Fault-Tolerant Integration of USB-Tethered Pi Zero Nodes into Duino-Coin Mining

Overview and Goals

Multiple Raspberry Pi Zero boards can be USB-tethered (via gadget mode) to a host, essentially turning each Pi into a network-connected compute node. In this setup, an x86 host runs the Duino-Coin PC Miner as a master controller, delegating mining work to several Pi Zero “worker” nodes. Each Pi Zero will act like a virtual core dedicated to mining. The goals of this design are to distribute hashing tasks across the Pi Zeros, achieve near-linear scaling of hashrate, and ensure that if any Pi hangs or disconnects, the overall mining process continues unaffectedraspberrypi.stackexchange.com. The system must automatically detect unresponsive nodes, reassign or restart their tasks, and even allow the host’s own CPU cores to mine alongside the Pi Zeros.

Key Challenges: Developing a lightweight RPC or job distribution method to send Duino-Coin workloads to each Pi, minimizing communication overhead, and implementing robust fault tolerance. We need to handle node discovery (finding each Pi over USB/Ethernet), task scheduling, result collection, and error recovery. Additionally, each Pi’s mining service should start on boot and be monitored (via heartbeats or watchdogs) to reset or revive hung processes. The following sections outline a recommended architecture and specific techniques (with code examples and existing frameworks) to meet these requirements.

Architecture: Master-Worker with Lightweight RPC

1. Master Controller (Host): The host acts as the master node, maintaining a list of available Pi Zero workers (e.g. on a USB Ethernet subnet like 192.168.7.x). It runs the Duino-Coin PC Miner logic but offloads the actual hash computations to the Pi nodes. The host’s responsibilities include:

• Worker Discovery: Identify all Pi Zero devices on USB. This can be done by scanning a known IP range, mDNS hostnames, or a static list of worker IPs. (For example, if each Pi Zero is configured with a unique static IP over the USB gadget interface, the host can ping those addresses to discover active nodes.)

• Task Scheduling: For each mining job obtained from the Duino-Coin server, the host assigns it to a Pi worker (or to a local CPU thread). This could be a simple round-robin or next-available scheduling. In practice, Duino-Coin mining involves continuously fetching “jobs” (hash challenges) and submitting results (shares), so the host will distribute these jobs across the workers in parallel.

• Result Collection: The host waits for each Pi to compute the result, then it submits the share to the Duino-Coin network. Each Pi Zero effectively functions as a separate mining thread, and the host aggregates their shares. Importantly, the host should not block waiting on a single Pi – it can manage multiple asynchronous requests so that faster or free nodes are kept busy even if one node is slow.

2. Worker Nodes (Pi Zeros): Each Pi Zero runs a persistent listener service that starts on boot. This worker service awaits incoming tasks from the host, executes the mining computation, and returns the result. The Pi does not need to contact the Duino-Coin server or handle networking beyond the host connection – it’s a pure compute node. We want this service to be minimal-overhead: essentially listening on a socket (or other RPC mechanism), performing a hash calculation when prompted, and replying.

• Starting on Boot: Configure each Pi to enable gadget mode (USB Ethernet or serial) and to launch the worker service at startup (e.g. via a systemd service or a cron @reboot entry). Ensure SSH is enabled as a fallback for maintenance. Each Pi can have a watchdog to reboot on kernel hangs (the Linux watchdog can “detect when your Pi locks up and automatically restarts the device”pimylifeup.com). The mining service itself should also be supervised – for instance, using systemd with Restart=always so it relaunches if it crashes. This guarantees each Pi is ready to receive jobs whenever the host is running.

• Listener Implementation: A lightweight RPC listener can be implemented using Python sockets or any minimal RPC library. For example, a simple Python socket server can listen on a TCP port on the Pi’s USB-ethernet interface. When the host connects and sends a job (e.g. a string or JSON containing the data to hash), the Pi’s service computes the result (using Duino-Coin’s hashing algorithm) and sends back the answer. This avoids the overhead of HTTP or heavy frameworks. The protocol can be as simple as a one-request/one-response cycle per job. Below is a pseudo-code outline of such a Pi worker service:

  python
  CopyEdit
  # On Pi Zero: simple mining worker (pseudo-code)
  import socket, struct
  server = socket.socket()
  server.bind(('0.0.0.0', 5005))  # listen on port 5005 (for example)
  server.listen(1)
  while True:
      conn, addr = server.accept() 
      with conn:
          # Optionally implement a handshake or authentication here
          while True:
              data = conn.recv(1024)  
              if not data: 
                  break  # connection closed
              job = data.decode()  # e.g. job format as string
              result = do_duco_hash(job)  # perform the Duino-Coin hash computation
              conn.sendall(result.encode())
  

  In practice, you’d likely keep the connection open and send multiple jobs sequentially to avoid reconnect overhead. The do_duco_hash(job) would implement the Duino-Coin “DUCO-S1” algorithm (which is based on a simple hash chain using SHA1pcbway.com). The host can send the necessary parameters (like the last hash or seed and the target difficulty) and the worker returns the computed hash or nonce solution. This raw socket approach has minimal overhead – just a few bytes per job – which is ideal for maximizing the Pi’s useful work. Since cryptocurrency proof-of-work is “embarrassingly parallel” (many independent trials) it splits nicely across nodes with negligible coordination costraspberrypi.stackexchange.com.

• Alternative RPC options: Instead of manual sockets, you can use higher-level lightweight RPC frameworks. RPyC (Remote Python Call) is one such library that lets you call Python functions on the remote Pi as if they were local objectsrpyc.readthedocs.io. For example, the Pi could run an rpyc_classic.py server, and the host can obtain a handle to a remote compute_hash() function. Calling this function on the host will actually execute on the Pi and return the result. RPyC handles the network communication under the hood. Other options include Pyro4, gRPC, or even an SSH-based command execution, but those tend to add more overhead. RPyC and Pyro are designed for Python-to-Python communication with minimal fuss. XML-RPC (built into Python) could also work for simplicity, though it uses HTTP underneath which isn’t as efficient.

• Example – using RPyC: On each Pi, run an RPyC service (either the classic server or a custom service exposing a hash function). The host then does:

  python
  CopyEdit
  import rpyc 
  conn = rpyc.connect(pi_ip, 18812)            # Connect to RPyC server on Pi
  remote_hash = conn.root.compute_hash         # get reference to remote function
  result = remote_hash(job_parameters)         # call remote function to get hash
  

  RPyC makes remote objects “transparent” so this feels like a local callrpyc.readthedocs.io. This can simplify development since you can reuse Python code on the Pi. However, be mindful of network latency – for efficiency, send just the minimal data needed (e.g. the block seed or hash prefix) and return only the result or share. The Duino-Coin share payloads are small, so RPC call overhead is usually negligible compared to the Pi’s computation time per share.

Fault Tolerance and Reliability

A major requirement is that the system continues mining even if some workers crash or become unresponsive. We achieve fault tolerance through a combination of heartbeats, timeouts, and watchdogs:

• Heartbeat & Timeouts (Host-side): The host controller should implement a timeout for each job sent to a Pi. If a Pi does not respond within a reasonable time (e.g. a bit longer than an average share solve time), the host marks that Pi as hung. The pending task can be retried on another node or on the host CPU. One simple way is using Python’s socket.settimeout() on the client sockets, so any recv that exceeds the time limit raises an exception. The host can then log the error, close that connection, and trigger a recovery (stop assigning new work to that Pi until it comes back). This way, a stuck Pi won’t stall the entire mining loop – the host simply skips it and redistributes work.

• Periodic Heartbeats: Apart from job-related communication, it’s wise to have a periodic heartbeat signal to monitor liveness. For example, the host can send a lightweight “ping” message to each Pi at regular intervals (say every few seconds) when they are idle, and expect a quick “pong” response. If a worker fails to respond to heartbeats, it’s considered offline. This technique is exemplified in ZeroMQ’s Paranoid Pirate pattern, which adds heartbeating to a load-balanced worker poolzguide.zeromq.org. In that pattern, workers regularly send a heartbeat message to the broker; if the broker (master) hasn’t heard from a worker in a while, it drops that worker from the poolzguide.zeromq.org. We can emulate this: each Pi could periodically announce “I’m alive” to the host (or the host polls them), so we can detect silent failures quickly. A simple implementation is to have the Pi thread send a newline or small packet every X seconds over the socket if no job was sent in that period, just to reassure the host. If the host doesn’t receive anything or the next job request times out, it treats the node as down.

• Worker Auto-Restart (Pi-side): Each Pi Zero should monitor and restart its mining service in case of failure. Using systemd is very helpful here: create a service unit for the miner listener (with Restart=on-failure or always). Additionally, enable the Linux hardware watchdog on the Pi. The hardware watchdog will reset the entire Pi if the system freezes or if a user-space heartbeat isn’t fed to /dev/watchdog within a timeoutpimylifeup.com. This protects against low-level hangs. In practice, a well-behaved Python service on Pi should not crash the OS, but enabling the watchdog is an extra safety net for hard lock-ups. As one guide notes, “Watchdog... allows you to detect when your Pi locks up and automatically restarts the device”pimylifeup.com. With these measures, a Pi that crashes or hangs will reboot and rejoin the mining cluster automatically (after its service restarts on boot). The host can periodically try to reconnect to any offline Pi, so recovered nodes are brought back into the fold.

• Graceful Degradation: The mining software on the host should be written to handle disappearing workers gracefully. This means catching exceptions on network failures and simply logging a warning rather than crashing. The Duino-Coin master loop can continue with fewer workers. Ideally, the design should also tolerate late responses (e.g., if a Pi returns a result after timeout, the host can discard it if already retried – but it’s harmless). If a Pi comes back online, the host can add it back to the rotation of available workers. By isolating each Pi’s work, we ensure one stuck node doesn’t block others. This approach mirrors how large mining pools or clusters operate: they treat each worker independently and the failure of one just reduces total hashpower, not halt the whole operationraspberrypi.stackexchange.com.

• Testing Failure Modes: It’s wise to simulate Pi failures to ensure the system behaves as expected. For example, temporarily disconnect one Pi or kill its service to see if the host times out and continues mining on the rest. Also test network delays – ensure the timeout isn’t too short (causing false alarms) or too long (causing unnecessary delays waiting on a dead node).

Communication Framework Examples

To implement the above architecture, you can choose from a few approaches depending on your familiarity and the complexity you need:

• Custom Sockets + Threads/Async (Lightweight): As illustrated, using Python’s socket library directly gives full control and minimal overhead. You would likely use one thread (or asyncio coroutine) per Pi connection on the host. Each thread handles sending jobs and receiving results from one Pi in a loop. This maps well to Duino-Coin’s design: the official PC miner spawns multiple threads for multiple cores, so here you spawn threads for remote cores. Instead of actually hashing in that thread, it sends the work to the Pi and waits for the result. This approach is straightforward and efficient, but you will need to write some boilerplate for thread management and reconnection logic.

• ZeroMQ + Heartbeat Pattern: If you prefer not to manage low-level sockets and threads, ZeroMQ (with the pyzmq binding) offers a robust abstraction for distributed task queues. You can implement a broker-worker model where the host is the broker that distributes tasks to Pi workers. ZeroMQ can automatically load-balance messages to available workers, and with a pattern like Paranoid Pirate, you can add heartbeating easilyzguide.zeromq.org. In this scenario, each Pi runs a small ZeroMQ REP (reply) loop, and the host uses a REQ (request) socket, or you set up a ventilator / sink pattern for continuous streaming of tasks. The ZeroMQ Guide provides a reference implementation of a reliable request-reply worker queue with heartbeatszguide.zeromq.org. The benefit is that ZMQ handles queuing and asynchronous I/O under the hood; you get high throughput and scalability with little effort. The downside is adding an external dependency and learning the ZMQ patterns. For a cluster of ~8 nodes, this is very manageable. Each Pi could register with the host’s broker at startup (sending a readiness signal), then the host broker sends jobs as they arrive, and if a Pi doesn’t heartbeat, it’s removed from the queuezguide.zeromq.org. This aligns perfectly with our fault tolerance goals.

• Distributed Task Queue (Celery/RQ): Another approach is to use a higher-level distributed task queue like Celery (with Redis or RabbitMQ as a broker) or RQ (Redis Queue). In this model, the host would enqueue “hash jobs” into a broker, and Celery worker processes running on each Pi would pick up jobs, execute them, and return results. Celery has built-in reliability: if a worker dies, the task can be retried or redirected to another worker (if the task is marked as retryable). It also supports time limits – a task that exceeds a timeout can be terminated and re-queued. This gives fault tolerance and scaling, at the cost of extra components (a message broker service) and some overhead. Celery might be considered heavy for something as quick as hashing tasks, but it’s proven technology for distributing work across hosts. In fact, hobbyists have run Celery on Raspberry Pi clusters for distributed computing tasks. One such project reports “everything works well enough” using Celery with a Redis broker on a Pi cluster, though Redis will eagerly consume memory on these small devicesdeldreth.me. In our scenario, you’d run the Redis broker on the host (which has more resources) and just run Celery workers on the Pi Zeros. The host (as the Celery client) can issue tasks via a simple function call. For example, define a Celery task @app.task(name="compute_hash") that performs the Duco hash. The host then does compute_hash.delay(job_data) to send it to the queue, and some worker Pi will execute it. The result can be returned or the host can poll for it. This setup provides a lot of features (retries, monitoring, scheduling), but it’s more complex to set up than a raw socket. Given we only have ~8 workers and a very specific task, a full task queue might be overkill. However, it’s an option if you want to leverage its reliability. (For minimal overhead, ensure that the task payloads are tiny and that results are processed quickly; Celery’s overhead is mainly in managing the message broker and task marshalling.)

• MPI or MPI4Py: Since this is essentially a parallel computing problem, one could also consider MPI (Message Passing Interface) using MPI4Py in Python or MPICH. MPI would allow you to treat all Pi zeros and the host as participants in a single parallel program. For instance, rank 0 (host) could dispatch work to ranks 1–8 (Pis) via MPI.send/recv or a scatter/gather approach. While MPI is very efficient in C/C++ HPC contexts, using it for a Python hashing project might be unwieldy. Traditional MPI also doesn’t handle fault tolerance well – if one rank fails, the whole MPI job typically fails unless you have resilience features. As one expert noted, if your task needs coordination and message passing, you can either “reinvent the wheel” or use MPI, which is a well-tested toolraspberrypi.stackexchange.com. In our case, the coordination is master-slave and quite simple, so a full MPI setup is probably not necessary. MPI4Py could work, but you’d need to pre-launch processes on each Pi and manage hostfile configurations. The complexity is higher and flexibility lower than using sockets or a small queue. Therefore, MPI is mentioned for completeness but not recommended as the first choice here.

Recommendation: A custom Python socket implementation (possibly enhanced with ZeroMQ patterns) is the recommended architecture for this scenario. It hits the sweet spot of being lightweight, easy to tailor to Duino-Coin’s workflow, and allowing fine-grained control over timeout and reconnection logic. Libraries like RPyC or Pyro can simplify calling remote functions if you want to avoid designing a protocol, whereas Celery/Redis provides a more heavyweight but high-reliability job queue if you’re already familiar with it. For most DIY mining rigs, sticking to sockets (or even using the Duino-Coin’s existing serial protocol approach – see Pitfalls) will be simplest.

Implementation Steps and Code Snippets

On Each Pi Zero (Worker Setup):

1. Enable USB Gadget Networking: Configure the Pi Zero’s /boot/config.txt and /boot/cmdline.txt to set up g_ether (USB Ethernet gadget) or g_serial if you prefer serial. For networking, add dtoverlay=dwc2 and modules-load=dwc2,g_ether (for Raspbian). This will present the Pi to the host as a USB network interface. Set a unique static IP for each Pi’s usb0 interface (e.g., Pi1: 192.168.7.2, Pi2: 192.168.7.3, etc., with host side configured as .1 on each subnet, or bridging them). Alternatively, use a DHCP server on the host to assign distinct IPs. The key is that the host can reach each Pi at a known address. If using g_serial instead, the Pi will appear as a COM port on the host – in that case you could actually reuse the Duino-Coin AVR miner protocol (more on this in Pitfalls).

2. Install Dependencies: Ensure Python 3 is installed. If using RPyC or PyZMQ or Celery, install those (pip3 install rpyc pyzmq duinocoin etc.). Also deploy the Duino-Coin hashing algorithm code to the Pi – either by installing the Duino-Coin miner package or copying a function that computes the hash. (Duino-Coin’s hashing is not too intensive – it’s SHA1-based and meant for Arduino, so Pi can handle it easily in pure Pythonpcbway.com.)

3. Worker Service Script: Create a Python script (e.g., pi_worker.py) that implements the listening logic. You can base it on the pseudo-code provided earlier. If using a library (RPyC or Celery), set up the appropriate server: e.g., for RPyC, run rpyc_classic.py on startup or write a custom service that registers a compute_hash method. For a raw socket approach, the script would bind to a port and handle one connection at a time (or potentially multiple if you want to serve many hosts, but here only one host connects to each Pi). It’s fine for the host to maintain one persistent connection per Pi to send sequential jobs.

4. Autostart and Watchdog: Set this script to run on boot. For example, create a systemd unit file /etc/systemd/system/ducopi.service:

   ini
   CopyEdit
   [Unit]
   Description=DuinoCoin Worker Service
   After=network.target
   
   [Service]
   ExecStart=/usr/bin/python3 /home/pi/pi_worker.py
   Restart=always
   User=pi
   
   [Install]
   WantedBy=multi-user.target
   

   Enable the service (sudo systemctl enable ducopi). Also enable the watchdog service (sudo apt install watchdog && sudo systemctl enable watchdog). In /etc/watchdog.conf, set a heartbeat file or use the default (the systemd service may already pet the watchdog). This ensures the Pi reboots if completely unresponsivepimylifeup.com. The combination of systemd auto-restart and hardware watchdog provides two layers of resiliency for the worker process.

On the Host (Master Setup):

1. Networking and Power: Connect all Pi Zeros via a powered USB hub (to avoid power drops) to the host. If using USB Ethernet gadgets, the host OS will create interfaces (like usb0, usb1, etc.). You may need to assign the host IP for each interface or bridge them. Confirm you can ping each Pi’s IP. If using serial gadget, note the COM port names. (You might end up with /dev/ttyACM0, ACM1, ...).

2. Host Mining Controller: If the Duino-Coin PC Miner software is open-source (it is Python), you can modify or wrap it. One approach is to replace its internal hashing function with a remote call. The PC Miner normally spawns threads equal to the number of cores specified and each thread works on jobs. For our needs, we could spawn N threads where N = number of Pi workers (+ maybe some for local CPU). Instead of computing the hash in-thread, have each thread send the job to a designated Pi and wait for result. However, an easier method is to write a custom master script that handles everything: connect to Duino-Coin server, request jobs, distribute to Pis, and submit results. The pseudo-code flow:

   python
   CopyEdit
   workers = [Worker(pi_ip) for pi_ip in PI_ADDRESSES]  # set up connections to each Pi
   while True:
       job = request_job_from_duco()          # e.g. via Duco API or socket
       worker = pick_next_worker(workers)     # choose an available worker (round-robin or idle)
       try:
           result = worker.send_job(job)      # send job to Pi, get result (with timeout)
       except TimeoutError:
           mark_worker_offline(worker)
           workers.remove(worker)
           result = None
       if result:
           submit_result_to_duco(result)      # send the Pi's computed result back to Duino-Coin server
       # else, if no result (worker failed), optionally let host compute it or just skip (pool will give new job)
   

   Here, Worker could be a class encapsulating the connection to each Pi and implementing send_job with the necessary timeout and retry. If a worker fails, it’s removed from the active list. A background thread might periodically attempt to reconnect to dropped workers (ping their IP to see if they came back). The Duino-Coin server connection can be managed using the official PC miner code (it speaks a simple protocol over TCP to the Duino server). Each mining iteration yields a job (usually a seed and difficulty) and expects a result (nonce or hash) to accept the share. The exact API depends on DuinoCoin’s current protocol – you might integrate at a high level by running the PC Miner in “SOCAT/AVR” mode where it expects external devices. DuinoCoin’s documentation notes that Arduinos use a special AVR miner program where the PC asks the Arduino over serial for the resultduinocoin.com.

3. Using the Host CPU as well: The architecture can easily mix local threads and remote workers. For example, if you have a multi-core host, you might dedicate some threads to local mining (especially since an x86 core may be much faster than a Pi Zero). The PC Miner natively supports multi-threading, so you could simply run a few local mining threads alongside this distributed scheme. To integrate, you could treat the host as another “worker” in the scheduling loop (i.e. have a worker stub that directly computes the hash locally). Or run the standard PC Miner separately for local threads and a custom script for Pi – but then they’d compete for the same account, which DuinoCoin might allow (Duco does allow multiple miners on one username). A unified approach is cleaner: e.g., maintain a thread pool where some threads are bound to remote Pi calls and others run a local hash function. Just ensure the difficulty (Kolka) adjustments are handled: DuinoCoin assigns difficulty per device type. Pi Zeros would likely be classed similar to low-power CPU miners. If each Pi has its own miner instance (even virtually), the server might adjust difficulty per connection. Using one connection for all and splitting work could confuse the difficulty algorithm. A workaround is to open multiple connections to the Duino server – one per Pi – but manage them in one program. This way each Pi appears as a separate miner (with its own username_workerID). The host can maintain 8 connections and dispatch each connection’s jobs to its corresponding Pi. This is more complex but aligns with DuinoCoin’s expectation of one job stream per device. If simplicity is preferred, a single connection with sequential job assignment should still function, but the Pi that is fastest will end up doing most shares unless you manipulate the difficulty.

4. Monitoring & Logging: Implement logging on the host for each job submission and result, including which worker handled it and how long it took. This will help identify if one Pi is consistently slow or failing. If a Pi crashes and reboots, you’ll see it drop out and later the host should detect it responding to pings or heartbeats and bring it back. You can also monitor system metrics: perhaps run a small script on each Pi (or use something like Node-RED or Telegraph) to observe CPU temp and load, just to ensure they aren’t overheating or throttling when running 100% mining load continuously.

5. Testing the Whole Pipeline: Start the host controller and confirm that all Pi workers receive tasks and produce shares. You should see accepted shares in the Duino-Coin miner output. Test fault tolerance by disconnecting one Pi’s USB or killing its service: the host should log a timeout and continue with others. When the Pi is plugged back or reboots, ensure the host eventually reconnects (you might need a simple loop that tries to reconnect offline workers every 30 seconds, for example).

Pitfalls and Considerations

• USB Gadget Networking Quirks: With multiple Pi Zeros on USB, you may encounter networking quirks. If each Pi uses the same default RNDIS MAC address, it could confuse the host. It’s wise to assign a unique MAC to each Pi’s gadget (there’s a module parameter for g_ether or you can use usb_eth_addr in config.txt). Also note that Windows hosts may need the RNDIS driver and might not handle multiple identical RNDIS gadgets well. Linux hosts can name interfaces usb0, usb1, etc., but the ordering could change – consider binding by MAC or using udev rules for consistent naming. Alternatively, using a USB hub with Pi Zeros in serial gadget mode might be simpler if you leverage the AVR miner approach (see next point).

• Duino-Coin Protocol Integration: The approach we described treats the host as the sole entity talking to the Duino-Coin server, and the Pis as opaque compute helpers. However, Duino-Coin was designed with microcontroller mining in mind and already has a mechanism for multiple devices: the AVR Miner software can manage several Arduino-like devices over serial portsduinocoin.com. Each Arduino runs a simple firmware that receives a job and spits back a result over serial. In theory, you could mimic this with Pi Zeros by using USB serial gadget mode (g_serial). Each Pi would show up as a COM port on the host. If you flash or program each Pi with a tiny script that speaks the Duco Arduino protocol (basically, listen on serial for a job string, compute, respond), then you could use the official Duino-Coin AVR miner program unmodified – just point it to all the COM ports. This is an alternate architecture: it offloads hashing to Pis (which act like super-Arduinos) and leverages the existing reliability of the AVR miner. The downside is you’re constrained to Duino-Coin’s protocol and might not easily implement advanced fault tolerance (beyond what AVR miner does). Also, running an entire Python interpreter on Pi to basically act like an Arduino might be overkill relative to the custom network approach. But it’s good to know this option exists, especially if you prefer not to write the master logic from scratch. The user community might have scripts for Raspberry Pi acting as Duco miners directly – if so, integrating at that protocol level could save time. Just ensure the USB driver (g_serial) is stable for multiple devices; mixing serial and network gadgets on multiple Pi might be tricky.

• Performance and Overhead: The Pi Zero is a low-power 1GHz single-core CPU. Eight of them roughly offer the performance of a single mid-range desktop core (8 × ~10 H/s each vs. a desktop doing perhaps ~80 H/s on one thread, just hypothetical figures). The communication overhead (sending a small job and receiving a hash) is negligible in terms of bandwidth. Even over USB 1.1 speeds, a few dozen bytes per job is nothing. The latency of a USB round-trip is small (sub-millisecond). The dominant time will be the Pi computing the hash. Therefore, adding networking will not meaningfully slow down mining – you’ll nearly get the sum of each Pi’s hash rateraspberrypi.stackexchange.com. If you notice lower than expected performance, it could be due to each Pi being assigned higher difficulty by the pool when it performs well. Duino-Coin’s Kolka system adjusts difficulty per miner device to keep things balancedduinocoin.com. So, if you funnel all work through one connection, the server might increase difficulty thinking one “miner” is very fast. Using multiple miner connections (one per Pi) as mentioned can mitigate this, but then you have to manage multiple logins. Keep an eye on accepted shares and ensure the difficulty isn’t too high for Pi Zeros (which would lower their effective H/s). You might need to configure the miner software to treat each Pi as a separate identifier (Duino-Coin allows adding a miner ID in config).

• Scaling Limits: The design targets ~8 Pi Zeros, which is fine. If you scaled to dozens of nodes, you’d need to consider more sophisticated load balancing (where something like ZeroMQ or Celery becomes more attractive). But up to 8, a simple Python thread per node is manageable. The host CPU overhead for managing sockets is minimal. The network over USB can handle this traffic easily. Just ensure the USB hub and host ports can supply power – Pi Zeros might draw ~120mA each when busy, so 8 could be ~1A plus overhead. A powered hub is recommended.

• Code Robustness: Python is an easy choice but remember to handle exceptions like ConnectionResetError (if a Pi reboots mid-connection) and to close sockets properly. Use retries for transient errors. Logging each event will help troubleshoot. Also, when updating the Pi worker code or host code, consider versioning the protocol – if you change what data is sent, update both sides in sync to avoid mismatches.

• Security: Since this is all local and via USB, security isn’t a big concern, but note that your Pi services are potentially accessible on a network interface. If the host bridges USB Ethernet to your LAN for internet, the Pi’s listening port might be exposed. To be safe, you could restrict the listener to only accept the host’s IP (e.g., bind to 192.168.7.2 on the Pi side and have host at .1) or use a simple authentication handshake on connect (even a shared key). Generally, obscurity (private USB link) is fine here.

In summary, the recommended solution is to use a master-worker architecture with the host distributing mining tasks to each USB-tethered Pi Zero. A lightweight RPC mechanism (such as a persistent socket connection per Pi) will keep overhead low while enabling high-throughput task delegation. For reliability, implement heartbeats and timeouts so the system detects failures quickly and recovers gracefully – this way, the loss of one worker doesn’t interrupt the others or crash the miner. Code examples from Python’s networking libraries and frameworks like RPyC or Celery illustrate how to perform remote calls and distribute work across hosts with minimal effortrpyc.readthedocs.iodeldreth.me. By following these practices and using watchdogs to auto-reboot hung nodes, you can achieve a fault-tolerant mining cluster. Each Pi Zero will contribute as an independent “virtual core” to the Duino-Coin miner, and the overall rig will continue hashing away even if one or two go down. This design not only scales your Duino-Coin setup to ~8 modest nodes but also provides a learning platform for distributed computing techniques that can be applied to other projects beyond crypto mining.

Sources: The concepts and patterns recommended here draw on known distributed computing practices and Raspberry Pi cluster experiments. For instance, ZeroMQ’s guide details reliable request-reply patterns with heartbeating for worker poolszguide.zeromq.org, and Raspberry Pi developers have successfully used Celery+Redis on Pi clusters to offload tasks, noting that it “works well” albeit with some resource overheaddeldreth.medeldreth.me. The RPyC library documentation explains how remote procedure calls allow one process to invoke code on another as if it were localrpyc.readthedocs.io. These tools, combined with Duino-Coin’s existing multi-device support and basic networking, form the basis of a resilient Pi Zero mining cluster. The approach is validated by the expectation that splitting hashing work among many low-power nodes yields roughly the sum of their rates (minus small overhead)raspberrypi.stackexchange.com – effectively 8 Pi Zeros at ~10 H/s each can reach ~80 H/s together, just as a single device would doing the same work in parallel.