Rename lecture files so they appear in sorted order

Author: Chris Whealy

Lecture 2.md renamed to Lecture 02.md

@@ -4,7 +4,7 @@
 
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-| | [Lecture 3](./Lecture%203.md)
+| | [Lecture 3](./Lecture%2003.md)
 
 ## What is a Distributed System?
 
@@ -198,4 +198,4 @@ You know that if event `A` happened before event `B`, then you can ensure that y
 
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-| | [Lecture 3](./Lecture%203.md)
+| | [Lecture 3](./Lecture%2003.md)

Lecture 3.md renamed to Lecture 03.md

@@ -4,7 +4,7 @@
 
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-| [Lecture 2](./Lecture%202.md) | [Lecture 4](./Lecture%204.md)
+| [Lecture 2](./Lecture%2002.md) | [Lecture 4](./Lecture%2004.md)
 
 ## Causality and the "Happens Before" Relation
 
@@ -204,7 +204,7 @@ Whenever we talk about a relation being a partial order, we must first look at t
 
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-| [Lecture 2](./Lecture%202.md) | [Lecture 4](./Lecture%204.md)
+| [Lecture 2](./Lecture%2002.md) | [Lecture 4](./Lecture%2004.md)
 
 ---
 

Lecture 4.md renamed to Lecture 04.md

@@ -4,7 +4,7 @@
 
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-| [Lecture 3](./Lecture%203.md) | [Lecture 5](./Lecture%205.md)
+| [Lecture 3](./Lecture%2003.md) | [Lecture 5](./Lecture%2005.md)
 
 ## Recap
 
@@ -235,4 +235,4 @@ In order to remove this indeterminacy, we need a different type of clock, called
 
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-| [Lecture 3](./Lecture%203.md) | [Lecture 5](./Lecture%205.md)
+| [Lecture 3](./Lecture%2003.md) | [Lecture 5](./Lecture%2005.md)

Lecture 5.md renamed to Lecture 05.md

@@ -4,7 +4,7 @@
 
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-| [Lecture 4](./Lecture%204.md) | [Lecture 6](./Lecture%206.md)
+| [Lecture 4](./Lecture%2004.md) | [Lecture 6](./Lecture%2006.md)
 
 
 
@@ -303,6 +303,6 @@ This is an example of where a diagram provides a very useful way to represent th
 
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-| [Lecture 4](./Lecture%204.md) | [Lecture 6](./Lecture%206.md)
+| [Lecture 4](./Lecture%2004.md) | [Lecture 6](./Lecture%2006.md)
 
 

Lecture 6.md renamed to Lecture 06.md

@@ -4,7 +4,7 @@
 
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-| [Lecture 5](./Lecture%205.md) | [Lecture 7](./Lecture%207.md)
+| [Lecture 5](./Lecture%2005.md) | [Lecture 7](./Lecture%2007.md)
 
 ## Recap
 
@@ -257,5 +257,5 @@ Once this out-of-sequence message has been delivered, the message waiting in the
 
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-| [Lecture 5](./Lecture%205.md) | [Lecture 7](./Lecture%207.md)
+| [Lecture 5](./Lecture%2005.md) | [Lecture 7](./Lecture%2007.md)
 

Lecture 7.md renamed to Lecture 07.md

@@ -4,7 +4,7 @@
 
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-| [Lecture 6](./Lecture%206.md) | [Lecture 8](./Lecture%208.md)
+| [Lecture 6](./Lecture%2006.md) | [Lecture 8](./Lecture%2008.md)
 
 
 ## Causal Broadcast Using Vector Clocks
@@ -294,6 +294,6 @@ It has been designed to ensure that the entire event history of each process in
 
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-| [Lecture 6](./Lecture%206.md) | [Lecture 8](./Lecture%208.md)
+| [Lecture 6](./Lecture%2006.md) | [Lecture 8](./Lecture%2008.md)
 
 

Lecture 8.md renamed to Lecture 08.md

@@ -4,7 +4,7 @@
 
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-| [Lecture 7](./Lecture%207.md) | [Lecture 9](./Lecture%209.md)
+| [Lecture 7](./Lecture%2007.md) | [Lecture 9](./Lecture%2009.md)
 
 
 ## Rules of the Chandy-Lamport Algorithm
@@ -222,10 +222,7 @@ Management of an entire system snapshot needs to be handled by some external coo
 
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-| [Lecture 7](./Lecture%207.md) | [Lecture 9](./Lecture%209.md)
-
-
-
+| [Lecture 7](./Lecture%2007.md) | [Lecture 9](./Lecture%2009.md)
 
 ---
 

Lecture 9.md renamed to Lecture 09.md

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-| [Lecture 8](./Lecture%208.md) | [Lecture 10](./Lecture%2010.md)
+| [Lecture 8](./Lecture%2008.md) | [Lecture 10](./Lecture%2010.md)
 
 
 ## Big Picture View of the Chandy-Lamport Algorithm
@@ -262,5 +262,5 @@ Let's now briefly introduce the next topic, that of ***safety*** and ***liveness
 
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-| [Lecture 8](./Lecture%208.md) | [Lecture 10](./Lecture%2010.md)
+| [Lecture 8](./Lecture%2008.md) | [Lecture 10](./Lecture%2010.md)
 

Lecture 10.md

@@ -4,7 +4,7 @@
 
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-| [Lecture 9](./Lecture%209.md) | [Lecture 11](./Lecture%2011.md)
+| [Lecture 9](./Lecture%2009.md) | [Lecture 11](./Lecture%2011.md)
 
 ## Recap: Safety & Liveness Properties
 
@@ -274,7 +274,7 @@ The objective here is not to remove all uncertainty, but to lower it to a level
 
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-| [Lecture 9](./Lecture%209.md) | [Lecture 11](./Lecture%2011.md)
+| [Lecture 9](./Lecture%2009.md) | [Lecture 11](./Lecture%2011.md)
 
 
 ---

README.md

@@ -15,14 +15,14 @@ Notes have not been created for these events; however, you can watch the videos
 |  Date | Description | Subjects Recapped |
 |---|---|---|
 | Lecture 1 | There are no notes for this lecture as it was concerned with course administration and logistics  |
-| [Lecture 2](./Lecture%202.md)<br>April&nbsp;1<sup>st</sup>,&nbsp;2020 | Distributed Systems: What and why?<br>Time and clocks |
-| [Lecture 3](./Lecture%203.md)<br>April&nbsp;3<sup>rd</sup>,&nbsp;2020| Lamport diagrams<br>Causality and the "happens before" relation<br>Network models<br>State and events<br>Partial orders
-| [Lecture 4](./Lecture%204.md)<br>April&nbsp;6<sup>th</sup>,&nbsp;2020 | Total orders and Lamport clocks | Partial orders<br>Happens before relation
-| [Lecture 5](./Lecture%205.md)<br>April&nbsp;8<sup>th</sup>,&nbsp;2020 | Vector clocks<br>Protocol runs and anomalies<br>Message Delivery vs. Message Receipt<br>FIFO delivery | Lamport Clocks
-| [Lecture 6](./Lecture%206.md)<br>April&nbsp;10<sup>th</sup>,&nbsp;2020 | Causal delivery<br>Totally-ordered delivery<br>Implementing FIFO delivery<br>Preview of implementing causal broadcast | Delivery vs. Receipt<br>FIFO delivery
-| [Lecture 7](./Lecture%207.md)<br>April&nbsp;13<sup>th</sup>,&nbsp;2020 | Implementing causal broadcast<br>Uses of causality in distributed systems<br>Consistent snapshots<br>Preview of the Chandy-Lamport snapshot algorithm | Causal anomalies and vector clocks
-| [Lecture 8](./Lecture%208.md)<br>April&nbsp;15<sup>th</sup>,&nbsp;2020 | Chandy-Lamport Snapshot Algorithm |
-| [Lecture 9](./Lecture%209.md)<br>April&nbsp;17<sup>th</sup>,&nbsp;2020 | Chandy-Lamport wrap-up: limitations, assumptions and properties<br>Uses of snapshots<br>Centralized vs. decentralized algorithms<br>Safety and liveness | Delivery guarantees and protocols
+| [Lecture 2](./Lecture%2002.md)<br>April&nbsp;1<sup>st</sup>,&nbsp;2020 | Distributed Systems: What and why?<br>Time and clocks |
+| [Lecture 3](./Lecture%2003.md)<br>April&nbsp;3<sup>rd</sup>,&nbsp;2020| Lamport diagrams<br>Causality and the "happens before" relation<br>Network models<br>State and events<br>Partial orders
+| [Lecture 4](./Lecture%2004.md)<br>April&nbsp;6<sup>th</sup>,&nbsp;2020 | Total orders and Lamport clocks | Partial orders<br>Happens before relation
+| [Lecture 5](./Lecture%2005.md)<br>April&nbsp;8<sup>th</sup>,&nbsp;2020 | Vector clocks<br>Protocol runs and anomalies<br>Message Delivery vs. Message Receipt<br>FIFO delivery | Lamport Clocks
+| [Lecture 6](./Lecture%2006.md)<br>April&nbsp;10<sup>th</sup>,&nbsp;2020 | Causal delivery<br>Totally-ordered delivery<br>Implementing FIFO delivery<br>Preview of implementing causal broadcast | Delivery vs. Receipt<br>FIFO delivery
+| [Lecture 7](./Lecture%2007.md)<br>April&nbsp;13<sup>th</sup>,&nbsp;2020 | Implementing causal broadcast<br>Uses of causality in distributed systems<br>Consistent snapshots<br>Preview of the Chandy-Lamport snapshot algorithm | Causal anomalies and vector clocks
+| [Lecture 8](./Lecture%2008.md)<br>April&nbsp;15<sup>th</sup>,&nbsp;2020 | Chandy-Lamport Snapshot Algorithm |
+| [Lecture 9](./Lecture%2009.md)<br>April&nbsp;17<sup>th</sup>,&nbsp;2020 | Chandy-Lamport wrap-up: limitations, assumptions and properties<br>Uses of snapshots<br>Centralized vs. decentralized algorithms<br>Safety and liveness | Delivery guarantees and protocols
 | [Lecture 10](./Lecture%2010.md)<br>April&nbsp;20<sup>th</sup>,&nbsp;2020 | Reliable delivery<br>Fault classification and fault models<br>The Two Generals problem | Safety and liveness
 | [Lecture 11](./Lecture%2011.md)<br>April&nbsp;22<sup>nd</sup>,&nbsp;2020 | Implementing reliable delivery<br> Idempotence<br>At-least-once/at-most-once/exactly-once delivery<br>Unicast/Broadcast/Multicast<br>Reliable broadcast<br>Implementing reliable broadcast<br>Preview of replication
 | [Lecture 12](./Lecture%2012.md)<br>April&nbsp;24<sup>th</sup>,&nbsp;2020 | Replication<br>Total order vs. determinism<br>Consistency models: FIFO, causal and strong<br>Primary-backup replication<br> Chain replication<br>Latency and throughput