Bump version to 1.1.8

docs/src/apps/jsonrpc-api.md

@@ -980,7 +980,7 @@ The result field will be a JSON object with the following fields:
 // Request
 curl -X POST -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","id":1, "method":"getVersion"}' http://localhost:8899
 // Result
-{"jsonrpc":"2.0","result":{"solana-core": "1.1.7"},"id":1}
+{"jsonrpc":"2.0","result":{"solana-core": "1.1.8"},"id":1}
 ```
 
 ### getVoteAccounts

docs/src/cluster/synchronization.md

@@ -6,7 +6,7 @@ Solana takes a very different approach, which it calls _Proof of History_ or _Po
 
 Solana technically never sends a _block_, but uses the term to describe the sequence of entries that validators vote on to achieve _confirmation_. In that way, Solana's confirmation times can be compared apples to apples to block-based systems. The current implementation sets block time to 800ms.
 
-What's happening under the hood is that entries are streamed to validators as quickly as a leader node can batch a set of valid transactions into an entry. Validators process those entries long before it is time to vote on their validity. By processing the transactions optimistically, there is effectively no delay between the time the last entry is received and the time when the node can vote. In the event consensus is **not** achieved, a node simply rolls back its state. This optimisic processing technique was introduced in 1981 and called [Optimistic Concurrency Control](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.4735). It can be applied to blockchain architecture where a cluster votes on a hash that represents the full ledger up to some _block height_. In Solana, it is implemented trivially using the last entry's PoH hash.
+What's happening under the hood is that entries are streamed to validators as quickly as a leader node can batch a set of valid transactions into an entry. Validators process those entries long before it is time to vote on their validity. By processing the transactions optimistically, there is effectively no delay between the time the last entry is received and the time when the node can vote. In the event consensus is **not** achieved, a node simply rolls back its state. This optimisic processing technique was introduced in 1981 and called [Optimistic Concurrency Control](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.4735). It can be applied to blockchain architecture where a cluster votes on a hash that represents the full ledger up to some _block height_. In Solana, it is implemented trivially using the last entry's PoH hash.
 
 ## Relationship to VDFs
 

docs/src/introduction.md

@@ -6,9 +6,9 @@ Solana is an open source project implementing a new, high-performance, permissio
 
 ## Why Solana?
 
-It is possible for a centralized database to process 710,000 transactions per second on a standard gigabit network if the transactions are, on average, no more than 176 bytes. A centralized database can also replicate itself and maintain high availability without significantly compromising that transaction rate using the distributed system technique known as Optimistic Concurrency Control [\[H.T.Kung, J.T.Robinson (1981)\]](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.4735). At Solana, we are demonstrating that these same theoretical limits apply just as well to blockchain on an adversarial network. The key ingredient? Finding a way to share time when nodes cannot trust one-another. Once nodes can trust time, suddenly ~40 years of distributed systems research becomes applicable to blockchain!
+It is possible for a centralized database to process 710,000 transactions per second on a standard gigabit network if the transactions are, on average, no more than 176 bytes. A centralized database can also replicate itself and maintain high availability without significantly compromising that transaction rate using the distributed system technique known as Optimistic Concurrency Control [\[H.T.Kung, J.T.Robinson (1981)\]](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.4735). At Solana, we are demonstrating that these same theoretical limits apply just as well to blockchain on an adversarial network. The key ingredient? Finding a way to share time when nodes cannot trust one-another. Once nodes can trust time, suddenly ~40 years of distributed systems research becomes applicable to blockchain!
 
-> Perhaps the most striking difference between algorithms obtained by our method and ones based upon timeout is that using timeout produces a traditional distributed algorithm in which the processes operate asynchronously, while our method produces a globally synchronous one in which every process does the same thing at (approximately) the same time. Our method seems to contradict the whole purpose of distributed processing, which is to permit different processes to operate independently and perform different functions. However, if a distributed system is really a single system, then the processes must be synchronized in some way. Conceptually, the easiest way to synchronize processes is to get them all to do the same thing at the same time. Therefore, our method is used to implement a kernel that performs the necessary synchronization--for example, making sure that two different processes do not try to modify a file at the same time. Processes might spend only a small fraction of their time executing the synchronizing kernel; the rest of the time, they can operate independently--e.g., accessing different files. This is an approach we have advocated even when fault-tolerance is not required. The method's basic simplicity makes it easier to understand the precise properties of a system, which is crucial if one is to know just how fault-tolerant the system is. [\[L.Lamport (1984)\]](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.1078)
+> Perhaps the most striking difference between algorithms obtained by our method and ones based upon timeout is that using timeout produces a traditional distributed algorithm in which the processes operate asynchronously, while our method produces a globally synchronous one in which every process does the same thing at (approximately) the same time. Our method seems to contradict the whole purpose of distributed processing, which is to permit different processes to operate independently and perform different functions. However, if a distributed system is really a single system, then the processes must be synchronized in some way. Conceptually, the easiest way to synchronize processes is to get them all to do the same thing at the same time. Therefore, our method is used to implement a kernel that performs the necessary synchronization--for example, making sure that two different processes do not try to modify a file at the same time. Processes might spend only a small fraction of their time executing the synchronizing kernel; the rest of the time, they can operate independently--e.g., accessing different files. This is an approach we have advocated even when fault-tolerance is not required. The method's basic simplicity makes it easier to understand the precise properties of a system, which is crucial if one is to know just how fault-tolerant the system is. [\[L.Lamport (1984)\]](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.1078)
 
 Furthermore, and much to our surprise, it can be implemented using a mechanism that has existed in Bitcoin since day one. The Bitcoin feature is called nLocktime and it can be used to postdate transactions using block height instead of a timestamp. As a Bitcoin client, you would use block height instead of a timestamp if you don't trust the network. Block height turns out to be an instance of what's being called a Verifiable Delay Function in cryptography circles. It's a cryptographically secure way to say time has passed. In Solana, we use a far more granular verifiable delay function, a SHA 256 hash chain, to checkpoint the ledger and coordinate consensus. With it, we implement Optimistic Concurrency Control and are now well en route towards that theoretical limit of 710,000 transactions per second.