摘要:
An embodiment of the invention provides an apparatus and method for classifying a workload of a computing entity. In an embodiment, the computing entity samples a plurality of values for a plurality of parameters of the workload. Based on the plurality of values of each parameter, the computing entity determines a parameter from the plurality of parameters that the computing entity's response time is dependent on. Here, the computing entity's response time is indicative of a time required by the computing entity to respond to a service request from the workload. Further, based on the identified significant parameter, the computing entity classifies the workload of the computing entity by selecting a workload classification from a plurality of predefined workload classifications.
摘要:
A change in workload characteristics detected at one tier of a multi-tiered cache is communicated to another tier of the multi-tiered cache. Multiple caching elements exist at different tiers, and at least one tier includes a cache element that is dynamically resizable. The communicated change in workload characteristics causes the receiving tier to adjust at least one aspect of cache performance in the multi-tiered cache. In one aspect, at least one dynamically resizable element in the multi-tiered cache is resized responsive to the change in workload characteristics.
摘要:
Collaborative management of shared resources is implemented by a storage server receiving, from a first resource manager, notification of a violation for a service provided by the storage server or device coupled to the storage server. The storage server further receives, from each of a plurality of resource managers, an estimated cost of taking a corrective action to mitigate the violation and selects a corrective action proposed by one of the plurality of resource managers based upon the estimated cost. The storage server directs the resource manager that proposed the selected corrective action to perform the selected corrective action.
摘要:
Example embodiments provide various techniques for modeling network storage environments. To model a particular storage environment, component models that are associated with the components of the storage environment are loaded. Each component model is programmed to mathematically simulate one or more components of the storage environment. A system model is then composed from the component models and this system model is configured to simulate the storage environment.