# Patent

## Title
Distributing data among data storage partitions based on a forecasted demand in a networked computing environment

## Abstract

Embodiments of the present invention provide an approach to forecast a potential demand for partitioned/sharded data and to distribute the data among a set of data partitions based on forecasted demand to optimize network characteristics (e.g., network bandwidth) and/or expedite data retrieval. For example, the data may be distributed among the partitions based on a quantity of trends/requests/hits on the data, so that requests for the data can be balanced among the partitions geographically.

## Background
TECHNICAL FIELD
In general, embodiments of the present invention relate to data partitioning. Specifically, embodiments of the present invention relate to the distribution (or redistribution) of data (e.g., news data) among partitions of a data storage system based upon a forecasted demand for the data in a networked computing environment (e.g., a cloud computing environment).
BACKGROUND
The networked computing environment (e.g., cloud computing environment) is an enhancement to the predecessor grid environment, whereby multiple grids and other computation resources may be further enhanced by one or more additional abstraction layers (e.g., a cloud layer), thus making disparate devices appear to an end-consumer as a single pool of seamless resources. These resources may include such things as physical or logical computing engines, servers and devices, device memory, and storage devices, among others.
Systems that maintain a large volume of data may partition (e.g., shard) data across many physical or virtual machines in order to better distribute workloads. One technique is to select a “shard key” that is specified by a database administrator to evenly distribute the data. One way to shard data is by the first letter of an object's name. For example, in a system of three servers that support a database, the system might store objects based upon a name that starts with the letters A-G on the first system, H-R on the second system, and S-Z on the third system. However, challenges may exist in choosing a shard key and appropriate ranges for values of that shard key on a system. In the example above, it may be logical to distribute values by dividing the alphabet into three more or less equal groups of letters (9 letters, 9 letters, and 8 letters), but in reality it may be the case that there are several times more objects that start with the letter S than the letter Q. Such a configuration may result in an excessive volume of data being stored on one partition and/or or an overwhelming workload being driven to a particular server.
SUMMARY
Embodiments of the present invention provide an approach to forecast a potential demand for partitioned/sharded data and to distribute the data among a set of data partitions based on forecasted demand to optimize network characteristics (e.g., network bandwidth) and/or expedite data retrieval. For example, the data can be distributed among the partitions based on a quantity of trends/requests/hits on the data, so that requests for the data can be balanced among the partitions geographically. In a typical embodiment, geographical data will be associated with a data storage system having a plurality of data partitions. The plurality of data partitions may typically comprise news data assigned according to geography. Regardless, a set of geographic regions will be assigned to the plurality of data partitions based on the geographical data. A set of online feeds having data (e.g., news data) corresponding to the set of geographic regions will be identified, as will a set of trending topics from the set of online feeds. Then, the set of geographic regions can be re-assigned among the plurality of data partitions based on the set of trending topics. Responsive to the re-assignment, the news data associated with the set of trending topics will be distributed among the plurality of data partitions (e.g., to balance a forecasted demand therefor).
A first aspect of the present invention provides a computer-implemented method for distributing data among data partitions of based upon a forecasted demand in a networked computing environment comprising: associating geographical data with a data storage system having a plurality of data partitions; assigning a set of geographic regions to each of the plurality of data partitions based on the geographical data; identifying a set of online feeds having data corresponding to the set of geographic regions; identifying a set of trending topics from the set of online feeds; re-assigning the set of geographic regions to each of the plurality of data partitions based on the set of trending topics; and responsive to the re-assigning, distributing the data associated with the set of trending topics among the plurality of data partitions.
A second aspect of the present invention provides a system for distributing data among data partitions based upon a forecasted demand in a networked computing environment comprising: a memory medium comprising instructions; a bus coupled to the memory medium; and a processor coupled to the bus that when executing the instructions causes the system to: associate geographical data with a data storage system having a plurality of data partitions; assign a set of geographic regions to each of the plurality of data partitions based on the geographical data; identify a set of online feeds having data corresponding to the set of geographic regions; identify a set of trending topics from the set of online feeds; re-assign the set of geographic regions to each of the plurality of data partitions based on the set of trending topics; and responsive to the re-assignment, distribute the data associated with the set of trending topics among the plurality of data partitions.
A third aspect of the present invention provides a computer program product for distributing data among data partitions based upon a forecasted demand in a networked computing environment, the computer program product comprising a computer readable storage media, and program instructions stored on the computer readable storage media, to: associate geographical data with a data storage system having a plurality of data partitions; assign a set of geographic regions to each of the plurality of data partitions based on the geographical data; identify a set of online feeds having data corresponding to the set of geographic regions; identify a set of trending topics from the set of online feeds; re-assign the set of geographic regions to each of the plurality of data partitions based on the set of trending topics; and responsive to the re-assignment, distribute the data associated with the set of trending topics among the plurality of data partitions.
A fourth aspect of the present invention provides a method for deploying a system for distributing data among data partitions based upon a forecasted demand in a networked computing environment, comprising: providing a computer infrastructure being operable to: associate geographical data with a data storage system having a plurality of data partitions; assign a set of geographic regions to each of the plurality of data partitions based on the geographical data; identify a set of online feeds having data corresponding to the set of geographic regions; identify a set of trending topics from the set of online feeds; re-assign the set of geographic regions to each of the plurality of data partitions based on the set of trending topics; and responsive to the re-assignment, distribute the data associated with the set of trending topics among the plurality of data partitions.



BRIEF DESCRIPTION OF THE DRAWINGS
These and other features of this invention will be more readily understood from the following detailed description of the various aspects of the invention taken in conjunction with the accompanying drawings in which:
FIG. 1 depicts a cloud computing node according to an embodiment of the present invention.
FIG. 2 depicts a cloud computing environment according to an embodiment of the present invention.
FIG. 3 depicts abstraction model layers according to an embodiment of the present invention.
FIG. 4 depicts a system diagram according to an embodiment of the present invention.
FIG. 5 depicts a method flow diagram according to an embodiment of the present invention.



The drawings are not necessarily to scale. The drawings are merely schematic representations, not intended to portray specific parameters of the invention. The drawings are intended to depict only typical embodiments of the invention, and therefore should not be considered as limiting the scope of the invention. In the drawings, like numbering represents like elements.
DETAILED DESCRIPTION
Illustrative embodiments now will be described more fully herein with reference to the accompanying drawings, in which embodiments are shown. This disclosure may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this disclosure to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, the use of the terms “a”, “an”, etc., do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. The term “set” is intended to mean a quantity of at least one. It will be further understood that the terms “comprises” and/or “comprising”, or “includes” and/or “including”, when used in this specification, specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof.
Embodiments of the present invention provide an approach to forecast a potential demand for partitioned/sharded data and to distribute the data among a set of data partitions based on forecasted demand to optimize network characteristics (e.g., network bandwidth) and/or expedite data retrieval. For example, the data can be distributed among the partitions based on a quantity of trends/requests/hits on the data, so that requests for the data can be balanced among the partitions geographically. In a typical embodiment, geographical data will be associated with a data storage system having a plurality of data partitions. The plurality of data partitions may typically comprise news data assigned according to geography. Regardless, a set of geographic regions will be assigned to the plurality of data partitions based on the geographical data. A set of online feeds having data (e.g., news data) corresponding to the set of geographic regions will be identified, as will a set of trending topics from the set of online feeds. Then, the set of geographic regions can be re-assigned among the plurality of data partitions based on the set of trending topics. Responsive to the re-assignment, the news data associated with the set of trending topics will be distributed among the plurality of data partitions (e.g., to balance a forecasted demand therefor).
It is understood in advance that although this disclosure includes a detailed description of cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed, automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active consumer accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited consumer-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application-hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 1, a schematic of an example of a cloud computing node is shown. Cloud computing node 10 is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node 10 is capable of being implemented and/or performing any of the functionality set forth hereinabove.
In cloud computing node 10, there is a computer system/server 12, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
As shown in FIG. 1, computer system/server 12 in cloud computing node 10 is shown in the form of a general-purpose computing device. The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus.
Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.
System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
The embodiments of the invention may be implemented as a computer readable signal medium, which may include a propagated data signal with computer readable program code embodied therein (e.g., in baseband or as part of a carrier wave). Such a propagated signal may take any of a variety of forms including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium including, but not limited to, wireless, wireline, optical fiber cable, radio-frequency (RF), etc., or any suitable combination of the foregoing.
Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a consumer to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 12. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.
Referring now to FIG. 2, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as private, community, public, or hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms, and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 2 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
Referring now to FIG. 3, a set of functional abstraction layers provided by cloud computing environment 50 (FIG. 2) is shown. It should be understood in advance that the components, layers, and functions shown in FIG. 3 are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include mainframes. In one example, IBM® Systems z® and RISC (Reduced Instruction Set Computer) architecture based servers. In one example, IBM Systems p®, IBM System x® servers, IBM BladeCenter® systems, storage devices, networks, and networking components. Examples of software components include network application server software. In one example, IBM WebSphere® application server software and database software. In one example, IBM DB2® database software. (IBM, System z, System p, System x, BladeCenter, WebSphere, and DB2 are trademarks of International Business Machines Corporation registered in many jurisdictions worldwide.)
Virtualization layer 62 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients.
In one example, management layer 64 may provide the functions described below. Resource provisioning provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and pricing provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. Consumer portal provides access to the cloud computing environment for consumers and system administrators. Service level management provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. Further shown in management layer is data distribution, which represents the functionality that is provided under the embodiments of the present invention.
Workloads layer 66 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation; software development and lifecycle management; virtual classroom education delivery; data analytics processing; transaction processing; and consumer data storage and backup. As mentioned above, all of the foregoing examples described with respect to FIG. 3 are illustrative only, and the invention is not limited to these examples.
It is understood that all functions of the present invention as described herein typically may be performed by the data distribution functionality (of management layer 64, which can be tangibly embodied as modules of program code 42 of program/utility 40 (FIG. 1). However, this need not be the case. Rather, the functionality recited herein could be carried out/implemented and/or enabled by any of the layers 60-66 shown in FIG. 3.
It is reiterated that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, the embodiments of the present invention are intended to be implemented with any type of networked computing environment now known or later developed.
Referring now to FIG. 4, a system diagram according to an aspect of the present invention is shown. As depicted, a data distribution engine (engine 70) is shown within networked computing environment 84 (e.g., comprising cloud computing environment 50). In general, engine 70 can be implemented as program 40 on computer system 12 of FIG. 1 and can implement the functions recited herein as depicted in management layer 64 of FIG. 3. In general, engine 70 (in one embodiment) comprises a rules and/or computational engine that processes a set (at least one) of rules 74 and/or performs computations to access electronic/online feeds, and distribute the data therein among a plurality of storage system partitions based on a forecasted demand for the data.
Along these lines, engine 70 may perform multiple functions similar to a general-purpose computer using rules 74. Specifically, among other functions, engine 70 may: associate geographical data 72 (e.g., a world fact book) with a data storage system 75 having a plurality of data partitions 76A-N; assign a set of geographic regions (e.g., countries) to each of the plurality of data partitions 76A-N based on geographical data 72 (e.g., alphabetical based upon geographic region); identify a set of online feeds 82A-N (e.g., social networking trends, or news media sources) having data corresponding to the set of geographic regions; identify a set of trending topics 86A-N from the set of online feeds 82A-N; re-assign the set of geographic regions to each of the plurality of data partitions 76A-N based on the set of trending topics 86A-N; responsive to the re-assignment, distribute the data associated with the set of trending topics 86A-N among the plurality of data partitions 76A-N.
In general, the re-assignment of the set of geographic regions can be based on a quantity of the set of trending topics 86A-N associated with each geographic region. Along these lines, engine 70 may forecast a volume of access of the set of trending topics 86A-N (e.g., by users/consumers) based on at least one of the following: the quantity of trending topics 86A-N and/or historical data 80A-N (stored in one or more storage devices 78A-N) associated with access of the set of online feeds 82A-N for the set of geographic regions.
Illustrative Example
This section comprises an illustrative example according to the teachings set forth above. It is understood that this example is intended to be illustrative only, and that other examples exist within the teachings recited herein.
In a traditional implementation, one can assume that a database administrator or the like inserts data (e.g., a fact book of country profiles, a world intellectual property organization listing of countries/country codes, etc.), into a partitioned data storage system using an appropriate distribution of values based on a shard/partition key that is based on the first letter of the country's name. In this example, there are 260 countries that are evenly distributed onto 4 partitions of 65 entities each as shown in the table below:













Partition 1:
Partition 2:
Partition 3: Laos-
Partition 4:


Afghanistan-
Czech Republic-
Saint Kitts and
Afghanistan-


Cyprus
Kyrgyzstan
Nevis
Cyprus







Afghanistan
Czech Republic
Laos
Saint Lucia


Akrotiri
Denmark
Latvia
Saint Martin


Albania
Dhekelia
Lebanon
Saint Pierre and


Algeria
Djibouti
Lesotho
Miquelon


American
Dominica
Liberia
Saint Vincent and


Samoa
Dominican
Libya
the


Andorra
Republic
Liechtenstein
Grenadines


Angola
Ecuador
Lithuania
Samoa


Anguilla
Egypt
Luxembourg
San Marino


Antarctica
El Salvador
Macau
Sao Tome and


Antigua and
Equatorial
Macedonia
Principe


Barbuda
Guinea
Madagascar
Saudi Arabia


Arctic Ocean
Eritrea
Malawi
Senegal


Argentina
Estonia
Malaysia
Serbia


Armenia
Ethiopia
Maldives
Seychelles


Aruba
Falkland Islands
Mali
Sierra Leone


Ashmore and
(Islas
Malta
Singapore


Cartier
Malvinas)
Marshall Islands
Sint Maarten


Islands
Faroe Islands
Mauritania
Slovakia


Atlantic Ocean
Fiji
Mauritius
Slovenia


Australia
Finland
Mayotte
Solomon Islands


Austria
France
Mexico
Somalia


Azerbaijan
French Polynesia
Micronesia,
South Africa


Bahamas, The
French Southern
Federated
South Georgia


Bahrain
and
States of
and


Bangladesh
Antarctic Lands
Moldova
South Sandwich


Barbados
Gabon
Monaco
Islands


Belarus
Gambia, The
Mongolia
Southern Ocean


Belgium
Gaza Strip
Montenegro
Spain


Belize
Georgia
Montserrat
Spratly Islands


Benin
Germany
Morocco
Sri Lanka


Bermuda
Ghana
Mozambique
Sudan


Bhutan
Gibraltar
Namibia
Suriname


Bolivia
Greece
Nauru
Svalbard


Bosnia and
Greenland
Navassa Island
Swaziland


Herzegovina
Grenada
Nepal
Sweden


Botswana
Guam
Netherlands
Switzerland


Bouvet Island
Guatemala
New Caledonia
Syria


Brazil
Guernsey
New Zealand
Tajikistan


British Indian
Guinea
Nicaragua
Tanzania


Ocean
Guinea-Bissau
Niger
Thailand


Territory
Guyana
Nigeria
Timor-Leste


British Virgin
Haiti
Niue
Togo


Islands
Heard Island and
Norfolk Island
Tokelau


Brunei
McDonald Islands
Northern Mariana
Tonga


Bulgaria
Holy See
Islands
Trinidad and


Burkina Faso
(Vatican City)
Norway
Tobago


Burma
Honduras
Oman
Tunisia


Burundi
Hong Kong
Pacific Ocean
Turkey


Cambodia
Hungary
Pakistan
Turkmenistan


Cameroon
Iceland
Palau
Turks and Caicos


Canada
India
Panama
Islands


Cape Verde
Indian Ocean
Papua New
Tuvalu


Cayman
Indonesia
Guinea
Uganda


Islands
Iran
Paracel Islands
Ukraine


Central
Iraq
Paraguay
United Arab


African
Ireland
Peru
Emirates


Republic
Isle of Man
Philippines
United Kingdom


Chad
Israel
Pitcairn Islands
United States


Chile
Italy
Poland
United States


China
Jamaica
Portugal
Pacific


Christmas
Jan Mayen
Puerto Rico
Island Wildlife


Island
Japan
Qatar
Refuges


Clipperton
Jersey
Romania
Uruguay


Island
Jordan
Russia
Uzbekistan


Cocos
Kazakhstan
Rwanda
Vanuatu


(Keeling)
Kenya
Saint Barthelemy
Venezuela


Islands
Kiribati
Saint Helena,
Vietnam


Colombia
Korea, North
Ascension, and
Virgin Islands


Comoros
Korea, South
Tristan
Wake Island


Congo,
Kosovo
da Cunha
Wallis and


Democratic
Kuwait
Saint Kitts and
Futuna


Republic of
Kyrgyzstan
Nevis
West Bank


the Congo,


Western Sahara


Republic


Yemen


of the


Zambia


Cook Islands


Zimbabwe


Coral Sea


Taiwan


Islands


European Union


Costa Rica


Cote d'lvoire


Croatia


Cuba


Curacao


Cyprus









However, this system may be subject to random re-distribution of reads (e.g., of online feeds) based on the trends that occur in news media or social networks based on events around the world. For example, if France, Italy and Honduras all make headlines due to sporting events that drive interest thereto, Server 2 will take the bulk of the user reads. This may overload that particular system, while the remaining three servers stay underutilized.
In contrast to the traditional implementation described above, the embodiments of the present invention instead identify the trending topics, and re-distribute each country automatically into a different partition in order to balance the load and keep the system evenly utilized. For example, based on emerging trends of the country, names in the news media, or on social network “A”, it may proactively switch a record for Egypt onto Server 1, leave South Korea on Server 2, moves Italy onto Server 3, and Ireland onto Server 4.













Partition 1:
Partition 2: Czech
Partition 3: Laos-
Partition 4:


Afghanistan-
Republic-
Saint Kitts and
Afghanistan-


Cyprus
Kyrgyzstan
Nevis
Cyprus







. . .
. . .
. . .
. . .


Egypt
Haiti
Iran
Israel









The system may repeat the process over time without human intervention. Further, the system may swap a trending topic onto a less utilized server by querying for the least requested value from that machine. For example, if Brunei did not have any mentions in the news or social media, Brunei may be moved to Server 2, when Egypt is moved to Server 1. Similarly, Palau may be moved to Server 2 when Italy is moved to Server 3, and San Marino may be moved to Server 2 when Ireland is moved to Server 4.
Referring now to FIG. 5, a method flow diagram according to an embodiment of the present invention is shown. In step S1, geographical data is associated with a data storage system having a plurality of data partitions. In step S2, a set of geographic regions will be assigned to each of the plurality of data partitions based on the geographical data. In step S3, a set of online feeds having data corresponding to the set of geographic regions will be identified. In step S4, a set of trending topics will be identified from the set of online feeds. In step S5, the set of geographic regions will be re-assigned to each of the plurality of data partitions based on the set of trending topics. In step S6, responsive to the re-assignment, the data associated with the set of trending topics will be distributed among the plurality of data partitions.
While shown and described herein as a data distribution solution, it is understood that the invention further provides various alternative embodiments. For example, in one embodiment, the invention provides a computer-readable/useable medium that includes computer program code to enable a computer infrastructure to provide data distribution functionality as discussed herein. To this extent, the computer-readable/useable medium includes program code that implements each of the various processes of the invention. It is understood that the terms computer-readable medium or computer-useable medium comprise one or more of any type of physical embodiment of the program code. In particular, the computer-readable/useable medium can comprise program code embodied on one or more portable storage articles of manufacture (e.g., a compact disc, a magnetic disk, a tape, etc.), on one or more data storage portions of a computing device, such as memory 28 (FIG. 1) and/or storage system 34 (FIG. 1) (e.g., a fixed disk, a read-only memory, a random access memory, a cache memory, etc.).
In another embodiment, the invention provides a method that performs the process of the invention on a subscription, advertising, and/or fee basis. That is, a service provider, such as a Solution Integrator, could offer to provide data distribution functionality. In this case, the service provider can create, maintain, support, etc., a computer infrastructure, such as computer system 12 (FIG. 1) that performs the processes of the invention for one or more consumers. In return, the service provider can receive payment from the consumer(s) under a subscription and/or fee agreement and/or the service provider can receive payment from the sale of advertising content to one or more third parties.
In still another embodiment, the invention provides a computer-implemented method for data distribution. In this case, a computer infrastructure, such as computer system 12 (FIG. 1), can be provided and one or more systems for performing the processes of the invention can be obtained (e.g., created, purchased, used, modified, etc.) and deployed to the computer infrastructure. To this extent, the deployment of a system can comprise one or more of: (1) installing program code on a computing device, such as computer system 12 (FIG. 1), from a computer-readable medium; (2) adding one or more computing devices to the computer infrastructure; and (3) incorporating and/or modifying one or more existing systems of the computer infrastructure to enable the computer infrastructure to perform the processes of the invention.
As used herein, it is understood that the terms “program code” and “computer program code” are synonymous and mean any expression, in any language, code, or notation, of a set of instructions intended to cause a computing device having an information processing capability to perform a particular function either directly or after either or both of the following: (a) conversion to another language, code, or notation; and/or (b) reproduction in a different material form. To this extent, program code can be embodied as one or more of: an application/software program, component software/a library of functions, an operating system, a basic device system/driver for a particular computing device, and the like.
A data processing system suitable for storing and/or executing program code can be provided hereunder and can include at least one processor communicatively coupled, directly or indirectly, to memory elements through a system bus. The memory elements can include, but are not limited to, local memory employed during actual execution of the program code, bulk storage, and cache memories that provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. Input/output and/or other external devices (including, but not limited to, keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening device controllers.
Network adapters also may be coupled to the system to enable the data processing system to become coupled to other data processing systems, remote printers, storage devices, and/or the like, through any combination of intervening private or public networks. Illustrative network adapters include, but are not limited to, modems, cable modems, and Ethernet cards.
The foregoing description of various aspects of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed and, obviously, many modifications and variations are possible. Such modifications and variations that may be apparent to a person skilled in the art are intended to be included within the scope of the invention as defined by the accompanying claims.

## Claims

1. A computer-implemented method for distributing data among data partitions based upon a forecasted demand in a networked computing environment comprising:
associating geographical data with a data storage system having a plurality of data partitions;
assigning a set of geographic regions to each of the plurality of data partitions based on the geographical data;
identifying a set of informational online feeds having data corresponding to the set of geographic regions;
identifying a set of trending topics from the set of online feeds, the set of trending topics including a first trending topic identified with a first geographic region and a second trending topic identified with a second geographic region;
determining, in response to the identifying, whether the first geographic region and the second geographic region are assigned to a common data partition;
re-assigning, response to a determination that the first geographic region and the second geographic region are assigned to the common data partition, the set of geographic regions to each of the plurality of data partitions based on the set of trending topics by re-assigning the second geographic region to a less utilized data partition and re-assigning a third geographic region that is not identified as having a trending topic to the common data partition; and
responsive to the re-assigning, distributing the data associated with the set of trending topics among the plurality of data partitions by moving all all data corresponding to the second geographic region to the less utilized data partition and moving all data corresponding to the third geographic region to the common data partition.

2. The computer-implemented method of claim 1, the set of online feeds comprising at least one of the following: social networking trends, or news media sources.

3. The computer-implemented method of claim 1, the assigning being alphabetical based upon geographic region.

4. The computer-implemented method of claim 1, the re-assigning being based on a quantity of the set of trending topics associated with each geographic region.

5. The computer-implemented method of claim 4, further comprising forecasting a volume of access of the set of trending topics based on at least one of the following: the quantity or historical data associated with access of the set of online feeds for the set of geographic regions.

6. The computer-implemented method of claim 1, the geographical data comprising a world fact book.

7. The computer-implemented method of claim 1, the networked computing environment comprising a cloud computing environment.

8. A system for distributing data among data partitions based upon a forecasted demand in a networked computing environment comprising:
a memory medium comprising instructions;
a bus coupled to the memory medium; and
a processor coupled to the bus that when executing the instructions causes the system to:
associate geographical data with a data storage system having a plurality of data partitions;
assign a set of geographic regions to each of the plurality of data partitions based on the geographical data;
identify a set of informational online feeds having data corresponding to the set of geographic regions;
identify a set of trending topics from the set of online feeds, the set of trending topics including a first trending topic identified with a first geographic region and a second trending topic identified with a second geographic region;
determine, in response to the identifying, whether the first geographic region and the second geographic region are assigned to a common data partition;
re-assign, in response to a determination that the first geographic region and the second geographic region are assigned to the common data partition, the set of geographic regions to each of the plurality of data partitions based on the set of trending topics by re-assigning the second geographic region to a less utilized data partition and re-assigning a third geographic region that is not identified as having a trending topic to the common data partition; and
responsive to the re-assignment, distribute the data associated with the set of trending topics among the plurality of data partitions by moving all all data corresponding to the second geographic region to the less utilized data partition and moving all data corresponding to the third geographic region to the common data partition.

9. The system of claim 8, the set of online feeds comprising at least one of the following: social networking trends, or news media sources.

10. The system of claim 8, the assignment being alphabetical based upon geographic region.

11. The system of claim 8, the re-assignment being based on a quantity of the set of trending topics associated with each geographic region.

12. The system of claim 11, the memory medium further comprising instructions for causing the system to forecast a volume of access of the set of trending topics based on at least one of the following: the quantity or historical data associated with access of the set of online feeds for the set of geographic regions.

13. The system of claim 8, the geographical data comprising a world fact book.

14. The system of claim 8, the networked computing environment comprising a cloud computing environment.

15. A computer program product for distributing data among data partitions based upon a forecasted demand in a networked computing environment, the computer program product comprising a computer readable storage media, and program instructions stored on the computer readable storage media, to:
associate geographical data with a data storage system having a plurality of data partitions;
assign a set of geographic regions to each of the plurality of data partitions based on the geographical data;
identify a set of informational online feeds having data corresponding to the set of geographic regions;
identify a set of trending topics from the set of online feeds, the set of trending topics including a first trending topic identified with a first geographic region and a second trending topic identified with a second geographic region;
determine, in response to the identifying, whether the first geographic region and the second geographic region are assigned to a common data partition;
re-assign, in response to a determination that the first geographic region and the second geographic region are assigned to the common data partition, the set of geographic regions to each of the plurality of data partitions based on the set of trending topics by re-assigning the second geographic region to a less utilized data partition and re-assigning a third geographic region that is not identified as having a trending topic to the common data partition; and
responsive to the re-assignment, distribute the data associated with the set of trending topics among the plurality of data partitions by moving all all data corresponding to the second geographic region to the less utilized data partition and moving all data corresponding to the third geographic region to the common data partition.

16. The computer program product of claim 15, the set of online feeds comprising at least one of the following: social networking trends, or news media sources.

17. The computer program product of claim 15, the assignment being alphabetical based upon geographic region.

18. The computer program product of claim 15, the re-assignment being based on a quantity of the set of trending topics associated with each geographic region.

19. The computer program product of claim 18, the computer readable storage media further comprising instructions to forecast a volume of access of the set of trending topics based on at least one of the following: the quantity or historical data associated with access of the set of online feeds for the set of geographic regions.

20. The computer program product of claim 15, the geographical data comprising a world fact book.

21. The computer program product of claim 15, the networked computing environment comprising a cloud computing environment.

22. A method for deploying a system for distributing data among data partitions based upon a forecasted demand in a networked computing environment, comprising:
providing a computer infrastructure being operable to:
associate geographical data with a data storage system having a plurality of data partitions;
assign a set of geographic regions to each of the plurality of data partitions based on the geographical data;
identify a set of informational online feeds having data corresponding to the set of geographic regions;
identify a set of trending topics from the set of online feeds, the set of trending topics including a first trending topic identified with a first geographic region and a second trending topic identified with a second geographic region;
determine, in response to the identifying, whether the first geographic region and the second geographic region are assigned to a common data partition;
re-assign, in response to a determination that the first geographic region and the second geographic region are assigned to the common data partition, the set of geographic regions to each of the plurality of data partitions based on the set of trending topics by re-assigning the second geographic region to a less utilized data partition and re-assigning a third geographic region that is not identified as having a trending topic to the common data partition; and
responsive to the re-assignment, distribute the data associated with the set of trending topics among the plurality of data partitions by moving all all data corresponding to the second geographic region to the less utilized data partition and moving all data corresponding to the third geographic region to the common data partition.