doc: update readme (#388)

README.md

@@ -1,11 +1,41 @@
 # Tortoise
 
-**Tortoise is under the active development and not production ready yet.**
-
 <img alt="Tortoise" src="docs/images/tortoise_big.jpg" width="400px"/> 
 
 Get cute Tortoises into your Kubernetes garden and say goodbye to the days optimizing your rigid autoscalers. 
 
+## Motivation
+
+At Mercari, the responsibilities of the Platform team and the service development teams are clearly distinguished. Not all service owners possess expert knowledge of Kubernetes.
+
+Also, Mercari has embraced a microservices architecture, currently managing over 1000 Deployments, each with its dedicated development team.
+
+To effectively drive FinOps across such a sprawling landscape, 
+it's clear that the platform team cannot individually optimize all services. 
+As a result, they provide a plethora of tools and guidelines to simplify the process of the Kubernetes optimization for service owners.
+
+But, even with them, manually optimizing various parameters across different resources, 
+such as resource requests/limits, HPA parameters, and Golang runtime environment variables, presents a substantial challenge. 
+
+Furthermore, this optimization demands engineering efforts from each team constantly - 
+adjustments are necessary whenever there’s a change impacting a resource usage, which can occur frequently: 
+Changes in implementation can alter resource consumption patterns, fluctuations in traffic volume are common, etc.
+
+Therefore, to keep our Kubernetes clusters optimized, it would necessitate mandating all teams to perpetually engage in complex manual optimization processes indefinitely, 
+or until Mercari goes out of business.
+
+To address these challenges, the platform team has embarked on developing Tortoise, 
+an automated solution designed to meet all Kubernetes resource optimization needs.
+
+This approach shifts the optimization responsibility from service owners to the platform team (Tortoises), 
+allowing for comprehensive tuning by the platform team to ensure all Tortoises in the cluster adapts to each workload. 
+On the other hand, service owners are required to configure only a minimal number of parameters 
+to initiate autoscaling with Tortoise, significantly simplifying their involvement.
+
+See more details in the blog post:
+- [Tortoise: Outpacing the Optimization Challenges in Kubernetes at Mercari](https://engineering.mercari.com/en/blog/entry/20240206-3a12bb1288/) 
+- [人間によるKubernetesリソース最適化の”諦め”とそこに見るリクガメの可能性](https://engineering.mercari.com/blog/entry/20240206-3a12bb1288/)
+
 ## Install
 
 You cannot get it from the breeder, you need to get it from GitHub instead.
@@ -19,34 +49,17 @@ make deploy
 
 You don't need a rearing cage, but need VPA in your Kubernetes cluster before installing it.
 
-## Motivation
-
-Many developers are working in Mercari, and not all of them are the experts of Kubernetes. 
-The platform has many tools and guides to simplify the task of optimizing resource requests,
-but the optimization takes engineering cost in every team constantly. 
-
-The optimization should be done every time the situation around the service get changed, which could happen easily and frequently. 
-(e.g., the implementation change could change the way of consuming resources, the amount of traffic could be changed, etc)
-
-Also, when it comes to HorizontalPodAutoscaler(HPA), it's nearly impossible for human to optimize.
-It’s not a simple problem which we just set the target utilization as high as possible – 
-there are many scenarios where the actual resource utilization doesn’t reach the target resource utilization in the first place
-(because of multiple containers, minReplicas, unbalanced container’s size etc).
+## Usage
 
-To overcome those challenges,
-the platform team start to have Tortoise, which is the automated solution for all optimization needs to be done for Kubernetes resource.
+As described in [Motivation](#motivation) section, Tortoise exposes many global parameters to a cluster admin, while it exposes few parameters in Tortoise resource.
 
-It aims to move the responsibility of optimizing the workloads from the application teams to tortoises (Platform team). 
-Application teams just need to set up Tortoise, and the platform team will never bother them again for the resource optimization - 
-all actual optimization is done by Tortoise automatically. 
+### Cluster admin
 
-See a detailed motivation in the blog post:
-- [Tortoise: Outpacing the Optimization Challenges in Kubernetes at Mercari](https://engineering.mercari.com/en/blog/entry/20240206-3a12bb1288/) 
-- [人間によるKubernetesリソース最適化の”諦め”とそこに見るリクガメの可能性](https://engineering.mercari.com/blog/entry/20240206-3a12bb1288/)
+See [Admin guide](./docs/admin-guide.md) to understand how to configure the tortoise controller to make it fit your workloads in one cluster.
 
-## Usage
+### Tortoise users
 
-Tortoise has a very simple interface:
+Tortoise CRD itself has a very simple interface:
 
 ```yaml
 apiVersion: autoscaling.mercari.com/v1beta3
@@ -62,12 +75,11 @@ spec:
       name: sample
 ```
 
-Then, Tortoise creates fully managed autoscalers (HPA and VPA). 
-
-Despite its simple appearance, it contains a rich collection of historical data on resource utilization beneath its shell, 
+Then, Tortoise creates HPA and VPA under the hood. 
+Despite its simple appearance, each tortoise stores a rich collection of historical data on resource utilization beneath its shell, 
 and cleverly utilizes them to manage parameters in autoscalers. 
 
-Please refer to [User guide](./docs/user-guide.md) for other parameters.
+Please refer to [User guide](./docs/user-guide.md) to learn more about other parameters.
 
 ## Documentations