Abstract: A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

Abstract: Methods and apparatus are provided for evaluating potential resource capacity in a system where there is elasticity and competition between a plurality of containers. A dynamic potential capacity is determined for at least one container in a plurality of containers competing for a total capacity of a larger container. A current utilization by each of the plurality of competing containers is obtained, and an equilibrium capacity is determined for each of the competing containers. The equilibrium capacity indicates a capacity that the corresponding container is entitled to. The dynamic potential capacity is determined based on the total capacity, a comparison of one or more of the current utilizations to one or more of the corresponding equilibrium capacities and a relative resource weight of each of the plurality of competing containers. The dynamic potential capacity is optionally recalculated when the set of plurality of containers is changed or after the assignment of each work element.

Abstract: A method, information processing system, and computer readable storage medium, vary a maximum heap memory size for one application of a plurality of applications based on monitoring garbage collection activity levels for the plurality of applications, each application including a heap memory, and unused memory in the heap memory being reclaimed by a garbage collector.

Abstract: A method and structure for dynamic memory re-allocation for an application runtime environment (ARE) includes receiving, through an interface of an application runtime environment (ARE), a first set of internal operational metrics of the ARE executing at a current setting S1 on a processor of a computer. A first performance P1 of the ARE is determined at the current setting S1 using the received first set of internal operation metrics. The current setting S1 of the ARE is varied to a new setting S2. A second set of internal operational metrics of the ARE executing at the new setting S2 is received through the interface of the ARE. A second performance P2 of the ARE is determined at the new setting S2, using the received second set of internal operation metrics. A memory allocation for the ARE is re-allocated, based on the determined performances P1 and P2.

Abstract: A method, processing system, and computer readable storage medium, reduce heap memory used by an application, where unused memory in the heap memory is reclaimed by a garbage collector. A processor periodically monitors the application's memory usage including maximum heap memory size, committed heap memory size, in use heap memory size, and a garbage collection activity level. The processor, based on determining that the monitored garbage collection activity level is below a threshold, releases unused heap memory from the application by reducing the maximum heap memory size.

Abstract: A method, information processing system, and computer readable storage medium, vary a maximum heap memory size for one application of a plurality of applications based on monitoring garbage collection activity levels for the plurality of applications, each application including a heap memory, and unused memory in the heap memory being reclaimed by a garbage collector.

Abstract: A method, processing system, and computer readable storage medium, reduce heap memory used by an application, where unused memory in the heap memory is reclaimed by a garbage collector. A processor periodically monitors the application's memory usage including maximum heap memory size, committed heap memory size, in use heap memory size, and a garbage collection activity level. The processor, based on determining that the monitored garbage collection activity level is below a threshold, releases unused heap memory from the application by reducing the maximum heap memory size.

Abstract: A method, information processing system, and computer readable storage medium, vary a maximum heap memory size for one application of a plurality of applications based on monitoring garbage collection activity levels for the plurality of applications, each application including a heap memory, and unused memory in the heap memory being reclaimed by a garbage collector.

Abstract: A method for allocating memory among a plurality of computing systems includes assigning a free memory threshold to each computing system, dynamically varying at least one free memory threshold for at least one computing system based on a past memory usage of the at least one computing system, periodically monitoring each computing system for memory usage and in response to the monitoring determining that an amount of free memory for the computer system is below the free memory threshold for the computing system, allocating memory from the free memory pool to the computing system.

Abstract: Methods and apparatus are provided for scheduling parallel jobs in a system of virtual containers. At least one parallel job is assigned to a plurality of containers competing for a total capacity of a larger container, wherein the at least one parallel job comprises a plurality of tasks. The assignment method comprises determining a current utilization and a potential free capacity for each of the plurality of competing containers; and assigning the tasks to one of the plurality of containers based on the potential free capacities and at least one predefined scheduling policy. The predefined scheduling policy may comprise, for example, one or more of load balancing, server consolidation, maximizing the current utilizations, minimizing a response time of the parallel job and satisfying quality of service requirements. The load balancing can be achieved, for example, by assigning a task to a container having a highest potential free capacity.

Abstract: Methods and apparatus are provided for scheduling parallel jobs in a system of virtual containers. At least one parallel job is assigned to a plurality of containers competing for a total capacity of a larger container, wherein the at least one parallel job comprises a plurality of tasks. The assignment method comprises determining a current utilization and a potential free capacity for each of the plurality of competing containers; and assigning the tasks to one of the plurality of containers based on the potential free capacities and at least one predefined scheduling policy. The predefined scheduling policy may comprise, for example, one or more of load balancing, server consolidation, maximizing the current utilizations, minimizing a response time of the parallel job and satisfying quality of service requirements. The load balancing can be achieved, for example, by assigning a task to a container having a highest potential free capacity.

Abstract: A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

Abstract: A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

Abstract: Techniques for dynamic management of virtual machine environments are disclosed. For example, a technique for automatically managing a first set of virtual machines being hosted by a second set of physical machines comprises the following steps/operations. An alert is obtained that a service level agreement (SLA) pertaining to at least one application being hosted by at least one of the virtual machines in the first set of virtual machines is being violated. Upon obtaining the SLA violation alert, the technique obtains at least one performance measurement for at least a portion of the machines in at least one of the first set of virtual machines and the second set of physical machines, and a cost of migration for at least a portion of the virtual machines in the first set of virtual machines. Based on the obtained performance measurements and the obtained migration costs, an optimal migration policy is determined for moving the virtual machine hosting the at least one application to another physical machine.

Abstract: To provide collaborative hosting of applications, the present invention first generates a virtual operating system image consisting of operating system code, application code, runtime code and supporting data repositories. The virtual operating system image may then be sent to and deploying on a physical computer or computer terminal at another location.

Abstract: Methods and apparatus are provided for scheduling parallel jobs in a system of virtual containers. At least one parallel job is assigned to a plurality of containers competing for a total capacity of a larger container, wherein the at least one parallel job comprises a plurality of tasks. The assignment method comprises determining a current utilization and a potential free capacity for each of the plurality of competing containers; and assigning the tasks to one of the plurality of containers based on the potential free capacities and at least one predefined scheduling policy. The predefined scheduling policy may comprise, for example, one or more of load balancing, server consolidation, maximizing the current utilizations, minimizing a response time of the parallel job and satisfying quality of service requirements. The load balancing can be achieved, for example, by assigning a task to a container having a highest potential free capacity.

Abstract: Historical data is measured for a computer server system. Future demand for service in the computer server system is forecast based on the historical data, and the mapping of virtual machines to physical machines is updated based on the forecast of the future demand. Measurement, forecasting, and placement modules can be employed.

Abstract: Methods and apparatus are provided for scheduling parallel jobs in a system of virtual containers. At least one parallel job is assigned to a plurality of containers competing for a total capacity of a larger container, wherein the at least one parallel job comprises a plurality of tasks. The assignment method comprises determining a current utilization and a potential free capacity for each of the plurality of competing containers; and assigning the tasks to one of the plurality of containers based on the potential free capacities and at least one predefined scheduling policy. The predefined scheduling policy may comprise, for example, one or more of load balancing, server consolidation, maximizing the current utilizations, minimizing a response time of the parallel job and satisfying quality of service requirements. The load balancing can be achieved, for example, by assigning a task to a container having a highest potential free capacity.

Abstract: Methods and apparatus are provided for evaluating potential resource capacity in a system where there is elasticity and competition between a plurality of containers. A dynamic potential capacity is determined for at least one container in a plurality of containers competing for a total capacity of a larger container. A current utilization by each of the plurality of competing containers is obtained, and an equilibrium capacity is determined for each of the competing containers. The equilibrium capacity indicates a capacity that the corresponding container is entitled to. The dynamic potential capacity is determined based on the total capacity, a comparison of one or more of the current utilizations to one or more of the corresponding equilibrium capacities and a relative resource weight of each of the plurality of competing containers. The dynamic potential capacity is optionally recalculated when the set of plurality of containers is changed or after the assignment of each work element.

Abstract: To provide collaborative hosting of applications, the present invention first generates a virtual operating system image consisting of operating system code, application code, runtime code and supporting data repositories. The virtual operating system image may then be sent to and deploying on a physical computer or computer terminal at another location.