Most simple web clusters rely on a single database sever that all the application servers connect to. This way, all of the application servers have concurrent access to the same data. This can be problematic in the elastic use case when workloads increase, and more servers are added to the cluster. If the work is bottle-necked on storing or accessing data in the database server, adding additional application servers will not help. It will actually make the problem worse.

I spoke on a panel at Zendcon yesterday, which was covered in an Infoworld article where my remarks were published. The article says:

Panelists also debated use of SQL and database connectivity in clouds. SQL as a design pattern for storage “is not ideal for cloud applications,” said Adrian Otto, senior technical strategist for Rackspace Cloud. Afterward, he described SQL issues as “typically the No. 1 bottleneck” to elasticity in the cloud. With elasticity, applications use more or fewer application servers based on demand. Otto recommended that developers who want elasticity should have a decentralized data model that scales horizontally. “SQL itself isn’t the problem. The problem is row-oriented data in an application,” which causes performance bottlenecks, said Otto.

The author Paul Krill did a good job here of accurately reporting my position on this subject. Data stored in databases are arranged in tables of rows and columns. A new piece of data adds a new row. Each row has multiple columns that separate fields of a single record of data in the table. The truth is that most web applications work very well with this data design pattern. Those should continue to use SQL databases with row oriented data. However, there are some applications where data may be arranged differently to make reading the data more efficient.

If you have a big table of data, and you want to pull out just a little bit of it using a query, the database server must determine the location of that data in the table by consulting the table’s index, and return the desired portion that matches the constraints given in the query. This makes the reading of data relatively expensive from a computational perspective. If data were instead arranged in lots of columns instead, it could be retrieved more efficiently, and the data could be more easily distributed over a larger number of servers yielding the horizontal scalability that cloud applications want. This works very well in cases where the number of reads are very high, but the data is not updated very frequently in proportion to the reads.

Let’s use a blog application as an example. Blog posts are written once, and maybe updated a few times, possibly once each time a comment is submitted. However, on a busy web site, a blog post may be read millions of times. If the posts were stored in a column oriented storage system like Cassandra, they could be quickly and easily retrieved using the id number of the blog post. The listing of recent blog posts can also be arranged in a column so that the front page of the blog site with the listing of the articles can be generated. Using this approach requires that the data be properly arranged as it’s stored, putting the computational burden on the (infrequent) write rather than on the (frequent) read.

Using a distributed system to store data in columns allows the data to be evenly distributed over an arbitrary number of servers, eliminating the central data bottleneck. Adding more servers in the correct proportion of application servers and storage servers can result in true horizontal scalability, meaning that the capacity increases as a direct proportion of how many servers are in the cluster.

Why doesn’t everyone do this already? For some good reasons:

1. The concept of running applications in clouds is still relatively new. The related technology is still maturing.
2. Existing software tends to use SQL already. If you want to use an existing CMS platform, chances are it will require a central SQL database.
3. Most heavy-read workloads can be scaled well using data caching techniques. If applications don’t write data very often, it may not be necessary to scale beyond a single database server.
4. You must anticipate exactly how the application will use the data, and arrange it just right.
5. It may be harder to analyze the data. Once your data is arranged in a column store, you may not be able to query it in arbitrary ways. You may only be able to pull it out using it’s id numbers, or by systematically scanning all of it to find the parts you want.
6. Distributed data storage (aka: NoSQL) systems like Cassandra, Hbase, Redis, etc. are complicated, and there is a considerable learning curve associated with setting them up and maintaining them. In some cases these systems are not as good in terms of data durability or data consistency as the prevailing SQL database systems. These tradeoffs can be difficult to navigate.
7. Today’s software developers are very familiar with SQL as a data storage and access paradigm. They can very quickly develop software that relies on the ACID qualities of a SQL database.

If you have an application that you want to deploy into a cloud, and you want it to be very elastic, you should think about the subject of how you arrange your data. If you use a centralized data design, you will probably have scalability bottlenecks when you add lots of servers. You should aim to decentralize the data in a way that you can easily add more servers to horizontally scale the environment, and not stumble on the limits of the database server. This is particularly important in situations where you need the application to write a lot of data, and a cache is not a suitable solution for you.

Over time, the reasons why not to use column oriented data will begin to shrink, and better tools and services will make it easier to do. Until then, I suggest that you carefully consider if you need maximum elasticity. If not, then it’s perfectly appropriate to keep using the same centralized row-oriented data paradigm. Use a cache like memcached in cases where you have heavy reads, and when it’s acceptable to show slightly outdated information to readers. The truth is that traditional solutions work really well for most web applications. However, if you have one of the more unique situations where you need true horizontal scalability, take a good look at a different arrangement for your data, and the systems and tools to make that possible for you in the cloud.

The more I found the less I liked any of them. Some of them actually had bugs or typos and did not work at all, and most of them would incorrectly validate a credit card number that was all zeros.

I wrote my own that I’m pretty happy with. It’s a good deal more efficient that most that I found. It does not repeat the same math on the same figures like some of them out there do.

The function contains 7 lines of code. Can you make this function better without making it harder to read and understand? Please let me know.

A system where software applications may be run in an environment composed of utility cloud services in a logically abstract environment.

Definition of PaaS:

Platform as a Service. A Cloud Platform offered by a service provider as a hosted service which facilitates the deployment of software applications without the cost and complexity of acquiring and managing the underlying hardware and software layers.

Examples of PaaS:

1. Google AppEngine
2. Force.com

The key drawback of most PaaS services that exist today is vendor lock-in. Implementing an application using a proprietary platform means that it becomes difficult or impossible to move a deployed application from one service provider to another, unless they have compatible Cloud Platforms

I plan to address this problem by embracing open source solutions that allow numerous service providers to host applications in a compatible PaaS model where applications can be easily moved with little or no modification required.

Keep an eye on the OpenStack project in the upcoming weeks and months for blueprints for a PaaS system for the whole world to use. I invite you to participate in the design and future implementation of the cloud platform that will end vendor lock-in concerns, and simplify application hosting for software developers.

Charter

• A database optimized for Cloud infrastructure and Web applications
• Design for massive concurrency on modern multi-cpu architecture
• Optimize memory for increased performance and parallelism
• Open source, open community, open design

Scope

• Re-designed modular architecture providing plugins with defined APIs
• Simple design for ease of use and administration
• Reliable, ACID transactional

There are many exciting changes, such as optimizing everything for 64-bit CPU’s and Multi-Core. You can’t hardly even buy 32-bit and Single Core servers nowadays if you want them. It makes no sense to have software that’s optimized for these antiquated hardware designs. No effort is spent optimizing software to work with rotational hard drives because SSD drives are the way of the future. All the language collations have simply been replaced with UTF-8 only, because the web uses UTF-8. Plus, this is tested with 41 different language translations. Drizzle has a new scheduler. The legacy MySQL scheduler was designed to work for a thread-per-session setup. In Drizzle, sessions are handled independently from the threads. The new scheduler allows this to work.

Drizzle uses InnoDB as its default storage engine, which is great for OLTP. It also supports the PBXT storage engine. There are available plugins for the InnoDB Embedded Engine and HailDB which will soon be the new default. DDL Operations (like ALTER TABLE) can actually roll back in the event that something goes wrong in the process, rather than leaving you with incomplete or corrupt data.

The code base in Drizzle has been fully modernized, and brought up to today’s standards of C++ with extensive use of the C++ STL to replace MySQL’s usage of obscure custom data type implementations that offered no real benefit compared to what the STL has today. Another example of improvements in this area is the replacement of the legacy REGEX implementation with a more standard library. All of these changes reduce the amount of Drizzle source code dramatically compared to MySQL. Less code and simpler code means less bugs, plain and simple. Drizzle is well on its way to being an ideal fit for web applications that need a reliable, and high performance transactional database.

Features in Drizzle7 Beta

• New micro kernel
• Migration Tool
• Instance Catalog Support
• Universal Replication
• User query analysis
• Mutli-core Support

What “Beta” means

• Your data is safe. Transactional engine by default and stable for over 2 years.
• Upgrade the system in-place without exporting/importing data.
• Replication is still being tested.

In Microsoft terms, it means that this project would have launched about a year ago. In Google terms, it probably would have launched six months ago. Simply put, if you trust your data to a MySQL system today running InnoDB, you should feel comfortable trying Drizzle. There have been some changes to the InnoDB setup, such as the elimination of the FRM files from disk which eliminate possible inconsistency between the state on disk and the state in InnoDB. I am in the process of moving a few of my produciton applications to use the Drizzle Beta. If you’re an accomplished system administrator and DBA, you should seriously consider putting at least one of your production applications on Drizzle now, and see how it works for you.

What’s Next?

• Beta announced today 2010-09-29.
• GA February 2011
• GA May 2011 for Multi-Tenancy features that allow an arbitrary number of logical databases (Schemas, Tables, etc.) to exist concurrently with full data isolation between them. This allows for individual security and resource controls (Threads, Memory, IO), and individual database backups, rather than system level backups. This feature will be called “Catalogs”.

Download Drizzle

Time to get started with the beta. Download the beta today!

About OpenStack Object Storage

OpenStack Object Storage was originally developed by Rackspace, and was released as Open Source Software earlier this year as part of the OpenStack Project. It was written for hosting the Rackspace Cloud Files service. It’s original project code name was swift, so you may see references to that in various documentation.

OpenStack Object Storage aggregates commodity servers to work together in clusters for reliable, redundant, and large-scale storage of static objects. Objects are written to multiple hardware devices in the datacenter, with the OpenStack software responsible for ensuring data replication and integrity across the cluster. Storage clusters can scale horizontally by adding new nodes, which are automatically configured. Should a node fail, OpenStack works to replicate its content from other active nodes. Because OpenStack uses software logic to ensure data replication and distribution across different devices, inexpensive commodity hard drives and servers can be used in lieu of more expensive equipment. [1]

The system uses a flat namespace, and has a concept an account (how you access the system),  a container (like a directory) and an object (like a file). You can have an arbitrary number accounts each with an arbitrary number of containers. Each container can hold an arbitrary number of objects.

OpenStack Object Storage is very good for is storing unstructured data using an object name as a lookup key (like a filename). You access your data from a web client using the web service REST API, not like a filesystem. Download an object (like a file) using an HTTP GET request, fetch object metadata with an HTTP HEAD request, delete an object with an HTTP DELETE request, etc. There are multiple language bindings so you can access your files in OpenStack Object Storage from your favorite language natively (Java, Python, Perl, PHP, .NET, etc.).

The system has no central point of failure, so it’s extremely fault tolerant, and the data and related metadata are distributed throughout the system, so there are no central scalability constraints. You can store arbitrary amounts of data in the system in both large and small sizes. It performs very well, even under very high levels of concurrency. It keeps multiple replicas of each object, so it’s reliable, and the storage is very durable, without any expensive hardware. You don’t need any RAID on any of the servers unless you want it for additional performance.

Use OpenStack Object Storage For…

Here are some good use cases for OpenStack Object Storage:

• Storing media libraries (photos, music, videos, etc.)
• Archiving video surveillance files
• Archiving phone call audio recordings
• Archiving compressed log files
• Archiving backups (<5GB each object)
• Storing and loading of OS Images, etc.
• Storing file populations that grow continuously on a practically infinite basis.
• Storing small files (<50 KB). OpenStack Object Storage is great at this.
• Storing billions of files.
• Storing Petabytes (millions of Gigabytes) of data.

Recognize the Limitations

Objects must be <5GB

This is an arbitrary size limit, but it can not be set to an unlimited value because of the system design. If you want to store a backup something larger than 5GB, you’ll need to have a way of breaking it up into chunks, and storing some manifest of the parts so you can later join them back together again when you want to download the data and use it again.

Not a Filesystem

Uses a REST API, or a language binding that consumes the REST API. It does not use the typical POSIX filesystem semantics like open(), read(), write(), seek(), and close().

No User Quotas

There are no maximums that can be configured on a per-user basis to limit how much storage is used.

No Directory Hierarchies

You can create an arbitrary number of containers, but there is no nested container capability. You can simulate a directory structure using creative object names, but this is limited to a maximum string length. If you only need a shallow hierarchy, or don’t have long directory names, this might be fine. Just remember that I warned you this is generally a bad idea.

No writing to a byte offset in a file

The only way to update a file is to essentially overwrite it. The system creates a new version of an object each time you upload one with the same name.

No ACL’s

Per-Container ACL’s will probably be added in a later release. Per-Object ACL’s will probably not be supported, but maybe.

No Append Support

It’s possible that this may be added at a later time using a versioning trick.

No File Locking

Most filesystems integrate with the kernel to offer advisory locking. This is not possible with OpenStack Object Storage.

Eventual Consistency

Don’t expect version consistency between multiple nodes when data is being updated.

If you upload a new version of an object, and immediately GET that object from another client, you may get a previous version of the file. There is no way to know which version of a given object the system is responding with, unless you set version metadata on each object yourself. If there is any problem with the network, you may get outdated versions of objects, or be able to see objects that were deleted, but the local node may not yet know are deleted.

No Support for Data Encryption

You must encrypt the data yourself. The current version does not have SSL support either. Use an SSL proxy to work around this by terminating the SSL sessions on the same network where the OpenStack Object Storage system runs.

Not Compatible With Web Browsers

You must supply a storage token header to authorize each request. Regular web browsers can’t do this. This can be solved using a proxy between the client and the system to handle token authentication. This is not a problem is you are using one of the language bindings. They will take care of this when you integrate your web app with the system.

Not a Database

It supports no querying or processing of data on the servers. All you can do is list the objects within a given container. There is no way to search based on object metadata. You need to keep your own external search indexes.

Don’t try to frequently update large objects.

All updates produce a new version of an object, because objects are immutable.

Don’t store unlimited objects per container

You can store as many objects in a container as you wish. However, your per-object upload latency will increase considerably one you reach a certain point. I found the optimal number of objects per container to be just under one million. This number will vary depending on your equipment, and how heavy of a workload it’s subjected to.

Changing Swift Into a Filesystem

You might think of using FUSE to access objects and containers in OpenStack Object Storage as files and directories with a filesystem interface, but you’ll quickly discover that this is only really good for very simple use cases. Most of the things you need to implement what we think of as a filesystem are missing.

If you are a developer, and you are thinking of building a filesystem on top of OpenStack Object Storage using objects as blocks, that could possibly work, but would probably not perform very well compared to existing alternatives that are actually designed for distributed block storage. The blocks would need to be pretty large to keep the network/protocol overhead down. Frequent writing is not likely to work well. Most users of filesystems are not expecting eventual consistency behavior. They want strong data consistency. You would also want some strategy to handle read/write concurrency with some locking capability. Plus, you would need to have a way to keep track of the blocks like a filesystem does in some data structure or database. Frankly speaking, OpenStack Object Storage is probably not the right tool for the job.

Conclusion

You should probably only use OpenStack Object Storage for use cases it’s intended for. If what you really want is a clustered filesystem, you’re probably better off looking at other solutions like Lustre, GlusterFS, GFS, OCFS, etc. Keep in mind that each of these have their own strengths and weaknesses. Pay particular attention to what they are designed for, and use them accordingly. If you want to use OpenStack Object Storage for something that it was designed for, then you will probably be very happy with it. Keep in mind that it’s a blob storage system. It’s not a filesystem, not a file server, not a database, etc. To learn more about OpenStack Object Storage, please check out the Developer Documentation.

This is very exciting for consumers of Cloud Computing because:

1. It allows individual companies to run their own clouds inside their own data centers and on their own equipment using the same scalable technology that powers some of the largest cloud infrastructures in the world.
2. An individual company can develop applications on their own cloud, and know with confidence that they can run their application on a number of different public clouds without any special adapter software. They need only find a cloud computing or cloud storage provider that uses OpenStack software to ensure compatibility.
3. If an application is hosted on one OpenStack public cloud, it can be easily moved to another without changes to the application source code, and without using any cloud middleware. This can completely eliminate all fears relating to single-vendor lock-in.
4. Applications can be run simultaneously in multiple clouds using the exact same software that needs to only implement a single API for universal access to computing and object storage resources.

I assumed I must have a bug in my server, and reviewed my code over and over, until I decided to try http_load and compare it to the results I got with ‘ab’. To my delight, the http_load client actually sent exactly the right number of requests, and my internal hit counter figure matched. I ran several comparisons to confirm it. The ‘ab’ tool does in fact measure the completion of its requests properly, but it may actually send more requests than you ask it to. That’s because it counts replies not requests.

So, if you use a concurrency setting of 1, then requests will equal responses. If you use a concurrency setting of 100, you might end up with 30 or 40 more requests that arrive at the server that the ‘ab’ client does not count in its results. Mystery solved.

I will publish results from my performance study. Initial results are showing that my libev server can produce roughly 10,000 responses per second on a single CPU. This is in the same ballpark as other high performance web servers. Stay tuned.

Recently you may have noticed my writing about Drizzle, but that’s not the only database system I love. I’m also a fan of Cassandra, and I’m proud to work with the same company sponsoring both projects.

Drizzle is the way to go if you want an SQL system, and Cassandra is the way to go if you have a huge data set or if you have a data insert/update rate that’s too high for and RDBMS to keep up with.

First of all, let’s review some definitions:

• Bandwidth: The amount of data that can be passed along a communications channel in a given period of time.
• Latency: The time it takes for a packet to cross a network connection, from sender to receiver.
• Speed: Fast and rapid moving, going, traveling, proceeding, or performing; swiftness.
• Throughput: The quantity data transmitted by a computer network over a given period of time.

Now, all of these terms are related, and I want to highlight some of the minutia here:

Bandwidth

The higher the bandwidth is on a network connection, the more data it’s capable of transmitting in a given period of time. Higher bandwidth is better.

Latency

This is very very important, because latency effectively limits the amount of bandwidth you can consume if you are using a synchronous data transmission, like a TCP/IP download. Lower latency is better, and will yield faster speed.

Throughput

Throughput is another way of expressing speed. The higher the throughput, the faster your network communications will be. Note that your maximum possible throughput is your bandwidth. Actual throughput is equal to or less than your bandwidth.

Speed

If your network is high speed, you should observe high bandwidth, low latency, and high throughput.

Latency and Bandwidth are Inversely Proportional

For TCP/IP transmissions, the higher your latency is, the lower your throughput will be. Let’s explore why. The most common use of TCP/IP is for the web, which uses the HTTP protocol. HTTP works by making a TCP/IP connection to a remote server, issuing a request for a document, and then receiving the response. The protocol is text based. A simple HTTP transmission is illustrated below.

Client Request:

GET / HTTP/1.1
User-Agent: Wget
Host: www.example.com

Server Response:

HTTP/1.1 200 OK
Server: Apache/2.2.3 (Red Hat)
Last-Modified: Tue, 15 Nov 2005 13:24:10 GMT
ETag: "b300b4-1b6-4059a80bfd280"
Accept-Ranges: bytes
Content-Type: text/html; charset=UTF-8
Connection: Keep-Alive
Date: Wed, 18 Nov 2009 22:36:34 GMT
Age: 1010
Content-Length: 438

  Example Web Page

You have reached this web page by typing "example.com",
"example.net",
  or "example.org" into your web browser.

These domain names are reserved for use in documentation and are not available
  for registration. See &lta href="http://www.rfc-editor.org/rfc/rfc2606.txt">RFC
  2606</a>, Section 3.

Here is a trace of the TCP/IP packets that make up that request:

14:57:47.146665 IP 192.168.144.2.39556 > 192.0.32.10.80: S 3717672264:3717672264(0) win 5840
14:57:47.220092 IP 192.168.144.2.39556 > 192.0.32.10.80: . ack 1 win 183
14:57:47.220309 IP 192.168.144.2.39556 > 192.0.32.10.80: P 1:123(122) ack 1 win 183  (GET Request)
14:57:47.300962 IP 192.0.32.10.80 > 192.168.144.2.39556: P 1:728(727) ack 123 win 4502  (200 OK Response)
14:57:47.300993 IP 192.168.144.2.39556 > 192.0.32.10.80: . ack 728 win 228
14:57:47.302035 IP 192.168.144.2.39556 > 192.0.32.10.80: F 123:123(0) ack 728 win 228
14:57:47.375475 IP 192.0.32.10.80 > 192.168.144.2.39556: . ack 124 win 4502
14:57:47.375499 IP 192.0.32.10.80 > 192.168.144.2.39556: F 728:728(0) ack 124 win 4502
14:57:47.375510 IP 192.168.144.2.39556 > 192.0.32.10.80: . ack 729 win 228

Notice that there are 10 packets in the above trace. It’s a three way handshake to set up the TCP session, then a round trip to send the data, then two more round trips to close down the connection. Each time the server receives a packet from the client, the connection may wait in the server’s connection queue to be processed, which can further increase the interactive protocol latency. Consider the impact of high latency on a connection like this. Suppose that it takes 0.2 seconds for each round trip. That connection would have a total throughput of 727 bytes downloaded in 0.8 seconds. That’s a rate of 909 Bytes/sec. Maybe your internet connection is 15 Mb/sec. bandwidth did not matter. Latency caused the throughput to be low.

Now, you might be wondering why we can’t just improve networking technology to make latency lower. We can, but that’s not going to help much, because we are still bounded by the speed of light, among other factors. The speed of light is slow when you consider the distance it has to travel to cross continents on the earth. Let’s look at some match to explain that:

• The speed of light in vacuum is 299,792,458 m/s.
• The speed of light in fiber optic cable is ~200,000,000 m/s.
• The distance from Anaheim, CA to New York is 4,494,898 meters
• The one-way latency to New York is 4,494,898 / 200,000,000 = 22.47ms
• The round-trip time between Anaheim, CA and New York is 44.95ms
• The current ping time from Anaheim, CA to New York is 72 ms

Tracing the route to sl-gw33-nyc.sprintlink.net (144.228.243.82)
  1 sl-crs1-ana-0-14-2-0.sprintlink.net (144.232.11.9) 0 msec
    sl-crs2-ana-0-14-2-0.sprintlink.net (144.232.11.11) 0 msec
    sl-crs1-ana-0-14-2-0.sprintlink.net (144.232.11.9) 4 msec
  2 sl-crs2-fw-0-13-3-0.sprintlink.net (144.232.19.197) 28 msec
    sl-crs2-fw-0-9-5-0.sprintlink.net (144.232.20.130) 28 msec
    sl-crs1-fw-0-3-3-0.sprintlink.net (144.232.9.65) 28 msec
  3 sl-crs2-kc-0-0-0-2.sprintlink.net (144.232.19.141) 40 msec
    144.232.20.57 40 msec
    sl-crs1-kc-0-5-5-0.sprintlink.net (144.232.24.9) 40 msec
  4 sl-crs2-chi-0-13-5-0.sprintlink.net (144.232.20.109) 52 msec
    sl-crs1-chi-0-1-0-3.sprintlink.net (144.232.18.214) 56 msec
    sl-crs2-chi-0-15-2-0.sprintlink.net (144.232.24.206) 52 msec
  5 sl-crs1-nyc-0-8-0-3.sprintlink.net (144.232.18.123) 72 msec
    sl-crs2-nyc-0-8-0-1.sprintlink.net (144.232.20.119) 72 msec
    sl-crs1-chi-0-10-3-0.sprintlink.net (144.232.9.148) 72 msec
  6 sl-gw33-nyc-14-0-0.sprintlink.net (144.232.6.56) 72 msec *
    sl-gw33-nyc-15-0-0.sprintlink.net (144.232.6.58) 72 msec

This round trip time includes all of the switching and routing to get the packet through its full round trip. That means that even if all switching and routing were instantaneous, and we had a perfectly straight fiber path between all points on the earth, that we could only reduce latency by about 40%. We can not accelerate the speed of light, so without a significant advance in data transmission technology (perhaps a quantum physics approach) we must accept the speed of light as a performance boundary.

Making Web Sites Faster

If you’re a web content publisher, you can set up your systems to work around these natural limitations. One way to make interactive web performance faster is to place copies of your data in various geographic locations that are physically closer to your end users. Using a CDN for your media content is one way to do this. You can also make your web server as fast as possible so that your dynamically generated content can be processed as quickly as possible. Using memcached to speed up your web application can help. Also, take a look at some best practices for web developers for good performance.

Yes, if you have similar entropy pools it is easier to break encryption dependent on it. It’s reasonably easy to work around this and make sure your entropy pool is uniquely initialized. You can consult the random manual for the Linux Kernel for information about how to seed your entropy pool with a particular set of data. If you are running an application in the cloud that utilizes encryption, and you are concerned about the initial state of your entropy pool, you can solve that. Use this procedure:

1) Seed your own pool from a long running system that has sufficient entropy in it, rather than relying on what you read from the kernel at startup.

2) Produce a network service that you use to seed your initial entropy pools. This service could be as simple as an entropy file that you create on pseudo-random time intervals, and just discard them as you serve them to cloud server instances (as they boot up) so you never serve the same one twice. At boot time from your VM, simply connect to wherever you run this service and download an input file to seed your entropy pool with. Restrict access to this so that it’s only available to your own server instances.

3) Make sure that your custom entropy pool initialization takes place prior to starting your encryption software.

4) If you are creating an AMI, or other server image that you plan to clone, be sure that it does not have a host key generated yet. Delete it and allow your initialization scripts to create it when the server is created (after step rather than making copies of the same one.

If you don’t trust what /dev/random or /dev/urandom emit, you can optionally use OpenSSL with prngd or egd as alternate entropy sources, and potentially feed in your own sensory input data. If you want to go hardcore, you could add environmental noise such as resistor noise on the microphone input of a sound card, or some other sensory data. There is existing software for doing just that. There’s all sorts of possibilities. Among them are a number of hardware solutions for RNG, most of which are pretty expensive and are not options for a cloud environment. There are sources of random numbers provided as a service from various sources.

There are things that we can do as Cloud Computing service providers to pre-initialize your entropy pools for you when the given server instance is created so the procedure above would be redundant. This still leaves the question as to the quality of the RNG available to you on a cloud server.

There are two standard randomness sources that you should know about:

/dev/random = produces actual entropy, if you have some, and blocks otherwise.
/dev/urandom = produces available entropy regardless of quality, but does not block.

The Linux kernel has a paravirtual entropy driver which provides kernel-side support for the virtual RNG hardware. The kernel compile option CONFIG_HW_RANDOM_VIRTIO enables it, and it can be built as a kernel module. There are drivers that run within the hypervisor host kernel that connect this with the RNG hardware available on the server (if any).

drivers/char/hw_random/amd-rng.ko = H/W RNG driver for AMD chipsets
drivers/char/hw_random/intel-rng.ko = H/W RNG driver for Intel chipsets
drivers/char/hw_random/virtio-rng.ko = VirtIO Random Number Generator support

How it works is the hypervisor host (dom0) runs rngd to read data from /dev/hwrandom (using the Intel or AMD modules mentoined above) and feeds it into /dev/random, then the guest VM (domU) does the same thing. The rngd can mixes data from both /dev/random and /dev/urandom so you get as much random data as you need in a non-blocking fashion. You can consult the kernel source code to learn more. Then you run rngd in the guest VM to feed that into the kernel.

What happens if multiple guest VM’s are reading this data at the same time using this arrangement? I’m not sure if it’s possible to deplete the entropy pool of the hypervisor host and produce PRNG patterns that are therefore less random. So if one guest VM emptied the entropy pool by aggressively reading from the /dev/hwrandom device, you might cause someone else’s guest VM to get less data. This could be solved if there were a simply a rate limit enforced on the consumption of RNG data allowed per guest VM. There is further discussion of that as well.

The truth is that for most needs you can have reasonably secure encryption by simply having an ordinary PRNG source like /dev/urandom that’s properly initialized with random data. I suggest that you use that approach in your cloud deployments.