Designing distributed applications with code mobility paradigms

When we are working on a distributed systems, many times during design phase we automatically assume the location of code as static. In other words, once a component is created, it cannot change either the location or the code during its life time. 

But there are also scenarios where considering concepts of code location and mobility etc during design phase makes a huge difference to the underlying distributed application in terms of fault-tolerance, concurrency, lower latencies and higher flexibility.

Rest of this post covers two notions of code mobility, various code mobility paradigms and some scenarios where a distributed application can benefit considerably by exploiting mobile code paradigms.

Understanding RBAC (Role based Access Control) model

Nearly all enterprise and large scale systems need authentication and authorization for using the underlying system resources. The key model and probably the most popular one is RBAC.

The central idea in RBAC is

• Permissions (or Permission sets) are associated with Roles. 
• Users (or User Groups) are associated with Roles.
• A permission is basically an approval of an operation on one or more objects.

Software Resilience Patterns

Good slide deck on patterns for software resiliency...

Patterns of resilience from Uwe Friedrichsen

Scalability, Availability and Stability Patterns

Came across this sometime back. Good slide deck...

Scalability, Availability & Stability Patterns from Jonas Bonér

Techniques to ensure eventual consistency

Recently I came across a paper on eventual consistency that touches among other things techniques to ensure eventual consistency for the updates to replicas. While the term eventual consistency is fairly popular now a days, I am not sure some of its subtleties are equally well known. So a small detour before diving into the techniques to ensure eventual consistency.

Eventual consistency basically guarantees that if there were no additional updates to a given data item then all reads for that data item will eventually return the same value. 

What the above also means that in an eventually consistent system,

• The system can return any arbitrary data and still be eventually consistent. So client has no way to know if the read response is wrong behavior. 
• If there are multiple concurrent updates, under eventually consistency you do not know which update gets eventually chosen. The order is unpredictable. Only guarantee is, there will be eventually a convergence. 
• In short, what eventual consistency tells us is something good will happen eventually but no guarantees as to what happens in the interim and no behavior is ruled out in the meantime!

Consistency considerations in distributed data stores

In a large distributed system, network partitions are a given. This means we cannot achieve both consistency and availability (CAP theorem). So our choices are either to relax the consistency for system to be highly available under partitions (or) make consistency a priority and system will not be available under certain conditions. Both of these options require developers to be aware of what the system is offering.

For example, if the system is emphasizing consistency, then the developer has to deal with availability issues. So if an update fails because of system unavailability, then developer need to plan on what to do with that update.

On other hand, if the system is emphasizing availability, then the developer should assume there will be times when the reads will not return latest updates. The application needs to be tolerant i.e., work with slightly stale data.

Many faces of replication...

Recently I was looking into replication and consistency papers and thought would be good topic to summarize. Replication is one of the most studied topics and is a quite important tool for designer.

It improves system availability by removing single point of failures, improves performance by reducing communication overheads and improves scalability by enabling system to grow with acceptable response times. But the benefits of replication comes with its own challenges. Nothing comes for free in distributed systems...

For example, some of the challenges anyone dealing with replication have to address are -

• How to manage the updates i.e., replication strategy? 
• Data Consistency & Availability tradeoffs? 
• How to handle downtime during new replica creation 
• Maintenance Overhead 
• Lower write performance etc. 

Designing scalable failure detection for distributed sensor nodes

Distributed systems need to provide reliable and continuous service despite failure of some of the components or nodes. So failure detection is one of the key aspects in building these systems. Also we generally will need failure detection for lot of other stuff like consensus, group membership, high availability etc.

The challenge is designing failure detection in a way that is both accurate and efficient is not an easy task though. One reason is the delays in a network are unpredictable. Another reason is the FLP impossibility theorem i.e., in any asynchronous distributed systems, it is impossible to determine accurately whether a remote process failed or has just taking its own time to respond. 

Bright side, a node really don't need to know if a remote process has failed or just taking its own time. As long as we have a way for all nodes to come to same conclusion (i.e., failed or delay) about a remote process then we can work from there. You do this by assuming an upper bound on the delays (ex: timeouts) after which a remote process is considered as failed. In academic speak, it is called a partially synchronous model

In rest of the post we go thru 

1. Eight factors to consider in designing failure detection, 
2. Four key metrics to consider in evaluating failure detection and 
3. Static, Lazy, Gossip, Hierarchical, Adaptive and Accrual based failure detection techniques.
4. Failure detection approaches in some popular distributed systems.