Agent Parallel Processing & Task Distribution

Enable horizontal workload scaling by executing tasks concurrently across multiple isolated DVM sandbox instances.

Objective: Enable AI agents to efficiently process large workloads by distributing tasks across multiple isolated sandboxes for parallel execution.

Parallel Execution & Workload Distribution

Decentra increases throughput and operational efficiency by decomposing complex workloads into independent units and executing them concurrently across multiple isolated DVM sandboxes. This horizontal scaling model enables Decentra to process tasks that would be inefficient or impractical under sequential execution.

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Role of DVM Sandboxes

DVM sandboxes function as independent execution containers that operate simultaneously without shared state or interference. Each sandbox runs in full isolation with its own allocated resources, allowing Decentra to process separate workload segments in parallel. Errors are contained within individual sandboxes, and resource profiles can be tailored to the specific requirements of each task.

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Why Parallelism Matters

Without parallel execution, Decentra is constrained to linear task processing, which limits scalability and increases completion times for large workloads. By leveraging multiple sandboxes, Decentra can:

• Scale horizontally Handle arbitrarily large workloads by increasing the number of active sandboxes.

• Reduce end-to-end latency Complete large operations significantly faster through concurrent execution.

• Optimize resource allocation Assign CPU and memory per sandbox based on task complexity.

• Isolate failures Prevent individual execution errors from impacting other workload segments.

• Enable true concurrency Run tasks independently with no shared execution context.

This shifts Decentra from a single-threaded execution model to a distributed system capable of enterprise-scale processing.

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Practical Applications

Content & Media Processing

Decentra can analyze or transform large volumes of images or video by distributing subsets of media across multiple sandboxes, each operating concurrently.

Data Pipelines & ETL

Complex data workflows can be parallelized, with individual sandboxes handling separate data sources, transformations, or validation steps.

API & System Testing

Extensive test suites can be executed in parallel, with different sandboxes running isolated test categories at the same time.

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Scenario: High-Volume Data Processing

Decentra is tasked with processing 10,000 files, each requiring analysis and transformation. Rather than handling them sequentially, it provisions 50 DVM sandboxes, assigns 200 files to each, and executes all workloads in parallel. As a result, the entire operation completes in minutes instead of hours.

Implementation: Distributed Task Execution

1

Analyze Task

Agent identifies parallelizable components of the workload.

2

Create Sandboxes

Agent creates multiple sandboxes with appropriate resource allocation.

3

Distribute Work

Agent splits the workload into chunks and assigns each to a sandbox.

4

Execute in Parallel

All sandboxes execute their assigned tasks simultaneously.

5

Monitor Progress

Agent tracks execution status across all sandboxes.

6

Handle Failures

Agent retries failed tasks or redistributes work as needed.

7

Aggregate Results

Agent collects and combines results from all sandboxes.

Example (TypeScript)

parallel.ts

import SandboxSDK from '@dvmcodes/sandbox-sdk';

const client = new SandboxSDK({
  apiKey: process.env['SANDBOX_SDK_API_KEY'],
});

async function processInParallel(tasks: string[], concurrency: number = 10) {
  // Create multiple sandboxes
  const sandboxes = await Promise.all(
    Array.from({ length: concurrency }, (_, i) =>
      client.sandboxes.create({
        name: `Worker ${i + 1}`,
        resources: {
          cpus: 2,
          memory: 512,
        },
      })
    )
  );

  // Distribute tasks across sandboxes
  const taskChunks = [];
  for (let i = 0; i < tasks.length; i += concurrency) {
    taskChunks.push(tasks.slice(i, i + concurrency));
  }

  const results = [];
  for (const chunk of taskChunks) {
    // Execute tasks in parallel across sandboxes
    const chunkResults = await Promise.all(
      chunk.map((task, idx) =>
        client.sandboxes.execute(sandboxes[idx].id, {
          command: `python process.py "${task}"`,
          timeout: 300,
        })
      )
    );
    results.push(...chunkResults);
  }

  // Aggregate results
  return results.filter(r => r.status === 'completed' && r.exit_code === 0);
}

Next Steps

• Enable workload-driven sandbox autoscaling Provision and retire DVM sandboxes dynamically based on queue depth, task duration, and resource pressure to maintain throughput without overprovisioning.

• Implement intelligent load distribution Add load balancing that assigns tasks using real-time signals (CPU/RAM availability, estimated task cost, and sandbox health) to keep execution evenly spread and efficient.

• Add fault tolerance with retries and recovery Introduce failure isolation policies, automatic retries with backoff, checkpointing where applicable, and rerouting to healthy sandboxes to prevent stalled pipelines.

• Create observability for parallel runs Build dashboards that expose sandbox count, task states, throughput, latency percentiles, failure rates, retry counts, and per-sandbox resource usage for end-to-end visibility.