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How can you scale and adapt distributed systems to changing workloads?

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Distributed systems are composed of multiple independent nodes that communicate and coordinate to achieve a common goal. However, distributed systems often face dynamic and unpredictable workloads that can affect their performance, reliability, and availability. How can you scale and adapt distributed systems to changing workloads? In this article, you will learn some basic principles and techniques that can help you design and manage distributed systems that can handle varying demands and conditions.

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Marcély Scariot

Supervisora de Infraestrutura na Mosaic Brasil
Fadi Almabrok

Digital Transformation | Senior Enterprise Architect | Hybrid Cloud Architect | AWS | GCP | TOGAF

1 Horizontal vs vertical scaling

One of the main challenges of distributed systems is to balance the load among the nodes and avoid bottlenecks, failures, or resource contention. Scaling is the process of adding or removing resources to adjust the system's capacity and performance. There are two types of scaling: horizontal and vertical. Horizontal scaling involves adding or removing nodes to the system, while vertical scaling involves increasing or decreasing the resources of each node, such as CPU, memory, or disk. Horizontal scaling is more common and preferred in distributed systems, as it allows for more flexibility, fault tolerance, and parallelism. However, horizontal scaling also introduces more complexity, overhead, and coordination costs. Vertical scaling is simpler and faster, but it has physical and economic limits, and it can create single points of failure.

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Thiago Luz Alves de Matos

Azure Tech Lead @ Delaware Brasil | MBA, Azure Cloud
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Horizontal scaling is like adding more waiters to handle more tables at a restaurant. You duplicate your servers, spreading the load. It's cost-effective and great for handling traffic spikes. Vertical scaling is like upgrading to a bigger, fancier table. You increase the power of an existing server. It's good for handling increased resource demands on a single machine. Choosing between them depends on your needs; horizontal for flexibility and cost-efficiency, vertical for boosting power when a single server needs more muscle. It's like deciding between adding more tables or getting a bigger one at the restaurant – depends on the feast!

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2 Load balancing and partitioning

Another challenge of distributed systems is to distribute the workload evenly among the nodes and avoid overloading or underutilizing some nodes. Load balancing and partitioning are two techniques that can help achieve this goal. Load balancing is the process of assigning requests or tasks to nodes based on their availability, capacity, or performance. Load balancing can be done at different levels, such as network, application, or data. Load balancing can improve the system's throughput, latency, and availability, but it can also introduce challenges, such as consistency, synchronization, or security. Partitioning is the process of dividing the data or state of the system into smaller and independent chunks that can be stored or processed by different nodes. Partitioning can reduce the data size, complexity, and contention, but it can also introduce challenges, such as replication, consistency, or coordination.

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Marcély Scariot

Supervisora de Infraestrutura na Mosaic Brasil
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é possível expandir para cima (vertical) ou para fora (horizontal). Escalabilidade vertical Expans?o vertical é quando se aumenta a capacidade de um componente, como um servidor, para melhorar o desempenho dele. Por exemplo: quanto mais tráfego chega ao servidor, pior o desempenho dele. A adi??o de RAM e de unidades de armazenamento aumenta o desempenho do servidor para que ele suporte o aumento do tráfego. A expans?o vertical é fácil de implementar, economiza dinheiro (n?o precisa adquirir novos recursos) e é de fácil manuten??o. Porém, por serem um ponto único de falha, podem resultar num tempo significativo de inatividade.

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Thiago Luz Alves de Matos

Azure Tech Lead @ Delaware Brasil | MBA, Azure Cloud
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Load balancing is like a traffic cop for servers, distributing incoming requests among multiple machines. It ensures no single server is overwhelmed, optimizing performance and reliability. Think of it as assigning lanes on a highway to avoid jams. Partitioning, on the other hand, involves dividing a database into smaller, manageable chunks called partitions. Each partition operates independently, enhancing data retrieval speed. It's like having different folders for A-M and N-Z in a giant book, making searches quicker. In a nutshell, load balancing keeps servers in check like a traffic cop, while partitioning organizes data for faster access. It's about smooth traffic flow and finding info in record time!

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3 Elasticity and adaptability

Another challenge of distributed systems is to adapt to changing workloads and conditions without compromising the system's quality or functionality. Elasticity and adaptability are two properties that can help achieve this goal. Elasticity is the ability of the system to automatically scale up or down the resources according to the workload demand or the performance requirements. Elasticity can improve the system's efficiency, cost, and user satisfaction, but it can also introduce challenges, such as monitoring, automation, or configuration. Adaptability is the ability of the system to modify its behavior or structure according to the workload characteristics or the environmental factors. Adaptability can improve the system's robustness, resilience, and evolution, but it can also introduce challenges, such as learning, feedback, or self-organization.

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4 Distributed algorithms and protocols

Another challenge of distributed systems is to ensure the correctness and consistency of the system's operations and outcomes despite the uncertainty and variability of the network and the nodes. Distributed algorithms and protocols are the rules and methods that govern the communication and coordination among the nodes and the resolution of conflicts and failures. Distributed algorithms and protocols can be classified into different categories, such as consensus, replication, synchronization, or fault tolerance. Distributed algorithms and protocols can improve the system's reliability, availability, and scalability, but they can also introduce challenges, such as complexity, overhead, or trade-offs.

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5 Testing and debugging

Another challenge of distributed systems is to verify and validate the system's functionality and performance under different scenarios and conditions. Testing and debugging are the processes of detecting and correcting errors, defects, or anomalies in the system's design, implementation, or execution. Testing and debugging can be done at different levels, such as unit, integration, or system. Testing and debugging can improve the system's quality, security, and usability, but they can also introduce challenges, such as coverage, reproducibility, or isolation.

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6 Best practices and patterns

Another challenge of distributed systems is to apply and adopt the best practices and patterns that can help design and manage distributed systems effectively and efficiently. Best practices and patterns are the guidelines and solutions that have been proven to work well in similar problems or contexts. Best practices and patterns can be derived from different sources, such as theory, experience, or literature. Best practices and patterns can improve the system's maintainability, modularity, and interoperability, but they can also introduce challenges, such as suitability, compatibility, or evolution.

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Fadi Almabrok

Digital Transformation | Senior Enterprise Architect | Hybrid Cloud Architect | AWS | GCP | TOGAF
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It's better to adopt Microservices Architecture where your system is decomposed into smaller, independent services that can be deployed and scaled independently, actually this will support in allocate resources more efficiently based on the specific needs of each service and makes it easier to adapt to changing workloads.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you scale and adapt distributed systems to changing workloads?

1

2

3

4

5

6

7

1 Horizontal vs vertical scaling

2 Load balancing and partitioning

3 Elasticity and adaptability

4 Distributed algorithms and protocols

5 Testing and debugging

6 Best practices and patterns

7 Here’s what else to consider

Operating Systems

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How can you scale and adapt distributed systems to changing workloads?

1

2

3

4

5

6

7

1 Horizontal vs vertical scaling

2 Load balancing and partitioning

3 Elasticity and adaptability

4 Distributed algorithms and protocols

5 Testing and debugging

6 Best practices and patterns

7 Here’s what else to consider

Operating Systems

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