Abstract: Techniques are provided for allocating resources. Performance metrics for a transaction are received. It is determined whether one or more service level objectives are being violated based on the received performance metrics. In response to determining that the one or more service level objectives are being violated, additional resources are allocated to the transaction. In response to allocating the additional resources, a resource allocation event is published.

Abstract: Provided are a method, system, and program for distributing application transactions among work servers. Application transaction rates are determined for a plurality of applications supplying transactions to process. For each application, available partitions in at least one server are assigned to process the application transactions based on partition transaction rates of partitions in the servers. For each application, a determination is made of weights for each server including partitions assigned to the application based on a number of partitions in the server assigned to the application. The determined weights for each application are used to distribute application transactions among the servers including partitions assigned to the application.

Abstract: The invention relates to scheduling multiple tasks running on multiple platforms by analysis and consideration of various factors and metrics, e.g., priority of execution, balancing the work load, balancing of resources, resource availability, time constraints, etc. through such expedients as task assignment, (i.e., deciding which processor or other resources will be used to execute one or more tasks). The purpose is to minimize processing execution time and client waiting time by efficiently distributing workload among operational computers, processors and other system resources. The relationship of real world server workload versus time, measured against various metrics and historical data, with an intermediate result used to simulate future demand. This simulation of future demand is then used to reconfigure the system to meet the demand, thereby providing higher degrees of self management and autonomy to the web site.