Patent Publication Number: US-10778522-B2

Title: Endpoint-based mechanism to apply network optimization

Description:
TECHNOLOGY 
     The present invention relates generally to optimizing network policies in content delivery, and in particular, to an endpoint-based mechanism to apply the optimized network policies. 
     BACKGROUND 
     Cellular networks are very volatile and diverse. Due to the nature of the wireless channel, link conditions change at a fine timescale. Metrics such as latency, jitter, throughput, and losses are hard to bound or predict. The diversity comes from the various network technologies, plethora of devices, platforms, and operating systems in use. 
     Techniques that rely on compression or right-sizing content do not address the fundamental issues of network volatility and diversity as they impact the transport of data. Irrespective of the savings in compression, the data still weathers the vagaries of the network, operating environment, and end device. 
     Mobile applications as well as web applications are powered by cloud data: images, video, audio, messaging, and so forth. The ability to quickly exchange—download or upload—assets with the cloud is a key factor in the user experience. While various parameters may affect network latency, there are no “golden values” that can be a “one size fits all” approach. As a result, mobile application developers lack methods and techniques to optimize operational dimensions that affect mobile performance. 
     The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, issues identified with respect to one or more approaches should not assume to have been recognized in any prior art on the basis of this section, unless otherwise indicated. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which: 
         FIG. 1  illustrates a high-level block diagram, according to an embodiment of the invention; 
         FIG. 2  illustrates a high-level block diagram, including an example dynamic network configuration optimizer according to an embodiment of the invention; 
         FIG. 3  illustrates a high-level interaction flow diagram of dynamic network configuration optimization, according to an embodiment of the invention; 
         FIG. 4  illustrates a flowchart for dynamic network configuration optimization, according to an embodiment of the invention; and 
         FIG. 5  illustrates an example hardware platform on which a computer or a computing device as described herein may be implemented. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Example embodiments, which relate to cognitive analysis of network performance data, are described herein. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are not described in exhaustive detail, in order to avoid unnecessarily occluding, obscuring, or obfuscating the present invention. 
     Example embodiments are described herein according to the following outline:
         1. GENERAL OVERVIEW   2. USING OPERATIONAL CONTEXTS TO IDENTIFY STRATEGIES FOR OPTIMIZATION   3. DYNAMICALLY GENERATING STRATEGIES FOR OPTIMIZATION USING MACHINE LEARNING   4. CONVERGENCE ON OPTIMUM STRATEGY FOR OPERATIONAL CONTEXTS   5. IMPLEMENTATION MECHANISMS—HARDWARE OVERVIEW   6. EQUIVALENTS, EXTENSIONS, ALTERNATIVES AND MISCELLANEOUS       

     1. GENERAL OVERVIEW 
     This overview presents a basic description of some aspects of an embodiment of the present invention. It should be noted that this overview is not an extensive or exhaustive summary of aspects of the embodiment. Moreover, it should be noted that this overview is not intended to be understood as identifying any particularly significant aspects or elements of the embodiment, nor as delineating any scope of the embodiment in particular, nor the invention in general. This overview merely presents some concepts that relate to the example embodiment in a condensed and simplified format, and should be understood as merely a conceptual prelude to a more detailed description of example embodiments that follows below. 
     Modern data transport networks feature a huge variety of network technologies, Internet service providers (ISPs), telecommunication infrastructure, end-user devices, and software. Some of the common network technologies include cellular networks (e.g., LTE, HSPA, 3G, older technologies, etc.), WiFi (e.g., 802.11xx series of standards, etc.), satellite, microwave, etc. In terms of devices and software, there are smartphones, tablets, personal computers, network-connected appliances, electronics, etc., that rely on a range of embedded software systems such as Apple iOS, Google Android, Linux, and several other specialized operating systems. There are certain shared characteristics that impact data delivery performance:
         a. Many of these network technologies feature a volatile wireless last mile. The volatility manifests itself in the application layer in the form of variable bandwidth, latency, jitter, loss rates and other network related impairments.   b. The diversity in devices, operating system software and form factors results in a unique challenge from the perspective of user experience.   c. The nature of content that is generated and consumed on these devices is quite different from what was observed with devices on the wired Internet. The new content is very dynamic and personalized (e.g., adapted to location, end-user, other context sensitive parameters, etc.).       

     A consequence of these characteristics is that end-users and applications experience inconsistent and poor performance. This is because most network mechanisms today are not equipped to tackle this new nature of the problem. In terms of the transport, today&#39;s client and server software systems are best deployed in a stable operating environment where operational parameters either change a little or do not change at all. When such software systems see unusual network feedback they tend to over-react in terms of remedies. From the perspective of infrastructure elements in the network that are entrusted with optimizations, current techniques like caching, right sizing, and compression fail to deliver the expected gains. The dynamic and personalized nature of traffic leads to low cache hit-rates and encrypted traffic streams that carry personalized data make content modification much harder and more expensive. 
     Modern heterogeneous networks feature unique challenges that are not addressed by technologies today. Unlike the wired Internet where there was a stable operating environment and predictable end device characteristics, modern heterogeneous networks require a new approach to optimize data delivery. To maximize improvement in perceived network performance for various applications, through asset download time and network throughput, various parameters may be configured as a policy strategy using a data driven approach by analyzing prior operational contexts. Because wireless networks are volatile and non-stationary (i.e., statistics change with time), configuring network parameters poses several challenges. The network parameter configuration should be adaptive to capture volatilities in the wireless network, but also stable and not overly sensitive to short term fluctuations. Further, raw network traffic data does not capture the performance in improvement of throughput and download complete time of a particular set of operational contexts. Methods and techniques described herein provide situational policy strategies for client-based compilation of network parameter configurations using machine learning techniques that operate on past data. In various embodiments, the techniques and methods described here work on wireless cellular and WiFi networks, individually and in combination. 
     Various modifications to the preferred embodiments and the generic principles and features described herein will be readily apparent to those skilled in the art. Thus, the disclosure is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features described herein. 
     2. USING OPERATIONAL CONTEXTS TO IDENTIFY STRATEGIES FOR OPTIMIZATION 
     The performance of data delivery is closely tied to the operating conditions within which the end-device is operating. With ubiquitous wireless access over cellular and WiFi networks, there is a lot of volatility in operating conditions, so acceleration techniques should adapt to such a network by adapting to these conditions, e.g., the performance achievable over a private WiFi hotspot is very different from that with a cellular data connection. An accelerator  116 , as illustrated in  FIG. 1 , dynamically adapts to these conditions and selects optimal strategies  118  based on the operating context  104 . 
     An operating context  104  captures the information about the operating conditions in which data transfer requests are being made. This includes, but not limited to, any combination of:
         Type of device, e.g., iPhone, iPad, Blackberry, etc.
           This may also include the version of the device and manufacturer information.   
           Device characteristics, e.g., the type of its modem, CPU/GPU, encryption hardware, battery, NFC (Near Field Communication) chipset, memory size and type or any other hardware information that impacts performance   Mobility of device, e.g., whether the device is on a moving vehicle/train etc., or is stationary/semi-stationary.   Operating System on the device.   Operating System characteristics, e.g., buffering, timers, public and hidden operating system facilities (APIs), etc.
           This may also include operating system limitations such as number of simultaneous connections allowed to a single domain, etc.   
           Usage information related to various device elements, e.g., Memory, Storage, CPU/GPU etc.   Battery charge and mode of powering the device.   Time of day.   Location where available.   IP Address and port numbers.   Network type, e.g., WiFi or Cellular, or 3G/4G/LTE, etc., or Public/Home WiFi, etc.
           SSID (Service Set Identifier) in WiFi networks.   802.11 network type for WiFi networks.   
           Service Provider information, e.g., AT&amp;T or Verizon for cellular, Time Warner or Comcast for WiFi, etc.   Strength of signal from the access point (e.g., Wi-Fi hot spot, cellular tower, etc.) for both upstream and downstream direction.   Cell-Tower or Hot-Spot identifier in any form.   Number of sectors in the cell tower or hot spot.   Spectrum allocated to each cell tower and/or sector.   Any software or hardware limitation placed on the hot-spot/cell tower.   Any information on the network elements in the path of traffic from device to the content server.   Firewall Policy rules, if available.   Any active measurements on the device, e.g., techniques that measure one-way delay between web-server and device, bandwidth, jitter, etc.   Medium of request, e.g., native app, hybrid app, web-browser, etc.
           Other information describing the medium, e.g., web browser type (e.g., Safari, Chrome, Firefox etc.), application name, etc.   
           Any other third party software that is installed on the device which impacts data delivery performance.   Content Type, e.g., image, video, text, email, etc.
           Also includes the nature of content if it is dynamic or static.   
           Content Location, e.g., coming from origin server or being served from a CDN (Content Delivery Network).
           In the case of a CDN, any optimization strategies being employed, if available.   
           Recent device performance statistics, e.g., dropped packets, bytes transferred, connections initiated, persistent/on-going connections, active memory, hard disk space available, etc.   Caching strategies if any, that are available or in use on the device or by the application requesting the content.   In the case of content, where multiple objects have to be fetched to completely display the content, the order in which requests are placed and the order in which objects are delivered to the device. The request method for each of these objects is also of interest.   Concurrency: the maximal number of objects downloaded at the same time   Protocol: HTTPv1, HTTPv2, QUIC or any proprietary transport protocol   Server port: sometimes using non-standard ports is beneficial   Pipelining: starting a new request on a connection before the previous one ended   Fail fast: faster retransmission timeouts for requests       

     Based on the operating context, machine learning techniques may be able to recommend, but is not limited to, any combination of: end-device based data delivery strategies and accelerator-based data delivery strategies. There typically are no “golden values” for these parameters that can be hardcoded in the application. Different users of the same application would need different settings, and even a single user would need different settings per the content type it consumes. As mentioned above, operational dimensions that affect mobile performance include network technology (e.g., 3G, HSPA, LTE, etc.), network carrier, location, device type, operating system version, content type, and Uniform Resource Locator (URL) domain. 
     End-device based data delivery strategies refer to methods deployed by an application (an application could be natively running on the end-device operating system, or running in some form of a hybrid or embedded environment, e.g., within a browser, etc.) to request, receive or, transmit data over the network. These data delivery strategies include, but are not limited to, any combination of:
         Methods used to query the location of service point, e.g., DNS, etc.
           This may involve strategies that include, but are not limited to, any combination of: choosing the best DNS servers based on response times, DNS prefetching, DNS refreshing/caching, etc.   
           Protocols available for data transport, e.g., UDP, TCP, SCTP, RDP, ROHC, etc.   Methods to request or send data as provided by the operating system, e.g., sockets, CFHTTP or NSURLConnection in Apple&#39;s iOS, HttpUrlConnection in Google&#39;s Android, etc.   Session oriented protocols available for requests, e.g., HTTP, HTTPS, FTP, RTP, Telnet, etc.   Full duplex communication over data transport protocols, e.g., SPDY, Websockets, etc.   Caching and or storage support provided in the Operating System.   Compression, right sizing or other support in the devices to help reduce size of data communication.   Transaction priorities which outline the order in which network transactions to be completed:
           E.g., this may be a list of transactions where the priority scheme is simply a random ordering of objects to be downloaded.   
           Content specific data delivery mechanisms, e.g., HTTP Live Streaming, DASH, Multicast, etc.   Encryption support in the device:
           Also includes secure transport mechanisms, e.g., SSL, TLS, etc.   
           VPN (Virtual Private Network) of any kind where available and/or configured on the device.   Any tunneling protocol support available or in use on the device.   Ability to use or influence rules on the device which dictate how the data needs to be accessed or requested or delivered.
           This includes, but is not limited to, any combination of: firewall rules, policies configured to reduce data usage, etc.   
           Ability to pick the radio technology to use to get/send data. For example, if allowed, the ability to choose cellular network to get some data instead of using a public Wi-Fi network.   Ability to run data requests or process data in the background.   Threading, locking, and queuing support in the Operating System.   Ability to modify radio power if available.   Presence and/or availability of any error correction scheme in the device.   In cases where middle boxes in the network infrastructure have adverse impact on performance, capabilities on the end-device to deploy mitigations such as encrypted network layer streams (e.g. IPSec, etc.).       

     A range of parameters determines the performance of tasks such as data delivery. With volatility and diversity, there is an explosion in the number of parameters that may be significant. By isolating parameters, significant acceleration of data delivery may be achieved. Networks, devices and content are constantly changing. Various methods of optimizing data delivery are described in U.S. Patent Publication No. 2014/0304395, entitled “Cognitive Data Delivery Optimizing System,” filed Nov. 12, 2013, and which is hereby incorporated by reference in its entirety for all purposes. Embodiments are not tied down by assumptions on the current nature of the system. An dynamic network configuration optimizer  106  may use raw network traffic data to generate an adaptive learning dataset. 
       FIG. 1  and the other figures use like reference numerals to identify like elements. A letter after a reference numeral, such as “ 102   a ,” indicates that the text refers specifically to the element having that particular reference numeral. A reference numeral in the text without a following letter, such as “ 102 ,” refers to any or all of the elements in the figures bearing that reference numeral (e.g. “ 102 ” in the text refers to reference numerals “ 102   a ,” and/or “ 102   b ” in the figures). Only one user device  102  (end-devices as described above) is shown in  FIG. 1  in order to simplify and clarify the description. 
     As illustrated in  FIG. 1 , a system  100  includes a user device  102  that communicates data requests through a network  104 . A proxy server  108  may receive the data requests and communicate the requests to a data center  110 . The dynamic network configuration optimizer  106  may gather information from the proxy server  108  and store information in the strategy data store  112 , in an embodiment. For example, with a priori knowledge of the operational contexts, a strategy for network configuration may be determined to be mapped to a set of values. Then, over time, operational contexts may be assigned parameters from these strategies at random and performance data may be stored in the strategy data store  112 . The operating context  104  received from agents  114  may be stored as operational context data in the strategy data store  112 . 
     Each database record in the strategy data store  112  may include performance metrics comparing selected strategy performance data against a baseline configuration performance data. For example, data representing outcomes of the download such as the throughput, download complete time, and time to first byte, may be captured in each database record in the strategy data store  112  for each strategy. Performance metrics such as percentage improvement in throughput and download complete time of the strategy applied compared to a baseline operational context may also be stored in the strategy data store  112 , in one embodiment. 
     Other information may also be included in each database record, in other embodiments. Typical sources of data relating to the network environment are elements in the network infrastructure that gather statistics about transit traffic and user devices that connect to the network as clients or servers. The data that can be gathered includes, but is not limited to, any combination of: data pertaining to requests for objects, periodic monitoring of network elements (which may include inputs from external source(s) as well as results from active probing), exceptional events (e.g., unpredictable, rare occurrences, etc.), data pertaining to the devices originating or servicing requests, data pertaining to the applications associated with the requests, data associated with the networking stack on any of the devices/elements that are in the path of the request or available from any external source, etc. 
     In an embodiment, a component may be installed in the user device  102  (agent  114 ) that provides inputs about the real-time operating conditions, participates and performs active network measurements, and executes recommended strategies. The agent  114  may be supplied in a software development kit (SDK) and is installed on the user device  102  when an application that includes the SDK is installed on the user device  102 . By inserting an agent  114  in the user device  102  to report the observed networking conditions back to the accelerator  116 , estimates about the state of the network can be vastly improved. The main benefits of having a presence (the agent  114 ) on the user device  102  include the ability to perform measurements that characterize one leg of the session, e.g., measuring just the client-to-server leg latency, etc., that are not easily measurable outside of the user device  102 . 
     An accelerator  116  sits in the path of the data traffic within a proxy server  108  and executes recommended strategies in addition to gathering and measuring network-related information in real-time. The accelerator  116  may propagate network policies from the dynamic network configuration optimizer  106  to the proxy server  108 , in one embodiment. In another embodiment, the agent  114  may implement one or more network policies from the dynamic network configuration optimizer  106 . For example, the optimal number of simultaneous network connections may be propagated as a network policy from the dynamic network configuration optimizer  106  through the network  104  to the agent  114  embedded on the user device  102 . As another example, the transmission rate of file transfer may be limited to 20 MB/sec by the accelerator  116  as a network policy propagated by the dynamic network configuration optimizer  106  based on supervised learning and performance metrics. Here, the term “supervised learning” is defined as providing datasets to train a machine to get desired outputs as opposed to “unsupervised learning” where no datasets are provided and data is clustered into classes. 
     As described above, many parameters may be adjusted by the mobile application on the endpoint, or the client or user devices  102 , that may enhance network latency by reducing the time to download various assets powered by cloud data, such as images, audio, video, messaging, and so forth. For example, the maximal number of objects downloaded at the same time, or concurrency as a parameter, may be adjusted at the client. As another example, a choice of transport protocol, including HTTPv1, HTTPv2, QUIC, or any proprietary transport protocol, may be adjusted as a strategy at the user device  102 . A further example of a parameter that can be configured at the endpoint device  102  is selecting non-standard server ports. Depending on a set of circumstances, or an operational context  104  in which a user device  102  is operating, selecting a non-standard server port may be beneficial to network latency. Another factor may include pipelining by starting a new request on a connection before the previous request ends. Yet another factor or parameter may be failing fast by generating faster retransmission timeouts for requests. These endpoint specific mechanisms for network stack configuration represent a multitude of strategies that may be used in different combinations based on operating context  104 . While examples included here describe interactions with a mobile application, it is understood that other applications, such as web applications and other software applications, may similarly use the techniques described. 
     In an embodiment, an operating context  104   a  is sent from an agent  114   a  on a user device  102  to an accelerator  116  on a proxy server  108  in communication with a data center  110  and a dynamic network configuration optimizer  106 . Based on the received operating context  104   a , a strategy  118   a  is selected from a strategy data store  112  by the dynamic network configuration optimizer  106  which is then transmitted to the agent  114   a . Strategies in the strategy data store  112  may be configured manually or automatically using machine learning techniques. A policy compiler  120   a  on the agent  114   a  then compiles the received strategy  118   a  at the user device  102   a . User devices  102  that even operate on the same network may have different operating contexts  104 . Similarly, a single user device  102  may have different operating contexts  104  based on time of day, user using the user device, and network availability. 
     An example client-side policy (CSP), depicted as a strategy  118  in  FIG. 1 , is sent from the server  108  via the accelerator  116  to the client, or user device  102  via the agent  114 . The client receives the strategy  118  and starts evaluating the CSP that includes programmable code with variables. Variable may come from both the server side and from the client. The CSP engine, depicted as a policy compiler  120  in  FIG. 1 , is aware which variables belong to the server and which are dynamically substituted by the client during compilation. The policy compiler  120  performs a validity check to ensure the program, or CSP, is correct and understood by the client. For example, if the CSP included a client-side variable that the client does not know about, the CSP engine would detect this and the compilation would fail. As another example, if the server sends the program which the handset, or user device  102 , does not yet support, then the compilation would also fail. When the compilation succeeds, the CSP and variables are stored on the user device  102  (e.g., cached). The compiled CSP will run each time the handset makes a request for an asset, such as a video, image, messaging, etc. All incoming requests received after a successful compilation will then be evaluated with the CSP. 
     The accelerator  116  translates an operational context to one or more optimization strategies. In an embodiment, the user device  102  compiles a received strategy once. As a default, the agent  114  may be configured with initial values for the network stack parameters. Once the agent  114  receives a strategy  118  from the accelerator  116 , the agent  114  compiles the strategy  118  using the policy compiler  120  to configure its network stack accordingly. To a viewing user of the user device  102 , the network stack configuration is seamless. Some parts of the strategy  118  may be conditional such that a strategy may only apply to specified URLs. For example, a regular expression may detect URLs that have been selected to use QUIC as the transport protocol for network exchanges and/or transactions. 
     In the strategy data store  112 , performance metrics may be stored based on application of strategies to network performance from the view of the endpoint. Once a multitude of performance data associated with data requests between user devices  102  and the data centers  110  are logged in the strategy data store  112 , it becomes possible to aggregate this performance data by strategy and operating context. For example, the performance metrics about outcomes of a download network transaction, such as download complete time and time to first byte, may be stored in the strategy data store  112  as performance data associated with a strategy that was applied based on an operational context, in an embodiment. Various statistical aggregations may be used to increase the relevance of a parameter in a strategy as stored in the strategy data store  112  according to different embodiments. Using information related to operational context, a more efficient strategy may be selected by the system to deliver a set of best values based on the operational context. The more efficient and optimized selection of the set of best values improves the overall performance of the specific device making the request for a client side policy. This improves the technology of the client by improving overall network performance above and beyond previous approaches. 
     3. DYNAMICALLY GENERATING STRATEGIES FOR OPTIMIZATION USING MACHINE LEARNING 
       FIG. 2  illustrates a high-level block diagram, including an example dynamic network configuration optimizer, according to an embodiment. A dynamic network configuration optimizer  106  may include an operational context data gatherer  202 , a data aggregator  204 , a heuristics engine  206 , a data model generator  208 , a supervised machine learning trainer  214 , a statistical prediction generator  216 , a training data set store  218 , and an optimized network strategy selector  212 , in one embodiment. The dynamic network configuration optimizer  106  may communicate data over one or more networks  210  with other elements of system  100 , such as user devices  102 , one or more proxy servers  108 , data centers  110 , and one or more strategy data stores  112 . 
     An operational context data gatherer  202  gathers network stack values associated with data requests between user devices  102  and data centers  110  through one or more proxy servers  108 . In one embodiment, a network stack data value may be gathered by an agent  114  of a user device  102  or from an accelerator  116  on the proxy server  108 . The operational context data gatherer  202  stores operational context data, defined as a set of network stack data values associated with a particular user device  102  during a particular asset transaction, in training data set store  218 , in an embodiment. Each operational context data is a result of configuration data values at the user device  102  associated with a network stack at a point in time, in an embodiment. 
     A data aggregator  204  aggregates data values over a fixed period of time (e.g., a month, a week, a day, an hour, a minute, a second, etc.) for each combination of operational context and strategy. A particular combination of operational context and strategy may be referred to herein as a control field. Each aggregated row becomes a data point with information on the “goodness” of the strategy used. Further, the distribution of control field values in this data set is representative of the mobile network traffic that is aimed for optimization. Every strategy can be modeled as an inverse problem: a function of the download outcomes. For example, a moving average of the download complete time values for a particular combination of an operational context having a set of network stack values and strategy may be identified as the lowest (e.g., the fastest, etc.) download complete time across all strategies. As a result, the particular combination of operational context and strategy may be a good estimate of the best value for the network stack values in the strategy. This good estimate of the best value for the network stack value may be used as a set of data points on which a machine may be trained in a “supervised” way, further described below as supervised learning method  400 , in one embodiment. 
     A heuristics engine  206  incorporates knowledge known to administrators of the dynamic network configuration optimizer  106 . A heuristic is a technique, method, or set of rules designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. Here, the heuristics engine  206  may incorporate knowledge known to the designers of the supervised learning method and techniques described herein to estimate network stack values, such as supervised learning method  400  below. For example, a particular carrier, such as AT&amp;T, may have a maximum throughput of 50 MB/sec based on historical data. Thus, an endpoint, or user device  102 , for example, may throttle a transmission rate, a particular network stack value, to a range of 20 to 30 MB/sec to ensure faster transmission and minimize the risk of packet loss. 
     A data model generator  208  generates one or more data models to estimate network stack values as described above. Given the possibility of network changes over time and the deterministic nature of identifying optimal network stack values using operational contexts and strategies, the data model generator  208  identifies an iterative process for a supervised learning algorithm, or method  400 , to train a machine to achieve desired outputs. Here, the estimation of the best value of a single parameter given the control fields using the performance information in the data points follows a two-step Bayesian learning algorithm. First, the estimation of the best value is based on a generative module where the network stack value is an inverse function of the download outcomes such as throughput, time to first byte, and download complete time. A prediction algorithm is used to estimate the optimal value of this network stack value. In this way, a set of data points may be generated to train the machine as a result of the supervised learning algorithm, or method  400 . 
     After the best value of a single parameter is estimated based on a model generated by the data model generator  208 , the posteriori probability of good performance is measured conditioned on the parameter estimate and other strategies. For example, if the posteriori probability is high, the optimizer  106  may then choose this policy for use on future network traffic. This probability is estimated using information from other estimated or set network stack values hence taking into account possible dependencies using a statistical prediction generator  216 , for example. For multiple network stack value estimation, this process is either parallelized if the parameters are independent in probability distribution or the estimation of the parameters is performed in cascade if independence cannot be determined. A supervised machine learning trainer  214  may iterate this two-step Bayesian learning algorithm using the generated datasets described above, stored in a training data set store  218 . 
     An optimized network strategy selector  212  selects a network configuration strategy based on the optimal choice. The objective function of the black box optimization is a function of performance improvement in throughput and download complete time, network congestion, and other network parameters. The optimization is constrained on thresholds for performance improvement metrics and traffic share. The black box algorithm outputs a set of network stack values which optimizes the objective function subject to the constraints. It operates on data aggregated over some period of time (e.g., one or more years, months, days, hours, minutes, seconds, etc.) and has no memory in the choice of statistics used to calculate this objective function and is purely deterministic. 
     In order to constrain the parameter space and generate relevant data sets to train the model on, the black box algorithm and the generation of strategies may be used in tandem by a supervised machine learning trainer  214  over multiple iterations. This gives the learning framework an adaptive nature. The strategies ensure that the adaptive learning framework explores the entire network stack value space and does not lead to focusing on local optima. The black box optimization algorithm guides the learning framework to focus on parts of the space where performance improvements are likely to result. Because the learning algorithm has memory and is used in tandem with the above elements, the network stack value estimates have achieved a tradeoff between maximizing performance improvement over unoptimized network performance and generating stable estimates that do not fluctuate with short term network fluctuations, while enabling estimates to evolve over time. Here, the system is dynamically improved using machine learning techniques and the technology that is used to perform network transactions overall, as a client device using the selected set of best values, improves network performance of that client device and thereby improves the user experience above and beyond previous approaches. 
     A statistical prediction generator  216  may be used to generate calculations used in statistical prediction, including probability distributions, Bayesian probability, moving averages, regression analysis, predictive modeling, and other statistical computations. A training data set store  218  may be used to store training set data for generated data models, as described above. The training data set store  218  may include a subset of data stored on the strategy data store  112 , in one embodiment. 
     The optimized network strategy selector  212  ensures the selected strategy is delivered to a user device  102  based on the received operational context. A network policy may be chosen based on the above described techniques and is propagated by configuring a network interface on the user device  102  through an agent  114 , in an embodiment. The optimized network strategy selector  212  sends instructions (e.g., as part of a client-side policy) to a user device  102  where, upon compilation of the instructions, implements the chosen network configuration strategy by setting the appropriate network stack values. 
     4. CONVERGENCE ON OPTIMUM STRATEGY FOR OPERATIONAL CONTEXTS 
       FIG. 3  illustrates a high-level interaction diagram of dynamic network configuration optimization, according to an embodiment. A user device  102  sends  302  a first operational context to a proxy server  108 . A dynamic network configuration optimizer  106  configures  304  one or more strategies based on operational context. In response to the proxy server  108  receiving a first operational context, the proxy servers  108  retrieves  306  a first strategy based on the first operational context. The user device  102  receives  308  the first strategy based on the first operational context from the proxy server  108 . Such a first strategy may include network stack values such as transport protocols to use for particular URLs that may affect network performance such as download completion time, time to first byte, and throughput, for example. 
     The first strategy is compiled  310  at the user device  102  to configure a first set of network parameters. Later, a second operational context is determined  312 . The second operational context may include different usage patterns, different networks being used to transact data, and the other types of operational context mentioned above. The second operational context is sent  314  to the proxy server  108 . A second strategy is requested  316  based on the second operational context from the dynamic network configuration optimizer  106 . 
     A second strategy is determined  318  based on the second operational context that maximizes network performance. This second strategy is then sent to the proxy server  108  responsive to the request and then the second strategy is received  320  by the user device  102  based on the second operational context. The second strategy is then compiled  322  to configure a second set of network parameters different from the first set of network parameters. 
     This interaction diagram illustrates how a strategy is converged upon based on differing operational context. For example, file size may be a dominant characteristic that affects a strategy that enables throughput of 1 MB to 20 MB. Because file size may vary according to the user device task, such as small file downloads (e.g., web browsing, etc.) versus large file downloads (e.g., video streaming, etc.), file size may be a hidden variable that dominates the strategy, causing a failure of convergence on a single strategy. Here, the differing operational context translates to different strategies being applied. Other hidden variables may include server behavior, user device behavior, and network congestion. 
     The following is an example code snippet of the CSP (compilation stage):
         Server sends:
           S_POLICY (static, default):
               S_Enable_acceleration=0   S_Enable_logging=0   
               S_POLICY (dynamic; applied only when CSP matches)
               S_MATCH
                   C_Current_URL   S_REGEX: {circumflex over ( )}haps://(.*)jpg   
                   S_Enable_acceleration=1   S_Enable_logging=1   
               
           Client received the CSP and compiles it successfully
           CSP is stored in the memory now.
 
S_variables are server-side specific. C_variables are client-side specific.
 
Now, an example of the Client serving request to www dot salesforce dot com with the above mentioned CSP compiled and stored in the memory:
   
           Client received the CSP and compiles it successfully
           S_POLICY (static, default):
               S_Enable_acceleration=0   S_Enable_logging=0   
               S_POLICY (dynamic; applied only when CSP matches)
               S_MATCH
                   https://www.salesforce.com   S_REGEX: {circumflex over ( )}haps://(.*)jpg$   
                   S_Enable_acceleration=1   S_Enable_logging=1   
               
           CSP engine runs the program.
           Attempts to match a pattern {circumflex over ( )}https://(.*)jpg$ fails (request isn&#39;t for a file ending with jpg)   
           Resulting policy is:
           S_POLICY (static, default):
               S_Enable_acceleration=0   S_Enable_logging=0
 
Counter-example resulting policy with a request to www dot salesforce dot com/logo.jpg would be:
   
               
           S_POLICY (static, default):
           S_Enable_acceleration=1   S_Enable_logging=1   
               

     Because the requested URL (www dot salesforce dot com/logo.jpg) would match the pattern {circumflex over ( )}https://(.*)jpg$, the values from a dynamic policy would be applied. 
       FIG. 4  illustrates a flowchart for dynamic network configuration optimization, according to an embodiment of the invention. Method  400 , using the supervised machine learning trainer  214  and data model generator  208 , among other components in the dynamic network configuration optimizer  106  as described above, may be used in dynamic network configuration optimization, in an embodiment. A policy strategy having a range of values set for at least one network parameter may be defined  402 . This policy strategy may be defined  402  based on known information and/or heuristics, for example. Using a machine learning model, multiple policy strategies having different ranges of values set for one or more network parameters are determined  404 . Network stack parameters are gathered  406  from client devices using one or more components embedded in a mobile application operating on the client devices. 
     For a set of network stack parameters, the policy strategy of the multiple policy strategies is selected  408  to be mapped to the set of network stack parameters based on the range of values set for the at least one network parameter. 
     Good performance of the machine learning model is verified  410  based on a measured performance improvement using the selected policy strategy over a threshold amount. For example, a threshold amount for a network stack parameter, such as transmission rate, may be 10%, meaning that the performance improvement should be over 10% of the unoptimized network configuration. If the model is not verified  410 , method  400  may repeat until the model converges. 
     The machine learning model enabled method  400  above allows more efficient analysis of system problems. This may enable automatic or operator-initiated modifications to system parameters that increases efficiency of the overall system performance, increases the efficiency of server and/or client computing performance, and aides in the systematic handling of problems that cause network performance issues from the viewpoint of the client device. 
     Characteristics of modern networks change at a very rapid clip. The diversity of devices, content, device types, access mediums, etc., further compound the volatility of the networks. These facets make the problem hard to characterize, estimate or constrain resulting in inefficient, slow and unpredictable delivery of any content over these networks. However, there is a lot of information about the network available in the transit traffic itself—from billions of devices consuming data. This information that describes network operating characteristics and defines efficacy of data delivery strategies is called a “network imprint”. The approaches described herein allow embodiments to compute this network imprint. Embodiments include an apparatus comprising a processor and configured to perform any one of the foregoing methods. Embodiments include a computer readable storage medium, storing software instructions, which when executed by one or more processors cause performance of any one of the foregoing methods. Note that, although separate embodiments are discussed herein, any combination of embodiments and/or partial embodiments discussed herein may be combined to form further embodiments. 
     5. IMPLEMENTATION MECHANISMS—HARDWARE OVERVIEW 
     According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques. 
     For example,  FIG. 5  is a block diagram that illustrates a computer system  500  upon which an embodiment of the invention may be implemented. Computer system  500  includes a bus  502  or other communication mechanism for communicating information, and a hardware processor  504  coupled with bus  502  for processing information. Hardware processor  504  may be, for example, a general purpose microprocessor. 
     Computer system  500  also includes a main memory  506 , such as a random access memory (RAM) or other dynamic storage device, coupled to bus  502  for storing information and instructions to be executed by processor  504 . Main memory  506  also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor  504 . Such instructions, when stored in non-transitory storage media accessible to processor  504 , render computer system  500  into a special-purpose machine that is device-specific to perform the operations specified in the instructions. 
     Computer system  500  further includes a read only memory (ROM)  508  or other static storage device coupled to bus  502  for storing static information and instructions for processor  504 . A storage device  510 , such as a magnetic disk or optical disk, is provided and coupled to bus  502  for storing information and instructions. 
     Computer system  500  may be coupled via bus  502  to a display  512 , such as a liquid crystal display (LCD), for displaying information to a computer user. An input device  514 , including alphanumeric and other keys, is coupled to bus  502  for communicating information and command selections to processor  504 . Another type of user input device is cursor control  516 , such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor  504  and for controlling cursor movement on display  512 . This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. 
     Computer system  500  may implement the techniques described herein using device-specific hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system  500  to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system  500  in response to processor  504  executing one or more sequences of one or more instructions contained in main memory  506 . Such instructions may be read into main memory  506  from another storage medium, such as storage device  510 . Execution of the sequences of instructions contained in main memory  506  causes processor  504  to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. 
     The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device  510 . Volatile media includes dynamic memory, such as main memory  506 . Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge. 
     Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus  502 . Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. 
     Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor  504  for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system  500  can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus  502 . Bus  502  carries the data to main memory  506 , from which processor  504  retrieves and executes the instructions. The instructions received by main memory  506  may optionally be stored on storage device  510  either before or after execution by processor  504 . 
     Computer system  500  also includes a communication interface  518  coupled to bus  502 . Communication interface  518  provides a two-way data communication coupling to a network link  520  that is connected to a local network  522 . For example, communication interface  518  may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface  518  may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface  518  sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. 
     Network link  520  typically provides data communication through one or more networks to other data devices. For example, network link  520  may provide a connection through local network  522  to a host computer  524  or to data equipment operated by an Internet Service Provider (ISP)  526 . ISP  526  in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet”  528 . Local network  522  and Internet  528  both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link  520  and through communication interface  518 , which carry the digital data to and from computer system  500 , are example forms of transmission media. 
     Computer system  500  can send messages and receive data, including program code, through the network(s), network link  520  and communication interface  518 . In the Internet example, a server  530  might transmit a requested code for an application program through Internet  528 , ISP  526 , local network  522  and communication interface  518 . 
     The received code may be executed by processor  504  as it is received, and/or stored in storage device  510 , or other non-volatile storage for later execution. 
     6. EQUIVALENTS, EXTENSIONS, ALTERNATIVES AND MISCELLANEOUS 
     In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. Thus, the sole and exclusive indicator of what is the invention, and is intended by the applicants to be the invention, is the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. Hence, no limitation, element, property, feature, advantage or attribute that is not expressly recited in a claim should limit the scope of such claim in any way. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.