Tauchain: The Social Dispersed Computer introduced as a Social Network? By Dana Edwards. Posted on Steemit. October 12, 2018.
What might a Tau Operating System via a Tau Social Dispersed Computer function like?
We know from tauchain.org that the first iteration of Tau is to be a discussion platform not too dissimilar from Facebook. Of course this would simply be the front end or the "face" of what could behind the scenes evolve toward a social dispersed computer complete with a dispersed operating system. The resources have to be managed and a kernel could provide for this in a manner not dissimilar to what we see with EOS. The Agoras or AGRS token specifically represents "resources" as it is the tokenization of resources for whichever application Tauchain will use.
TML provides the basis from which to create the necessary languages to produce a dispersed operating system computer. Zennet even has an algorithm which Ohad himself worked on for the purpose of calculating the resource requirements. All minds will be able to contribute towards the computational resources (at least in theory) of Tauchain.
Because of Zennet there may in fact not be a limit to the amount of computation resources which we could throw at the super computer. It will of course depend on resource management which is where a kernel likely comes into play because any smart apps built to run on Tau will have to ask for resources. Resource management is one of the core functions of a kernel and of an operating system which is why I think it is likely that Tauchain will have one. I think the Ethereum route shows problems with scaling as applications also have to compete for resources in a way where the network cannot self manage it. Cryptokitties for example can render the whole Ethereum network lagged and if this is a computer then it could mean a nonsense app could disrupt more critical apps.
A prime example of a potential smart app for Tauchain
An example (which may or may not be feasible) is a health and fitness app. The app in theory could allow any user to provide data such as genetic information, blood test results, exercise tracking, blood pressure, blood sugar and anything else. All of this could provide a feedback loop back to the patient on how to improve their health over time based on the knowledge of Tau. As technology gets better the users could add more devices to provide more data for a better feedback loop. As technology evolves FGPAs could be added to meet the demand for calculations and storage can be rented as well.
An operating system could give priority to this kind of app by load balancing the resources. How would it know to do this? Tau could learn the morals, legal ramifications, and a consensus can emerge that health related apps deserve a premium access to resources because it can save lives.
The Paradigm of Social Dispersed Computing and the Utility of Agoras. By Dana Edwards. Posted on Steemit. October 12, 2018.
Social Dispersed Computing
What is socially dispersed computing? It is an edge oriented computing paradigm which goes beyond cloud and fog computing. To understand socially dispersed computing we first have to discuss dispersed computing and how it differs from the previous paradigm of cloud and fog computing. The current trend toward decentralized networks which we first saw with the peer to peer technologies such as Napster, Limewire, Bittorrent, and later with Bitcoin, have brought to us an opportunity to conceptually new paradigms. The original model most people are familiar with is the client server model which was very much limited in that the server was always vulnerable to DDOS attack. The client server model has never been and could likely never be censorship resistant.
In the client server model the server could simply shut down as was the case with Bitconnect or it could be raided. The server could also be shut down by hackers who simply flood the site with requests. As we can see from the problems the client server model presented we discovered the utility of the peer to peer model. The peer to peer model was all about censorship resistance and promoted a network which was to have no single point of failure (single point of attack) which could be result in the shutdown of access points to the information. One of the first applications for these peer to peer networks was file sharing networks and networks such as Freenet/Tor etc. This of course eventually evolved into the Bitcoin which ultimately led to the development of Steem.
In dispersed computing a concept is introduced called "Networked Computation Points". An NCP can execute a function in support of user applications. To elaborate further I'll offer something below.
Consider that every component in a network is a node. Now consider that every component node is an NCP in that it can execute some function to support some user application. If we think of for example a blockchain then we know mining would fit into this category because it is both a node in the network and it also can execute a function in support of Bitcoin transactions. Why is any of this important? Parallelism is something we can gain from dispersed computing and please note that it is distinct form concurrent computing. When we rely on parallelism we can reap the benefits in terms of performance when executing code by breaking it up into many small tasks which can be performed across many CPUs.
EOS attempts to leverage parallelism specifically to enable it's performance boost. The benefit is speed and flexibility. Think for example of the hardware side also with FGPAs which can do similar tasks of a microprocessor. FGPAs (not ASICs) which unlike ASICs would provide generalized flexible parallel computing. Consider that just like with mining a company could add more and more FGPAs to scale an application as needed.
To understand Social Dispersed Computing we have to make note of the fact that there are other users at any given time. For example the other users in the network participate to provide resources to the network for the benefit of other users whilst using the network. So in Steem for example as you add content to Steem you are adding value to Steem in a direct way, but also in a dynamic way. The resources on Steem also can adapt dynamically to the demand provided that the incentive mechanism (Resource Credits) works as intended.
EOS as an example DOSC (Dispersed Operating System Computer)
Because EOS seems to be the first to approach this holistically I will give credit to the EOS network for pioneering dispersed computing in the crypto space. All resources are representable by tokenization in a dispersed computing network. EOS and even Steem have this. Steem has it in the form of "Resource Credits" which represent the available resources on the Steem network. If more resources are needed then theoretically the resource credits could act as an incentive to provide these resources to the Steem network. This provides a permanent price floor to Steem represented as the amount of Steem which would have to be purchased in order to have enough resources to run Steem (if I have the correct theoretical understanding). This would put Steem on a trajectory toward dispersed computing.
Operating systems typically sit between the hardware and software as a sort of abstraction layer. This traditionally has been valuable because programmers don't have to directly speak to the hardware and hardware designers don't have to directly communicate by their designs to the programmer. In essence the operating system in the traditional model is centralized and made by a company such as Microsoft or Apple. This centralized operating system typically runs on a device or set of devices and provides some standard services such as email, a web browser, and maybe even a Bitcoin wallet.
Typically the most valuable or high utility software people consider on a computer is the operating system. In our smart phones this is Android OS and in PCs it may be Windows or Linux. This is of course thrown on it's head under the new paradigm of dispersed computing and the new conceptual model of the "decentralized" operating system. EOS is the first to attempt a decentralized operating system using current blockchain technology but the upcoming technology easily eclipses what EOS could do. Tauchain is a technology which if successful will leave EOS in the stone age in terms of what it will be able to do. EOS while ambitious also has had it's problems with regard to the voting mechanisms and the ease at which collusion can take place.
To better understand how decentralized operating systems emerge learn about:
If we look at OSKit we see that it is the tools necessary for operating system development. If we look at Tauchain we realize that it is strategically the most important tool for the development of a decentralized operating system being provided in the form of TML (a partial evaluator). If we think of the primary tool necessary to develop from we have to initially start with a compiler. A compiler generator is more like what TML allows with it's partial evaluator. More specifically it is the feature of Futamura projection which can provide the ability to generate compilers.
If we look at the next most important part of an operating system it is typically the kernel. Let's have a look at what an exokernel is:
Operating systems generally present hardware resources to applications through high-level abstractions such as (virtual) file systems. The idea behind exokernels is to force as few abstractions as possible on application developers, enabling them to make as many decisions as possible about hardware abstractions. Exokernels are tiny, since functionality is limited to ensuring protection and multiplexing of resources, which is considerably simpler than conventional microkernels' implementation of message passing and monolithic kernels' implementation of high-level abstractions.
By Thorben Bochenek [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons
From this at minimum we can see that an exokernel is a more efficient and direct way for programmers to communicate with hardware. To be more specific, "programs" communicate with hardware directly by way of an exokernel. We know the most basic function of a kernel in an operating system is the management of resources. We know in a decentralized context that tokenization allows for incentives for management of resources. When we combine them we get kernel+tokenization to produce an elementary foundation of an operating system. In a distributed context we could apply a decentralized operating system in such a way that the network could be treated as a unified computer.
Abstraction is still important by the way. In an operating system we know the object oriented way of abstraction. Typically the programmer works with the concept of objects. In an "Application Operating Environment" an "Application Object" can be another useful abstraction. Abstraction can of course be taken further but that is for another blog post.
The Utility of Agoras
Agoras+TML is interesting. Agoras is the resource management component of what may evolve into the Tau Operating System. This Tau Operating System or TOS is something which would be vastly superior to EOS or anything else out there because of the unique abilities of Agoras. The main abilities have been announced on the website such as the knowledge exchange (knowledge market) where humans and machines alike can contribute knowledge to the network in exchange for the token reward. We also know that Agoras will have a more direct resource contribution incentive property in the form of the AGRS token so as to facilitate the sale or trade of storage, bandwidth or computation resources.
The possible (likely?) emergence of the Tau Operating System
In order for Tauchain to evolve into a Dispersed Operating System Computer it will need an equivalent to a kernel. Some means of allowing whomever is responsible for the Tauchain network to control and manage the resources of that network. If for example the users decide then by way of discussion there would be a formal specification or model of a future iteration of the Tauchain network. This according to current documents is what would produce the requirements for the Beta version of the network to apply program synthesis. Program synthesis in essence could result in a kernel and from there the components of a Tau Operating System could be synthesized in the same way. Just remember that all that I write is purely speculative as we have no way to predict with certainty the direction the community will take during the alpha.
Tauchain Update: Significant code changes in Github and discussion of progress. By Dana Edwards. Posted on Steemit. September 30, 2018.
Just several hours ago lead developer and founder of the Tauchain project Ohad Asor released his most significant code update yet. This blog post will be to discuss some of those updates and put it into context. In order to make sense of the current codebase : "Tauchain Codebase" I will also discuss a bit about the makeup of the code.
The significant breakthrough - Ohad implements the BDD
First some might be wondering what is BDD? BDD is a data structure called binary decision diagram. This data structure in my opinion is as significant to Tauchain as the "blockchain" data structure was to Bitcoin. For those who do not have a computer science degree I will elaborate on what exactly a data structure is below before discussing what a BDD is and why it is so significant.
Brief discussion on what a data structure is
In programming a data structure is a concept which represents a data organization method. For example blockchain is all about how records are stored as blocks. There are other similar data structures which represent decentralized data management and storage such as for instance the distributed hash table data structure.
A blockchain data structure looks like this for visualization:
By Matthäus Wander [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Commons
A hash table looks like this for a visual:
By Jorge Stolfi [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0) or GFDL (http://www.gnu.org/copyleft/fdl.html)], from Wikimedia Commons
The really good programmers choose the appropriate data structure to meet the requirements of the project. BDD was chosen specifically by Ohad because it provides efficiency boosts in a key area necessary for Tauchain to function as intended. In specific we know Tauchain requires partial fixed point logic in order to have decidability in P-SPACE. We also know Tauchain requires decentralization and efficiency. Efficiency can be understood better in terms of the trade off between time and space. We do not have unlimited time or space so we must sacrifice one in order to get more of the other.
When we look at the code base we know that Ohad can optimize the code either by sacrificing space in which the executable will be bigger (but the code runs faster) or he can choose to sacrifice time in which the code is a smaller executable to save memory but might run slightly slower. This highlights the essential trade off between time and space when optimizing code but of course there is more to it because algorithms within a code base have to make similar trade offs.
Now what exactly is a BDD (binary decision diagram)?
Now that we understand the basics about efficiency and what a data structure is we can make a bit more sense of what a BDD is. In order to understand why BDD as a data structure is so important to Tauchain we have to remember that Tauchain is about logic. We can take the most basic example of Socrates:
A predicate takes an entity or entities in the domain of discourse as input while outputs are either True or False. Consider the two sentences "Socrates is a philosopher" and "Plato is a philosopher". In propositional logic, these sentences are viewed as being unrelated and might be denoted, for example, by variables such as p and q. The predicate "is a philosopher" occurs in both sentences, which have a common structure of "a is a philosopher". The variable a is instantiated as "Socrates" in the first sentence and is instantiated as "Plato" in the second sentence. While first-order logic allows for the use of predicates, such as "is a philosopher" in this example, propositional logic does not.
Based on the rules of first order logic we can have our inputs and receive our outputs. In the most basic example above we an see a bit about how logic works. To elaborate further:
Relationships between predicates can be stated using logical connectives. Consider, for example, the first-order formula "if a is a philosopher, then a is a scholar". This formula is a conditional statement with "a is a philosopher" as its hypothesis and "a is a scholar" as its conclusion. The truth of this formula depends on which object is denoted by a, and on the interpretations of the predicates "is a philosopher" and "is a scholar".
A truth table has one column for each input variable (for example, P and Q), and one final column showing all of the possible results of the logical operation that the table represents (for example, P XOR Q). Each row of the truth table contains one possible configuration of the input variables (for instance, P=true Q=false), and the result of the operation for those values. See the examples below for further clarification. Ludwig Wittgenstein is often credited with inventing the truth table in his Tractatus Logico-Philosophicus, though it appeared at least a year earlier in a paper on propositional logic by Emil Leon Post.
When we are dealing with logic we may find that a truth table helps with visualization.
Now with this knowledge we have the most basic Socrates example:
This can be represented via truth table and is called a syllogism. To solve this we simply apply a kind of reasoning called deductive reasoning. This would indicate that if All men are mortal is true and if Socrates is a man is also true then Socrates is a mortal must be true. If we were to say all men are mortal but Socrates is immortal then Socrates cannot be a man. So if Socrates is a man he must be moral or there is what we call a contradiction. Logic is all about avoiding these sorts of contradictions and in specific binary or boolean logic is to reach a conclusion which always must be one of two possible values.
If I ask you to play a game which we want to guarantee will end with either one of two possible outcomes then we have a good example of a boolean function. 1 or 0, true or false, on or off, a or b.
Some of you may be familiar with data structure we call a DAG (directed acyclic graph). For those of you who understand this concept you can visualize a BDD as being very similar to a propositional DAG.
By David Eppstein [CC0], from Wikimedia Commons
We know from DAGs that it's a finite amount of vertices, edges, etc. We may also be able to visualize topological ordering and if you remember my post on transitive closure you might also remember the visuals on how that can work:
A binary decision diagram can represent a truth table:
By The original uploader was IMeowbot at English Wikipedia. (Transferred from en.wikipedia to Commons.) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0/)], via Wikimedia Commons
And from these visuals now it should be abundantly clear how this is critical to the functioning of Tauchain. The BDD data structure allows for efficient model checking as well. To understand we have to consider the boolean satisfiability problem.
This highlights the fact that BDD can be used to create a SAT solver.
A DPLL SAT solver employs a systematic backtracking search procedure to explore the (exponentially sized) space of variable assignments looking for satisfying assignments. The basic search procedure was proposed in two seminal papers in the early 1960s (see references below) and is now commonly referred to as the Davis–Putnam–Logemann–Loveland algorithm ("DPLL" or "DLL"). Theoretically, exponential lower bounds have been proved for the DPLL family of algorithms.
Without getting overwhelmed by technical details the key points are below:
To read the code for yourself and track the progress of Tauchain development take a look at Github:
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Suggested readings to better understand the Tau ecosystem, Tau Meta Language, Tau-Chain and Agoras, and collaborate in the development of the project.
Lecturas sugeridas para entender mejor el ecosistema Tau, Tau Meta Lenguaje, Tau-Chain y Agoras, y colaborar en el desarrollo del proyecto.