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# System prepended metadata

title: Thesis TODO

---

Thesis TODO
===

## 千鈞
- Short term
    - [ ] try output port as label
    - [ ] learn RNN model
    - [ ] try input: dimention = number of edges
    - [ ] impact of seq length
    - [ ] reward = 1 / delta, delta = (r1 - r2)/r1
    - [ ] input = symmetric matrix
    - [ ] add one more edge to topo
    - [ ] random topology, reward: comparing to the rate of K-shortest paths
        - 單一source destination的測試情況下，即使是8個nodes20個edges的case大概也能做到最大reward的80%甚至接近90%
    - [ ] extend to multiple SD-pair scenario
        - topo: 4 nodes complete graph
            - 在跟K條最短路徑的比較當中，大約能做到最大reward的90%左右
        - topo: 8 nodes 15 edges
            - 在跟K條最短路徑的比較當中，大約能做到最大reward的80%左右
    - [x] test reward = rate (normalized)
    - [x] test topo with random bw, but test sequential flows
- Mid term
    - train local models
        - LSTM: input: historical N flow requests, output: output port distribution
        - Stream mining
- Long term
    - implement streaming mining decision tree in P4


## 暐倫
- Short-term
    - 1/26
        - [ ] find experiments of paper 
        - [ ] prove pipe table detain flow
    - Interval length vs. total uploading cost / per-interval uploading cost
    - Interval length vs. accuracy
    - [x] design pipe + cm architecture to detect QoS variation 
    - evaluate the performance of sketch
        - [ ] impact of number of sketches
        - [ ] impact of number of flows
        - [ ] impact of sketch sizes
        - [ ] impact of interval of feedback/data collection on detection latency and network throughput
        - [ ] accuracy of flow variation (detected deviation, true deviation)
        - [ ] latency of detection
    - design how to estimate up-to-date rewards
- Mid-term
    - feed the updated reward and new data sets to re-train the model
    - example scenarios
        - your admission control model
        - flow offloading (example code found by Hung)
- Long-term
    - integrate RL with sketch and see if you can get on-line retraining
    - evaluate the performane, as compared to conventional offline training
        - metrics
            - time required to converge
            - average network performance
            - instaneous network performance
            - impact of network changes

## 嘉容
- Short-term
    - 1/27
        - [ ] check the throughput of each time-slot
        - [ ] try beam distribution as the output
    - 1/19
        - [ ] check the reason for low throughput
        - [ ] check demand distribution when UE=20,40
        - [ ] check UE number that allocated RB
        - [ ] check the relation between throughput and utilization when UE=5,10,...,40 for both input feature (1.demand 2.demand and interference)
        - [ ] calculate 20 times test avg. throughput
        - [ ] paper [Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays
](https://arxiv.org/abs/2101.01336)
    - [ ] auto convergence
    - [x] check inter-beam interference
    - [x] try new input matrix (# of power levels x |B|)
    - [ ] check the impact of performance differences among beams on model accuracy
    - [x] reward convergence
    - [x] test different input features based on demands
- Mid-term
    - extend your model to support sequention flow requests
    - Check whether you can further output the resource allocations (maybe just UE prioritization)
- Long-term
    - Consider inter-beam interference
    - implement multi-agent models
    - Extend the model to the multi-cell scenario

## 杜津
* DT
    * Fig 1:
        * x: number of coflows
        * y: throughput, accuracy
        * line: without DT, with small DT, with heavy DT 
    * Fig 2: latency in switch
        * ingress timestamp -> egress timestamp (average)
        * CDF of latency
        * test different feature set
        * test different intervals of model training
        * impact of different tree depths (width) on accuracy and latency
    - queue requirement (design architecture priority queue)
        
- Short-term
    - [ ] test impact of network fluctuation
    - [ ] verify performance of DT in BMV2/P4
    - [ ] plot computing time

## 謙仁
- Short-term
    - [ ] complete solver for multi-cell
    - [ ] test difference between integer and linear programming
    - [ ] output results for various network settings
    - [ ] evaluate the impact of parameter configurations
    - [ ] design the centralized greedy algorithm
- Mid-term
    - try linear relaxation
    - compare it with the original model
- Long-term
    - design greedy algorithms
    - centralized algo v.s. distributed algo.
    - evaluation

## 瑋綱
- [ ] check dataset for camera filtering
- [ ] give more details about the related work (university, conference)
- [ ] read: Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Camera (and its related work)

## 元毓
- [ ] group meeting
- [ ] buy optics cable
- [ ] read paper

## 昱萱
- PCC revision complexity

## 韋德
-