Abstract: A group trial system is described herein that facilitates an automated group trial of various products or services. The system provides an online platform with distributed networks of local affiliates and consumer trial groups that enable consumers to easily locate, see or try products, or share related experiences directly with friends, before purchase. A trial group can check out trial products or a product as a group from the system. Then the trial products will be distributed directly among the group members according to the trial policy, trial setting and scheduling algorithms. The trial status of each group member and states of the trial products are tracked. After the group trial, the products may remain for further instruction, be consumed, be passed to next waiting group or returned to a designated location. This system provides a highly distributed, scalable, dynamic and extremely low-cost marketing/distributing platform.

Abstract: A hierarchial tree is provided to harvest data from data sources. The hierarchial tree includes multiple tree node entities arranged in multiple levels. In one case, leaf node entities of the hierarchial tree are used to collect data from the data sources. The hierarchial tree includes storage resources for storing the collected data. The hierarchial tree further includes computing resources for aggregating the collecting data in one or more aggregation operations and performing other processing operations. A receiving entity can selectively and independently receive parts of the collected data and aggregated data.

Abstract: Analysis may be made of the amount that a load on a machine impacts the machine's performance. Performance counters on the machine record raw statistical data, such as a given resource's current utilization. The values of these counters may be captured. A n-bin histogram may be created that shows how many of the captured performance counter values occur within various ranges, such as 0-10% utilization, 10-20%, etc. A weight may be assigned to each bin. A weighted sum of the bins may be calculated by multiplying the number of occurrences in each bin by the bin's weight, and adding the products together. The weights may be chosen to reflect the relative amounts that particular performance counter values impact the overall performance of a machine. Thus, a metric that represents performance impact may be calculated based on the weighted sum.

Abstract: Analysis may be made of the amount that a load on a machine impacts the machine's performance. Performance counters on the machine record raw statistical data, such as a given resource's current utilization. The values of these counters may be captured. A n-bin histogram may be created that shows how many of the captured performance counter values occur within various ranges, such as 0-10% utilization, 10-20%, etc. A weight may be assigned to each bin. A weighted sum of the bins may be calculated by multiplying the number of occurrences in each bin by the bin's weight, and adding the products together. The weights may be chosen to reflect the relative amounts that particular performance counter values impact the overall performance of a machine. Thus, a metric that represents performance impact may be calculated based on the weighted sum.

Abstract: A hierarchial tree is provided to harvest data from data sources. The hierarchial tree includes multiple tree node entities arranged in multiple levels. In one case, leaf node entities of the hierarchial tree are used to collect data from the data sources. The hierarchial tree includes storage resources for storing the collected data. The hierarchial tree further includes computing resources for aggregating the collecting data in one or more aggregation operations and performing other processing operations. A receiving entity can selectively and independently receive parts of the collected data and aggregated data.