price that is evaluated against a bid received from client bidder to accept or reject the bid. Alternatively, the price may be returned as a quote. For batch application-type jobs, performance parameters include a work volume parameter and a deadline or the like. For an interactive-type application job, example performance-related parameters may include an average load parameter, a peak load parameter, an acceptance rate parameter, a minimum capacity parameter, a maximum capacity parameter, and/or a time window parameter over which load is specified.

Abstract: There is provided a method and system for process migration in a data center network. The method includes selecting processes to be migrated from a number of overloaded servers within a data center network based on an overload status of each overloaded server. Additionally, the method includes selecting, for each selected process, one of a number of underloaded servers to which to migrate the selected process based on an underload status of each underloaded server, and based on a parameter of a network component by which the selected process is to be migrated. The method also includes migrating each selected process to the selected underloaded server such that a migration finishes within a specified budget.

Abstract: Upon receiving a request to improve one or more conditions of a datacenter network, a fault management system may analyze information of the datacenter network including communication patterns among services provided in the network. The fault management system determines one or more logical machines associated with one or more services to be moved from one or more devices to one or more other devices of the network. The fault management system may select these one or more logical machines for migration based on a cost function including factors for fault tolerance, bandwidth usage, number of moves and/or response time latency. The fault management system may improve the fault tolerance of the network without significantly affecting the bandwidth usage of the network.

Abstract: A method for adaptively allocating resources to a plurality of jobs. The method comprises selecting a first policy from a plurality of policies for a first job in the plurality of jobs by using a policy selection mechanism, allocating at least one resource to the first job in accordance with the first policy, and in response to completion of the first job, updating the policy selection mechanism to obtain an updated policy selection mechanism by using at least one processor. Updating the policy selection mechanism comprises evaluating the performance of the first policy with respect to the first job by calculating a value of a metric of utility for the first policy based on conditions associated with execution of the first job and updating the policy selection mechanism based on the calculated value and a delay of execution of the first job.

Abstract: A plurality of requests for execution of computing jobs on one or more devices that include a plurality of computing resources may be obtained, the one or more devices configured to flexibly allocate the plurality of computing resources, each of the computing jobs including job completion values representing a worth to a respective user that is associated with execution completion times of each respective computing job. The computing resources may be scheduled based on the job completion values associated with each respective computing job.

Abstract: A system for managing allocation of resources based on service level agreements between application owners and cloud operators. Under some service level agreements, the cloud operator may have responsibility for managing allocation of resources to the software application and may manage the allocation such that the software application executes within an agreed performance level. Operating a cloud computing platform according to such a service level agreement may alleviate for the application owners the complexities of managing allocation of resources and may provide greater flexibility to cloud operators in managing their cloud computing platforms.

Abstract: A system for managing allocation of resources based on service level agreements between application owners and cloud operators. Under some service level agreements, the cloud operator may have responsibility for managing allocation of resources to the software application and may manage the allocation such that the software application executes within an agreed performance level. Operating a cloud computing platform according to such a service level agreement may alleviate for the application owners the complexities of managing allocation of resources and may provide greater flexibility to cloud operators in managing their cloud computing platforms.

Abstract: This document describes techniques for dynamically placing computing jobs. These techniques enable reduced financial and/or energy costs to perform computing jobs at data centers.

Abstract: A data center system is described which includes multiple data centers powered by multiple power sources, including any combination of renewable power sources and on-grid utility power sources. The data center system also includes a management system for managing execution of computational tasks by moving data components associated with the computational tasks within the data center system, in lieu of, or in addition to, moving power itself. The movement of data components can involve performing pre-computation or delayed computation on data components within any data center, as well as moving data components between data centers. The management system also includes a price determination module for determining prices for performing the computational tasks based on different pricing models. The data center system also includes a “stripped down” architecture to complement its use in the above-summarized data-centric environment.