What other test-cases can we have ?

[fnikolai]

Port these cases into Frisbee testplans.

https://github.com/tikv/pd/tree/master/tools/pd-simulator/simulator/cases

[fnikolai]

Scalability Analysis:

Example Experiment 1: Scaling Web Servers in a Load Balancing Scenario
    Deploy multiple web servers behind a load balancer using the framework.
    Gradually increase the number of web servers and measure response times and throughput.
    Record the system's ability to handle increased load and how it scales horizontally.

Example Experiment 2: Scaling Peer-to-Peer Network Size in File Sharing System
    Implement a peer-to-peer file-sharing system using the framework with a variable number of nodes.
    Measure the time taken for a file search and download as the network size increases.
    Evaluate the impact of network size on file availability and search efficiency.

Fault Tolerance Assessment:

Example Experiment 1: Network Partition Recovery in a Distributed Database
    Create a distributed database using the framework with replica sets.
    Simulate a network partition between two replica sets and observe the system's behavior.
    Measure the time it takes for the database to restore consistency after the partition heals.

Example Experiment 2: Node Failure Recovery in a Clustered Application
    Deploy a clustered application using the framework with multiple nodes.
    Simulate the failure of one or more nodes and monitor the system's recovery process.
    Measure the time it takes for the system to redistribute load and restore full functionality.

Network Latency and Bandwidth:

Example Experiment 1: Evaluating Video Streaming Quality in a Content Delivery Network (CDN)
    Set up a CDN using the framework with multiple edge servers located in different regions.
    Measure video streaming quality (e.g., bitrate, buffer time) for users in different geographical locations.
    Analyze how network latency affects the streaming experience and how the CDN optimizes content delivery.

Example Experiment 2: Analyzing Communication Latency in a Distributed Multiplayer Game
    Develop a distributed multiplayer game using the framework with players in different regions.
    Measure the time it takes for game state updates to propagate between players.
    Assess how latency affects the gameplay experience and the game's synchronization mechanisms.

Consistency and Replication:

Example Experiment 1: Testing Consistency Levels in a Distributed Key-Value Store
    Implement a distributed key-value store using the framework with configurable consistency levels.
    Evaluate read and write operations under strong, eventual, and causal consistency models.
    Measure the impact of consistency settings on data accuracy and performance.

Example Experiment 2: Evaluating Multi-Datacenter Replication in a Distributed Database
    Deploy a distributed database with multiple data centers using the framework.
    Measure data replication latency and consistency across data centers.
    Analyze how replication strategies impact data availability and performance in geographically distributed scenarios.

Load Testing:

Example Experiment 1: Load Testing a Distributed Messaging System
    Implement a distributed messaging system using the framework with message brokers and consumers.
    Generate a high volume of messages using a load testing tool and measure the system's message processing capacity.
    Analyze message delivery rates, potential message backlogs, and how the system scales to handle load.

Example Experiment 2: Load Balancing in a Microservices Architecture
    Deploy a microservices-based application using the framework with load balancing.
    Generate different types of load on various microservices and observe the load balancing behavior.
    Measure how the system distributes traffic across instances and how it adapts to varying workloads.

Message Passing and Communication Overhead:

Example Experiment 1: Analyzing Message Latency in a Distributed Sensor Network
    Set up a distributed sensor network using the framework with sensors sending data to a central server.
    Measure the time taken for messages from sensors to reach the server.
    Analyze communication overhead and how it impacts data freshness and real-time processing.

Example Experiment 2: Evaluating Communication Overhead in a Peer-to-Peer File Sharing System
    Develop a peer-to-peer file-sharing system using the framework with file requests and responses between nodes.
    Measure message transmission times and communication overhead under various network conditions.
    Assess the system's efficiency in sharing files among peers.

Synchronization and Concurrency:

Example Experiment 1: Evaluating Locking Mechanisms in a Distributed File System
    Create a distributed file system using the framework with multiple clients accessing shared files.
    Implement different locking mechanisms (e.g., centralized, distributed) to manage file access.
    Measure file access times, contention rates, and the occurrence of conflicts.

Example Experiment 2: Concurrency Control in a Distributed Database Transaction
    Deploy a distributed database using the framework with multiple concurrent transactions.
    Measure transaction completion times and the occurrence of transaction conflicts.
    Analyze how different concurrency control protocols (e.g., two-phase locking, optimistic concurrency control) impact performance.

Dynamic Configuration Changes:

Example Experiment 1: Dynamic Load Balancing in a Cloud Computing Environment
    Set up a cloud computing platform using the framework with autoscaling capabilities.
    Introduce varying workloads and observe how the system automatically adjusts resource allocation.
    Measure the time it takes for the system to scale up or down based on workload changes.

Example Experiment 2: Dynamic Network Topology Management in a Peer-to-Peer Network
    Implement a peer-to-peer network using the framework with nodes joining and leaving dynamically.
    Measure the time it takes for the network to stabilize after topology changes.
    Assess the system's ability to maintain connectivity and routing efficiency during dynamic changes.

Security and Resilience:

Example Experiment 1: DDoS Mitigation in a Distributed Web Application Firewall
    Implement a distributed web application firewall using the framework with DDoS detection and mitigation capabilities.
    Launch simulated DDoS attacks and measure the system's ability to detect and mitigate them.
    Analyze the impact on legitimate user traffic during the attacks.

Example Experiment 2: Evaluating Byzantine Fault Tolerance in a Consensus Protocol
    Implement a distributed consensus protocol using the framework with Byzantine fault tolerance.
    Simulate Byzantine nodes in the network and measure the system's ability to achieve consensus.
    Assess the protocol's resiliency against malicious attacks.

Resource Management:

Example Experiment 1: Dynamic Resource Allocation in a Cloud-based Video Encoding Service
    Develop a cloud-based video encoding service using the framework with dynamic resource allocation.
    Measure resource usage (CPU, memory, storage) for encoding jobs of varying complexity.
    Analyze the system's ability to optimize resource allocation and meet service level agreements.

Example Experiment 2: Resource Partitioning in a Distributed Machine Learning Framework
    Set up a distributed machine learning framework using the framework with multiple workers.
    Measure the time taken for model training with varying resource partitioning schemes.
    Analyze how resource allocation affects model convergence and training efficiency.

Comparative Studies:

Example Experiment 1: Benchmarking Two Distributed Database Systems for E-Commerce Applications
    Deploy two distributed databases with different architectures using the framework.
    Execute a set of representative queries and transactions on both systems.
    Compare