Intro to Load Balancing for Developers – The Algorithms
If you’re new to this series, you can find the complete list of articles in the series on my personal page here If you are writing applications to sit behind a Load Balancer, it behooves you to at least have a clue what the algorithm your load balancer uses is about. We’re taking this week’s installment to just chat about the most common algorithms and give a plain- programmer description of how they work. While historically the algorithm chosen is both beyond the developers’ control, you’re the one that has to deal with performance problems, so you should know what is happening in the application’s ecosystem, not just in the application. Anything that can slow your application down or introduce errors is something worth having reviewed. For algorithms supported by the BIG-IP, the text here is paraphrased/modified versions of the help text associated with the Pool Member tab of the BIG-IP UI. If they wrote a good description and all I needed to do was programmer-ize it, then I used it. For algorithms not supported by the BIG-IP I wrote from scratch. Note that there are many, many more algorithms out there, but as you read through here you’ll see why these (or minor variants of them) are the ones you’ll see the most. Plain Programmer Description: Is not intended to say anything about the way any particular dev team at F5 or any other company writes these algorithms, they’re just an attempt to put the process into terms that are easier for someone with a programming background to understand. Hopefully a successful attempt. Interestingly enough, I’ve pared down what BIG-IP supports to a subset. That means that F5 employees and aficionados will be going “But you didn’t mention…!” and non-F5 employees will likely say “But there’s the Chi-Squared Algorithm…!” (no, chi-squared is theoretical distribution method I know of because it was presented as a proof for testing the randomness of a 20 sided die, ages ago in Dragon Magazine). The point being that I tried to stick to a group that builds on each other in some connected fashion. So send me hate mail… I’m good. Unless you can say more than 2-5% of the world’s load balancers are running the algorithm, I won’t consider that I missed something important. The point is to give developers and software architects a familiarity with core algorithms, not to build the worlds most complete lexicon of algorithms. Random: This load balancing method randomly distributes load across the servers available, picking one via random number generation and sending the current connection to it. While it is available on many load balancing products, its usefulness is questionable except where uptime is concerned – and then only if you detect down machines. Plain Programmer Description: The system builds an array of Servers being load balanced, and uses the random number generator to determine who gets the next connection… Far from an elegant solution, and most often found in large software packages that have thrown load balancing in as a feature. Round Robin: Round Robin passes each new connection request to the next server in line, eventually distributing connections evenly across the array of machines being load balanced. Round Robin works well in most configurations, but could be better if the equipment that you are load balancing is not roughly equal in processing speed, connection speed, and/or memory. Plain Programmer Description: The system builds a standard circular queue and walks through it, sending one request to each machine before getting to the start of the queue and doing it again. While I’ve never seen the code (or actual load balancer code for any of these for that matter), we’ve all written this queue with the modulus function before. In school if nowhere else. Weighted Round Robin (called Ratio on the BIG-IP): With this method, the number of connections that each machine receives over time is proportionate to a ratio weight you define for each machine. This is an improvement over Round Robin because you can say “Machine 3 can handle 2x the load of machines 1 and 2”, and the load balancer will send two requests to machine #3 for each request to the others. Plain Programmer Description: The simplest way to explain for this one is that the system makes multiple entries in the Round Robin circular queue for servers with larger ratios. So if you set ratios at 3:2:1:1 for your four servers, that’s what the queue would look like – 3 entries for the first server, two for the second, one each for the third and fourth. In this version, the weights are set when the load balancing is configured for your application and never change, so the system will just keep looping through that circular queue. Different vendors use different weighting systems – whole numbers, decimals that must total 1.0 (100%), etc. but this is an implementation detail, they all end up in a circular queue style layout with more entries for larger ratings. Dynamic Round Robin (Called Dynamic Ratio on the BIG-IP): is similar to Weighted Round Robin, however, weights are based on continuous monitoring of the servers and are therefore continually changing. This is a dynamic load balancing method, distributing connections based on various aspects of real-time server performance analysis, such as the current number of connections per node or the fastest node response time. This Application Delivery Controller method is rarely available in a simple load balancer. Plain Programmer Description: If you think of Weighted Round Robin where the circular queue is rebuilt with new (dynamic) weights whenever it has been fully traversed, you’ll be dead-on. Fastest: The Fastest method passes a new connection based on the fastest response time of all servers. This method may be particularly useful in environments where servers are distributed across different logical networks. On the BIG-IP, only servers that are active will be selected. Plain Programmer Description: The load balancer looks at the response time of each attached server and chooses the one with the best response time. This is pretty straight-forward, but can lead to congestion because response time right now won’t necessarily be response time in 1 second or two seconds. Since connections are generally going through the load balancer, this algorithm is a lot easier to implement than you might think, as long as the numbers are kept up to date whenever a response comes through. These next three I use the BIG-IP name for. They are variants of a generalized algorithm sometimes called Long Term Resource Monitoring. Least Connections: With this method, the system passes a new connection to the server that has the least number of current connections. Least Connections methods work best in environments where the servers or other equipment you are load balancing have similar capabilities. This is a dynamic load balancing method, distributing connections based on various aspects of real-time server performance analysis, such as the current number of connections per node or the fastest node response time. This Application Delivery Controller method is rarely available in a simple load balancer. Plain Programmer Description: This algorithm just keeps track of the number of connections attached to each server, and selects the one with the smallest number to receive the connection. Like fastest, this can cause congestion when the connections are all of different durations – like if one is loading a plain HTML page and another is running a JSP with a ton of database lookups. Connection counting just doesn’t account for that scenario very well. Observed: The Observed method uses a combination of the logic used in the Least Connections and Fastest algorithms to load balance connections to servers being load-balanced. With this method, servers are ranked based on a combination of the number of current connections and the response time. Servers that have a better balance of fewest connections and fastest response time receive a greater proportion of the connections. This Application Delivery Controller method is rarely available in a simple load balancer. Plain Programmer Description: This algorithm tries to merge Fastest and Least Connections, which does make it more appealing than either one of the above than alone. In this case, an array is built with the information indicated (how weighting is done will vary, and I don’t know even for F5, let alone our competitors), and the element with the highest value is chosen to receive the connection. This somewhat counters the weaknesses of both of the original algorithms, but does not account for when a server is about to be overloaded – like when three requests to that query-heavy JSP have just been submitted, but not yet hit the heavy work. Predictive: The Predictive method uses the ranking method used by the Observed method, however, with the Predictive method, the system analyzes the trend of the ranking over time, determining whether a servers performance is currently improving or declining. The servers in the specified pool with better performance rankings that are currently improving, rather than declining, receive a higher proportion of the connections. The Predictive methods work well in any environment. This Application Delivery Controller method is rarely available in a simple load balancer. Plain Programmer Description: This method attempts to fix the one problem with Observed by watching what is happening with the server. If its response time has started going down, it is less likely to receive the packet. Again, no idea what the weightings are, but an array is built and the most desirable is chosen. You can see with some of these algorithms that persistent connections would cause problems. Like Round Robin, if the connections persist to a server for as long as the user session is working, some servers will build a backlog of persistent connections that slow their response time. The Long Term Resource Monitoring algorithms are the best choice if you have a significant number of persistent connections. Fastest works okay in this scenario also if you don’t have access to any of the dynamic solutions. That’s it for this week, next week we’ll start talking specifically about Application Delivery Controllers and what they offer – which is a whole lot – that can help your application in a variety of ways. Until then! Don.X-Forwarded-For Log Filter for Windows Servers
For those that don't know what X-Forwarded-For is, then you might as well close your browser because this post likely will mean nothing to you… A Little Background Now, if you are still reading this, then you likely are having issues with determining the origin client connections to your web servers. When web requests are passed through proxies, load balancers, application delivery controllers, etc, the client no longer has a direct connection with the destination server and all traffic looks like it's coming from the last server in the chain. In the following diagram, Proxy2 is the last hop in the chain before the request hits the destination server. Relying on connection information alone, the server thinks that all connections come from Proxy2, not from the Client that initiated the connection. The only one in the chain here who knows who the client really is (as determined by it's client IP Address, is Proxy1. The problem is that application owners rely on source client information for many reasons ranging from analyzing client demographics to targeting Denial of Service attacks. That's where the X-Forwarded-For header comes in. It is non-RFC standard HTTP request header that is used for identifying the originating IP address of a client connecting to a web server through a proxy. The format of the header is: X-Forwarded-For: client, proxy1, proxy, … X-Forwarded-For header logging is supported in Apache (with mod_proxy) but Microsoft IIS does not have a direct way to support the translation of the X-Forwarded-For value into the client ip (c-ip) header value used in its webserver logging. Back in September, 2005 I wrote an ISAPI filter that can be installed within IIS to perform this transition. This was primarily for F5 customers but I figured that I might as well release it into the wild as others would find value out of it. Recently folks have asked for 64 bit versions (especially with the release of Windows 2008 Server). This gave me the opportunity to brush up on my C skills. In addition to building targets for 64 bit windows, I went ahead and added a few new features that have been asked for. Proxy Chain Support The original implementation did not correctly parse the "client, proxy1, proxy2,…" format and assumed that there was a single IP address following the X-Forwarded-For header. I've added code to tokenize the values and strip out all but the first token in the comma delimited chain for inclusion in the logs. Header Name Override Others have asked to be able to change the header name that the filter looked for from "X-Forwarded-For" to some customized value. In some cases they were using the X-Forwarded-For header for another reason and wanted to use iRules to create a new header that was to be used in the logs. I implemented this by adding a configuration file option for the filter. The filter will look for a file named F5XForwardedFor.ini in the same directory as the filter with the following format: [SETTINGS] HEADER=Alternate-Header-Name The value of "Alternate-Header-Name" can be changed to whatever header you would like to use. Download I've updated the original distribution file so that folks hitting my previous blog post would get the updates. The following zip file includes 32 and 64 bit release versions of the F5XForwardedFor.dll that you can install under IIS6 or IIS7. Installation Follow these steps to install the filter. Download and unzip the F5XForwardedFor.zip distribution. Copy the F5XForwardedFor.dll file from the x86\Release or x64\Release directory (depending on your platform) into a target directory on your system. Let's say C:\ISAPIFilters. Ensure that the containing directory and the F5XForwardedFor.dll file have read permissions by the IIS process. It's easiest to just give full read access to everyone. Open the IIS Admin utility and navigate to the web server you would like to apply it to. For IIS6, Right click on your web server and select Properties. Then select the "ISAPI Filters" tab. From there click the "Add" button and enter "F5XForwardedFor" for the Name and the path to the file "c:\ISAPIFilters\F5XForwardedFor.dll" to the Executable field and click OK enough times to exit the property dialogs. At this point the filter should be working for you. You can go back into the property dialog to determine whether the filter is active or an error occurred. For II7, you'll want to select your website and then double click on the "ISAPI Filters" icon that shows up in the Features View. In the Actions Pane on the right select the "Add" link and enter "F5XForwardedFor" for the name and "C:\ISAPIFilters\F5XForwardedFor.dll" for the Executable. Click OK and you are set to go. I'd love to hear feedback on this and if there are any other feature request, I'm wide open to suggestions. The source code is included in the download distribution so if you make any changes yourself, let me know! Good luck and happy filtering! -JoeThe Top 10, Top Predictions for 2012
Around this time of year, almost everyone and their brother put out their annual predictions for the coming year. So instead of coming up with my own, I figured I’d simply regurgitate what many others are expecting to happen. Security Predictions 2012 & 2013 - The Emerging Security Threat – SANS talks Custom Malware, IPv6, ARM hacking and Social Media. Top 7 Cybersecurity Predictions for 2012 - From Stuxnet to Sony, a number of cyberattacks emerged in 2011 that experts have predicted for quite some time. Webroot’s top seven forecasts for the year ahead. Zero-day targets and smartphones are on this list. Top 8 Security Predictions for 2012 – Fortinet’s Security Predictions for 2012. Sponsored attacks and SCADA Under the Scope. Security Predictions for 2012 - With all of the crazy 2011 security breaches, exploits and notorious hacks, what can we expect for 2012? Websense looks at blended attacks, social media identity and SSL. Top 5 Security Predictions For 2012 – The escalating change in the threat landscape is something that drives the need for comprehensive security ever-forward. Firewalls and regulations in this one. Gartner Predicts 2012 – Special report addressing the continuing trend toward the reduction of control IT has over the forces that affect it. Cloud, mobile, data management and context-aware computing. 2012 Cyber Security Predictions – Predicts cybercriminals will use cyber-antics during the U.S. presidential election and will turn cell phones into ATMs. Top Nine Cyber Security Trends for 2012 – Imperva’s predictions for the top cyber security trends for 2012. DDoS, HTML 5 and social media. Internet Predictions for 2012 – QR codes and Flash TOP 15 Internet Marketing Predictions for 2012 – Mobile SEO, Social Media ROI and location based marketing. Certainly not an exhaustive list of all the various 2012 predictions including the doomsday and non-doomsday claims but a good swath of what the experts believe is coming. Wonder if anyone predicted that Targeted attacks increased four-fold in 2011. ps Technorati Tags: F5, cyber security, predictions, 2012, Pete Silva, security, mobile, vulnerabilities, crime, social media, hacks, the tube, internet, identity theft
HTTP Pipelining: A security risk without real performance benefits
Everyone wants web sites and applications to load faster, and there’s no shortage of folks out there looking for ways to do just that. But all that glitters is not gold, and not all acceleration techniques actually do all that much to accelerate the delivery of web sites and applications. Worse, some actual incur risk in the form of leaving servers open to exploitation. A BRIEF HISTORY Back in the day when HTTP was still evolving, someone came up with the concept of persistent connections. See, in ancient times – when administrators still wore togas in the data center – HTTP 1.0 required one TCP connection for every object on a page. That was okay, until pages started comprising ten, twenty, and more objects. So someone added an HTTP header, Keep-Alive, which basically told the server not to close the TCP connection until (a) the browser told it to or (b) it didn’t hear from the browser for X number of seconds (a time out). This eventually became the default behavior when HTTP 1.1 was written and became a standard. I told you it was a brief history. This capability is known as a persistent connection, because the connection persists across multiple requests. This is not the same as pipelining, though the two are closely related. Pipelining takes the concept of persistent connections and then ignores the traditional request – reply relationship inherent in HTTP and throws it out the window. The general line of thought goes like this: “Whoa. What if we just shoved all the requests from a page at the server and then waited for them all to come back rather than doing it one at a time? We could make things even faster!” Tada! HTTP pipelining. In technical terms, HTTP pipelining is initiated by the browser by opening a connection to the server and then sending multiple requests to the server without waiting for a response. Once the requests are all sent then the browser starts listening for responses. The reason this is considered an acceleration technique is that by shoving all the requests at the server at once you essentially save the RTT (Round Trip Time) on the connection waiting for a response after each request is sent. WHY IT JUST DOESN’T MATTER ANYMORE (AND MAYBE NEVER DID) Unfortunately, pipelining was conceived of and implemented before broadband connections were widely utilized as a method of accessing the Internet. Back then, the RTT was significant enough to have a negative impact on application and web site performance and the overall user-experience was improved by the use of pipelining. Today, however, most folks have a comfortable speed at which they access the Internet and the RTT impact on most web application’s performance, despite the increasing number of objects per page, is relatively low. There is no arguing, however, that some reduction in time to load is better than none. Too, anyone who’s had to access the Internet via high latency links can tell you anything that makes that experience faster has got to be a Good Thing. So what’s the problem? The problem is that pipelining isn’t actually treated any differently on the server than regular old persistent connections. In fact, the HTTP 1.1 specification requires that a “server MUST send its responses to those requests in the same order that the requests were received.” In other words, the requests are return in serial, despite the fact that some web servers may actually process those requests in parallel. Because the server MUST return responses to requests in order that the server has to do some extra processing to ensure compliance with this part of the HTTP 1.1 specification. It has to queue up the responses and make certain responses are returned properly, which essentially negates the performance gained by reducing the number of round trips using pipelining. Depending on the order in which requests are sent, if a request requiring particularly lengthy processing – say a database query – were sent relatively early in the pipeline, this could actually cause a degradation in performance because all the other responses have to wait for the lengthy one to finish before the others can be sent back. Application intermediaries such as proxies, application delivery controllers, and general load-balancers can and do support pipelining, but they, too, will adhere to the protocol specification and return responses in the proper order according to how the requests were received. This limitation on the server side actually inhibits a potentially significant boost in performance because we know that processing dynamic requests takes longer than processing a request for static content. If this limitation were removed it is possible that the server would become more efficient and the user would experience non-trivial improvements in performance. Or, if intermediaries were smart enough to rearrange requests such that they their execution were optimized (I seem to recall I was required to design and implement a solution to a similar example in graduate school) then we’d maintain the performance benefits gained by pipelining. But that would require an understanding of the application that goes far beyond what even today’s most intelligent application delivery controllers are capable of providing. THE SILVER LINING At this point it may be fairly disappointing to learn that HTTP pipelining today does not result in as significant a performance gain as it might at first seem to offer (except over high latency links like satellite or dial-up, which are rapidly dwindling in usage). But that may very well be a good thing. As miscreants have become smarter and more intelligent about exploiting protocols and not just application code, they’ve learned to take advantage of the protocol to “trick” servers into believing their requests are legitimate, even though the desired result is usually malicious. In the case of pipelining, it would be a simple thing to exploit the capability to enact a layer 7 DoS attack on the server in question. Because pipelining assumes that requests will be sent one after the other and that the client is not waiting for the response until the end, it would have a difficult time distinguishing between someone attempting to consume resources and a legitimate request. Consider that the server has no understanding of a “page”. It understands individual requests. It has no way of knowing that a “page” consists of only 50 objects, and therefore a client pipelining requests for the maximum allowed – by default 100 for Apache – may not be seen as out of the ordinary. Several clients opening connections and pipelining hundreds or thousands of requests every second without caring if they receive any of the responses could quickly consume the server’s resources or available bandwidth and result in a denial of service to legitimate users. So perhaps the fact that pipelining is not really all that useful to most folks is a good thing, as server administrators can disable the feature without too much concern and thereby mitigate the risk of the feature being leveraged as an attack method against them. Pipelining as it is specified and implemented today is more of a security risk than it is a performance enhancement. There are, however, tweaks to the specification that could be made in the future that might make it more useful. Those tweaks do not address the potential security risk, however, so perhaps given that there are so many other optimizations and acceleration techniques that can be used to improve performance that incur no measurable security risk that we simply let sleeping dogs lie. IMAGES COURTESTY WIKIPEDIA COMMONSBack to Basics: Least Connections is Not Least Loaded
#webperf #ado When load balancing, "least connections" does not mean "least loaded" Performance is important, and that means it's important that our infrastructure support the need for speed. Load balancing algorithms are an integral piece of the performance equation and can both improve - or degrade - performance. That's why it's important to understand more about the algorithms than their general selection mechanism. Understanding that round robin is basically an iterative choice, traversing a list one by one is good - but understanding what that means in terms of performance and capacity on different types of applications and application workloads is even better. We last checked out "fastest response time" and today we're diving into "least connections" which, as stated above, does not mean "least loaded." INTRA-APPLICATION WORKLOADS The industry standard "Least connections" load balancing algorithm uses the number of current connections to each application instance (member) to make its load balancing decision. The member with the least number of active connections is chosen. Pretty simple, right? The premise of this algorithm is a general assumption that fewer connections (and thus fewer users) means less load and therefore better performance. That's operational axiom #2 at work - if performance decreases as load increases it stands to reason that performance increases as load decreases. That would be true (and in the early days of load balancing it was true) if all intra-application workloads required the same resources. Unfortunately, that's no longer true and the result is uneven load distribution that leads to unpredictable performance fluctuations as demand increases. Consider a simple example: a user logging into a system takes at least one if not more database queries to validate credentials and then update the system to indicate the activity. Depending on the nature of the application, other intra-application activities will require different quantities of resources. Some are RAM heavy, others CPU heavy, others file or database heavy. Furthermore, depending on the user in question, the usage pattern will vary greatly. One hundred users can be logged into the same system (requiring at a minimum ten connections) but if they're all relatively idle, the system will be lightly loaded and performing well. Conversely, another application instance may boast only 50 connections, but all fifty users are heavily active with database queries returning large volumes of data. The system is far more heavily loaded and performance may be already beginning to suffer. When the next request comes in, however, the load balancer using a "least connections" algorithm will choose the latter member, increasing the burden on that member and likely further degrading performance. The premise of the least connections algorithm is that the application instance with the fewest number of connections is the least loaded. Except, it's not. The only way to know which application instance is the least loaded is to monitor its system variables directly, gathering CPU utilization and memory and comparing it against known maximums. That generally requires either SNMP, agents, or other active monitoring mechanisms that can unduly tax the system in and of itself by virtue of consuming resources. This is a quandary for operations, because "application workload" is simply too broad a generalization. Certainly some applications are more I/O heavy than others, still others are more CPU or connection heavy. But all applications have both a general workload profile and an intra-application workload profile. Understanding the usage patterns - the intra-application workload profile - of an application is critical to being able to determine how best to not only choose a load balancing algorithm but specify any limitations that may provide better overall performance and use of capacity during execution. As always, being aware of the capabilities and the limitations of a given load balancing algorithm will assist in choosing one that is best able to meet the performance and availability requirements of an application (and thus the business).Back to Basics: The Many Faces of Load Balancing Persistence
Finally! It all makes sense now! Thanks to cloud and the very generic "sticky sessions", many more people are aware of persistence as it relates to load balancing. It's a critical capability of load balancing without which stateful applications (which is most of them including VDI, most web applications, and data analysis tools) would simply fail to scale. Persistence is, in general, like the many moods of Spock. They all look pretty much the same from the outside - ensure that a user, once connected, continues to be connected to the same application instance to ensure access to whatever state is stored in that instance. But though they act the same (and Spock's expression appears the same) deep down, where it counts, persistence is very different depending on how it's implemented. It requires different processing, different inspection, different data, even. Understanding these differences is important because each one has a different impact on performance. The Many Faces of Persistence There are several industry de facto standard types of persistence: simple, SSL, and cookie. Then there are more advanced forms of persistence: SIP, WTS, Universal and Hash. Generally speaking the de facto standard types of persistence are applicable for use with just about any web application. The more advanced forms of persistence are specific to a protocol or rely on a capability that is not necessarily standardized across load balancing services. Without further adieu, let's dive in! Simple Persistence Simple persistence is generally based on network characteristics, like source IP address. It can also include the destination port, to give the load balancer a bit more capacity in terms of simultaneously applications supported. Best practices avoid simple persistence to avoid reoccurrence of the mega-proxy problem which had a tendency to overwhelm application instances. Network load balancing uses a form of simple persistence. SSL Session ID Persistence SSL Session ID persistence became necessary when SSL was broadly accepted as the de facto means of securing traffic in flight for web applications. Because SSL sessions need to be established and are very much tied to a session between client and server, failing to "stick" SSL-secured sessions results in renegotiation of the session, which takes a noticeable amount of time and annoys end-users. To avoid unnecessary renegotiation, load balancers use the SSL Session ID to ensure sessions are properly routed to the application instance to which they first connected. Cookie Persistence Cookie persistence is a technique invented by F5 (shameless plug) that uses the HTTP cookie header to persist connections across a session. Most application servers insert a session id into responses that is used by developers to access data stored in the server session (shopping carts, etc... ). This value is used by load balancing services to enable persistence. This technique avoids the issues associated with simple persistence because the session id is unique. Universal Persistence Universal persistence is the use of any piece of data (network, application protocol, payload) to persist a session. This technique requires the load balancer to be able to inspect and ultimately extract any piece of data from a request or response. This technique is the basis for application-specific persistence solutions addressing popular applications like SIP, WTS, and more recently, VMware View. SIP, WTS, Username Persistence Session Initiation Protocol (SIP) and Windows Terminal Server (WTS) persistence are application-specific persistence techniques that use data unique to a session to persist connections. Username persistence is a similar technique designed to address the needs of VDI - specifically VMware View solutions - in which sessions are persisted (as one might expect) based on username. When a type of persistence becomes very commonly used it is often moved from being a customized, universal persistence implementation to a native, productized persistence profile. This improves performance and scalability by removing the need to inspect and extract the values used to persist sessions from the data flow and results in an application-specific persistence type, such as SIP or WTS. Hash Persistence Hash persistence is the use of multiple values within a request to enable persistence. To avoid problems with simple persistence, for example, a hash value may be created based on Source IP, Destination IP, Destination Port. While not necessarily unique to every session, this technique results in a more even distribution of load across servers. Non-unique value-based persistence techniques (simple, hash) are generally used with stateless applications or streaming content (video, audio) as a means to more evenly distribute load. Unique value-based persistence techniques (universal, application-specific, SSL ID) are generally used with stateful applications that depend on the client being connected to the same application instance through the session's life. Cookie persistence can be used with both techniques, provided the application is web based and uses HTTP headers for each request (Web Sockets breaks this technique).X-Forwarded-For HTTP Module For IIS7, Source Included!
For those who of you that are having problems with logging client addresses in their server logs because you are running your web servers behind a proxy of some sort, never fear, your solution is here. For those that don't, I already discussed in my previous posts about what the X-Forwarded-For header is so feel free to click back into those to read about it. History Back in September, 2005 I wrote and posted a 32-bit ISAPI filter that extracted the X-Forwarded-For header value and replaced the c-ip value (client ip) that is stored in the server logs. Lots of folks have found this useful over time and I was eventually asked for a 64-bit version which I posted about in August, 2009. The Question Well, it looks like it's time for the next generation for this filter… I received an email from a colleague here at F5 telling me that his customer didn't want to deploy any more ISAPI filters in their IIS7 infrastructure. IIS7 introduced the concept of IIS Modules that are more integrated into the whole pipeline and was told that Microsoft is recommending folks move in that direction. I was asked if I had plans to port my ISAPI filter into a HTTP Module. The Answer Well, the answer was "probably not", but now it's changed to a "yes"! The Solution In reading about IIS Module, I found that you can develop in managed (C#/VB) or Native (C++) code. I loaded up the test C# project to see if I could get it working. In a matter of minutes I had a working module that intercepted the event when logging occurs. The only problem was that from managed code, I could find no way to actually modify the values that were passed to the logging processor. This was a problem so I scrapped that and moved to a native C++ module. After a little while of jumping through the documentation, I found the things I needed and pretty soon I had a working HTTP module that implemented the same functionality as the ISAPI filter. Download The new Http Module hasn't had much testing done so please test it out before you roll it out into production. I've made the source available as well if you find an issue and care to fix it. Just make sure you pass back the fixes to me B-). X-Forwarded-For Http Module Binary Distribution X-Forwarded-For Http Module Source Distribution The filter will require installation into IIS in order for you to be able to add it to your applications. Both distributions include a readme.txt file with an example installation procedure. Make sure you use the Release builds for the appropriate platform (x86 or x64) unless you are in need of some troubleshooting as the Debug build will dump a lot of data to a log file. The module supports customizable headers if you are using something other than X-Forwarded-For. Instructions for using that are in the readme.txt file as well. If you have any issues with using this, please let me know on this blog. Keep in mind that this is an unsupported product, but I'll do my best to fix any issues that come up. I'm even open to enhancements if you can think of any. Enjoy! -JoeUnix To PowerShell - Wc
PowerShell is definitely gaining momentum in the windows scripting world but I still hear folks wanting to rely on unix based tools to get their job done. In this series of posts I’m going to look at converting some of the more popular Unix based tools to PowerShell. wc The Unix “wc” (word count) command will print the character, word, and newline counts for each file specified and a total line if more than one file is specified. This command is useful for quickly scanning a directory for small and large files or to quickly look at a file and determine it’s relative size. The Get-Content Cmdlet will return the number of characters in the full but not the number of lines and words. The following script will emulate the behavior of the Unix “wc” command with a few changes in the way parameters are supplied.Infrastructure Architecture: Whitelisting with JSON and API Keys
Application delivery infrastructure can be a valuable partner in architecting solutions …. AJAX and JSON have changed the way in which we architect applications, especially with respect to their ascendancy to rule the realm of integration, i.e. the API. Policies are generally focused on the URI, which has effectively become the exposed interface to any given application function. It’s REST-ful, it’s service-oriented, and it works well. Because we’ve taken to leveraging the URI as a basic building block, as the entry-point into an application, it affords the opportunity to optimize architectures and make more efficient the use of compute power available for processing. This is an increasingly important point, as capacity has become a focal point around which cost and efficiency is measured. By offloading functions to other systems when possible, we are able to increase the useful processing capacity of an given application instance and ensure a higher ratio of valuable processing to resources is achieved. The ability of application delivery infrastructure to intercept, inspect, and manipulate the exchange of data between client and server should not be underestimated. A full-proxy based infrastructure component can provide valuable services to the application architect that can enhance the performance and reliability of applications while abstracting functionality in a way that alleviates the need to modify applications to support new initiatives. AN EXAMPLE Consider, for example, a business requirement specifying that only certain authorized partners (in the integration sense) are allowed to retrieve certain dynamic content via an exposed application API. There are myriad ways in which such a requirement could be implemented, including requiring authentication and subsequent tokens to authorize access – likely the most common means of providing such access management in conjunction with an API. Most of these options require several steps, however, and interaction directly with the application to examine credentials and determine authorization to requested resources. This consumes valuable compute that could otherwise be used to serve requests. An alternative approach would be to provide authorized consumers with a more standards-based method of access that includes, in the request, the very means by which authorization can be determined. Taking a lesson from the credit card industry, for example, an algorithm can be used to determine the validity of a particular customer ID or authorization token. An API key, if you will, that is not stored in a database (and thus requires a lookup) but rather is algorithmic and therefore able to be verified as valid without needing a specific lookup at run-time. Assuming such a token or API key were embedded in the URI, the application delivery service can then extract the key, verify its authenticity using an algorithm, and subsequently allow or deny access based on the result. This architecture is based on the premise that the application delivery service is capable of responding with the appropriate JSON in the event that the API key is determined to be invalid. Such a service must therefore be network-side scripting capable. Assuming such a platform exists, one can easily implement this architecture and enjoy the improved capacity and resulting performance boost from the offload of authorization and access management functions to the infrastructure. 1. A request is received by the application delivery service. 2. The application delivery service extracts the API key from the URI and determines validity. 3. If the API key is not legitimate, a JSON-encoded response is returned. 4. If the API key is valid, the request is passed on to the appropriate web/application server for processing. Such an approach can also be used to enable or disable functionality within an application, including live-streams. Assume a site that serves up streaming content, but only to authorized (registered) users. When requests for that content arrive, the application delivery service can dynamically determine, using an embedded key or some portion of the URI, whether to serve up the content or not. If it deems the request invalid, it can return a JSON response that effectively “turns off” the streaming content, thereby eliminating the ability of non-registered (or non-paying) customers to access live content. Such an approach could also be useful in the event of a service failure; if content is not available, the application delivery service can easily turn off and/or respond to the request, providing feedback to the user that is valuable in reducing their frustration with AJAX-enabled sites that too often simply “stop working” without any kind of feedback or message to the end user. The application delivery service could, of course, perform other actions based on the in/validity of the request, such as directing the request be fulfilled by a service generating older or non-dynamic streaming content, using its ability to perform application level routing. The possibilities are quite extensive and implementation depends entirely on goals and requirements to be met. Such features become more appealing when they are, through their capabilities, able to intelligently make use of resources in various locations. Cloud-hosted services may be more or less desirable for use in an application, and thus leveraging application delivery services to either enable or reduce the traffic sent to such services may be financially and operationally beneficial. ARCHITECTURE is KEY The core principle to remember here is that ultimately infrastructure architecture plays (or can and should play) a vital role in designing and deploying applications today. With the increasing interest and use of cloud computing and APIs, it is rapidly becoming necessary to leverage resources and services external to the application as a means to rapidly deploy new functionality and support for new features. The abstraction offered by application delivery services provides an effective, cross-site and cross-application means of enabling what were once application-only services within the infrastructure. This abstraction and service-oriented approach reduces the burden on the application as well as its developers. The application delivery service is almost always the first service in the oft-times lengthy chain of services required to respond to a client’s request. Leveraging its capabilities to inspect and manipulate as well as route and respond to those requests allows architects to formulate new strategies and ways to provide their own services, as well as leveraging existing and integrated resources for maximum efficiency, with minimal effort. Related blogs & articles: HTML5 Going Like Gangbusters But Will Anyone Notice? Web 2.0 Killed the Middleware Star The Inevitable Eventual Consistency of Cloud Computing Let’s Face It: PaaS is Just SOA for Platforms Without the Baggage Cloud-Tiered Architectural Models are Bad Except When They Aren’t The Database Tier is Not Elastic The New Distribution of The 3-Tiered Architecture Changes Everything Sessions, Sessions EverywhereWhat is server offload and why do I need it?
One of the tasks of an enterprise architect is to design a framework atop which developers can implement and deploy applications consistently and easily. The consistency is important for internal business continuity and reuse; common objects, operations, and processes can be reused across applications to make development and integration with other applications and systems easier. Architects also often decide where functionality resides and design the base application infrastructure framework. Application server, identity management, messaging, and integration are all often a part of such architecture designs. Rarely does the architect concern him/herself with the network infrastructure, as that is the purview of “that group”; the “you know who I’m talking about” group. And for the most part there’s no need for architects to concern themselves with network-oriented architecture. Applications should not need to know on which VLAN they will be deployed or what their default gateway might be. But what architects might need to know – and probably should know – is whether the network infrastructure supports “server offload” of some application functions or not, and how that can benefit their enterprise architecture and the applications which will be deployed atop it. WHAT IT IS Server offload is a generic term used by the networking industry to indicate some functionality designed to improve the performance or security of applications. We use the term “offload” because the functionality is “offloaded” from the server and moved to an application network infrastructure device instead. Server offload works because the application network infrastructure is almost always these days deployed in front of the web/application servers and is in fact acting as a broker (proxy) between the client and the server. Server offload is generally offered by load balancers and application delivery controllers. You can think of server offload like a relay race. The application network infrastructure device runs the first leg and then hands off the baton (the request) to the server. When the server is finished, the application network infrastructure device gets to run another leg, and then the race is done as the response is sent back to the client. There are basically two kinds of server offload functionality: Protocol processing offload Protocol processing offload includes functions like SSL termination and TCP optimizations. Rather than enable SSL communication on the web/application server, it can be “offloaded” to an application network infrastructure device and shared across all applications requiring secured communications. Offloading SSL to an application network infrastructure device improves application performance because the device is generally optimized to handle the complex calculations involved in encryption and decryption of secured data and web/application servers are not. TCP optimization is a little different. We say TCP session management is “offloaded” to the server but that’s really not what happens as obviously TCP connections are still opened, closed, and managed on the server as well. Offloading TCP session management means that the application network infrastructure is managing the connections between itself and the server in such a way as to reduce the total number of connections needed without impacting the capacity of the application. This is more commonly referred to as TCP multiplexing and it “offloads” the overhead of TCP connection management from the web/application server to the application network infrastructure device by effectively giving up control over those connections. By allowing an application network infrastructure device to decide how many connections to maintain and which ones to use to communicate with the server, it can manage thousands of client-side connections using merely hundreds of server-side connections. Reducing the overhead associated with opening and closing TCP sockets on the web/application server improves application performance and actually increases the user capacity of servers. TCP offload is beneficial to all TCP-based applications, but is particularly beneficial for Web 2.0 applications making use of AJAX and other near real-time technologies that maintain one or more connections to the server for its functionality. Protocol processing offload does not require any modifications to the applications. Application-oriented offload Application-oriented offload includes the ability to implement shared services on an application network infrastructure device. This is often accomplished via a network-side scripting capability, but some functionality has become so commonplace that it is now built into the core features available on application network infrastructure solutions. Application-oriented offload can include functions like cookie encryption/decryption, compression, caching, URI rewriting, HTTP redirection, DLP (Data Leak Prevention), selective data encryption, application security functionality, and data transformation. When network-side scripting is available, virtually any kind of pre or post-processing can be offloaded to the application network infrastructure and thereafter shared with all applications. Application-oriented offload works because the application network infrastructure solution is mediating between the client and the server and it has the ability to inspect and manipulate the application data. The benefits of application-oriented offload are that the services implemented can be shared across multiple applications and in many cases the functionality removes the need for the web/application server to handle a specific request. For example, HTTP redirection can be fully accomplished on the application network infrastructure device. HTTP redirection is often used as a means to handle application upgrades, commonly mistyped URIs, or as part of the application logic when certain conditions are met. Application security offload usually falls into this category because it is application – or at least application data – specific. Application security offload can include scanning URIs and data for malicious content, validating the existence of specific cookies/data required for the application, etc… This kind of offload improves server efficiency and performance but a bigger benefit is consistent, shared security across all applications for which the service is enabled. Some application-oriented offload can require modification to the application, so it is important to design such features into the application architecture before development and deployment. While it is certainly possible to add such functionality into the architecture after deployment, it is always easier to do so at the beginning. WHY YOU NEED IT Server offload is a way to increase the efficiency of servers and improve application performance and security. Server offload increases efficiency of servers by alleviating the need for the web/application server to consume resources performing tasks that can be performed more efficiently on an application network infrastructure solution. The two best examples of this are SSL encryption/decryption and compression. Both are CPU intense operations that can consume 20-40% of a web/application server’s resources. By offloading these functions to an application network infrastructure solution, servers “reclaim” those resources and can use them instead to execute application logic, serve more users, handle more requests, and do so faster. Server offload improves application performance by allowing the web/application server to concentrate on what it is designed to do: serve applications and putting the onus for performing ancillary functions on a platform that is more optimized to handle those functions. Server offload provides these benefits whether you have a traditional client-server architecture or have moved (or are moving) toward a virtualized infrastructure. Applications deployed on virtual servers still use TCP connections and SSL and run applications and therefore will benefit the same as those deployed on traditional servers. I am wondering why not all websites enabling this great feature GZIP? 3 Really good reasons you should use TCP multiplexing SOA & Web 2.0: The Connection Management Challenge Understanding network-side scripting I am in your HTTP headers, attacking your application Infrastructure 2.0: As a matter of fact that isn't what it means
