I found the discussion of the available bandwidth of tree vs higher dimensional virtual networks topologies quite, to quote Spock, fascinating: A mathematical analysis by Ritter (2002) (one of the original developers of Napster) presented a detailed numerical argument demonstrating that the Gnutella network could not scale to the capacity of its competitor, the Napster network. Essentially, that model showed that the Gnutella network is severely bandwidth-limited long before the P2P population reaches a million peers. In each of these previous studies, the conclusions have overlooked the intrinsic bandwidth limits of the underlying topology in the Gnutella network: a Cayley tree (Rains and Sloane 1999) (see Sect. 9.4 for the definition). Trees are known to have lower aggregate bandwidth than higher dimensional topologies, e.g., hypercubes and hypertori. Studies of interconnection topologies in the literature have tended to focus on hardware implementations (see, e.g., Culler et al. 1996; Buyya 1999), which are generally limited by the cost of the chips and wires to a few thousand nodes. P2P networks, on the other hand, are intended to support from hundreds of thousands to millions of simultaneous peers, and since they are implemented in software, hyper-topologies are relatively unfettered by the economics of hardware. In this chapter, we analyze the scalability of several alternative topologies and compare their throughput up to 2–3 million peers. The virtual hypercube and the virtual hypertorus offer near-linear scalable bandwidth subject to the number of peer TCP/IP connections that can be simultaneously kept open.
ScaleOut StateServer is an in-memory distributed cache across a server farm or compute grid. Unlike middleware vendors, StateServer is aims at being a very good data cache, it doesn't try to handle job scheduling as well. StateServer is what you might get when you take Memcached and merge in all the value added distributed caching features you've ever dreamed of. True, Memcached is free and ScaleOut StateServer is very far from free, but for those looking a for a satisfying out-of-the-box experience, StateServer may be just the caching solution you are looking for. Yes, "solution" is one of those "oh my God I'm going to pay through the nose" indicator words, but it really applies here. Memcached is a framework whereas StateServer has already prepackaged most features you would need to add through your own programming efforts. Why use a distributed cache? Because it combines the holly quadrinity of computing: better performance, linear scalability, high availability, and fast application development. Performance is better because data is accessed from memory instead of through a database to a disk. Scalability is linear because as more servers are added data is transparently load balanced across the servers so there is an automated in-memory sharding. Availability is higher because multiple copies of data are kept in memory and the entire system reroutes on failure. Application development is faster because there's only one layer of software to deal with, the cache, and its API is simple. All the complexity is hidden from the programmer which means all a developer has to do is get and put data. StateServer follows the RAM is the new disk credo. Memory is assumed to be the system of record, not the database. If you want data to be stored in a database and have the two kept in sync, then you'll have to add that layer yourself. All the standard memcached techniques should work as well for StateServer. Consider however that a database layer may not be needed. Reliability is handled by StateServer because it keeps multiple data copies, reroutes on failure, and has an option for geographical distribution for another layer of added safety. Storing to disk wouldn't make you any safer. Via email I asked them a few questions. The key question was how they stacked up against Memcached? As that is surely one of the more popular challenges they would get in any sales cycle, I was very curious about their answer. And they did a great job differentiation themselves. What did they say? First, for an in-depth discussion of their technology take a look ScaleOut Software Technology, but here a few of the highlights:
Why use ScaleOut StateServer instead of Memcached?I've [Dan McMillan, VP Sales] included some data points below based on our current understanding of the Memcached product. We don't use and haven't tested Memcached internally, so this comparison is based in part upon our own investigations and in part what we are hearing from our own customers during their evaluation and comparisons. We are aware that Memcached is successfully being used on many large, high volume sites. We believe strong demand for ScaleOut is being driven by companies that need a ready-to-deploy solution that provides advanced features and just works. We also hear that Memcached is often seen as a low cost solution in the beginning, but development and ongoing management costs sometimes far exceed our licensing fees. What sets ScaleOut apart from Memcached (and other competing solutions) is that ScaleOut was architected from the ground up to be a fully integrated and automated caching solution. ScaleOut offers both scalability and high availability, where our competitors typically provide only one or the other. ScaleOut is considered a full-featured, plug-n-play caching solution at a very reasonable price point, whereas we view Memcached as a framework in which to build your own caching solution. Much of the cost in choosing Memcached will be in development and ongoing management. ScaleOut works right out of the box. I asked ScaleOut Software founder and chief architect, Bill Bain for his thoughts on this. He is a long-time distributed caching and parallel computing expert and is the architect of ScaleOut StateServer. He had several interesting points to share about creating a distributed cache by using an open source (i.e. build it yourself) solution versus ScaleOut StateServer. First, he estimates that it would take considerable time and effort for engineers to create a distributed cache that has ScaleOut StateServer's fundamental capabilities. The primary reason is that the open source method only gives you a starting point, but it does not include most capabilities that are needed in a distributed cache. In fact, there is no built-in scalability or availability, the two principal benefits of a distributed cache. Here is some of the functionality that you would have to build:
Do you find yourself in competition with the likes of Terracotta, GridGain, GridSpaces, and Coherence type products?Our ScaleOut technology has previously been targeted to the ASP.Net space. Now that we are entering the Java/Linux space, we will be competing with companies like the ones you mentioned above, which are mainly Java/Linux focused as well. We initially got our start with distributed caching for ecommerce applications, but grid computing seems to be a strong growth area for us as well. We are now working with some large Wall Street firms on grid computing projects that involve some (very large) grid operations. I would like to reiterate that we are very focused on data caching only. We don't try to do job scheduling or other grid computing tasks, but we do improve performance and availability for those tasks via our distributed data cache.
What architectures your customers are using with your GeoServer product?A. GeoServer is a newer, add-on product that is designed to replicate the contents of two or more geographically separated ScaleOut object stores (caches). Typically a customer might use GeoServer to replicate object data between a primary data center site and a DR site. GeoServer facilitates continuous (async.) replication between sites, so if site A goes offline, the other site B is immediately available to handle the workload. Our ScaleOut technology offers 3 primary benefits: Scalability, performance & high availability. From a single web farm perspective, ScaleOut provides high availability by making either 1 or 2 (this is configurable) replica copies of each master object and storing the replica on an alternate host server in the farm. ScaleOut provides uniform access to the object from any server, and protects the object in the case of a server failure. With GeoServer, these benefits are extended across multiple sites. It is true that distributed caches typically hold temporary, fast-changing data, but that data can still be very critical to ecommerce, or grid computing applications. Loss of this data during a server failure, worker process recycle or even a grid computation process is unacceptable. We improve performance by keeping the data in-memory, while still maintaining high availability.
Hi, we've got a web application, which runs without the common standalone application servers like tomcat or jboss, rather it runs with an embedded jetty server. Now we are planing to run instances of this application on multiple machines, with a load balancer serving the requests. The big question is: is there a common scenario on how to update these applications? Lets think of 10 instances on 10 machines (one instance per machine), where we want to update each of these applications version. The brute force approach would be, to stop all instances, update and then restart it. This is a lot of manual work ;) Another problem is down-time: so someone must only shutdown one server after another, but then there are multiple application versions around. Can someone please provide us with a hint for this problem? Perhaps papers, tools or something like that? Thanks a lot :)
Looks interesting... Abstract: Today’s data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfortunately, even when deploying the highest-end IP switches/routers, resulting topologies may only support 50% of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Nonuniform bandwidth among data center nodes complicates application design and limits overall system performance. In this paper, we show how to leverage largely commodity Ethernet switches to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements. Similar to how clusters of commodity computers have largely replaced more specialized SMPs and MPPs, we argue that appropriately architected and interconnected commodity switches may deliver more performance at less cost than available from today’s higher-end solutions. Our approach requires no modifications to the end host network interface, operating system, or applications; critically, it is fully backward compatible with Ethernet, IP, and TCP.
G'day, I noticed the default sort order for the forum is to show the posts with the most replies first. That seems a bit odd for a forum. Would it not make sense to show the posts with the most recently replies first? It is possible to re-sort the forum threads that way by clicking on the "Last post" header (twice). It would seem like a more sensible default. I've checked and I see the same behaviour as both a registered (logged in) and anonymous user. Cheers - Callum.
G'day, I'm building an application to manage WordPress PHP code on many servers. Our application will push down code updates to each server, as well as performing backups and testing. I'm considering different methods of pushing updated code onto the individual servers. I'm considering something like Capistrano (I've no experience in Ruby though). I've also considered using subversion and then remotely calling svn commands via SSH. Are there any other tools specifically for this purpose? The servers will have persistent data (the WordPress databases) so I don't want to re-image them every update. Plus, they will each have a different set of plugins / themes, so building many images would be too complex. If there are any papers on code deployment, or other recommended reading, please point the links my way. Likewise, if anyone has any suggestions, or would like more details, just let me know. Cheers - Callum.
How do you design a reliable distributed file system when the expected availability of the individual nodes are only ~1/5? That is the case for P2P systems. Dominik Grolimund, the founder of a Swiss startup Caleido will show you how! They have launched Wuala, the social online storage service which scales as new nodes join the P2P network. The goal of Wua.la is to provide distributed online storage that is:
Hello! My first post here, so be patient please. I am developing site where I have lots of static content. But on many pages I have query to update count of views. I would say this is may cause lots of problems and was interested in another solution like storing these counts somewhere else. As my knowledge is bit limited in this way, I am asking you. I can say I understand PHP(OOP ofc) and MySQL. Nowadays I am getting into servers. Other question I have is: I read about making lots of things static.(in Flickr Architecture) and am interested how they do static sites? Lets say they make photo page static? And rebuild when tagg or comment is added? I am bit interested in it as I want to learn Smarty better(newbie) and serving content. Moreover, how about PHP? I have read many books about PHP theoretically but would love to see some RL example of using objects and exceptions(mainly this as I don't completely understand it) to learn some good programming habits. So if you can help me with some example or resource, please do :) I know I've covered huge area of things but these are what makes me mad everyday. So please be patient :) Greetings.
Update 2: Michael Galpin in Cache Money and Cache Discussions likes memcached for it's expiry policy, complex graph data, process data, but says MySQL has many advantages: SQL, Uniform Data Access, Write-through, Read-through, Replication, Management, Cold starts, LRU eviction. Update: Dormando asks Should you use memcached? Should you just shard mysql more?. The idea of caching is the most important part of caching as it transports you beyond a simple CRUD worldview. Plan for caching and sharding by properly abstracting data access methods. Brace for change. Be ready to shard, be ready to cache. React and change to what you push out which is actually popular, vs over planning and wasting valuable time. Feedster's François Schiettecatte wonders if Fotolog's 21 memcached servers wouldn't be better used to further shard data by adding more MySQL servers? He mentions Feedster was able to drop memcached once they partitioned their data across more servers. The algorithm: partition until all data resides in memory and then you may not need an additional memcached layer. Parvesh Garg goes a step further and asks why people think they should be using MySQL at all?
Pre-generating static files is an oldy but a goody, and as Thomas Brox Røst says, it's probably an underused strategy today. At one time this was the dominate technique for structuring a web site. Then the age of dynamic web sites arrived and we spent all our time worrying how to make the database faster and add more caching to recover the speed we had lost in the transition from static to dynamic. Static files have the advantage of being very fast to serve. Read from disk and display. Simple and fast. Especially when caching proxies are used. The issue is how do you bulk generate the initial files, how do you serve the files, and how do you keep the changed files up to date? This is the process Thomas covers in his excellent article Serving static files with Django and AWS - going fast on a budget", where he explains how he converted 600K thousand previously dynamic pages to static pages for his site Eventseer.net, a service for tracking academic events. Eventseer.net was experiencing performance problems as search engines crawled their 600K dynamic pages. As a solution you could imagine scaling up, adding more servers, adding sharding, etc etc, all somewhat complicated approaches. Their solution was to convert the dynamic pages to static pages in order to keep search engines from killing the site. As an added bonus non logged-in users experienced a much faster site and were more likely to sign up for the service. The article does a good job explaining what they did, so I won't regurgitate it all here, but I will cover the highlights and comment on some additional potential features and alternate implementations... They estimated it would take 7 days on single server to generate the initial 600K pages. Ouch. So what they did was use EC2 for what it's good for, spin up a lot of boxes to process data. Their data is backed up on S3 so the EC2 instances could read the data from S3, generate the static pages, and write them to their deployment area. It took 5 hours, 25 EC2 instances, and a meager $12.50 to perform the initial bulk conversion. Pretty slick. The next trick is figuring out how to regenerate static pages when changes occur. When a new event is added to their system hundreds of pages could be impacted, which would require the effected static pages to be regenerated. Since it's not important to update pages immediately they queued updates for processing later. An excellent technique. A local queue of changes was maintained and replicated to an AWS SQS queue. The local queue is used in case SQS is down. Twice a day EC2 instances are started to regenerate pages. Instances read twork requests from SQS, access data from S3, regenerate the pages, and shutdown when the SQS is empty. In addition they use AWS for all their background processing jobs.