Abstract: A decentralized process to ensure the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Abstract: Techniques are disclosed for determining placements of application instances on computing resources in a computing system such that the application instances can be executed thereon. By way of example, a method for determining an application instance placement in a set of machines under one or more resource constraints includes the following steps. An estimate is computed of a value of the first metric that can be achieved by a current application instance placement and a current application load distribution. A new application instance placement and a new application load distribution are determined, wherein the new application instance placement and the new load distribution optimize the first metric.

Abstract: Systems and methods for managing a system of heterogeneous workloads are provided. Work that enters the system is separated into a plurality of heterogeneous workloads. A plurality of high-level quality of service goals is gathered. At least one of the plurality of high-level quality of service goals corresponds to each of the plurality of heterogeneous workloads. A plurality of control functions are determined that are provided by virtualizations on one or more containers in which one or more of the plurality of heterogeneous workloads run. An expected utility of a plurality of settings of at least one of the plurality of control functions is determined in response to the plurality of high-level quality of service goals. At least one of the plurality of control functions is exercised in response to the expected utility to effect changes in the behavior of the system.

Abstract: Apparatus, systems and methods for service and/or business for coordinating tasks of performance management and application placement management in a dynamic fashion. An example process is dynamic in the face of fluctuations in the request load to the distributed computer system and the periodic adjustments to the placement of applications onto servers in said distributed computer system. There are two opposite functional flows in said process: a demand estimation function and a capacity adjustment function. The coordination system involves two subsystems: a demand estimator and a capacity adjuster, along with appropriate interfaces to of the performance manager and the application placement manager.

Abstract: A computer implemented method, apparatus, and computer usable program code for load balancing in a cluster. In response to receiving a notification of an impending state change event in the server, a weight adjustment value for a server is identified to form an identified weight adjustment. An adjusted routing weight for the server is generated using the identified weight adjustment to form a current routing weight for the server. An incoming service request is routed to a selected server in the cluster using current routing weight associated with servers in the cluster.

Abstract: An optimization problem models the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Abstract: Techniques for managing membership of a network of nodes are provided. Nodes of the network are organized into a tree structure having a plurality of vertices. Each vertex of the tree structure comprises a group having a plurality of nodes. Membership information is maintained at each node. The membership information for a given node comprises information about a group to which the given node belongs and child groups of the group to which the given node belongs in the tree structure.

Abstract: Techniques for managing a system of heterogeneous applications are provided. A plurality of applications is classified into a plurality of application types. One or more of the plurality of applications in each of the plurality of application types are classified into one or more collections. A utility function of possible resource allocations is computed for each of the one or more collections. An application placement is computed that optimizes a global utility of the plurality of applications in accordance with the one or more utility functions. Placement and resource allocation of the plurality of applications are modified in the system in accordance with the application placement.

Abstract: Techniques are disclosed for determining placements of application instances on computing resources in a computing system such that the application instances can be executed thereon. By way of example, a method for determining an application instance placement in a set of machines under one or more resource constraints includes the following steps. An estimate is computed of a value of the first metric that can be achieved by a current application instance placement and a current application load distribution. A new application instance placement and a new application load distribution are determined, wherein the new application instance placement and the new load distribution optimize the first metric.

Abstract: Embodiments of the present invention address deficiencies of the art in respect to deploying heterogeneous workloads in separate resource pools and provide a method, system and computer program product for on-demand application scheduling in a heterogeneous environment. In one embodiment of the invention, a method for balancing nodal allocations in a resource pool common to both transactional workloads and long running workloads can include parsing a service policy for both transactional workloads and also long running workloads. An allocation of nodes for a common resource pool for the transactional and long running workloads can be determined to balance performance requirements for the transactional workloads and long running workloads specified by the service policy. Subsequently, the determined allocation can be applied to the common resource pool.

Abstract: A decentralized process to ensure the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Abstract: A solution to a variant of a class constrained multiple knapsack problem. Previous solutions require that memory demand of every application be identical and do not consider minimizing placement changes. Previous techniques do not consider optimizing placement to improve load balancing as is described subsequently. Thus, the present invention provides systems, methods and apparatus, encapsulated in software, to provide the dynamic placement of application instances on a heterogeneous cluster of server machines. It depends on the existence of a visible and controllable platform, systems management and other business services that signal events and accept commands. It provides dynamically placing applications on servers such as to maximize a total amount of satisfied application demands, minimize a number of placement changes from a current placement, and minimize imbalance of load distribution of said applications on said at least one server.

Abstract: An optimization problem models the dynamic placement of applications on servers under two types of simultaneous resource requirements, those that are dependent on the loads placed on the applications and those that are independent. The demand (load) for applications changes over time and the goal is to satisfy all the demand while changing the solution (assignment of applications to servers) as little as possible.

Abstract: A method, system and apparatus for on-demand application resource allocation. In accordance with the method of the invention, an anticipated workload can be compared to a measured capacity for an application cluster in one or more server computing nodes in a server farm. If the measured capacity warrants a re-configuration of the application clusters, a new placement can be computed for application server instances in individual ones of the server computing nodes. Subsequently, the new placement can be applied to the server computing nodes in the server farm. In this regard, the applying step can include starting and stopping selected ones of the application server instances in different ones of the server computing nodes to effectuate the new placement.

Abstract: The present invention provides a method, system, and computer program product for supporting a large number of intermittently used application clusters without consuming all the resources needed to run server processes for all the application clusters all the time. A method in accordance with the present invention comprises: holding an application request for a dormant application cluster in a holding zone; changing a status of the dormant application cluster to active; changing a status of an unused active application cluster to dormant; stopping server processes for the newly dormant application cluster; starting server processes for the newly active application cluster; and directing the application request held in the holding zone to the server processes for the newly active application cluster.

Abstract: Apparatus, systems and methods for service and/or business for coordinating tasks of performance management and application placement management in a dynamic fashion. An example process is dynamic in the face of fluctuations in the request load to the distributed computer system and the periodic adjustments to the placement of applications onto servers in said distributed computer system. There are two opposite functional flows in said process: a demand estimation function and a capacity adjustment function. The coordination system involves two subsystems: a demand estimator and a capacity adjuster, along with appropriate interfaces to of the performance manager and the application placement manager.