Abstract:
An automatic scaling system and method for reducing state space in reinforced learning for automatic scaling of a multi-tier application uses a state decision tree that is updated with new states of the multi-tier application. When a new state of the multi-tier application is received, the new state is placed in an existing node of the state decision tree only if a first attribute of the new state is same as a first attribute of any state contained in the existing node and a second attribute of the new state is sufficiently similar to a second attribute of each existing state contained in the existing node based on a similarity measurement of the second attribute of each state contained in the existing node with the second attribute of the new state.
Abstract:
A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.
Abstract:
Exemplary methods, apparatuses, and systems include a first host system configuring storage of the first host to serve as a primary cache for a virtual machine running on the first host. A second host system configures storage of the second host to serve as a secondary cache and boots a placeholder virtual machine. The first host transmits, in response to write operations from the virtual machine directed to the primary cache, copies of the write operations to the second host to create mirrored copies on the secondary cache. The first host acknowledges each write operation from the virtual machine when the write operation is committed to both the primary cache and the secondary cache. When the virtual machine is restarted on the second host in response to a failure or migration event, the secondary cache is promoted to serve as a new primary cache for the virtual machine.
Abstract:
A system and method for performing a hypothetical power management analysis on a distributed computer system uses chronologically consecutive snapshots of the distributed computer system. The snapshots are used to extract demands of clients running in the distributed computer system for a resource for different time intervals, which are then stitched together to produce a workload trace. The snapshots and the workload trace are used to construct modeling scenarios for the distributed computer system. The modeling scenarios are used to perform analyses to simulate the operation of the distributed computer system during which the power management module is enabled to compute potential power savings.