Frequently Asked Questions

Arcology is a radically new blockchain ecosystem that is self-organizing and runs on a unique multifactor consensus algorithm.

Arcology solves several problems that plague existing decentralized networks:

• Expensive, slow storage
• Unscalable system architecture
• Lack of serial execution
• Unsophisticated consensus algorithms
• Inefficient network communication
• Weak security models
• Fragmented ecosystems

We’ve employed artificial intelligence and machine learning to create the world’s first self-organizing, hierarchical blockchain architecture that is simultaneously scalable, secure and fully decentralized. 

For details, please refer to our Technical Paper, found here.

• Microservice architecture
• Fully parallel transaction processing
• Cluster computation
• Multifactor consensus algorithm
• Self-organization of nodes

Arcology is fast. Very fast. It’s also secure and stubbornly decentralized. This is made possible by our unique integration of the five features named above. Perhaps most important is a node cluster’s ability to process transactions in parallel. This means there’s no theoretical limit on how many transactions a single node cluster can process — it’s simply a matter of adding more machines. 

Conventionally, a node is a single machine with one copy of client software installed.  Users can synchronize blocking, process transactions and so forth, all on the same machine.

Arcology divides major functional modules of the client software into smaller, more independent parts. These modules can be installed individually on different machines; these machines are then connected by a high-speed network connection to function as whole.

This is a “node cluster.”

Thanks to this design, a single node cluster can scale without limits. Whereas a physical machine has limited resources — CPU, RAM, storage space — Arcology’s cluster can accommodate additional resources on-demand.

In this manner, Arcology has supercomputer-like scalability.

On blockchain networks, nodes are independent and self-contained entities that, by design, don’t trust each other. In contrast, machines within a node cluster belong to the entity and they trust each other, so they can share the workload.

Cluster computation is a well-studied subject for centralized systems, but efforts to leverage their advantages on DLT have, until now, been unsuccessful. To learn more about Arcology’s cluster computing framework, please refer to our Technical Paper, found here.

Laurent Zhang is the founder of Arcology. He got his B.Sc. Degree in Computing from Oxford Brookes university M.Sc.  in Intelligent systems. Mr. Zhang previously worked as senior research engineer and managers at MKS Instrument Inc, University of Alberta and Baker Hughes. He has extensive experience in solving large-scale, real-world applications with creative solutions. His is an expert in artificial intelligence, distributed computation, distributed ledger technologies, decentralized networks and machine learning algorithms.

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