---
title: 'FCFS Blockchain Networks: Latency & MEV'
url: https://www.emergentmind.com/topics/first-come-first-served-fcfs-blockchain-networks
type: topic
---

# FCFS Blockchain Networks: Latency & MEV

A First-Come-First-Served (FCFS) blockchain network establishes transaction sequencing strictly according to the arrival time at block proposers or relevant nodes, diverging fundamentally from fee-based chains. FCFS ordering enforces temporal prioritization, so the optimization of network latency becomes paramount for both user-facing applications and adversarial actors such as MEV searchers. Algorand is a prominent implementation of FCFS ordering, providing a technical ecosystem that has driven empirical and theoretical investigation into arbitrage extraction, adversarial game dynamics, and network infrastructure adaptations.

## 1. Transaction Ordering and Mechanics

FCFS ordering in blockchain is implemented at the node or relay network level, rather than directly within the consensus protocol. Each participation node maintains a mempool (T = {t₁, ..., t_N}) of pending transactions, stamped with their local arrival time τ_i. The block construction algorithm selects transactions in strict order of increasing τ_i; only if congestion exceeds a threshold does the algorithm switch to fee-based prioritization. Formally, the ordering function σ is defined as:

\[
σ(T) = \langle t_{(1)}, t_{(2)}, ..., t_{(N)} \rangle \quad \text{where} \quad τ_{(1)} \leq τ_{(2)} \leq \cdots \leq τ_{(N)}
\]

Thus, absent network congestion, transaction fees play no direct role in intrablock sequencing. Block proposers receive transactions through a distributed set of relays, leading to local (but highly consistent) reception orderings [2308.06513]. Block times on Algorand are typically T_round ≈ 3.3 s, and propagation delays are constrained (Δ ≈ 50–200 ms) [2401.07992].

## 2. MEV Extraction and Adversarial Game Framework

In an FCFS system, Maximal Extractable Value (MEV) optimization pivots away from gas auctions to latency races. Searchers S = {S₁, ..., S_M} monitor liquidity pools and transaction flows, seeking arbitrage opportunities that can be seized by rapid issuance. The payoff structure is starkly temporal: a searcher’s transaction tx realizing arbitrage on pools {p₁, ..., p_l} must be included at the earliest position touching those pools. Competing searchers may issue nearly simultaneous transactions—only the fastest transaction (lowest reception time τ) captures the profit π > 0, with all subsequent attempts either reverting or earning zero net revenue. The adversarial framework thus centers around minimizing ℓ_i,p, the end-to-end latency from searcher i to proposer p.

Proposer selection on Algorand uses a Verifiable Random Function (VRF), yielding committees P_r of 20 candidates weighted by stake; the eventual block builder is chosen from this set [2401.07992].

## 3. Arbitrage Detection Algorithms and Latency Analysis

MEV extraction in FCFS networks requires detection algorithms tuned to per-round time constraints. The cyclic-arbitrage detection method operates on multigraphs G=(V,E) over asset set V=A, using runtime filtering of cycle implementations involving recently updated pools. Pseudocode for Algorithm 1 (CyclicArbDetect):

```python
# Inputs: Multigraph G=(V,E), cycle implementations I_total, block state S^r, time budget τ
A_candidates = []
t0 = now()
for i in I_total:
    if i uses at least one pool updated in S^r:
        if now() - t0 > τ:
            break
        Pi = compute_exchange_rate_product(i, S^r)
        if Pi > 1:
            x_max = argmax Profit_i(x) subject to CPMM constraints
            if Profit_i > pi_min:
                A_candidates.append((i, x_max, Profit_i))
return A_candidates
```

Strict enforcement of the time budget τ is necessary; in practice, the majority of relevant arbitrages can be discovered within τ≥T_round [2401.07992]. The window of opportunity for executing an arbitrage (Δ_update) is empirically ≈6 blocks (≈19.8 s), so real-time detection is required. The revenue degradation formula is:

\[
\frac{revenue(τ)}{revenue(∞)} \approx 1 - e^{-\frac{τ}{T_{round}}}
\]

This formula models the sharp decline in recovered MEV when detection times are significantly below one block interval [2401.07992].

## 4. Empirical MEV Patterns and Attack Vectors

Network-state backrunning is the dominant MEV extraction pattern in Algorand and similar FCFS chains. An adversary observes mempool updates and issues a transaction designed to execute immediately after a “victim” trade. This backrun exploits real-time simulation on the last confirmed state. The probability of slot-winning is modeled as:

\[
P_s \approx Pr[Δ + Δ_{\text{backrun}} < Δ_{\text{other}}]
\]

On-chain analysis reveals that arbitrage opportunities are uniformly distributed across block positions; e.g., an index of 1,142,970 arbitrages in 401,679 blocks, with profits peaking marginally in later octiles but counts remaining flat [2308.06513]. Techniques such as destructive frontrunning and batch transaction issuance (BTI, i.e., clogging) are also observed. BTI can temporarily trigger congestion, reverting the network to fee-based ordering, thus permitting classical MEV attacks (sandwich, replay). BTI events are frequent, inexpensive, and carry significant ordering influence.

## 5. Network-Layer Optimizations and Latency Strategies

Transaction fees are largely irrelevant in FCFS ordering, so searchers must invest in minimizing latency to proposers. Optimum network strategy focuses on direct, low-latency relay connections to relays controlling high-stake proposer candidates. For searcher node np and relay r, with total relay-stake weight W_r, the win-probability is:

\[
Pr_i \propto \sum_{r \in \text{Relays}} 1_{L_{i,r} \leq L_{j,r}\,\forall j \neq i} \cdot W_r
\]

Empirical measurement shows that direct relay connectivity can yield up to 75% win rates in contest scenarios, independent of fee magnitude [2401.07992]. Practical searcher tactics include maintaining relay diversity, candidate-cycle ranking (by pool liquidity), and tuning detection pipelines to round intervals. Network-level fairness can be improved by batching transaction arrival (e.g., frequent batch auctions on 100–200 ms scales).

## 6. FCFS, Deferred Choice, and Oracle Constructions

FCFS ordering semantics map directly to deferred choice in event-driven blockchain applications: given a set E of external events, the system must atomically select the earliest as the canonical “winner.” Formal models distinguish environment-driven state transitions and transaction-driven state transitions, with race-resolution occurring via atomic comparison of on-chain detection times. Oracle architectures supporting FCFS/deferred choice include:

- Storage oracles (on-chain, synchronous)
- Request-response oracles (off-chain, asynchronous)
- On-chain/off-chain history oracles (tracking (t, value) pairs)
- Publish-subscribe oracles (push updates on event trigger)

Each pattern provides different latency, trust, and auditability tradeoffs [2104.10520]. Consensus among multiple oracles can be implemented via median timestamp selection.

## 7. Protocol Design Implications and Recommendations

FCFS blockchain design constrains adversarial MEV extraction to latency optimization rather than fee escalation. Searchers achieve best results through real-time monitoring, rapid simulation, and strategic relay connectivity. Protocol-level recommendations include tightening congestion thresholds, dynamic fee adjustment under mempool saturation, and introduction of order-fair consensus mechanisms (e.g., Aequitas) or frequent batch auctions. The adoption of global proof-of-arrival times (e.g., VRF stamping) can further dampen latency races [2308.06513]. For deferred choice applications, atomic transaction-based event selection and robust oracle architectures are essential to prevent race conditions and ensure non-repudiation.

Taken together, FCFS blockchain networks redefine transaction ordering and MEV optimization as latency-driven competitions, with practical adversarial tactics centered around timing, network topology, and protocol-level batching. The engineering of detection, issuance, and relay connectivity now determines economic outcomes and fairness in decentralized systems [2401.07992][2308.06513][2104.10520].

Source: https://www.emergentmind.com/topics/first-come-first-served-fcfs-blockchain-networks