Update disaggregated.md

docs/architecture/concepts/disaggregated.md

@@ -4,23 +4,27 @@ description: "Disaggregated storage represents a modern approach to distributed
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-Disaggregated storage represents a modern approach to distributed storage architectures, where compute and storage
-resources are decoupled. This separation allows for greater flexibility, scalability, and efficiency in managing data
-across large-scale distributed environments.
+Simplyblock supports disaggregated storage deployments, in which dedicated storage nodes operate separately from compute, in addition to
+hyper-converged and hybrid deployment topologies.
 
-Traditional storage architectures typically integrate compute and storage within the same nodes, leading to resource
-contention and inefficiencies. Disaggregated storage solutions address these limitations by separating storage resources
-from compute resources, enabling independent scaling of each component based on workload demands.
+It is important to note that, while we support plain Linux (Docker-based) disaggregated deployments, they can also run fully in the
+Kubernetes-native operations model, either within the same cluster as the compute workloads or in separate clusters.
 
-Key characteristics of disaggregated storage solutions include:
+The main difference from the hyper-converged model is the separation of storage and compute resources.
+
+The biggest benefit of the disaggregated model is the decoupling the lifecycle of storage operations from
+compute lifecycle management (worker node upgrades, node maintenance, and node reboots), as well as independent scaling.
+For example, a small compute cluster with I/O-intensive workloads may still require a lot of storage capacity and IOPS.
+
+The biggest disadvantages are the loss of data locality and possible misalignments in scaling (required minimum scale of the
+storage cluster and scaling of the storage cluster with compute).
+
+Key characteristics of our disaggregated storage solutions include:
 
 - **Independent Scalability:** Compute and storage can be scaled separately, optimizing resource utilization and
-  reducing unnecessary hardware expansion.
-- **Resource Efficiency:** Storage is pooled and accessible across multiple compute nodes, reducing data duplication and
-  improving overall efficiency.
-- **Improved Performance:** By reducing bottlenecks associated with tightly coupled storage, applications can achieve
-  better latency and throughput.
-- **Flexibility and Adaptability:** Different storage technologies (e.g., NVMe-over-Fabrics, object storage) can be
-  integrated seamlessly, allowing organizations to adopt the best-fit storage solutions for specific workloads.
-- **Simplified Management:** Centralized storage management reduces complexity, enabling easier provisioning,
-  monitoring, and maintenance of storage resources.
+  reducing unnecessary hardware expansion. IO demand and compute sizing are treated independently.
+- **Independent Cluster Lifecycle:** Storage and compute can be maintained, upgraded, and replaced independently of each other.
+- **Controlled Storage Performance:** It is easier to control latency, throughput, and IOPS output in a disaggregated model. 
+- **Hardware Independence:** This feature is shared with hyper-converged storage. Hardware component and node replacements
+  can be performed entirely independently of the software, choosing different vendors in accordance with procurement strategies,
+  and supporting gradual and rolling replacements.