Abstract: A system for processing data is provided. During operation, the system obtains a current window of one or more intervals of timeseries data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Abstract: Techniques for automatically recommending a new data center, such as a point-of-presence (POP) center are provided. In one technique, multiple candidate locations for a new POP center are considered. An impact of adding a new POP to each candidate location is estimated and a score is generated. Each candidate location is ranked based on the score. In a related technique, an impact score for a candidate location is based on a prediction of whether and how much a new POP center at the candidate location would reduce response times of clients that would connect to the new POP center. The prediction may be based on a model that is generated based on response data generated by clients that are connecting to existing POP centers. The model may take into account multiple factors, such as geographic distance, network distance, type of browser executing on the clients, type of operating system, etc.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a current window of one or more intervals of time-series data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Abstract: Techniques for automatically recommending a new data center, such as a point-of-presence (POP) center are provided. In one technique, multiple candidate locations for a new POP center are considered. An impact of adding a new POP to each candidate location is estimated and a score is generated. Each candidate location is ranked based on the score. In a related technique, an impact score for a candidate location is based on a prediction of whether and how much a new POP center at the candidate location would reduce response times of clients that would connect to the new POP center. The prediction may be based on a model that is generated based on response data generated by clients that are connecting to existing POP centers. The model may take into account multiple factors, such as geographic distance, network distance, type of browser executing on the clients, type of operating system, etc.

Abstract: The disclosed embodiments provide a system that processes data. During operation, the system obtains a set of records, wherein each of the records comprises one or more metrics and at least one dimension associated with the one or more metrics. Next, the system creates a data segment from the records by sorting the records by a primary sort column having a first set of possible values, creating a set of dictionaries for a set of columns in the records, and building a set of forward indexes for the columns using the set of dictionaries.

Abstract: A system and methods are provided for serving content in response to content queries or requests. When a request is received, for content to be presented to a specified user, candidate content items are identified, possibly based on matches between attributes of the user and attributes of the items' target audiences. For each item, a history indicating the frequency (e.g., total number) and/or recency with which impressions of the candidate item were previously presented to the user is retrieved and used to determine a modifier value, which is applied to a calculated or generated probable click-through-rate (pCTR) to produce a modified probability that the user would act on the item if it is served to him or her. Each item's estimated value is computed by multiplying a bid associated with the item and the modified probability; the results are ranked and the top-ranked item(s) are served.

Abstract: A system and methods are provided for serving content in response to content queries or requests. When a request is received, for content to be presented to a specified user, candidate content items are identified, possibly based on matches between attributes of the user and attributes of the items' target audiences. For each item, a history indicating the frequency (e.g., total number) and/or recency with which impressions of the candidate item were previously presented to the user is retrieved and used to determine a modifier value, which is applied to a calculated or generated probable click-through-rate (pCTR) to produce a modified probability that the user would act on the item if it is served to him or her. Each item's estimated value is computed by multiplying a bid associated with the item and the modified probability; the results are ranked and the top-ranked item(s) are served.

Abstract: A system and methods are provided for serving content in response to content queries or requests. When a request is received, for content to be presented to a specified user, candidate content items are identified, possibly based on matches between attributes of the specified user and attributes of the items' target audiences. For each item, a history indicating the frequency (e.g., total number) and/or recency with which impressions of the candidate item (and/or related items) were previously presented to the specified user is retrieved and used to filter out candidate content items and/or collections of content items that have been already been served to the specified user a threshold number of times. Each remaining item's estimated value is computed, the results are ranked and the top-ranked item(s) are served.

Abstract: Apparatus and method are provided for indexing electronic content to be served to users that operate mobile and/or stationary communications and computing devices. An index is composed of multiple slices, with each slice capable of storing multiple entries and each entry representing one content item. A content item's index entry is populated with integer values representing attribute/value pairs of a target audience of the content item. A query or request to identify content items for serving to a particular user is similarly formatted with integers representing attribute/value pairs of the user. Queries can then be executed rapidly across any or all index entries in any or all slices. Within a slice, entries may be sorted by value or score, and integer components within an individual entry may be sorted to facilitate rapid comparison with a query.