In the previous era, architecture design predominantly focused on optimizing resource utilization. However, in the current cloud era, there has been a significant shift in perspective. The crucial difference between architecture designs aimed at maximizing resource utilization and those dedicated to minimizing resource occupancy remains of paramount importance. This differentiation ensures that users are spared from the costs associated with excessive performance while achieving the necessary level of performance with minimal resource utilization. In essence, architecture design strives to strike a balance that meets performance requirements without unnecessary resource consumption, emphasizing efficiency and cost-effectiveness. This approach aligns technology solutions more closely with actual needs and sustainability considerations.
In traditional computing, tasks are typically handled on physical servers with fixed memory allocations. This can result in slower memory management and sometimes leads to wasted resources. To ensure smooth performance during high-demand periods, it often requires advance planning and purchasing of memory. On the other hand, cloud computing is more flexible, using virtual servers or containers. Memory resources can be easily adjusted based on the workload. If a task needs more memory, you can add it instantly without waiting for new physical equipment. This not only reduces resource waste but also saves costs. In simple terms, in cloud computing, you can allocate more resources to complete tasks quickly and then release them when you’re done, maximize resource utilization by ensuring that no resource remains idle. which is a better cost-effective way.
In a fixed-server environment, the IO bandwidth is constant, which means that the IO overhead is primarily determined by the size of the data that needs to be loaded. However, in a scalable platform where resources can be flexibly allocated, the available bandwidth is not fixed, and users have the ability to distribute their workloads across different hardware as needed. This allows for a more efficient utilization of IO bandwidth, often exceeding what a single server can provide.
What’s more, this additional bandwidth allocation typically doesn’t incur extra charges; users can allocate it as part of their existing resources. Therefore, utilizing more bandwidth at a minimal cost becomes a more effective architectural approach.
In essence, the key difference lies in the ability to dynamically allocate and utilize bandwidth in a scalable platform, offering improved efficiency compared to relying solely on the fixed bandwidth of a single server, and this often comes without additional expenses.
In order to attain this objective, it is crucial to enhance the dynamic deployability of system components. This necessitates ensuring that these components are stateless. Traditional database practices and designs, typically operating on a single server, do not inherently prioritize statelessness. Therefore, achieving a stateless state for components within a replicated system requires substantial redesign and implementation efforts.
Database systems inherently require some degree of state, making complete avoidance impractical. For such modules, traditional system designs typically aim to store as little information as possible to reduce memory pressure. However, in the context of systems focused on minimal resource occupancy, the absolute data size may not be the primary concern. Instead, the focus shifts to reducing memory pressure within a single request. It’s not about achieving the smallest memory overhead, but rather implementing strategies such as partial loading and minimizing the duration for which components hold onto memory resources during a request. This approach prioritizes efficient memory management over strict minimization of memory usage.