Abstract: A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Abstract: One or more non-transitory machine-readable storage mediums storing program instructions for operating a first cluster of servers. The program instructions are configured to be executable by one or more processors of the first cluster of servers to perform various operations. The operations may include storing a user interface of a cloud service consumer and receiving, from a user, a request to access services of the cloud service consumer. The operations may further include retrieving, from a second cluster of servers maintained by the cloud service consumer, user data required in response to the request and providing the user interface and the user data to the user.

Abstract: One or more non-transitory machine-readable storage mediums storing program instructions for operating a first cluster of servers. The program instructions are configured to be executable by one or more processors of the first cluster of servers to perform various operations. The operations may include storing a user interface of a cloud service consumer and receiving, from a user, a request to access services of the cloud service consumer. The operations may further include retrieving, from a second cluster of servers maintained by the cloud service consumer, user data required in response to the request and providing the user interface and the user data to the user.

Abstract: A first cluster of servers maintains a shared database that stores, for each of a plurality of cloud service consumers, user interface data for the cloud service consumer, wherein each server in the first cluster can read and write to the shared database. The first cluster further receives, from a customer computer, a request to access services of an identified cloud service consumer among the plurality of cloud service consumers, wherein the customer computer is associated with a customer of the identified cloud service consumer. Still further, the first cluster retrieves the user interface data of the identified cloud service consumer from the shared database; retrieves, from a second cluster of servers maintained by the identified cloud service consumer, user data required in response to the request; and provides the user interface data of the identified cloud service consumer and the user data to the customer computer.

Abstract: A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Abstract: A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Abstract: A system and method for providing quality of service (QoS) for clients running on host computers to access a common resource uses a resource pool module and a local scheduler in at least one of the host computers. The resource pool module operates to compute an entitlement of each client for the common resource based on a current capacity for the common resource and demands of the clients for the common resource. In addition, the resource pool module operates to assign a portion of the computed current capacity for the common resource to a particular host computer using the computed entitlement of each client running on the particular host computer. The local scheduler operates to allocate the portion of the computed current capacity among the clients running on the particular host computer.

Abstract: A first cluster of servers maintains a shared database that stores, for each of a plurality of cloud service consumers, user interface data for the cloud service consumer, wherein each server in the first cluster can read and write to the shared database. The first cluster further receives, from a customer computer, a request to access services of an identified cloud service consumer among the plurality of cloud service consumers, wherein the customer computer is associated with a customer of the identified cloud service consumer. Still further, the first cluster retrieves the user interface data of the identified cloud service consumer from the shared database; retrieves, from a second cluster of servers maintained by the identified cloud service consumer, user data required in response to the request; and provides the user interface data of the identified cloud service consumer and the user data to the customer computer.

Abstract: Approaches for automated provisioning of a private cloud infrastructure. A cloud service provider maintains a first cluster of servers which stores a set of user interfaces. Each first cluster node comprises a first distributed management layer that manages, monitors, and self-heals first cluster nodes and facilitates communication with nodes of a second cluster of servers. A cloud service consumer maintains the second cluster which stores sets of user data. Second cluster nodes comprise a second distributed management layer that manages, monitors, and self-heals second cluster nodes and facilitates communication with first cluster nodes. When a first node in the first cluster receives a request from an end-user of a cloud-based service, the first node (a) communicates with the second distributed management layer to obtain a required set of user data and (b) prepares and transmits, to the end-user, a response that comprises the appropriate user interface and required user data.

Abstract: A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles in a storage system. One method includes operations for receiving requests to be processed, and for associating each request to one task. A foreground task is for processing input/output requests, and the foreground task includes one or more flows. Each flow is associated with a queue and a flow counter value, where each queue is configured to hold requests. The method further includes an operation for selecting one task for processing by the CPU based on an examination of the number of cycles processed by the CPU for each task. When the selected task is the foreground task, the flow having the lowest flow counter is selected. The CPU processes a request from the queue of the selected flow, and the flow counter of the selected flow is increased based on the data consumption of the processed task.

Abstract: A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles and disk Input/Output's (IOs) to resource-creating processes based on dynamic weights that change according to the current percentage of resource utilization in the storage device. One method includes operations for assigning a first weight to a processing task that increases resource utilization of a resource for processing incoming input/output (IO) requests, and for assigning a second weight to a generating task that decreases the resource utilization of the resource. Further, the method includes an operation for dynamically adjusting the second weight based on the current resource utilization in the storage system. Additionally, the method includes an operation for allocating the CPU cycles and disk IOs to the processing task and to the generating task based on their respective first weight and second weight.

Abstract: A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles and disk Input/Output's (IOs) to resource-creating processes based on dynamic weights that change according to the current percentage of resource utilization in the storage device. One method includes operations for assigning a first weight to a processing task that increases resource utilization of a resource for processing incoming input/output (IO) requests, and for assigning a second weight to a generating task that decreases the resource utilization of the resource. Further, the method includes an operation for dynamically adjusting the second weight based on the current resource utilization in the storage system. Additionally, the method includes an operation for allocating the CPU cycles and disk IOs to the processing task and to the generating task based on their respective first weight and second weight.

Abstract: Maximum throughput of a storage unit, and workload and latency values of the storage unit corresponding to a predefined fraction of the maximum throughput are estimated based on workloads and latencies that are monitored on the storage unit. The computed metrics are usable in a variety of different applications including admission control, storage load balancing, and enforcing quality of service in a shared storage environment.

Abstract: A management server and method for performing resource management operations in a distributed computer system uses at least one sampling parameter to estimate demand of a client for a resource. The sampling parameter has a correlation with at least one target performance goal of an application that the client is running. The demand estimation can then be used to make at least one decision in a resource management operation.

Abstract: A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles and disk Input/Output's (IOs) to resource-creating processes based on dynamic weights that change according to the current percentage of resource utilization in the storage device. One method includes operations for assigning a first weight to a processing task that increases resource utilization of a resource for processing incoming input/output (IO) requests, and for assigning a second weight to a generating task that decreases the resource utilization of the resource. Further, the method includes an operation for dynamically adjusting the second weight based on the current resource utilization in the storage system. Additionally, the method includes an operation for allocating the CPU cycles and disk IOs to the processing task and to the generating task based on their respective first weight and second weight.

Abstract: One or more embodiments of the present invention provide a technique for effectively managing virtualized computing systems with an unlimited number of hardware resources. Host systems included in a virtualized computer system are organized into a scalable, peer-to-peer (P2P) network in which host systems arrange themselves into a network overlay to communicate with one another. The network overlay enables the host systems to perform a variety of operations, which include dividing computing resources of the host systems among a plurality of virtual machines (VMs), load balancing VMs across the host systems, and performing an initial placement of a VM in one of the host systems.