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Canadian Open stats & predictions

Overview of Tomorrow's Matches at the Canadian Open

The Canadian Open, a prestigious event in the tennis calendar, is set to captivate audiences with its thrilling matches tomorrow. As the tournament progresses, spectators and enthusiasts eagerly anticipate the performances of top-ranked players. This guide provides expert insights and betting predictions for the key matches scheduled for tomorrow.

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Key Matches to Watch

  • Top Seed Showdown: The highlight of the day features a clash between the top seed and a formidable challenger. Known for their powerful serves and strategic play, this match promises to be a spectacle.
  • Rising Star vs. Veteran: A thrilling encounter between a young, talented player and an experienced veteran. This match is expected to showcase a blend of raw talent and seasoned expertise.
  • Local Favorite: A match featuring a Canadian player, drawing significant local support. Fans are eager to see if their favorite can advance further in the tournament.

Betting Predictions and Insights

Expert analysts have provided betting predictions based on current form, head-to-head records, and recent performances. Here are some key insights:

  • Top Seed vs. Challenger: Analysts favor the top seed due to their consistent performance throughout the tournament. However, the challenger's aggressive style could pose a threat.
  • Rising Star vs. Veteran: Betting odds slightly favor the veteran, given their experience in high-pressure situations. Nonetheless, the rising star's unpredictability adds an element of excitement.
  • Local Favorite: With strong local support, the Canadian player is expected to perform well. Betting tips suggest considering an upset if they maintain their current form.

Detailed Match Analysis

Top Seed vs. Challenger

The top seed enters the court with an impressive record this season, having won multiple titles and consistently ranked at the top of the leaderboard. Their serve-and-volley technique has been particularly effective against opponents with weaker returns.

The challenger, known for their aggressive baseline play, has been making waves in recent tournaments. Their ability to sustain long rallies and force errors from opponents makes them a formidable adversary.

Key Factors:

  • The top seed's ability to control the pace of the game.
  • The challenger's stamina and consistency in maintaining pressure.

Rising Star vs. Veteran

This match pits youthful exuberance against seasoned wisdom. The rising star has been turning heads with their explosive power and fearless approach to the game.

The veteran, on the other hand, brings years of experience and a tactical mindset that often outsmarts younger opponents. Their ability to read opponents' moves and adapt strategies mid-match is unparalleled.

Key Factors:

  • The rising star's mental resilience under pressure.
  • The veteran's strategic adjustments during critical points.

Local Favorite: Canadian Player's Journey

The Canadian player has captured the hearts of fans with their dedication and skillful play. Representing their home country on such a prestigious stage adds an extra layer of motivation.

Known for their strong baseline game and precise volleys, this player has shown remarkable improvement over recent tournaments. Their ability to stay composed under pressure is a testament to their growing confidence.

Key Factors:

  • The player's connection with the home crowd.
  • Their performance in high-stakes matches earlier in the tournament.

Expert Betting Tips

Betting on Top Seed vs. Challenger

Given the top seed's track record, placing bets on their victory seems prudent. However, consider placing smaller bets on specific set outcomes or total points played to capitalize on potential upsets.

  • Top Seed to win in straight sets: High probability but lower returns.
  • Total points played over/under: Analyze recent matches for trends.
  • Challenger to win a set: A safer bet if you believe they can break through.
<|repo_name|>chrisblakey/Distributed-Systems-Lab<|file_sep|>/src/lab6/lab6_1.py #!/usr/bin/env python # coding=utf-8 import socket import time from multiprocessing import Process def handler(client_socket): try: while True: data = client_socket.recv(1024) if data: print 'received', data client_socket.sendall(data) else: break except Exception as e: print "Exception", e finally: client_socket.close() def main(): server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR,1) server_socket.bind(('localhost',9999)) server_socket.listen(10) while True: (client_socket,address) = server_socket.accept() print 'accepted', address handler_process = Process(target=handler,args=(client_socket,)) handler_process.start() if __name__ == '__main__': main() <|repo_name|>chrisblakey/Distributed-Systems-Lab<|file_sep|>/src/lab4/README.md Lab4 ==== Write programs that implement each of these protocols. 1. Write a simple reliable data transfer protocol (without explicit flow control) using Go-Back-N ARQ. Test your program by simulating packet loss. Solution: To test packet loss I ran my program with netem -L1% which will lose about one percent of all packets sent. A typical run will look like this: $ ./lab4_1.py Server started Client started Server received packet number:0 Server received packet number:1 Server received packet number:2 Server received packet number:3 Server received packet number:4 Server received packet number:5 Server received packet number:6 Server received packet number:7 Server received packet number:8 Server received packet number:9 This shows that it successfully recovered from lost packets. ![Lab4_1](./lab4_1.png) ![Lab4_2](./lab4_2.png) ![Lab4_3](./lab4_3.png) ![Lab4_4](./lab4_4.png) 2. Write a simple reliable data transfer protocol (without explicit flow control) using Selective Repeat ARQ. Test your program by simulating packet loss. Solution: To test packet loss I ran my program with netem -L1% which will lose about one percent of all packets sent. A typical run will look like this: $ ./lab4_2.py server starting... client starting... server received packet num:0 server received packet num:1 server received packet num:2 server received packet num:3 server received packet num:4 server received packet num:5 server received packet num:6 server received packet num:7 server received packet num:8 server received packet num:9 This shows that it successfully recovered from lost packets. ![Lab4_5](./lab4_5.png) ![Lab4_6](./lab4_6.png) ![Lab4_7](./lab4_7.png) ![Lab4_8](./lab4_8.png) 3. Modify your Go-Back-N protocol so that it implements explicit flow control using stop-and-wait. Test your program by simulating high network load. Solution: To test high network load I ran my program with netem -delay 100ms which will add a hundred milliseconds delay before every sent package. A typical run will look like this: bash $ ./lab4_3.py Starting server... Starting client... Server sent ACK number :0 , client got ACK :0 , sending next frame... Server sent ACK number :1 , client got ACK :1 , sending next frame... Server sent ACK number :2 , client got ACK :2 , sending next frame... Server sent ACK number :3 , client got ACK :3 , sending next frame... Server sent ACK number :4 , client got ACK :4 , sending next frame... Server sent ACK number :5 , client got ACK :5 , sending next frame... Server sent ACK number :6 , client got ACK :6 , sending next frame... Server sent ACK number :7 , client got ACK :7 , sending next frame... Server sent ACK number :8 , client got ACK :8 , sending next frame... This shows that it successfully recovered from high network load. ![Lab4_9](./lab4_9.png) ![Lab4_10](./lab4_10.png) ![Lab4_11](./lab4_11.png) ![Lab4_12](./lab4_12.png)<|file_sep|># Lab5 ## UDP Reliable Data Transfer Protocol ### Description The following is an implementation of UDP Reliable Data Transfer Protocol. ### Usage To run as Server: $ python lab5.py -s -i IP_ADDRESS -n PORT_NUMBER To run as Client: $ python lab5.py -c -i IP_ADDRESS -n PORT_NUMBER ### Testing The tests were performed using `netem` tool for network emulation. #### Test Case #1 Simulating network delay Command used: $ sudo tc qdisc add dev eno1 root netem delay 100ms Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab5/lab5-1.png) #### Test Case #2 Simulating network loss Command used: $ sudo tc qdisc add dev eno1 root netem loss 10% Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab5/lab5-2.png) #### Test Case #3 Simulating network jitter Command used: $ sudo tc qdisc add dev eno1 root netem delay 100ms 50ms distribution normal Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab5/lab5-3.png) #### Test Case #4 Simulating network corruption Command used: $ sudo tc qdisc add dev eno1 root netem corrupt 20% Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab5/lab5-4.png) #### Test Case #5 Simulating network reordering Command used: $ sudo tc qdisc add dev eno1 root netem reorder 25% Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab5/lab5-5.png) #### Test Case #6 Simulating network duplication Command used: $ sudo tc qdisc add dev eno1 root netem duplicate 20% Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab5/lab5-6.png) ## Reference [Implementing Reliable Data Transfer over UDP in Python](http://www.binarytides.com/udp-reliable-data-transfer-in-python-linux/)<|file_sep|># Lab6 ## TCP Server Program ### Description The following is an implementation of TCP Server Program. ### Usage To run as Server: $ python lab6_1.py To run as Client: $ python lab6_client.py localhost port_number message_to_send ### Testing The tests were performed using `netem` tool for network emulation. #### Test Case #1 Simulating network delay using `netem -delay` option. Command used: bash $ sudo tc qdisc add dev eno1 root netem delay [100ms] Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab6/tcp-delay.jpg) #### Test Case #2 Simulating network loss using `netem -loss` option. Command used: bash $ sudo tc qdisc add dev eno1 root netem loss [10%] Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab6/tcp-loss.jpg) #### Test Case #3 Simulating network duplication using `netem -duplicate` option. Command used: bash $ sudo tc qdisc add dev eno1 root netem duplicate [20%] Result: ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab6/tcp-duplicate.jpg) ## TCP Congestion Control Algorithm ### Description The following is an implementation of TCP Congestion Control Algorithm. ### Usage To run as Server: bash $ python lab6_server.py To run as Client: bash $ python lab6_client.py localhost port_number message_to_send ### Testing The tests were performed using `netem` tool for network emulation. #### Test Case #1 Simulating network delay using `netem -delay` option. Command used: bash $ sudo tc qdisc add dev eno1 root netem delay [100ms] Result : ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab6/tcc-delay.jpg) #### Test Case #2 Simulating network loss using `netem -loss` option. Command used: bash $ sudo tc qdisc add dev eno1 root netem loss [10%] Result : ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab6/tcc-loss.jpg) #### Test Case #3 Simulating network duplication using `netem -duplicate` option. Command used: bash $ sudo tc qdisc add dev eno1 root netem duplicate [20%] Result : ![](https://github.com/chrisblakey/Distributed-Systems-Lab/blob/master/img/lab6/tcc-duplicate.jpg)<|repo_name|>chrisblakey/Distributed-Systems-Lab<|file_sep|>/src/README.md # Distributed Systems Lab ## Lab Description Each lab assignment contains implementations of various distributed systems algorithms. The labs cover topics such as: * Reliable Data Transfer Protocol * UDP Reliable Data Transfer Protocol * TCP Server Program * TCP Congestion Control Algorithm ## How To Run Run each lab by running its python script as follows: bash python script-name-here.py ## Notes Make sure you have Python installed on your system.<|repo_name|>chrisblakey/Distributed-Systems-Lab<|file_sep|>/src/lab6/README.md # Lab6 ## TCP Server Program ### Description The following is an implementation of TCP Server Program. ### Usage To run as Server: bash python lab6_1.py To run as Client: bash python lab6_client.py localhost port_number message_to_send ### Testing The tests were performed using `netem` tool for network emulation. #### Test Case #1 Simulating network delay using `netem -delay` option. Command used: bash sudo tc qdisc add dev eno1 root netem delay [100ms] Result: ![](tcp-delay.jpg) #### Test Case #2 Simulating network loss using `netem -loss` option. Command used: bash sudo tc qdisc add dev eno1 root netem loss [10%] Result: ![](tcp-loss.jpg) #### Test Case #3 Simulating network duplication using `netem -duplicate` option. Command used: bash sudo tc qdisc add dev eno1 root netem duplicate [20%] Result: ![](tcp-duplicate.jpg) ## TCP Congestion Control Algorithm ### Description The following is an implementation of TCP Congestion Control Algorithm. ### Usage To run as Server: bash python lab6_server.py To run as Client: bash python lab6_client.py localhost port_number message_to_send ### Testing The tests were performed using `netem` tool for network emulation. #### Test Case #1 Simulating network delay using `netem -delay` option. Command used: bash sudo tc qdisc add dev eno1 root netem delay [100ms] Result : ![](tcc-delay.jpg) #### Test Case #2 Simulating network loss using `netem -loss` option. Command used: bash sudo tc qdisc add dev eno1 root netem loss [10%] Result : ![](tcc-loss.jpg) #### Test Case #3 Simulating network duplication using `netem -duplicate` option. Command used: bash sudo tc qdisc add dev eno1 root netem duplicate [20%] Result : ![](tcc-duplicate.jpg)<|repo_name|>chrisblakey/Distributed-Systems-Lab<|file_sep|>/src/lab7/README.md # Lab7 ## Chord Implementation ### Description The following is an implementation of Chord distributed hash table protocol. ### Usage To start chord nodes: * Run python chord_node.py node_id host_ip host_port command for each node you want to start
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