Search results matching tags 'Best Practices', 'Cloud Computing', 'Cloud', and 'Data'

Cloud Computing Patterns: Using Data Transaction Commitment Models for Design

BuckWoody — Tue, 14 Feb 2012 20:45:47 GMT

There are multiple ways to store data in a cloud provider, specifically around Windows and SQL Azure. As part of a “Data First” architecture design, one decision vector – assuming you’ve already done a data classification of the elements you want to store – is to decide the transaction level you need for that datum. Once you’ve decided on what level of transactional commitment you need, you can make intelligent decisions about the storage engine, method of access and storage, speed and other requirements.

Although the list below is neither original nor exhaustive, these are the general considerations I use for a given data set. It’s important to note that in many on premises systems the engine choice at hand overrides these concerns. If you have a large Relational Database Management System (RDBMS) for instance, you might simply place all data there without further consideration. In a Platform as a Service (PaaS) like Windows and SQL Azure, however, selection of the proper engine for a particular dataset has implications ranging from cost to performance, and selecting the right engine is critical when you want to leverage the data across “Bid Data” analysis like Hadoop or other constructs.

Monolithic Consistent Transactional
The first selection is analogous to a local RDBMS system. The dataset is retrieved in a functionally single, monolithic transaction, i.e. kept together with ACID properties in mind. This is the most reliable type of data design for datasets that require a high degree of safety in the read/write pattern. As an example, a bank ATM transaction should be modeled in a monolithic way. If I make a transfer of funds from one account to another, I want the money to be subtracted from one account if and only if it is successfully added to the other. The bank, on the other hand, wants the money added to the second account if and only if it is subtracted from the first. This is a prime example of a monolithic (single atomic transaction), Consistent (if and only if) and Transactional (as a unit, with provision for roll-back and reporting if unsuccessful) data requirement.

The primary engine used for this type of data is often SQL Azure – an RDMBS in the same datacenters as Windows Azure. Placing both the calling application, whether that is a Data Access Layer-based code widget or a direct call from a Web or Worker Role, means that data is retrieved quickly and in a monolithic way. The costs for this method is based on overall database size. A consideration is how much data you can store this way. Database sizes have limits, although there are ways of overcoming size issues using technologies such as Sharding or SQL Azure Federations. There is also the consideration of performance. In an RDBMs that conforms to ACID properties, locking and other overhead for safety is at conflict with the highest possible read performance. But in some cases the ACID properties are worth the cost, as in the banking example.

You are not limited to SQL Azure in this model. Windows Azure Table storage, while similar to NoSQL offerings is different in that it is immediately consistent across all three replicated copies of data, offering a higher degree of safety. And while Table storage does not offer built-in support for transactions, there are ways to achieve certain transaction levels.

Monolithic Realtime
If consistency can be relaxed – meaning that a guaranteed read/write patter is not essential – then more options arise in Windows and SQL Azure. You can still use SQL Azure for this type of storage, with either automatic or programmatic hints allowing for “dirty reads”. Windows Azure Table storage is still consistent, but the selection of the method for querying the data such as separate copies of read and write data can be employed. Because of the relaxed transaction nature, higher speeds are possible by querying cached or separate datasets.

An example here is that same transaction from the bank, but a statement inquiry. Just after the money is deposited, the user wishes to query the current balance. The current balance – minus the transaction that just occurred – is retrieved and shown to the user, perhaps even combining the amount with the latest transaction, perhaps saved as a local cached object, with a caveat to the user.

Distributed Realtime
At some point, the data becomes too large to fit inside a single processing session, and parallelism is used. In this case, either separate databases in SQL Azure or Windows Azure Tables, local data storage on the Web or Worker Role, or a combination of all with Caches is the right approach for the data design.

The biggest implication in this type of system is speed – a higher degree of data separation is essential, and so the dataset selection must fit the pattern. It is unacceptable to force an ACID-properties type workload into this environment. Typical examples here are the actual data asset payload for streaming video or music, read-only documents and so on. This pattern is often separated from the meta-data, which is kept in more of a transactional model.

As an example, assume you log on to a website to watch a movie or listen to music. The provider needs to verify your identity and account balance, which are transactional data loads. After that process is complete, the workload shifts to a copy – perhaps one of several – of the asset to stream to your location.

In this case, Windows Azure Blob storage, along with the Content Delivery Network (CDN – a series of servers closer to the user) is employed along with the transactional realtime requirements for the metadata.

Distributed Eventual
At the furthest end of the data scale are large datasets that need deeper analysis, but not necessarily in realtime. Examples here are terrabytes of data requiring a Business Intelligence view, but with a tolerance of a few seconds to minutes or hours. In this case, Storage, Processing and Query methods, such as the Hadoop offering in Windows Azure, or perhaps the High Performance Computing (HPC) Windows Server in Windows Azure fit well. Here, the design of the data is often dictated by the source, and more emphasis is placed on the algorithms around processing and re-assembling the data.

There are, of course, other patterns. In many cases a single dataset may have needs in one or more of these categories – in fact, sitting at 30,000 feet typing this entry, I’m having that very design discussion with a gentleman sitting next to me. The key is to design data-first, and fit the technology to the requirement for each datum. Allow each function and engine to handle the data in the most efficient, effective way for cost, performance and utility.

Rip and Replace or Extend and Embrace?

BuckWoody — Tue, 13 Sep 2011 11:20:05 GMT

As most of you know, I don’t like the term “cloud” very
much. It isn’t defined, which means it can be anything. I prefer “distributed
computing”, which is more technically accurate and describes what you’re doing
in more concrete terms.

So when you think about Windows and SQL Azure, you don’t
have to think about an entire product – you can use parts of the system
together or independently to accomplish what you need to do. You can use the
computing functions, storage, and more and more I see folks leverage the
Service Bus to enable current applications to expose things to the web.

And that brings up the point of this post. Once you decide
that a distributed architecture works to solve a problem, you’re faced with a
decision: should you completely re-write your architecture to take advantage of
the current systems or should you just fold in new code that makes the data or
function available to the web?

Of course, the answer is always “it depends” on the situation
– and it does. But unless you’re fixing a problem with current code, I usually
advocate a migration approach. That means at the very least retaining the
business logic (again, unless it’s not currently working) and as much of the
code as you can. In fact, if you follow this paradigm, you’re on your way to
making a Service Bus out of the functions you currently have. You can expose
the results of a system rather than opening the system up. Let’s take an
example.

Assume for a moment that you have an order-taking system
on-premise. That system performs many functions, one of which might creating a
Purchase Order. Your system might be enclosed, meaning that it has an
application that talks to a middle-tier, and then from there to a database
system. A query is generated from a screen, and passed along to eventually
compute, store and return a Purchase Order Number, along with other
information. Imagine now that you wire up the code not only to return the PO
number to the client, but to make that number available on an endpoint –
actually really not that hard to do.

Now you can make that PO number available to the web using
Azure. You could restrict who can make that call to the system, or open it up
to a broader audience. Or instead of the PO Number, you could make a product
list available. And you can go further than that – EBay, for instance, uses the
OData protocol (which is very cool in and of itself) which you can query from
the web. You could compare your company’s product catalog to what is on EBay,
and list the items you have there if there are no competitors in that space.
And on and on it goes.

So the point is this – where you can, retain what works.
Fold in systems like Azure where they make sense. Extend and Embrace.

Should All Data Be Encrypted By Default?

BuckWoody — Tue, 09 Aug 2011 13:45:04 GMT

Recently several IT industry information outlets have reported that there has been a 10-year concentrated, organized effort on breaking through computer security at some of the largest companies in the world. Government sites have also been attacked in multiple countries. Add to this the regular loss of data by banking and other industries, and the fear of “the cloud” as a storage location, and it seems to beg the question asked in the title in this post: “should all data, everywhere, be encrypted by default?”

If you’re new to encryption, there’s an excellent video and overview here: http://blogs.msdn.com/b/plankytronixx/archive/2010/10/23/crypto-primer-understanding-encryption-public-private-key-signatures-and-certificates.aspx

If all data were encrypted, the break-in to websites would still continue, but the value would be lessened for some types of “orthogonal” attacks that only seek the pure stream of data.

Data States

Computing has two major components - static program elements and data. The program doesn’t change (until it is updated, of course) over the course of a transaction between a user and the ultimate data store. Data is classified as anything that is manipulated by the program. That implies three states of the data interchange: Creation, Transmission, and Storage. In on-premise systems, many times none of these states are encrypted. The entire system from user to data store is viewed as “secure”, which of course evidence has proved it is not. In some cases, even laptops are viewed as part of an on-premise system, and so is left unprotected. If all data were treated as “publicly viewable”, that mindset would lead to encrypting the data at all states, even for on-premise systems.

Creation

In this phase, a user, device or other input program creates data to send to the program. This can be entries on a web form, input from a weather sensor, or one service (program) sending information to another service. There are multiple ways to encrypt data at this state, most notably using client-side libraries such as the Windows Crypto API, hardware encryption and others. The reference for the Crypto API is here: http://msdn.microsoft.com/en-us/library/ms867086.aspx

Transmission

After the data is created, it needs to be transmitted to the processing and storage system. the references above explain how to secure the communications channel between the client systems and the various components used within the system. In the case of Windows Azure, the session can be protected with a secure session, and all communications within the Azure datacenters are encrypted. The key is that the transmission of data, regardless of method, should be considered to be “in the clear”, and treated as such. Without the decryption algorithm, it’s much harder to get to the ultimate goal.

Storage (data at rest)

It follows that f the data is encrypted at the source, and the decryption method is retained only with the code that processes the data, then the data “at rest” if obtained is less accessible. If the data is not encrypted at the source, then this step should be put into place at a minimum. In many cloud systems, including Windows and SQL Azure, the data is not encrypted at rest. There are various reasons for this, including performance, physical and logical security already in place, and the fact that the encryption process would expose customer data to the provider while it is being encrypted. In this case, the key is to encrypt the data before it is transmitted and stored, so that it is encrypted ahead of time.

Considerations

Encrypting data is a separate process, and must be factored into the original codebase. This means additional effort, and more CPU power for the encryption process (although many systems have security hardware included which help with this) and of course protecting the keys. If the keys are accessed, the data is considered unencrypted from then on, and all previous encryption with that particular key is now vulnerable. Key rotation and protection is essential. Even so, the benefits of treating all data as being at risk outweighs the efforts.

You can learn more about general encryption here: http://msdn.microsoft.com/en-us/library/aa380255(VS.85).aspx

Windows Azure Security Review

BuckWoody — Tue, 02 Aug 2011 13:24:50 GMT

Current as of 08/01/2011 - Check the Resources listed below for more up-to-date information on this topic

Background:

Security for any computing platform involves three primary areas:

Principals (users or programmatic access to an asset or other program)
Securables (objects, data or programs that can be accessed)
Channels (methods of access by Principals to Securables)

On-premise systems normally use a central system to control security. In a Windows operating system-based environment, this is often accomplished with Active Directory or other systems that provide sign-on and user identity information. While other networking security paradigms have different terminology, all involve the three areas defined above.

In addition to the names and passwords for a user, Active Directory (like other security mechanisms) store other information about Principals - called Claims. These claims can include any custom fields the provider allows. In many networks, these fields are not used heavily, because applications that eventually need to secure the assets they control are not always deployed on the same platforms everywhere.

In a single environment, security is often quite simple. A Principal is created such as a user or group, and then the Principal is granted access to a Securable such as a a folder, database or other asset. Permissions or Rights (or both) combine to allow a particular Principal to read, write, delete or edit data, or to access or run a particular program.

Figure 1 - On-premise security environment example

The simplicity of this arrangement is due to a single, homogenous boundary. Even if more than one location is used, the Principals and Securables are grouped into a single logical boundary that is managed from one location.

This background serves as the starting point for the Federating Security topic below.

Windows Azure Security Boundaries

Windows Azure is a series of resources - servers, data and service buses, in addition to other features. Developers write code, and the deploy that to the Azure environment.

Figure 2 - Azure Components

The code or data can be deployed to use one or more of the services. In other words, the Web Role in Windows Azure might host a simple website, and no other component need be used.

Figure 3 - Simple Azure Web Role Application - only one feature used

Or, a complex mix of Web, Worker and Data Services, along with a Service Bus, RDBS and even on-site systems can be grouped into a much larger program.

Figure 4 - Complex Windows and SQL Azure Application With Multiple Interactions

For a more basic introduction to Windows and SQL Azure, see this link: http://channel9.msdn.com/Events/TechEd/Europe/2010/COS322

Windows Azure, like any web-based property, has three general layers of security:

Physical Access
Operating Environment (Including the Operating System itself)
Data and Programmatic Security

Each of these layers have additional layers within themselves, and this forms the basis of a secure experience for the end user or program. Some of these layers are the responsibility of Microsoft; others are the responsibility of the architect and developer; others are a joint or shared responsibility of both Microsoft and the client.

Layer One: Physical Access

The first layer of security within a web property such as Windows or SQL Azure is a secure facility. the following data points are important to understand for the worldwide facilities that host Windows and SQL Azure:

Microsoft Global Foundation Services (GFS) is responsible for the physical security of the datacenters located worldwide for Windows and SQL Azure. Information on Microsoft datacenters can be found here: http://www.globalfoundationservices.com/
The address and exact locations facilities are not commonly documented for security reasons.
Microsoft runs it’s own data centers and does not contract this function out.
The GFS controlled facilities hold an ISO/IEC 27001:2005 certification, and are audited to SAS level II.
Standard secure operations protocols are in place, including least-privilege access.

Layer Two: Operating Environment

Windows Azure and SQL Azure do not currently hold certifications. Microsoft does not comment on the security certifications being pursued for Windows or SQL Azure. That being said, the Windows Azure environment is based on a modified Windows 2008 R2 Enterprise environment, developed using the Trustworthy Computing Initiative (TCI).

The system controlling the host machines and their guest environments that ultimately hold the Web and Worker Roles within Windows Azure is called the Fabric - not to be confused with the Application Fabric feature. The Fabric is not accessible by client code - it controls the inner workings of Windows Azure, including Load-balancing, system restarts, maintenance and monitoring.

Within the host machines that house the Web and Worker Roles, special networking constructs broker all conversations between Virtual Machines. Virtual Machines - even ones configured to communicate with each other - move through this network. Direct-machine to machine communication is not allowed, protecting one application from another or one data construct from another.

Figure 5 - Windows Azure Fabric

Windows and SQL Azure support only TCP-based communications. Ports commonly used are:

80 - Default public port used for Web Roles - can be enabled/disabled per configuration
443 - Default secure port used for Web roles - can be enabled/disabled per configuration
9350-9353 - These ports are used by the Windows Azure AppFabric service bus bindings. Refer to http://msdn.microsoft.com/en-us/library/ee732535.aspx for more details
1433 - SQL Azure
3389 - This port is used for RDP access to VM-based roles, only if enabled

Layer Three: Data and Programmatic Security

All internal access through use of keys only. Without the proper key, code or data will not transfer. Storage Accounts have individual keys, so in this manner different security layers may be applied not only programmatically but at the account layer.

Figure 6 - Windows Azure communications between components

Calls to Windows Azure are made using standard SOAP, XML or REST-based protocols. The communications channel can be encrypted between the client and Windows Azure or allow it to remain unencrypted based on security needs.

SQL Azure uses the standard SQL Server Tabular Data Stream (TDS) protocol, but only allows encrypted communications.

Data is unencrypted within Windows Azure Blob or Table Storage - but is only accessible via the key for a storage account. Data can be encrypted client-side and stored in Windows Azure in an encrypted fashion. Microsoft does not inspect internal data for validity or encryption enforcement. The key is that the data is client-side encrypted and decrypted.

Figure 7 - Example data at rest encryption scenario

Alternatively, a hybrid solution can store sensitive data locally and non-sensitive data in Azure Storage. The data can be coalesced at the client level such that the data is never transferred over any channel not owned or controlled by the organization.

Federating Security:

In the case of a single security boundary for Windows Azure, multiple security options are available. Users can be anonymously authorized, such as in the case of a public website for advertisement or informational purposes.

Another option is to create an Internet Information Services (IIS) Internal Security Store. This is not a best-practice (although still possible) approach since the Fabric services within Windows Azure may recycle an instance and the session may sever between a given role and a client. Architecting stateless applications is a preferred approach.

Using Claims-Based Authentication is a better solution. In this approach, the Principal is authenticated through a trusted party, such as Active Directory, OpenID, OpenAuthentication, or LiveID. Many web-properties use these methods, such as Microsoft, Google, Yahoo and Facebook to name a few. After authenticating with one of these services, the client is issued Claims using the WS-Federation (WS-Fed) or Security Assertion Markup Language (SAML) that are passed to Windows Azure. At no time does Windows Azure store, transfer or interrogate the Principal’s security token. Claims can be anything from a group or role membership to location or any other settable attribute. Assets are then secured allowing only the Claim, without regard to the user’s location or access method. In this fashion a single security paradigm covers the Securables, with the Principals being controlled in any number of other mechanisms. This allows single-sign-on and/or federated security access from multiple providers.

The simplest mechanism for building this environment is the Access Control Services (ACS) feature found in the Windows Azure Application Fabric component. It is a federated authorization management service that simplifies user access authorization across organizations and ID providers and performs claims transformation to map identities with access levels.

ACS can:

Create and manage scopes such as URLs
Create and manage claim types
Create and manage signing and encryption keys
Create and manage rules within an application scope
Chain claims rules
Manage permissions on scopes or perform delegation

Figure 8 - Federated Security Example

Full information on the Access Control Service is available at this link: http://social.technet.microsoft.com/wiki/contents/articles/windows-identity-foundation-wif-and-azure-appfabric-access-control-service-acs-survival-guide.aspx?wa=wsignin1.0

Since the Web and Worker Roles within Windows Azure are designed to be stateless, Microsoft created a Certification Store within the Management area to hold Certificates that can be called from within code. An example of using the Certification Store is here: http://blogs.msdn.com/b/jnak/archive/2010/01/29/installing-certificates-in-windows-azure-vms.aspx

Additional Resources:

Official, authoritative security resource list: http://msdn.microsoft.com/en-us/library/ff934690.aspx
Technical Overview of the Security Features in the Windows Azure Platform: http://www.microsoft.com/online/legal/?langid=en-us&docid=11.
Windows Azure Security Overview: http://www.globalfoundationservices.com/security/documents/WindowsAzureSecurityOverview1_0Aug2010.pdf
Windows Azure Privacy: http://www.microsoft.com/online/legal/?langid=en-us&docid=11
Securing Microsoft Cloud Infrastructure: http://www.globalfoundationservices.com/security/documents/SecuringtheMSCloudMay09.pdf.
A list of other security resources is here: http://blogs.msdn.com/b/buckwoody/archive/2010/12/07/windows-azure-learning-plan-security.aspx

Image Attribution: David Pallmann: http://davidpallmann.blogspot.com/2011/07/windows-azure-design-patterns-part-1.html

Cloud Computing and the Importance of Code Diagrams

BuckWoody — Tue, 03 May 2011 13:59:20 GMT

Most mature development shops use various code diagrams to give a symbolic representation of high-level and database code structures. Standards such as Business Process Model Notation (BPMN), Entity Relationship Diagrams (ERD) and the Unified Modeling Language (UML) are a few I use all the time.

In the Distributed Computing (Cloud Computing) paradigm, these three diagrams (or their equivalent) become essential. In the past, I’ve been able to rely on a single architecture where my code will run. I understand the servers, the networking and the path the code takes between the client and the components within that architecture.

With Distributed Computing (DC), the architecture changes. In fact, the reason I use the term “Distributed Computing” instead of “Cloud Computing” most often (except in the title of this post, as you can see) is that I feel it’s more technically accurate about how we write code. I don’t view DC coding as an “all or nothing” exercise – I view it as just another option to solve a computing problem. A “hybrid” approach, where I mix in the strengths of a cloud provider is often a great way to leverage the best cost, performance and other advantages of each part of your solution. It can also help keep data secure, provide options for High Availability and Disaster Recovery, and more.

To gain these advantages, we have to think more about the components of the application rather than a monolithic stack of components in a single architecture. And that brings us to the title of this post…

For us to correctly identify code components, database objects, security paths and other elements, we have to be able to conceptualize them. And that’s where those diagrams come into play. Starting with some sort of business or organizational need, we can use BPMN or UML Actor diagrams to explain what the program needs to do. That helps segregate the security and location requirements. For instance, if the BPMN shows a data access to Private Information, we can evaluate the need for an on-premise system that is federated to a DC provider. If the business users need global access, we can decide whether to set up a VPN to allow access to an on-premise system or whether a login component can be used on the web.

After determining the flow of the program, move on to the data the system will store. In the case of Windows and SQL Azure, there are several options for storing data. In the past, I’ve often selected a single storage type, such as an RDBMS, and stored program data there. Now we can store in multiple formats, in multiple locations and more. The ERD is pivotal, because it defines data types, which can help decisions around where things go. Another important aspect to the data decision which is not covered in an ERD (but perhaps should be) is the estimated size and growth of a datum, since that can also drive the decision on where to put a data component.

From there, the UML document helps me understand where each computing element can live. There are strengths for each type of computing, and using the UML diagram I can place each code component in the best environment for speed, security and other considerations.

So in the new Distributed Computing world, these graphical documents do much more than just help design the application – they can help define the architecture as well.