Source: https://patents.google.com/patent/US10084818B1/en
Timestamp: 2019-04-22 23:12:05+00:00

Document:
2012-08-20 Assigned to AMAZON TECHNOLOGIES, INC. reassignment AMAZON TECHNOLOGIES, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BAER, Graeme D., BRANDWINE, ERIC JASON, ROTH, Gregory B.
Techniques for processing data according to customer-defined rules are disclosed. In particular, methods and systems for implementing a data alteration service using one or resources of a distributed computing system are described. The data alteration service is flexibly configurable by entities using the distributed computing system, and may be used to augment, compress, filter or otherwise modify data crossing a customer boundary.
Large amounts of network data are generated and consumed in the course of modern electronic communication. Such data are often transmitted to and/or received from entities outside of the influence or control of customers, both at an enterprise level and at a granular and/or personal level. Such entities may require formatting, censorship, compliance or other requirements for incoming and outgoing data. However, the number of manifestations and combinations of such data requirements is nearly infinite, and it can be difficult to configure systems to modify, format, or otherwise alter data to meet such widely disparate sets of requirements. Additionally, such requirements may change on short notice, and necessary reconfiguration to meet the changed requirements can be burdensome.
FIG. 6 illustrates an environment in which various embodiments can be implemented.
Techniques described and suggested herein include systems and methods for implementing a customer-configurable packet mangler or other data alteration service in and/or using a distributed computing system. In particular, a customer may specify certain rules by which various types of data, inbound and/or outbound from customer devices, may be modified, upon which various components of the distributed computing system are utilized to implement the rules, e.g., using packet mangling techniques. For example, the customer may specify that outbound data to specific external device types or locales must conform to a customer-provided specification (e.g., for regulatory compliance and/or performance optimization), upon which the distributed computing system determines the necessary alterations and data patterns (or other markers) to which they apply. Thereafter, in the example given, the implementation may include redirection of some or all of the network traffic between the customer and the external device in question to a component of the distributed computing system. Upon processing in the determined manner, the redirected data is thereupon relayed to the original destination. As will be contemplated, a similar process may be utilized for inbound network traffic.
In another example, the data alteration service may be effective to modify data transiting between two virtualized instances of the distributed computing system, one of which is controlled by a customer requesting the service, while the other is controlled by a different customer. A separate virtualized instance of the distributed computing system may be invoked to implement the customer-requested rules, or in some embodiments, the same instance from whence (or to which) the data desired to be modified may perform the actual modification, e.g., in a quarantine or “sandbox.” It is contemplated that data transiting to or from any device or component controlled by a service-requesting customer, whether physical, virtual, aggregated or otherwise, to a device not under control by the customer (without regard to its nature), may be subject to packet mangling by the service. The service may, in some embodiments, be granularly controllable based on a number of criteria, including customer or external device type, geographic location, and the like.
An example implementation of such a data alteration service may include the generation of one or more policies that apply to some or all components involved in a given data transaction type. The policies, in some embodiments, are generated by the distributed computing system upon receipt of customer-defined rules, and may take into account data type, connected device type, customer characteristics, network topology, or any other characteristics relevant to the generation of an effective policy. Such policies, when broadcasted to the relevant devices, can ensure that all controlled devices (or a desired subset thereof) act to implement the associated customer-defined rules in a unified, predictable fashion. In embodiments where a customer controls a significant plurality of devices at varying levels of abstraction and/or access, such policies may also ensure that, e.g., mission-critical or strict rules take effect across all affected devices.
FIG. 1 illustrates an example environment 100 that implements a data alteration service. One or more customer devices 102 connect, via data connection 104, to distributed computing system 106. The distributed computing system connects via public network connection 108 to one or more public network entities 110. The one or more customer devices may have a public network connection 112 to the public network, as illustrated. Customer devices may include datacenters, mainframes, individual computing devices, distributed computing environments, or other devices or groups of devices capable of communicating to other devices, such as the distributed computing system described herein, via data connections. In some embodiments, the customer device may consist of one or more servers 114 or other resources. As may be contemplated, such servers or similar resources may be networked and configured to support a distributed computing system. For example, a customer, such as an enterprise customer, may have a datacenter comprising multiple servers, some or all of which are interconnected via network connections. Customer devices, and devices in general, may include devices and/or resources of the distributed computing system, such as the resources 118 described below. The datacenter or constituents thereof may nominally connect to a public network, such as the Internet, via a public network connection (e.g., the public network connection 112 as illustrated). However, the customer device (e.g., datacenter) may require one or more data transformations to be effected upon traffic transiting across a boundary of the customer's control or influence. The customer device may wish to exert a given transformation regime upon traffic that is inbound from external devices (e.g., those which are outside of the customer's influence or control), or outbound from authorized resources, such as the constituent nodes 114 of customer device 102, to such external devices. In some embodiments, to transform transiting data in a manner prescribed by a customer or customer device, the customer or customer device may avail of a data modification service provided by the distributed computing system 106 in a manner herein described in further detail.
In some embodiments, the distributed computing system 106 consists of a plurality of networked computing resources 116, such as storage devices, servers, processors, network equipment, and the like. The networked computing resources are abstracted and/or grouped into virtualized or grouped resources 118 that consist of any portion or number of underlying networked computing resources, which in some embodiments includes physical hardware. For example, the virtualized or grouped resources may include guest operating systems that run in virtual machines enabled by a hypervisor with direct access to the hardware (e.g., a bare metal hypervisor). As another example, such virtualized or grouped resources may include relational databases, data storage and archival services and/or devices, distributed computing services and/or devices, or any level of abstraction thereof (e.g., physical machines, virtual machines, instances, and the like). Such virtualized resources may, in some embodiments, be exposed to a plurality of connecting customers, such as the customer or customer device described above. In some embodiments, the underlying networked computing resources from which the virtualized resources are derived are obfuscated to connecting customers. In some embodiments, the distributed computing system is capable of supporting a plurality of customers simultaneously connecting to the virtualized resources.
In some embodiments, such a distributed computing system may support the provision, using a virtualized resource, of a data modification service upon request of a customer. Such a request may be propagated by a customer, a customer device, or any constituent thereof by programmatic, graphical, automatic and/or manual invocation. For example, a customer may request the service via an application programming interface (API) or user interface (UI) provided by a provider of the distributed computing system and/or service provider. In some embodiments, rules may be provided by the customer as part of the service request, and/or separately from such a request, and in certain embodiments may be provided by the customer via a similar interface (e.g., an API or UI). The rules may be defined in any manner that the service and/or system is configured to receive. For example, rules may be formed using relational database queries such as structured query language (SQL) queries, artificial intelligence language and/or predicate logic notation, natural language, specialized languages developed specifically for the service, and the like. In some embodiments, the rules specify, to varying levels of granularity, desired alterations to transiting data, and may also specify the characteristics of devices or network traffic to which such alterations should apply.
In some embodiments, as previously mentioned, the rules are implemented by the service to apply to traffic transiting across a boundary of customer control or influence, e.g., between devices authorized by or for access by a customer and external devices without such authorization. In the illustrated example, a customer is authorized to access the constituents of customer device 102 (and vice versa), while devices 110 across the public network and external to that of a customer or customer device lie beyond a boundary that lies in between. It is contemplated that, in some embodiments, such authorized devices may connect with customer assets across a public network (and thus not necessarily co-located with other customer devices), but still be included as an authorized device due to implemented trust mechanisms, e.g., by encrypting the public network connection using a virtual private network or similar securing mechanism. It will be appreciated that, as previously mentioned, such devices may include virtual resources such as those of distributed computing system 106.
In some embodiments, the service uses the virtualized instance to determine, by at least processing the received rules, a policy that is thereupon propagated to some or all authorized and/or customer devices. Such a policy may, for example, be provided to various devices as part of regularly scheduled maintenance, via a policy push by a system management system, upon association and/or connection with a different customer device (e.g., upon establishing a VPN connection), and/or manually. The policy, in some embodiments, includes instructions that, when implemented, causes the affected device to retrieve and/or redirect all or some data or network traffic that would otherwise be directed via a direct network connection (e.g., the public network connection 112) to the virtualized resource of the distributed computing system that provides the service. In some embodiments, multiple policies may be generated and propagated, for example, to address different alteration schemes, device types, and/or scenarios. In alternate embodiments, a single policy addresses all specified rules. The policy may, in some embodiments, be determined from the rules by considering the characteristics of the devices involved, the nature of the data, the volume and/or resources of the distributed computing system that are required to implement the rules, and other factors.
Upon implementation of the policy across some or all devices, it will be appreciated that a portion of data transiting across the aforementioned boundary will instead be routed through the distributed computing system implementing the service, in particular the virtualized instance. Such redirected data is processed, in some embodiments, by the virtualized instance according to the received rules. The virtualized resource may configure the data in one of a large plurality of ways, including packet mangling techniques to achieve the effects specified by the rules. As the rules are customer-specified, the data may be configured for a large number of applications. Some examples include rules that enable anonymous browsing or access to external devices (e.g., by stripping cookies, user agents or other personally identifiable information within the data stream), transparent language translation, restriction or redirection of traffic according to defined quotas, code sanitization (e.g., processing transiting code to comply with industry standards), watermarking of images, data compression (either lossless or lossy), UTF-8 inbound canonicalization, differentiation of transmitted and/or received data based on geographic location(s) of one or more of the customer devices or external devices, caching and/or de-duplication (e.g., to improve performance and/or throughput), government or regulatory compliance (e.g., PCI DSS, destination-dependent government data import/export requirements, obscenity and/or morality filters, insertion of third party certifications and the like), content filtering (e.g., with respect to a set of published signatures), distributed denial of service (DDoS) mitigation (e.g., rules may be written, generated and/or implemented in accordance with a given volume of traffic), security filtering and pattern-based vulnerability mitigation (e.g., protection from SQL injection), selective filtering of network traffic (e.g., filtering traffic in one direction of a given data stream based at least in part on content previously received in the opposing direction, such as an automated cross-site scripting (XSS) mitigation scheme that detects and filters hypertext markup language (HTML) code in outbound traffic that matches content in an associated inbound request) and many other transformative cases. In some embodiments, the rules may be operable to cause certain actions to occur within the customer environment, e.g., upon at least a subset of customer devices. Such actions may include tripping of warnings or alarms, propagation of notifications, augmentation of data streams within the customer environment, quarantining or termination of unresponsive or otherwise malfunctioning customer devices, modification of the rules themselves, and many other types of actions. As will be appreciated, rules, and thus the associated data modification, may include both the restriction and augmentation of flowing data, depending on the nature of the rules. In embodiments where multiple rules are specified, duplicate and/or overlapping rules may be combined in order to save resources, improve latency and decrease processing time and/or cost.
In some embodiments, the rules may be operable to cause a distributed computing system or customer environment to recognize that multiple subsets of resources thereof are capable of processing data according to the rules. In some of such embodiments, a first subset of resources applies a first transformation to data according to the implemented rules. Additionally, a second (or greater) subset of resources applies a second (or greater) transformation to the data in accordance with the rules. The data may flow to the disparate subsets of resources in parallel, in sequence, or in some combination thereof. In some embodiments, the second (or greater) transformation may include injecting an additional executable instruction into the data stream, or possibly installing another (or the same) rule to be implemented by the second (or greater) subset of resources. In some embodiments, the second (or greater) subset of resources caches a portion of the data transiting it in accordance with the implemented rule, and the first subset of resources is configured to deduplicate the cached data, such as by replacing data in the data stream with the corresponding data cached by the second resources. In some embodiments, the first subset of resources replaces at least some of the transferred and/or cached data, such as data cached by the second or greater subset of resources, with cache identifiers, placeholders, or pointers indicating that such data has been cached. In some embodiments, the second or greater subset of resources inverts the transformation effected by the first subset of resources, e.g., by re-inserting at least some of the data replaced by cache identifiers and the like. In such embodiments, the inversion of the transformation may be triggered by, or at least influenced by, characteristics of the received data in the data stream and/or in the cache. Such techniques, as may be utilized in a wide area network (WAN) optimization scheme or other bandwidth optimization and/or caching scheme, may be transparently or specifically implemented in order to decrease network traffic, decrease latency, and improve performance of applications requiring data to transit high-cost and/or capacity limited uplinks, including, for example, cellular or wireless data networks. Such techniques may also decrease traffic, latency, etc. from poorly implemented software or hardware solutions that fail to cache data independently.
Costs incurred to process the rules (e.g., by resource usage, per implemented rule, and/or by data volume and/or network traffic processed), including generation and propagation of the associated policy, may be tracked in a manner that allows the requesting customer to be billed for such usage. In such embodiments, accounting records may be updated, and in some embodiments, when an existing allocation or virtual resource under control of the customer is used to implement the service, such records and tracked usage may be used to adjust the amount charged to the customer for the virtual resource or allocation already in their possession. Other metrics may be tracked, such as those associated with traffic matching an implemented or unimplemented rule or set of rules. Such metrics may be exposed to the customer, a managed data transformation provider, and/or the distributed computing resource provider (e.g., to be used internally to improve efficiency of operations thereof).
FIG. 2 illustrates an example distributed computing environment 200 having a distributed computing system 202, which in some embodiments may include the distributed computing system 106 of FIG. 1, and implementing a data alteration service. In the example illustrated, the distributed computing system includes virtualized customer resources 204, 206, e.g., instances, which in some embodiments may be similar to the virtual resources 118 described in connection with FIG. 1, and may in some embodiments also be customer devices 102 or components thereof 114, also as described in connection with FIG. 1. The physical resources from which the virtual resource is abstracted is intentionally omitted in the illustration in order to emphasize the obfuscation of such physical resources from, e.g., customers. The virtualized customer resources may be networked together, and in some embodiments, may be segregated from other virtualized resources of the same distributed computing system. In some embodiments, the virtualized customer resources may be connected via a virtual private network (VPN) connection to other customer devices, e.g., the customer device 102 or constituents 114 described in connection with FIG. 1.
Data transiting across customer boundary 208 may, in whole or part as defined by customer rules or policies (e.g., the customer rules and policies described in connection with FIG. 1), be processed in a similar manner as described in connection with FIG. 1, e.g., using a virtualized resource 210. In the illustrated example, the customer boundary traverses between virtualized resources under control of the customer 204, 206 and other virtualized resources 214 of the same distributed computing system 202. The virtualized resource performing the data processing 210 may, as alluded to above, be a separate virtualized resource from that of a customer, may be bound to or otherwise associated with a customer, or may in some embodiments be an existing virtualized resource that is already associated with a customer. The data processing itself may occur on a portion of data redirected to the virtualized resource, as described in connection with FIG. 1, or the implementing virtualized resource may, based on the rules and/or policies previously described, detect in situ that data flowing through pre-existing mechanisms should be processed without the need for such redirection. In some embodiments, a combination of the approaches may be employed. For example, rules and/or derived policies may specify that data directed to, or originating from, certain devices or device types should be altered in a prescribed way. In such embodiments, the implementing virtualized resource may only redirect and/or process the subset of data to which the rules would apply, based, e.g., upon information received from listeners, connected devices, predictive mechanisms such as data pattern recognition, and the like. Such processing may apply to any set of data crossing the aforementioned boundary, e.g., via data connection 212 to the virtual resource 214, or, in some embodiments, to external device 220, e.g., Web hosts or application servers, outside of the distributed computing system via network connection 216 to external networks 218 (such as the Internet and/or to other distributed computing systems).
Although the virtualized resource performing the data processing is illustrated as straddling the customer boundary, such location is meant to be merely illustrative. Such a resource may, by virtue of being virtualized, be associated with physical resources located anywhere along, e.g., a network topology, and not necessarily on an external edge of such a topology. Additionally, the illustrated customer boundary is conceptual in nature, and does not necessarily require an implemented barrier, such as a firewall. For example, virtualized resources outside the control of the customer requesting the services (e.g., virtualized resource 214) may, in some embodiments, be under control of a different customer of the distributed computing system. A policy, such as the policy described in connection with FIG. 1, may, in some embodiments, be pushed out to precisely the set of resources under a given customer's control, and not to resources outside of said customer's control (e.g., even those springing from or associated with the same system, such as the virtualized resource 214, and not otherwise segregated from one another). However, the customer boundary may, in some embodiments, defined at least in part by other well-known techniques used for segregating certain resources, either virtual or physical, from one another. In some embodiments, the boundary itself is configurable, e.g., by a customer, a third party such as a managed data transformation provider, and/or by the distributed computing system. Managed data transformation providers include any provider of data-related services that use data transformation techniques, including but not limited to any of those described in connection with FIG. 1. Such managed data transformation providers may, in some embodiments, also request rules for implementation, e.g., on behalf of a customer requesting the aforementioned data transformation services as may be provided by managed data transformation providers. In embodiments where managed data transformation providers provide rules, services, boundaries and the like, such rules, services and boundaries may be associated with a defined pricing structure (e.g., per rule selected, implementing resource used, and/or data amount processed) wherein associated costs for such managed data transformation providers accrue to the customer's account, rather than that of the managed data transformation provider. In such embodiments, an additional rule may be implemented (e.g., by the managed data transformation provider and/or the distributed computing system) to redirect payments, upon remittal from the customer, to the managed data transformation provider.
FIG. 3 illustrates an example user interface (UI) 300 for selecting and defining rules. As previously mentioned in connection with at least FIG. 1, a customer may define rules and provide them to, e.g., the provider of the implementing distributed computing system or the data modification service, by a number of techniques. Such techniques, as previously alluded to, include submission and configuration using an application programming interface (API) or a user interface (UI). In some embodiments, the UI may be provided by the provider of the service, in some embodiments also the provider of the implementing distributed computing system, and presented to a requesting customer at the time of requisitioning. Such requisitioning may be specific to the service, or may be integrated into the requisitioning process for other products and services provided by the distributed computing system provider or the service provider.
An illustrated example UI 300 is shown. The UI may be used for initial requisitioning, configuration after initial requisitioning, or both. The rule selection portion 302 allows a customer to select among predetermined rule sets 304 or define a customer rule set 306. The predetermined rule sets may be preconfigured for ease of selecting common data transformation regimes and applications, including those examples provided above in connection with FIG. 1. The custom rule set, as previously mentioned in connection with at least FIG. 1, may be defined in any manner prescribed by the service provider and for which the underlying distributed computing system or implementing virtual instance is configured to receive. Such defined custom rule sets may be preserved for ease of future access, e.g., by being presented as a different “predetermined” rule set for selection upon subsequent visits to the UI.
In the example given, a device type restriction portion 308 is also shown. From this portion of the UI, the customer may select among predetermined device types 310 to which one or more selected rule sets 312 may apply. Such device types may be separate using any appropriate criteria. For example, one device type may include mobile devices, while another may include virtualized instances. Each selected device type may be subject to any number of rule sets selected in the rule selection portion described above. Additionally, the customer may define a custom device type 314 in a fashion similar to that of a custom rule set, also as described above. Such additional device types may be displayed in a dropdown, e.g., of all other device types known to the system, or defined manually. Upon completing selection, the customer's selections are passed to the implementing entity, e.g., the virtualized resource, for implementation, reconfiguration, and/or further processing.
FIG. 4 illustrates an example process 400 for implementing customer-defined rules across resources of a distributed computing system, in accordance with at least one embodiment. Some or all of the process 400 (or any other processes described herein, or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. The code may be stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable storage medium may be non-transitory.
In some embodiments, such as those shown and described in connection with FIGS. 1 and 2, a data alteration service provider or associated distributed computing system (e.g., the distributed computing system 106) receives customer-defined rules and/or a customer's specification of desired effect(s) upon transiting data 402. As previously described, such rules may be received using a number of techniques, such as the UI shown and described in connection with FIG. 3. The system or provider thereupon generates a policy from the received rules 404, using, e.g., a virtualized instance 118, 210 of the distributed computing system. At a time after generating the policy, the policy is distributed to some or all customer devices 406, e.g., on the customer side of a customer boundary 208. Such distribution may be effected using a number of techniques, including those previously described in connection with at least FIGS. 1 and 2 (e.g., using system management techniques or login scripts). The policy may, in some embodiments, cause the affected customer devices to retrieve and redirect data transiting across the boundary to the entity employed to perform the data transformations specified by the rules 408. As previously discussed, the entity may be a virtualized resource or instance of a distributed computing system. Once the entity has access to the target data, the cross-boundary data is then processed 410 using, e.g., packet mangling techniques, in order to implement the rules. Although packet mangling has been provided as an example of such techniques, other transformation techniques, including those described in connection with at least FIG. 1, may be employed.
FIG. 5 illustrates an example process 500 for altering data in accordance with customer-defined rules, in accordance with at least one embodiment. As will be contemplated, example process 500 may incorporate, or be incorporated in, some or all aspects of at least process 400. Upon receiving customer-defined rules according to various techniques, some of which are described in connection with at least FIGS. 1 through 4, a subset of transiting data (e.g., network traffic) is determined for processing according to the received rules 502, for example in accordance with an implemented policy described in connection with at least FIGS. 1-3 and 4. The subset of data is then processed to determine the specific data to which the received rules apply 504. Techniques used for determining the subset of data for processing are exemplified in connection with, e.g., FIG. 2. In embodiments where the rules or desired effects on transiting data (e.g., network traffic) are dependent upon or sensitive to one or more characteristics of a connecting external device to or from which the data is transiting, the characteristics of the external device, or device types, are determined 506. Applicable techniques may be employed in response to the device type restrictions described in connection with the UI of FIG. 3, and characteristics of external devices may include, for example, connection speed, connecting system type, geographic or network location, latency, and any other device characteristic supported by the service and/or specified by the customer, e.g., via an API or UI. The determined data is then processed, by techniques previously discussed in connection with at least FIGS. 1-2 and 4, according to the applicable rules and determined device characteristics 508. The processed (e.g., augmented, compressed, destructively compressed, scrubbed, or otherwise modified) data is thereon relayed to its intended destination 510, for example a customer device 102, its constituents 114, a virtualized customer resource 204, 206, an external device 110, 220, and/or a virtualized resource 214 beyond customer boundary 208. Accounting records are optionally updated 512, for example, to reflect monetary charges to the customer and/or, e.g., processing time used, such as described in connection with at least FIG. 1.
FIG. 6 illustrates aspects of an example environment 600 for implementing aspects in accordance with various embodiments. As will be appreciated, although a Web-based environment is used for purposes of explanation, different environments may be used, as appropriate, to implement various embodiments. The environment includes an electronic client device 602, which can include any appropriate device operable to send and receive requests, messages, or information over an appropriate network 604 and convey information back to a user of the device. Examples of such client devices include personal computers, cell phones, handheld messaging devices, laptop computers, set-top boxes, personal data assistants, electronic book readers, and the like. The network can include any appropriate network, including an intranet, the Internet, a cellular network, a local area network, or any other such network or combination thereof. Components used for such a system can depend at least in part upon the type of network and/or environment selected. Protocols and components for communicating via such a network are well known and will not be discussed herein in detail. Communication over the network can be enabled by wired or wireless connections, and combinations thereof. In this example, the network includes the Internet, as the environment includes a Web server 606 for receiving requests and serving content in response thereto, although for other networks an alternative device serving a similar purpose could be used as would be apparent to one of ordinary skill in the art.
The illustrative environment includes at least one application server 608 and a data store 610. It should be understood that there can be several application servers, layers, or other elements, processes, or components, which may be chained or otherwise configured, which can interact to perform tasks such as obtaining data from an appropriate data store. As used herein the term “data store” refers to any device or combination of devices capable of storing, accessing, and retrieving data, which may include any combination and number of data servers, databases, data storage devices, and data storage media, in any standard, distributed, or clustered environment. The application server can include any appropriate hardware and software for integrating with the data store as needed to execute aspects of one or more applications for the client device, handling a majority of the data access and business logic for an application. The application server provides access control services in cooperation with the data store, and is able to generate content such as text, graphics, audio, and/or video to be transferred to the user, which may be served to the user by the Web server in the form of HTML, XML, or another appropriate structured language in this example. The handling of all requests and responses, as well as the delivery of content between the client device 602 and the application server 608, can be handled by the Web server. It should be understood that the Web and application servers are not required and are merely example components, as structured code discussed herein can be executed on any appropriate device or host machine as discussed elsewhere herein.
The data store 610 can include several separate data tables, databases, or other data storage mechanisms and media for storing data relating to a particular aspect. For example, the data store illustrated includes mechanisms for storing production data 612 and user information 616, which can be used to serve content for the production side. The data store also is shown to include a mechanism for storing log data 614, which can be used for reporting, analysis, or other such purposes. It should be understood that there can be many other aspects that may need to be stored in the data store, such as for page image information and to access right information, which can be stored in any of the above listed mechanisms as appropriate or in additional mechanisms in the data store 610. The data store 610 is operable, through logic associated therewith, to receive instructions from the application server 608 and obtain, update, or otherwise process data in response thereto. In one example, a user might submit a search request for a certain type of item. In this case, the data store might access the user information to verify the identity of the user, and can access the catalog detail information to obtain information about items of that type. The information then can be returned to the user, such as in a results listing on a Web page that the user is able to view via a browser on the user device 602. Information for a particular item of interest can be viewed in a dedicated page or window of the browser.
In embodiments utilizing a Web server, the Web server can run any of a variety of server or mid-tier applications, including HTTP servers, FTP servers, CGI servers, data servers, Java servers, and business application servers. The server(s) also may be capable of executing programs or scripts in response requests from user devices, such as by executing one or more Web applications that may be implemented as one or more scripts or programs written in any programming language, such as Java®, C, C# or C++, or any scripting language, such as Perl, Python, or TCL, as well as combinations thereof. The server(s) may also include database servers, including without limitation those commercially available from Oracle, Microsoft®, Sybase®, and IBM®.
Other variations are within the spirit of the present disclosure. Thus, while the disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in the drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the invention to the specific form or forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention, as defined in the appended claims.
The use of the terms “a” and “an” and “the” and similar referents in the context of describing the disclosed embodiments (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. The term “connected” is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
configuring at least the first set of computing resources to implement a policy derived based at least in part on the received customer-defined rules, to cause the transiting subset of data to pass to a third set of computing resources of the computing resource provider that applies the customer-defined rules to the subset of data prior to reaching the second set of computing resources which are outside of control of the customer, with at least a portion of the transiting subset of data processed in accordance with at least one rule provided by the managed data transformation provider and the rule associated with the requested data transformation technique.
2. The computer-implemented method of claim 1, wherein the third set of computing resources is programmatically managed by the computing resource provider.
providing, to the customer, a plurality of predefined selectable rules; and receiving, from the customer, information identifying at least: a selection of rules from the plurality of predefined rules, the information thereby identifying customer-defined rules; and the request to apply the customer-defined rules.
4. The computer-implemented method of claim 1, wherein at least one of the second set of computing resources includes one or more devices that are physically separate from. the computing resource provider.
5. The computer-implemented method of claim 1, wherein the customer- defined rules are composed by the customer using a rule notation supported by a rule definition platform provided by the computing resource provider.
6. me computer-implemented method of claim 1, further comprising applying the customer-defined rules to the subset of data at a time after determining that the subset of data is transiting between the first set of computing resources and the second set of computing resources.
configuring at least the first resource to implement a policy derived based at least in part on the customer-defined rules to cause at least a portion of the transiting data to be processed by a third resource in accordance with the customer-defined rules prior to reaching the second resource, and at least a second portion of the transiting data processed in accordance with at least one rule provided by the managed data transformation provider associated with the requested data transformation technique and the computing resource provider.
8. The computer-implemented method of claim 7, wherein the customer-defined rules are received from the customer that selects the rules from a predetermined plurality of rule sets using at least one of a user interface (UI) or an application programming interface (API) exposed to the customer.
9. The computer-implemented method of claim 7, wherein the second resource is under control of a second customer of the computing resource provider.
10. The computer-implemented method of claim 7, wherein: at least one of the first resource or the second resource comprises one or more devices of the computing resource provider; and at least one of the first resource or the second resource is a device that is physically separate from the computing resource provider.
11. The computer-implemented method of claim 7, wherein the third resource is a subset of the first resource.
12. The computer implemented method of claim 7, further comprising configuring the first resource to implement a policy derived at least in part from the received customer-defined rules, the policy being implemented to configure a plurality of customer-controlled resources associated with the transiting data.
13. The computer-implemented method of claim 7, wherein the managed data transformation provider-provided rules include at least one rule that, when processed, causes at least a portion of customer-charged costs to be remitted to the managed data transformation provider, the customer-charged costs accruing to the customer in accordance with a pricing structure associated with the managed data transformation provider-provided rules.
provide the policy to the at least one customer device such that the system is configured to implement the customer-specified rules using at least a subset of the configured resources; and.
implement the customer-specified rules to cause the at least one alteration to be applied to the transferred data by another device prior to the transferred data reaching the external device.
15. The computer system of claim 14, wherein the customer-specified rules are implementable to alter at least a subset of the transferred data such that the transferred data complies with one or more content requirements of a recipient of the data.
16. The computer system of claim 14, wherein the customer-specified rules, when implemented, cause at least one action to be taken by the system upon at least a subset of the plurality of customer devices.
17. The computer system of claim 14, wherein the external device is connected to the customer device across a public network.
18. The computer system of claim 14, wherein the customer-specified rules include at least one subset of rules that, when implemented, restrict the transferred data based at least in part on a geographic location of the external device.
at a time after providing the policy to the at least one customer device, redirect the transferred data to the at least the subset of configured resources such that the at least the subset implements the customer-specified rules.
the customer-specified rules include a subset of rules that, when implemented, cause a second subset of configured resources to implement the subset of rules such that a second alteration is applied to the transferred data, the second subset of configured resources being recognized by the computer system as operable to apply the subset of rules.
21. The computer system of claim. 20, wherein the second alteration includes at least one of augmenting the transferred data with an executable instruction or implementing an additional customer-specified rule using the second subset of configured resources.
the second subset of resources replaces the at least one cache identifier with at least a portion of the cached data.
23. The computer system of claim 14, wherein the instructions cause the computer system to further generate metrics associated with the transferred data subject to the customer-specified rules.
24. The computer system of claim 14, wherein the instructions cause the computer system to further accrue costs associated with the processing of the customer-specified rules to the customer.
25. The computer system of claim 24, wherein the costs are accrued based at least in part on a level of resources of the computer system used to process the transferred data.
26. The computer system of claim 24, wherein the costs are accrued based at least in part on an amount of the transferred data subject to the customer-specified rules.
27. The computer system of claim 24, wherein the costs are accrued based at least in part on a quantity of the customer-specified rules applied to the transferred data.
process, using the at least one adapted resource, the redirected subset of network traffic such that the traffic is modified in accordance with the customer-defined rules prior to reaching the at least one external device.
29. The non-transitory computer-readable storage media of claim 28, Wherein the executable instructions further cause the computer system to process the subset of network traffic at a time after determining that the subset of network traffic either originates from or is destined to the external device.
30. The non-transitory computer-readable storage media of claim 28, wherein the executable instructions further cause the computer system to, in accordance with a subset of the received customer-defined rifles, process the subset of network traffic in accordance with determined characteristics of the external device.
31. The non-transitory computer-readable storage media of claim 28, wherein: the customer device includes a first adapted resource among the adapted resources, the first adapted resource being under control of a first customer of the computer system; and the external device includes a second adapted resource among the adapted resources, the second adapted resource being under control of a second customer of the computer system.
32. The non-transitory computer-readable storage media of claim 28, wherein the processing of the redirected subset of network traffic compresses the network traffic.
33. The non-transitory computer-readable storage media of claim 28, wherein the executable instructions further cause the computer system to process only a portion of the redirected subset of network traffic that is relevant to the customer-defined rules.
Australian Examination Report No. 1 for Patent Application No. 2017204853 dated Jun. 29, 2018, 4 pages.
Bernstein et al., "The Poly1305-AES Message-Authentication Code," Lecture Notes in Computer Science, Department of Mathematics, Statistics, and Computer Science (M/C 249), The University of Illinois at Chicago, Feb. 21, 2005, 18 pages.
Bethencourt et al., "Ciphertext-Policy Attribute-Based Encryption," IEEE Symposium on Security and Privacy 2007, 15 pages.
Canadian Notice of Allowance for Patent Application No. 2,899,014 dated Feb. 8, 2018, 1 page.
Canadian Notice of Allowance for Patent Application No. 2,916,954 dated Dec. 19, 2017, 1 page.
Canadian Office Action for Patent Application No. 2,898,995 dated Jul. 3, 2018, 9 pages.
Chinese Decision on Rejection for Patent Application No. 201480011965.9 dated Jan. 15, 2018, 6 pages.
Chinese First Office Action for Patent Application No. 201480020482.5, dated Dec. 28, 2017, 11 pages.
Chinese First Office Action for Patent Application No. 201480046236.7, dated Feb. 24, 2018, 11 pages.
Chinese Notice on Grant of Patent for Application No. 201480020500 dated Dec. 28, 2017, 4 pages.
Chinese Notice on the Second Office Action for Patent Application No. 201480020517.5 dated Jan. 3, 2018, 8 pages.
Chinese Second Office Action for Patent Application No. 20148002048.5 dated Jul. 19, 2018, 17 pages.
Chowdhury et al., "Network Virtualization: State of the Art and Research Challenges", IEEE Communications Magazine, Jul. 2009, 7 pages.
Chowdhury et al., "Virtual Network Embedding With Coordinated Node and Link Mapping", IEEE INFOCOM 2009, Apr. 2009, 9 pages.
European Official Letter for Patent Application No. 14751881.5 dated Feb. 21, 2018, 3 pages.
Guttman et al., "An Introduction to Computer Security: The NIST Handbook," Special Publication 800-12, Oct. 1, 1995, Sections 17.6 and 18.3.2 and Section 19 for cryptographic tools, XP055298016, retrieved on Aug. 26, 2016, from http://www.davidsalomon.name/CompSec/auxiliary/handbook.pdf.
IEEE, "Draft Standard for Identity-based Public-key Cryptography Using Pairings," IEEE P1363.3/D1, Apr. 2008, retrieved Sep. 22, 2015, from http://grouper.ieee.org/groups/1363/IBC/material/P1363.3-D1-200805.pdf, 85 pages.
International Search Report and Written Opinion mailed Apr. 29, 2014, in International Patent Application No. PCT/US2014/015404, filed Feb. 7, 2014.
International Search Report and Written Opinion mailed May 28, 2014, in International Patent Application No. PCT/US2014/15697, filed Feb. 11, 2014.
International Search Report and Written Opinion mailed May 30, 2014, in International Patent Application No. PCT/US2014/015408, filed Feb. 7, 2014.
International Search Report and Written Opinion mailed May 30, 2014, in International Patent Application No. PCT/US2014/015410, filed Feb. 7, 2014.
International Search Report and Written Opinion mailed May 30, 2014, International Patent Application No. PCT/US2014/015414, filed Feb. 7, 2014.
International Search Report and Written Opinion mailed Oct. 28, 2014, International Patent Application No. PCT/US2014/044861, filed Jun. 30, 2014.
Itani et al., "Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures," 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 711-716.
Japanese Notice and Report for Revocation of Reconsideration Report for Patent Application No. 2016-524288 dated Apr. 13, 2018, 6 pages.
Japanese Notice of Allowance for Patent Application No. 2015-558042, dated Dec. 27, 2017, 6 pages.
Krawczyk et al., "HMAC-based Extract-and-Expand Key Derivation Function (HKDF)," Internet Engineering Task Force (IETF), May 2010, retrieved Sep. 22, 2015, from https://tools.ietf.org/html/rfc5869, 14 pages.
Manulis, et al., "Group Signatures: Authentication with Privacy," retrieved from https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/Publications/Studies/GruPA/GruPA.pdf?_blob=publicationFile, Published May 15, 2010, 267 pages.
Nikkei Trendy, "Simple Input of URL Using Hosts File," Nikkei Business Publications, Inc., Feb. 6, 2006, retrieved Dec. 21, 2016, from http://trendy.nikkeibp.co.jp/article/tec/winxp/20060131/115213/?45=nocnt, 5 pages.
Rescorla, "Diffie-Hellman Key Agreement Method," Network Working Group, RTFM Inc., Jun. 1999, retrieved on Sep. 22, 2015, from https://tools.ietf.org/html/rfc2631, 13 pages.
Sieloff, "The new systems administrator: The role of becoming root," Inside Solaris, Oct. 2002, 8(10):6-9.
Singapore Notice of Eligibility for Grant for Patent Application No. 11201510650U, dated Feb. 5, 2018, 3 pages.
Wikipedia, "IEEE P1363" an Institute of Electrical and Electronics Engineers (IEEE) standardization project for public-key cryptography, retrieved Sep. 22, 2015, from https://en.wikipedia.org/wiki/IEEE_P1363, 3 pages.
Wikipedia, "Key derivation function," retrieved Sep. 22, 2015, from https://en.wikipedia.org/wiki/Key_derivation_function, 4 pages.
Zhu et al., "Algorithms for assigning substrate network resources to virtual network components", IEEE INFOCOM 2006, Apr. 2006, 12 pages.

References: Application No. 2017204853
 Application No. 2
 Application No. 2
 Application No. 2
 Application No. 201480011965
 Application No. 201480020482
 Application No. 201480046236
 Application No. 201480020500
 Application No. 201480020517
 Application No. 20148002048
 Application No. 14751881
 Application No. 2016
 Application No. 2015
 Application No. 11201510650