Scalable distributed computations utilizing multiple distinct clouds

• US 10,348,810 B1
• Filed: 08/22/2017
• Issued: 07/09/2019
• Est. Priority Date: 04/06/2015
• Status: Active Grant

• Current Assignee: Emc IP Holding Company LLC (Dell Technologies Inc.)
• Original Assignee: Emc IP Holding Company LLC (Dell Technologies Inc.)
• Inventors: Florissi, Patricia Gomes Soares, Masad, Ofri
• Primary Examiner(s): Tokuta, Shean
• Application Number:
  US15/683,269
• Time in Patent Office: 686 Days
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1097 for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• CPC Class Codes: H04L 67/566 Grouping or aggregating service requests, e.g. for unified processing

Abstract

An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to initiate distributed computations across a plurality of data processing clusters associated with respective data zones, and to combine local processing results of the distributed computations from respective ones of the data processing clusters. The data processing clusters are configured to perform respective portions of the distributed computations by processing data local to their respective data zones utilizing at least one local data structure configured to support at least one computational framework. A first one of data processing clusters is implemented in a first cloud of a first type provided by a first cloud service provider. At least a second one of the data processing clusters is implemented in a second cloud of a second type different than the first type, provided by a second cloud service provider.

Claims

1. A method comprising:

initiating distributed computations across a plurality of data processing clusters associated with respective data zones; and

combining local processing results of the distributed computations from respective ones of the data processing clusters;

the data processing clusters being configured to perform respective portions of the distributed computations by processing data local to their respective data zones utilizing at least one local data structure configured to support at least one computational framework;

a first one of data processing clusters being implemented in a first cloud of a first type provided by a first cloud service provider;

at least a second one of the data processing clusters being implemented in a second cloud of a second type different than the first type, provided by a second cloud service provider different than the first cloud service provider;

wherein the plurality of data processing clusters associated with the respective data zones are organized in accordance with a global computation graph for performance of the distributed computations and wherein the global computation graph comprises a plurality of nodes corresponding to respective ones of the data processing clusters and further wherein the plurality of nodes are arranged in multiple levels each including at least one of the nodes;

wherein a global data structure is organized in levels with different levels of the global data structure corresponding to respective ones of the levels of the global computation graph and wherein a given one of the levels of the global data structure comprises local processing results generated by nodes of the corresponding level in the global computation graph;

wherein the local processing results of the distributed computations from respective ones of the data processing clusters are combined utilizing the global data structure configured based at least in part on the at least one local data structure in order to produce global processing results of the distributed computations; and

wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

2. The method of claim 1 wherein each of the data processing clusters utilizes a first local data structure configured to support a first computational framework.

3. The method of claim 2 wherein the first computational framework comprises one of a Spark batch framework and a Spark streaming framework.

4. The method of claim 1 wherein the first one of data processing clusters implemented in the first cloud utilizes a first local data structure configured to support a first computational framework and wherein at least the second one of the data processing clusters implemented in the second cloud utilizes a second local data structure different than the first local data structure and configured to support a second computational framework different than the first computational framework.

5. The method of claim 4 wherein the first computational framework comprises a MapReduce framework and the second computational framework comprises a Spark framework.

6. The method of claim 5 wherein the Spark framework comprises one of a Spark batch framework and a Spark streaming framework.

14. The method of claim 1 wherein each of the data processing clusters generates its portion of the local processing results asynchronously with respect to portions of the local processing results generated by the other data processing clusters but the portions of the local processing results are eventually synchronized across the plurality of data processing clusters in conjunction with generation of the global processing results in accordance with the global data structure.

7. A method comprising:

initiating distributed computations across a plurality of data processing clusters associated with respective data zones; and

combining local processing results of the distributed computations from respective ones of the data processing clusters;

the data processing clusters being configured to perform respective portions of the distributed computations by processing data local to their respective data zones utilizing at least one local data structure configured to support at least one computational framework;

a first one of data processing clusters being implemented in a first cloud of a first type provided by a first cloud service provider;

at least a second one of the data processing clusters being implemented in a second cloud of a second type different than the first type, provided by a second cloud service provider different than the first cloud service provider;

wherein the first one of data processing clusters implemented in the first cloud utilizes a first local data structure configured to support a first computational framework and wherein at least the second one of the data processing clusters implemented in the second cloud utilizes a second local data structure different than the first local data structure and configured to support a second computational framework different than the first computational framework;

wherein the first computational framework comprises a Spark batch framework and the second computational framework comprises a Spark streaming framework;

wherein the local processing results of the distributed computations from respective ones of the data processing clusters are combined utilizing a global data structure configured based at least in part on the at least one local data structure in order to produce global processing results of the distributed computations; and

wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

8. The method of claim 7 wherein the Spark batch framework implements one or more batch mode extensions comprising at least one of a Spark SQL extension, a Spark MLlib extension and a Spark GraphX extension.

9. The method of claim 7 wherein the Spark streaming framework is configured to support at least one of Spark iterative processing and Spark interactive processing.

10. The method of claim 7 wherein the plurality of data processing clusters associated with the respective data zones are organized in accordance with a global computation graph for performance of the distributed computations and wherein the global computation graph comprises a plurality of nodes corresponding to respective ones of the data processing clusters and further wherein the plurality of nodes are arranged in multiple levels each including at least one of the nodes.

11. The method of claim 10 wherein the global data structure is organized in levels with different levels of the global data structure corresponding to respective ones of the levels of the global computation graph and wherein a given one of the levels of the global data structure comprises local processing results generated by nodes of the corresponding level in the global computation graph.

12. The method of claim 11 wherein portions of the local processing results at a given level of the global data structure are approximately synchronized with one another as belonging to a common iteration of the global data structure.

13. The method of claim 12 wherein the portions of the local processing results at the given level of the global data structure are determined to belong to the common iteration of the global data structure based at least in part on at least one of a time interval during which the portions of the local processing results were generated, a sequence number associated with generation of the portions of the local processing results and a time-stamp associated with generation of the portions of the local processing results.

15. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device:

to initiate distributed computations across a plurality of data processing clusters associated with respective data zones; and

to combine local processing results of the distributed computations from respective ones of the data processing clusters;

the data processing clusters being configured to perform respective portions of the distributed computations by processing data local to their respective data zones utilizing at least one local data structure configured to support at least one computational framework;

a first one of data processing clusters being implemented in a first cloud of a first type provided by a first cloud service provider;

at least a second one of the data processing clusters being implemented in a second cloud of a second type different than the first type, provided by a second cloud service provider different than the first cloud service provider;

wherein the plurality of data processing clusters associated with the respective data zones are organized in accordance with a global computation graph for performance of the distributed computations and wherein the global computation graph comprises a plurality of nodes corresponding to respective ones of the data processing clusters and further wherein the plurality of nodes are arranged in multiple levels each including at least one of the nodes;

wherein a global data structure is organized in levels with different levels of the global data structure corresponding to respective ones of the levels of the global computation graph and wherein a given one of the levels of the global data structure comprises local processing results generated by nodes of the corresponding level in the global computation graph; and

wherein the local processing results of the distributed computations from respective ones of the data processing clusters are combined utilizing the global data structure configured based at least in part on the at least one local data structure in order to produce global processing results of the distributed computations.

16. The computer program product of claim 15 wherein the first one of data processing clusters implemented in the first cloud utilizes a first local data structure configured to support a first computational framework and wherein at least the second one of the data processing clusters implemented in the second cloud utilizes a second local data structure different than the first local data structure and configured to support a second computational framework different than the first computational framework.

17. The computer program product of claim 16 wherein the first computational framework comprises a Spark batch framework and the second computational framework comprises a Spark streaming framework.

18. An apparatus comprising:

at least one processing device having a processor coupled to a memory;

wherein said at least one processing device is configured;

to initiate distributed computations across a plurality of data processing clusters associated with respective data zones; and

to combine local processing results of the distributed computations from respective ones of the data processing clusters;

the data processing clusters being configured to perform respective portions of the distributed computations by processing data local to their respective data zones utilizing at least one local data structure configured to support at least one computational framework;

a first one of data processing clusters being implemented in a first cloud of a first type provided by a first cloud service provider;

at least a second one of the data processing clusters being implemented in a second cloud of a second type different than the first type, provided by a second cloud service provider different than the first cloud service provider;

wherein the plurality of data processing clusters associated with the respective data zones are organized in accordance with a global computation graph for performance of the distributed computations and wherein the global computation graph comprises a plurality of nodes corresponding to respective ones of the data processing clusters and further wherein the plurality of nodes are arranged in multiple levels each including at least one of the nodes;

wherein a global data structure is organized in levels with different levels of the global data structure corresponding to respective ones of the levels of the global computation graph and wherein a given one of the levels of the global data structure comprises local processing results generated by nodes of the corresponding level in the global computation graph; and

wherein the local processing results of the distributed computations from respective ones of the data processing clusters are combined utilizing the global data structure configured based at least in part on the at least one local data structure in order to produce global processing results of the distributed computations.

19. The apparatus of claim 18 wherein the first one of data processing clusters implemented in the first cloud utilizes a first local data structure configured to support a first computational framework and wherein at least the second one of the data processing clusters implemented in the second cloud utilizes a second local data structure different than the first local data structure and configured to support a second computational framework different than the first computational framework.

20. The apparatus of claim 19 wherein the first computational framework comprises a Spark batch framework and the second computational framework comprises a Spark streaming framework.