System for Monitoring Elastic Cloud-Based Computing Systems as a Service

• US 20130238791A1
• Filed: 04/16/2013
• Published: 09/12/2013
• Est. Priority Date: 11/10/2011
• Status: Active Grant

• Current Assignee: CopperEgg Corp. (Idera, Inc.)
• Original Assignee: CopperEgg Corp. (Idera, Inc.)
• Inventors: Anderson, Eric Paul, Johnson, Scott Conrad, Perdue, David, Ehresman, Luke, Gordon, Robert B.
• Granted Patent: US 8,996,695 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 11/3006 where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems multiprogramming arrangements G06F9/46; allocation of resources G06F9/50
• CPC Class Codes: G06F 11/301 where the computing system is a virtual computing platform, e.g. logically partitioned systems virtual machines G06F9/45533; logical partitioning of resources G06F9/5077
• CPC Class Codes: G06F 11/3082 the data filtering being achieved by aggregating or compressing the monitored data
• CPC Class Codes: G06F 11/3089 Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents software debugging using additional hardware using a specific debug interface G06F11/3656; performance evaluation by tracing or monitoring G06F11/3466
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: G06F 2201/815 Virtual middleware or OS functionality using virtual machines to implement generic software techniques for error detection or fault masking G06F11/1484
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level

Abstract

Provided is a computing-system monitor configured to monitor a plurality of computing-systems each having a plurality of monitored computing-instances. The computing-system monitor may include a plurality of collectors, each collector executed by one of a plurality of monitored computing-instances, wherein the plurality of monitored computing-instances each are part of one of a plurality of separately monitored computing systems, and wherein each of the collectors is operable to output metrics of a corresponding monitored computing-instance executing that collector. The computing-system monitor may also include an analytics platform, the analytics platform having a plurality of analytic computing-instances, the analytics platform being operable to receive metrics output by the plurality of collectors, calculate statistics with the analytic computing-instances based on the received metrics, and output the calculated statistics.

Claims

1. A monitored computing system configured to transmit metrics to an analytics platform, the monitored computing system comprising:

a plurality of monitored computing-instances, each monitored computing-instance comprising;

one or more memories; and

one or more processors, wherein one or more of the one or more memories comprises collector code, the collector code, when executed by one or more of the one or more processors, being capable of causing the monitored computing-instance to perform steps comprising;

initiating a monitoring session with an analytics platform by transmitting a signal operable to initiate a monitoring session with the analytics platform to an Internet Protocol address of the analytics platform;

obtaining metrics of the monitored computing-instance; and

transmitting the obtained metrics of the monitored computing-instance to the analytics platform.

2. The monitored computing system of claim 1, wherein the steps further comprise:

storing the obtained metrics in a buffer; and

wherein transmitting the obtained metrics comprises transmitting the metrics stored in the buffer.

3. The monitored computing system of claim 2, wherein transmitting the metrics stored in the buffer comprises delaying transmission.

4. The monitored computing system of claim 3, wherein delaying transmission comprises delaying transmission based on a latency of previous transmissions to the analytics platform.

5. The monitored computing system of claim 3, wherein delaying transmission comprises throttling transmission of the metrics.

6. The monitored computing system of claim 1, wherein initiating a monitoring session comprises determining an identifier of the monitored computing-instance prior to communicating with the analytics platform.

7. The monitored computing system of claim 6, wherein determining an identifier of the monitored computing-instance comprises:

before establishing a monitoring session, determining a unique identifier based on properties of the monitored computing-instance; and

wherein establishing a monitoring session comprises;

transmitting to the analytics platform an identifier of a monitored computing system including the plurality of monitored computing-instances.

8. A method of operating an analytics platform, comprising:

receiving metrics of computing instances from a plurality of different computing-systems, each computing-system comprising a plurality of computing instances;

determining, with a processor, statistics of the computing-instances based on the received metrics; and

transmitting the determined statistics within less than 120 seconds of receiving at least some of the metrics upon which the statistics are based, to one or more user-devices, each of the one or more user-devices being associated with at least one of the different computing-systems.

9. The method of claim 8, wherein transmitting the determined statistics comprises transmitting the determined statistics within less than 20 seconds of receiving at least some of the metrics upon which the statistics are based.

10. The method of claim 8, wherein determining statistics comprises:

storing the received metrics in a non-relational database; and

analyzing the received metrics by querying the non-relational database for the received statistics and calculating the statistics based on the query results.

11. The method of claim 8, comprising:

receiving new-instance signals from new computing instances indicating that additional computing instances are were added to one of the plurality of different computing-systems; and

determining statistics for the new computing instances in response to the received new-instance signals and based on metrics from the new computing-instances.

12. The method of claim 8, comprising automatically scaling the analytics platform by adding computing instances to the analytics platform and terminating computing instances from the analytics platform based on a load of the analytics platform.

13. A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more computer systems effectuate operations comprising:

receiving, over a network, from a plurality of monitored computing instances, encrypted metric batches from collectors executed by the monitored computing instances;

decrypting the metric batches from the collectors;

associating the metric batches with one or more accounts, each metric batch being associated with one of the accounts;

parsing the decrypted metric batches from a serialized data format into hierarchical key-value pairs each associated with a respective one of the accounts;

adding the parsed hierarchical key-value pairs to an analysis queue of data to be analyzed;

retrieving the parsed hierarchical key-value pairs from the queue;

updating, with a processor, each of a plurality of statistics each describing values of metrics over a different temporal window based on the retrieved parsed hierarchical key-value pairs, the statistics being associated with the same account with which the corresponding metric batch is associated; and

receiving a request from a user device to view statistics of metrics of monitored computing instances, the request being associated with one of the accounts; and

sending the updated statistics associated with the account of the request to the user device.

14. The medium of claim 13, wherein the operations comprise:

receiving another request from another user device to view statistics of metrics of monitored computing instances, the other request being associated with another one of the accounts;

sending the updated statistics associated with the other account of the other request to the other user device for display, the account and the other account being different accounts associated with different monitored computing instances.

15. The medium of claim 13, wherein the operations comprise:

determining, with a process analyzing a shorter temporal window, that a duration of time of a longer temporal window has transpired and, in response, initiating an analysis of metric values of the longer temporal window.

16. The medium of claim 13, wherein a plurality of statistics are calculated for each of the temporal windows, and wherein each of the statistics is compared against alarm criteria.

17. The medium of claim 13, wherein the operations comprise:

automatically increasing a number of instances of monitoring computing devices in response to an increase in a rate of receiving the metric batches.

18. The medium of claim 13, wherein the operations comprise:

validating a subscription associated with the account of the request before sending the updated statistics; and

billing the account.

19. The medium of claim 13, wherein the metrics batches comprise metric values indicative of processor usage, memory usage, and network usage by a respective monitored computing instance.

20. The medium of claim 13, wherein the operations comprise:

grouping the parsed hierarchical key-value pairs according to user-defined groups associated with the same account as the parsed hierarchical key-value pairs; and

calculating statistics of the groups.