Comparative Examination

Transcription

1 IMAGINE A WORLD WHERE ANYONE CAN PROFIT BY JOINING A MASSIVE PROCESSING AND STORAGE PLATFORM Comparative Examination of Decentralized Processing and Storage Crypto-Platforms Version 2.0, June 21st, 2018

2 INTRODUCTION The rapid growth in introduction of new crypto-token projects and ICOs, virtually in every domain of business, industrial, IT and public operations creates wealth of innovation and inventive solutions that are based on the blockchain technology. Nonetheless, the forms in which information on each project, its roadmap and scope is disclosed require vast efforts from investors and community members attempting to identify the properties of each project, its direction of development, roadmap, operations and support. IAGON operates in the field of decentralized cloud services, developing a platform (i.e. a grid ) for conducting processing tasks and storing files to cater to needs of both conventional computational tasks and databases, as well as to the needs of the more complex AI operations and management of Big Data repositories that are significantly larger than organizational databases. IAGON conducted this study to assess the capabilities of other crypto-token and blockchain based projects that address decentralized storage of processing, and completed their ICO or plan to do so. The projects were compared according to prominent features of cloud services, decentralized storage and processing and the existing crypto-token platforms. Data were collected and analyzed from multiple resources published by the different projects: whitepapers, websites, videos, forums, project blogs, etc. Despite our efforts, no references to particular attributes of some projects were found online. We will be grateful for completing these details, including references from the originators of the projects, for the benefit of the blockchain, Big Data and AI community members..

3 THE CRITERIA FOR BENCHMARKING As decentralized cloud services provide both storage and processing capacity and as the recent emergence of AI technologies require not only CPU use but also GPU, the following dimensions were included in our analysis: AI powered grid Does the processing/storage grid utilizes advanced algorithms, such as Machine Learning techniques (data classification and clustering), Artificial Neural Networks or Deep Learning to continuously analyze its performance, improve and optimize it? The implementation of an AI powered grid can assist in the allocation of processing tasks and files for storage based on the current and predicted miners performance and accessibility Decentralized management of the grid - Is the processing/storage planned to be managed by a decentralized system for the allocation of processing tasks and files for storage by using multiple nodes, or is the management of the grid based on a central module that allocates them to the miners? Continuous optimization of the grid Is the grid planned to learn from its present performance by continuously analyzing data on it and dynamically modifying the allocation of processing tasks and files for storage to optimize it under changing conditions, or does it use a static and non-adaptive policy (such as random allocation)? Storage of file fragments on the grid CPU processing grid Is the grid planned to support conventional processing tasks that are based on the use of CPUs (such as SQL queries)? GPU processing grid - Is the grid planned to support conventional processing tasks that are based on the use of GPUs (such as Deep Learning analyses)? Multiple platform support Is the grid planned to support other cryptoplatforms, in addition to Ethereum? Ethereum platform - Is the grid planned to run on Ethereum? Tangle platform - Is the grid planned to run on the Tangle technology? OS support Which operating systems will the miner s application support? File encryption Which file encryption algorithm is planned to secure stored files?

4 RESULTS The benchmarking analysis was based on a comparative study of projects developing decentralized storage and processing platforms that completed or plan to complete an ICO. Data were collected from their whitepapers, websites, videos, forums, project blogs, as well as from other materials (such as FAQ and replies to questions) published online by the project teams. The results of the comparison considering the multiple dimensions of comparison are as follows: IAGON Storj SONM DADI Golem iexec dfinity Siacoin 0chain Friendup DxChain Hypernet Perlin Symbioses AI management of the grid Decentralized management of the grid Continuous optimization of the grid Storage of file fragments on the grid CPU processing grid GPU processing grid N/A TBA N/A Multiple platform support Ethereum platform Tangle platform N/A Marketplace OS support Linu x, Linu x,, Linu x,,, N/A N/A N/A, File encryption SHA- 256 AES 256 N/A N/A - N/A N/A blake 2b N/A N/A N/A - N/A N/A

5 IAGON IAGON s architecture is planned to support allocation of file fragments for storage and processing tasks operated on a decentralized grid of nodes that joins the storage and processing capacities of miners. The allocation of processing tasks and files for storage will be done by operation of an AI based coordination module that will operate in a decentralized manner by utilizing the miner s applications on the various nodes to select the miner s node for carrying out a processing task or for storing a file fragment. The project will support multiple technologies, including Ethereum and Tangle. STORJ Storj is planned to support the storage of files on a decentralized grid by using Ethereum. The management of the platform is decentralized and miner s applications will run on different operating systems. Files are protected by using the AES256 protocol to secure them before allocating them to miners. SONM SONM is planned to support both storage of files and processing tasks on a decentralized grid by using Ethereum. The management of the platform is decentralized and miner s applications will run on different operating systems. The encryption of files stored on miners nodes is not clearly indicated, though SONM s whitepaper mentioned that Hard disk drive analogue will be implemented using decentralized data storage solutions: IPFS (InterPlanetary File System), Storj, Sia, etc.. SONM will support processing tasks on both CPUs and GPUs. DADI DADI is planned to support the both storage of files and processing tasks on a decentralized grid by using Ethereum. The management of the platform is decentralized and miner s applications will run on different operating systems. The encryption of files stored on miners nodes is not clearly indicated. DADI clearly indicates that it will support processing tasks on CPUs, but does not mention whether processing on GPUs will be included in its scope of operations.

6 GOLEM Golem is planned to support processing tasks on a decentralized grid by using Ethereum. The management of the platform is decentralized and miner s applications will run on different operating systems. Golem will support processing tasks on CPUs. Support of processing tasks that require GPU operations will be announced by Golem s team. IEXEC iexec is planned to support both storage of files and processing tasks on a decentralized grid by using Ethereum. The management of the platform is decentralized and miner s applications will run on different operating systems. The encryption of files stored on miners nodes is not clearly indicated. iexec will support processing tasks on both CPUs and GPUs. DFINITY dfinity is planned to support both storage of files and processing tasks on a decentralized grid by using Ethereum. The management of the platform is decentralized and miner s applications will run on different operating systems, though dfinity s team does not indicate which operating systems will be supported. The encryption of files stored on miners nodes is not clearly indicated. dfinity will support processing tasks on both CPUs and GPUs. SIACOIN Siacoin is planned to support the storage of files on a decentralized grid by using Ethereum. The management of the platform is decentralized and miner s applications will run on different operating systems. Files are protected by using the blake2b protocol to secure them before allocating them to miners.

7 DXCHAIN DxChain aims at integrating the Hadoop framework with Ethereum, operating miners as MapReduce nodes for storage and processing activities. This framework will utilize the existing MapReduce parallel computing allocation algorithms (that are not based on AI) and will encrypt the files on the miner s machine or will require the user to do so before uploading files into the platform. The platform is limited to operating tasks that are written in Pig and Hive Hadoop s data processing languages. 0CHAIN 0chain is a multiple dimension Blockchain that supports n-dimensional Blockchain, with the ability to fork from the main Blockchain and to store data in blobs that are operated by miners. Data storage is based on sharding files that are stored in the miners computers, located along the multiple Blockchain networks. 0chain will also support CPU processing on the Blockchain forks. HYPERNET Hypernet aims at providing a distributed computing grid for multiple computational task, without storage of files. The grid is based on uniting the computational power of CPUs (no GPUs are mentioned) to carry out different tasks in multiple languages submitted to the CPU grid. The project utilizes the Ethereum Blockchain and multiple platform support is not planned. PERLIN Perlin is a distributed computing grid for smart contracts. It will operate by joining the RAM, CPU and disk space of miners (no GPU is mentioned) to carry out smart contracts for developers. Perlin claims to develop its own ledger technology that is 80 times faster than competitive technologies. SYMBIOSES Symbioses is a distributed computing grid that supports distributed file storage and CPU and GPU processing on the Ethereum Blockchain. The allocation of files and processing tasks is done by a reputation and availability model. Yet, the model is not dynamic and is based on static equations for calculating reputation and availability, thereby it does not apply the basic principles of AI and Machine Learning.

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