Question

Explain why distributed systems are inherently more scalable than centralised systems. What are the likely limits on the scalability of the system?

Short answer

Distributed systems scale better by adding nodes, but face limits from network and coordination complexities.

Step-by-step solution

Understanding Scalability

Scalability refers to the capacity of a system to handle an increasing amount of work or its potential to expand to accommodate growth. In centralized systems, all the processing and data storage are managed by a single entity or server, limiting scalability because it can only scale vertically by adding more resources, like CPU or memory, to that single unit.

Analyzing Distributed Systems

Distributed systems, on the other hand, distribute tasks and data across multiple nodes or servers. This allows them to scale horizontally by adding more nodes to the system, effectively spreading the workload and storage needs across many machines. This inherent ability to add more resources by increasing the number of nodes makes distributed systems more scalable.

Limits of Scalability in Distributed Systems

Although distributed systems can scale better than centralized ones, they face limits such as network bandwidth, latency issues, and complexities in coordinating and maintaining data consistency across multiple nodes. Furthermore, the law of diminishing returns may apply, where adding more nodes results in a proportionally lower increase in performance due to these overheads.

Conclusion on Scalability

Ultimately, distributed systems are inherently more scalable than centralized systems due to their ability to add and manage resources across multiple nodes. Despite this, physical limitations like bandwidth and coordination issues pose challenges that can limit scalability.

Key concepts

Scalability

Scalability is a key aspect in the realm of distributed systems and centralized systems alike. It denotes a system's ability to grow and manage increased demand efficiently. In centralized systems, scalability is typically limited to vertical scaling. This means enhancing the existing hardware, such as adding more powerful processors or memory. However, this approach has a ceiling. Eventually, the physical limitations of hardware lead to diminishing returns.
Distributed systems, however, bring a fresh perspective. They adapt by adding more machines, a concept known as horizontal scaling, enabling greater scalability by distributing workloads across multiple nodes. This avoids the pitfalls of maxed-out hardware. It allows systems to expand more dynamically, but also introduces new challenges, such as managing the coordination and communication across many machines.

Centralized Systems

Centralized systems are like one-man bands—they rely on a single server or group of closely connected servers to perform tasks. These systems centralize data and processing power in one place. While this can simplify management and reduce communication overhead, it creates a bottleneck.
Getting more out of centralized systems requires vertical scaling. This involves boosting the capacity of existing hardware, which isn’t always cost-effective or feasible.

• Vertical scaling has a limit due to hardware constraints.
• Increased performance comes at a cost, often with diminishing returns.

Overall, while they are simpler to run, centralized systems fall short when it comes to extensive scalability. Once the server reaches its maximum capacity, it becomes a hurdle in supporting growing user demands.

Horizontal Scaling

Horizontal scaling is the hero of distributed systems. Unlike the towering mountain approach of adding more power to a single server (vertical scaling), horizontal scaling spreads out the load. It brings new machines into the fold to share tasks, alleviating strain on any one node.
By increasing nodes, you reduce the risk of failure impacting the whole system.

• New tasks get easily distributed across several nodes.
• Resources are expanded by merely integrating additional servers.

However, with this boon comes coordination complexity. Ensuring all these nodes work together seamlessly is essential. Managing data consistency and communication between nodes turns into a crucial task making the system efficient but also complex.

Data Consistency

Data consistency in distributed systems is a cornerstone of reliability. When data is spread across multiple nodes, ensuring that each node reflects the same data picture becomes fundamental. It means that reading or writing from any node should give you the same result as accessing any other node. Yet, maintaining this consistency across a distributed setup can be challenging.
Here are some factors affecting data consistency:

• Network latencies can cause delays, impacting how quickly data updates propagate across all nodes.
• In synchronized operations, locking mechanisms might be needed to ensure data accuracy.

While data consistency is more easily achieved in centralized systems, distributed systems must often balance between consistency, availability, and tolerance to network failures, commonly known as the CAP Theorem. This balance is crucial for ensuring that the distributed system works effectively at scale.