Round Robin And Multi Level Queue - Learning Module

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Time Quantum Selection

The Goldilocks Problem of Scheduling

In the tale of Goldilocks, porridge could be too hot, too cold, or just right. The time quantum in Round Robin scheduling faces an identical challenge—it can be too small (excessive overhead), too large (poor responsiveness), or just right (optimal balance).

Choosing the time quantum is arguably the single most important tuning decision when deploying Round Robin scheduling. A poorly chosen quantum can degrade system performance by orders of magnitude, transforming a responsive interactive system into a sluggish batch processor, or wasting half the CPU on context switches.

This page provides a rigorous, comprehensive analysis of time quantum selection. We'll explore the mathematical foundations, examine the tradeoffs from multiple perspectives, study empirical research, and develop practical guidelines for selecting optimal quantum values across different workload types.

What You Will Master

By the end of this page, you will understand: the fundamental tradeoff between responsiveness and overhead, mathematical models for quantum optimization, the relationship between quantum and CPU burst characteristics, empirical guidelines from OS research, and strategies for adaptive quantum adjustment.

The Fundamental Tradeoff

At its core, quantum selection involves balancing two opposing forces:

Force 1: Responsiveness (favors small quantum)

Smaller time quanta mean processes cycle through the ready queue faster, reducing the maximum time any process must wait for CPU access. For interactive applications—text editors, web browsers, terminals—this responsiveness creates the illusion of simultaneous execution.

Force 2: Efficiency (favors large quantum)

Every context switch consumes CPU time that could otherwise be used for productive computation. Since switches occur at quantum boundaries, larger quanta mean fewer switches per unit time, maximizing the fraction of CPU time devoted to actual work.

The Core Equation:

Let's formalize this tradeoff. Given:

Q = time quantum
S = context switch time
n = number of ready processes

Responsiveness Metric (Maximum Response Time): $$T_{response,max} = (n - 1) \times Q$$

Efficiency Metric (CPU Utilization): $$U_{cpu} = \frac{Q}{Q + S}$$

Tradeoff Analysis: Response Time vs. CPU Utilization
Time Quantum	Max Response (n=10)	CPU Utilization (S=0.5ms)	Classification
1ms	9ms	66.7%	Highly responsive, poor efficiency
5ms	45ms	90.9%	Good responsiveness, acceptable efficiency
10ms	90ms	95.2%	Balanced (common default)
20ms	180ms	97.6%	Slightly sluggish, good efficiency
50ms	450ms	99.0%	Batch-like, excellent efficiency
100ms	900ms	99.5%	Nearly FCFS, maximum efficiency

The Non-Linear Impact

Notice that reducing Q from 10ms to 1ms decreases response time by 90% but drops CPU utilization from 95% to 67%—a 28% loss in productive capacity. The overhead impact accelerates dramatically at small quanta because switch time S is constant while Q shrinks.

Mathematical Analysis of Quantum Selection

Let's develop a rigorous mathematical framework for analyzing quantum selection.

Model Parameters:

n = number of processes in the system
Q = time quantum
S = context switch overhead
Bᵢ = CPU burst time of process i
B̄ = average CPU burst time

Throughput Analysis:

Throughput measures how many processes complete per unit time. In a fully loaded Round Robin system:

$$Throughput = \frac{1}{\bar{B} + \frac{S \times \lceil \bar{B}/Q \rceil}{\lceil \bar{B}/Q \rceil}}$$

Simplifying for the case where B̄ >> Q (many switches per burst):

$$Throughput \approx \frac{Q}{(Q + S) \times \bar{B}}$$

Waiting Time Analysis:

For n identical processes with burst time B, average waiting time is:

$$\bar{W} = \frac{(n-1) \times n \times Q + (n-1) \times \lceil B/Q \rceil \times S}{2n}$$

This formula accounts for both the time spent waiting behind other processes and the overhead of context switches.

Turnaround Time Analysis:

For a single process with burst B in a system with n-1 other processes:

$$T_{turnaround} = B + W$$

Where waiting includes all time spent in the ready queue while other processes execute.

Optimal Quantum Derivation:

To find the optimal Q that minimizes average turnaround time, we take the derivative and set to zero:

$$\frac{d\bar{T}}{dQ} = 0$$

The closed-form solution is complex, but empirically, the optimal quantum is approximately:

$$Q_{optimal} \approx \sqrt{2 \times S \times \bar{B}}$$

For typical values (S = 0.5ms, B̄ = 50ms): $$Q_{optimal} \approx \sqrt{2 \times 0.5 \times 50} = \sqrt{50} \approx 7ms$$

This aligns with the common practice of using 10-20ms quanta in general-purpose systems.

quantum_optimizer.py
Python
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import numpy as np
import matplotlib.pyplot as plt
from typing import List, Tuple
 
def analyze_quantum_tradeoffs(
    context_switch_ms: float,
    avg_burst_ms: float,
    num_processes: int,
    quantum_range: Tuple[float, float] = (0.5, 100.0)
) -> dict:
    """
    Comprehensive analysis of quantum selection tradeoffs.
    
    Returns metrics for various quantum values to visualize the tradeoff.
    """
    quanta = np.linspace(quantum_range[0], quantum_range[1], 200)
    
    # CPU Utilization: U = Q / (Q + S)
    utilization = quanta / (quanta + context_switch_ms)
    
    # Maximum Response Time: T_resp = (n-1) * Q
    max_response = (num_processes - 1) * quanta
    
    # Average number of context switches per burst
    switches_per_burst = np.ceil(avg_burst_ms / quanta)
    
    # Total overhead per burst
    overhead_per_burst = switches_per_burst * context_switch_ms
    
    # Effective throughput (relative to ideal)
    # Throughput = 1 / (burst + overhead)
    effective_throughput = 1 / (avg_burst_ms + overhead_per_burst)
    relative_throughput = effective_throughput / (1 / avg_burst_ms)
    
    # Approximate optimal quantum
    optimal_q = np.sqrt(2 * context_switch_ms * avg_burst_ms)
    
    return {
        'quanta': quanta,
        'utilization': utilization * 100,  # Percentage
        'max_response': max_response,
        'switches_per_burst': switches_per_burst,
        'relative_throughput': relative_throughput * 100,  # Percentage
        'optimal_quantum': optimal_q,
    }
 
def find_balanced_quantum(
    context_switch_ms: float,
    avg_burst_ms: float,
    num_processes: int,
    max_response_target: float
) -> float:
    """
    Find the largest quantum that still meets response time target.
    """
    # Max response = (n-1) * Q
    # Q = max_response_target / (n-1)
    max_quantum_for_target = max_response_target / (num_processes - 1)
    
    # Also consider threshold where overhead exceeds 10%
    # U = Q / (Q + S) = 0.90 => Q = 9S
    min_quantum_for_efficiency = 9 * context_switch_ms
    
    # Use the maximum of efficiency constraint with response target
    recommended = max(min_quantum_for_efficiency, 
                      min(max_quantum_for_target, 100.0))
    
    return recommended
 
 
# Example Analysis
if __name__ == "__main__":
    # System parameters
    S = 0.5    # Context switch time: 0.5ms
    B = 50.0   # Average CPU burst: 50ms
    n = 8      # Number of processes
    
    results = analyze_quantum_tradeoffs(S, B, n)
    
    print("=" * 60)
    print("Time Quantum Analysis")
    print(f"Context Switch: {S}ms | Avg Burst: {B}ms | Processes: {n}")
    print("=" * 60)
    
    print(f"\nTheoretical Optimal Quantum: {results['optimal_quantum']:.2f}ms")
    
    print("\nQuantum Selection Impact:")
    print("-" * 60)
    print(f"{'Quantum':^10} {'Util%':^8} {'MaxResp':^10} "
          f"{'Switches':^10} {'Throughput%':^12}")
    print("-" * 60)
    
    for q in [1, 2, 5, 10, 20, 50, 100]:
        idx = np.argmin(np.abs(results['quanta'] - q))
        print(f"{q:^10.0f} {results['utilization'][idx]:^8.1f} "
              f"{results['max_response'][idx]:^10.0f} "
              f"{results['switches_per_burst'][idx]:^10.0f} "
              f"{results['relative_throughput'][idx]:^12.1f}")
    
    # Find balanced quantum for 100ms response target
    balanced = find_balanced_quantum(S, B, n, 100.0)
    print(f"\nBalanced quantum for 100ms response target: {balanced:.1f}ms")

The CPU Burst Distribution

One of the most important insights for quantum selection comes from understanding the statistical distribution of CPU burst durations in real workloads.

Empirical Observations:

Decades of measurement studies have consistently shown that CPU burst durations follow a highly skewed distribution:

The majority of bursts are very short (< 10ms)
A small minority of bursts are very long (> 100ms)
The distribution often follows a hyperexponential or Pareto pattern

This is fundamentally because interactive processes (editors, browsers, shells) do small computations between I/O waits, while CPU-bound processes (compilers, simulations) have long uninterrupted computational phases.

Converting Mermaid diagram...

Implications for Quantum Selection:

This skewed distribution has profound implications:

1. Most bursts complete within a single quantum: If we choose Q ≈ 10ms, approximately 60-80% of all CPU bursts will complete without preemption. These processes never experience context switch overhead (except when first dispatched).

2. Only long bursts incur repeated overhead: The minority of long-running processes (typically CPU-bound batch jobs) will be preempted multiple times, but these are often less latency-sensitive anyway.

3. The 80% rule: A common heuristic is to set Q such that approximately 80% of CPU bursts complete without preemption. This typically yields Q ≈ 8-15ms for general-purpose workloads.

The Percentile Approach:

Given a burst time distribution, we can systematically determine quantum:

$$Q = B_{80} \text{ (80th percentile burst time)}$$

This ensures most interactive bursts complete in one quantum while still limiting the impact of long bursts.

CPU Burst Characteristics by Workload Type
Workload	Avg. Burst	80th Percentile	Recommended Q
Interactive Desktop	3-8ms	8-15ms	10-15ms
Web Server	2-5ms	5-10ms	5-10ms
Database (OLTP)	1-3ms	3-8ms	5-8ms
Scientific Computing	50-500ms	100-1000ms	50-100ms
Video Encoding	20-100ms	50-200ms	20-50ms
Mixed Workload	5-20ms	15-40ms	15-20ms

Measuring Your Workload

In production systems, tools like perf, SystemTap, or BPF can measure actual CPU burst distributions. Linux's /proc/schedstat and scheduling tracepoints provide burst duration histograms. Use this data to tune quantum for your specific workload rather than relying on generic defaults.

Impact on Different Process Types

Different process types are affected differently by quantum selection, making the choice of Q a policy decision about which workloads to favor.

I/O-Bound Processes:

These processes have short CPU bursts between I/O operations (disk reads, network requests, user input). They typically:

Use only a fraction of their quantum
Voluntarily yield CPU when blocking for I/O
Rarely get preempted by timer

Effect of Q on I/O-bound processes:

Small Q: No real benefit—they block before expiration anyway
Large Q: Minimal harm—they don't use the full quantum
Key concern: Time waiting for CPU between I/O completions

I/O-bound processes care more about how quickly they get CPU access after returning from I/O than about the quantum size.

CPU-Bound Processes:

These processes perform continuous computation with minimal I/O. They typically:

Use their entire quantum
Always get preempted by timer
Make steady progress but incur all switch overhead

Effect of Q on CPU-bound processes:

Small Q: Significant overhead—many switches per unit progress
Large Q: Better throughput—more work per switch
Key concern: Total time to completion (minimize switch overhead)

CPU-bound processes strongly prefer larger quanta to minimize the fraction of time lost to context switches.

The Mixed Workload Dilemma:

Real systems run both I/O-bound and CPU-bound processes simultaneously. The quantum must balance:

Responsiveness for I/O-bound: Smaller Q reduces waiting time
Throughput for CPU-bound: Larger Q improves efficiency

Example Analysis:

Consider a system with 4 I/O-bound processes (avg burst: 3ms) and 2 CPU-bound processes (burst: 100ms continuous):

Quantum	I/O-bound Wait	CPU-bound Overhead	Verdict
5ms	25ms max	20 switches = 10ms lost	Balanced
10ms	50ms max	10 switches = 5ms lost	Slight favor to CPU-bound
20ms	100ms max	5 switches = 2.5ms lost	Favors CPU-bound, I/O feels sluggish

With Q = 10ms, I/O-bound processes wait at most 50ms (acceptable for interactive), while CPU-bound processes waste only 5% on overhead.

The Scheduler's Impossible Choice

Pure Round Robin cannot perfectly serve mixed workloads because I/O and CPU-bound processes have fundamentally different needs. This limitation is precisely why Multi-Level Feedback Queues (MLFQ) were invented—to automatically detect process type and apply different quanta accordingly. We'll explore MLFQ in detail later in this module.

Empirical Guidelines and Research

Decades of operating systems research have produced empirical guidelines for quantum selection. These guidelines synthesize theoretical analysis with practical experience.

Historical Research Findings:

1. The 100ms Rule (Early Unix): Original Unix systems used 100ms quanta, reflecting the batch-oriented nature of early workloads and slow context switch times on 1970s hardware.

2. The 10-20ms Sweet Spot (Modern Interactive): As hardware improved and interactive workloads became dominant, research consistently found 10-20ms optimal for general-purpose systems:

Liedtke (1995): Measured context switch costs on modern hardware, recommending Q ≥ 10ms for < 5% overhead
Ousterhout (1982): Analyzed Unix scheduling, finding 10-20ms optimal for interactive responsiveness

3. The Variable Quantum Approach: Research by Nieh and Lam (1997) showed that varying quantum based on system load improves both responsiveness and throughput:

Light load: Use smaller Q for responsiveness
Heavy load: Use larger Q to reduce thrashing

Time Quantum in Production Operating Systems
Operating System	Default Quantum	Range/Policy	Notes
Linux (CFS)	~6ms (targeted)	1-20ms dynamic	Uses 'targeted latency' concept, not fixed RR
Windows 10/11	~15ms (desktop)	15-45ms	Foreground boost reduces quantum for server
macOS	~10ms	10-100ms	Dynamic based on load and priority
FreeBSD	~10ms	Configurable	ULE scheduler with dynamic quanta
Solaris	~10ms	10-100ms	Fair share scheduler with dynamic adjustment
RTOS (VxWorks)	~10ms typical	Application-defined	Hard real-time requires predictable quanta

Modern Hardware Considerations:

Context switch costs have decreased dramatically over the decades:

Era	Typical Switch Time	Recommended Min Q
1970s (PDP-11)	1-5ms	100ms
1990s (486/Pentium)	100-500μs	20-50ms
2000s (P4/Athlon)	10-50μs	5-20ms
2010s+ (Modern x86)	1-10μs	1-10ms

However, indirect costs (cache/TLB misses) haven't improved as dramatically, often adding 10-100μs of effective overhead on cache-cold switches.

The 10× Rule of Thumb

A robust guideline: set Q to at least 10× the context switch time. With modern switches at ~10μs, this suggests Q ≥ 100μs. In practice, 10-20ms provides a safety margin for indirect costs and covers most workload scenarios.

Adaptive Quantum Strategies

Given that optimal quantum depends on workload characteristics that vary over time, modern systems employ adaptive quantum strategies rather than fixed values.

Strategy 1: Load-Based Adjustment:

The quantum can be adjusted based on the number of runnable processes:

$$Q_{adaptive} = \frac{T_{target}}{n}$$

Where T_target is the desired maximum latency and n is the number of runnable processes.

Light load (n=2): Q = 50ms for efficiency
Moderate load (n=10): Q = 10ms for balance
Heavy load (n=50): Q = 2ms for responsiveness

This is essentially what Linux CFS does with its 'targeted latency' parameter.

adaptive_quantum.c
C
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/* Simplified adaptive quantum implementation */
 
#include <stdint.h>
 
/* System parameters */
#define TARGET_LATENCY_MS  100   /* Maximum scheduling latency target */
#define MIN_QUANTUM_MS     1     /* Minimum quantum (prevent thrashing) */
#define MAX_QUANTUM_MS     100   /* Maximum quantum (prevent starvation) */
#define MIN_GRANULARITY_MS 4     /* Minimum useful work unit */
 
/* Global scheduler state */
static uint32_t num_runnable_processes = 1;
static uint32_t current_quantum_ms = 10;  /* Default */
 
/**
 * Calculate adaptive quantum based on system load.
 * Called whenever num_runnable changes.
 */
uint32_t calculate_adaptive_quantum(void) {
    uint32_t ideal_quantum;
    
    if (num_runnable_processes == 0) {
        return MAX_QUANTUM_MS;  /* No contention */
    }
    
    /* Target: each process gets CPU within TARGET_LATENCY */
    ideal_quantum = TARGET_LATENCY_MS / num_runnable_processes;
    
    /* Apply bounds */
    if (ideal_quantum < MIN_QUANTUM_MS) {
        /* Too many processes - accept higher latency */
        ideal_quantum = MIN_QUANTUM_MS;
    }
    if (ideal_quantum > MAX_QUANTUM_MS) {
        ideal_quantum = MAX_QUANTUM_MS;
    }
    
    /* Ensure quantum is at least MIN_GRANULARITY for efficiency */
    if (ideal_quantum < MIN_GRANULARITY_MS && 
        num_runnable_processes * MIN_GRANULARITY_MS <= TARGET_LATENCY_MS * 2) {
        ideal_quantum = MIN_GRANULARITY_MS;
    }
    
    return ideal_quantum;
}
 
/**
 * Called when a process becomes runnable.
 */
void process_became_runnable(void) {
    num_runnable_processes++;
    current_quantum_ms = calculate_adaptive_quantum();
}
 
/**
 * Called when a process blocks or exits.
 */
void process_left_runqueue(void) {
    if (num_runnable_processes > 0) {
        num_runnable_processes--;
    }
    current_quantum_ms = calculate_adaptive_quantum();
}
 
/**
 * Returns current quantum for scheduling decisions.
 */
uint32_t get_current_quantum(void) {
    return current_quantum_ms;
}
 
/* Example behavior:
 * 
 * n=1:  Q = 100ms (max, single process gets full CPU)
 * n=2:  Q = 50ms  (each waits 50ms max)
 * n=5:  Q = 20ms  (each waits 80ms max)
 * n=10: Q = 10ms  (each waits 90ms max)
 * n=25: Q = 4ms   (each waits 96ms, at granularity floor)
 * n=100: Q = 1ms  (min, latency exceeds target but limited)
 */

Strategy 2: Process-Type Based Quantum:

Different quanta can be assigned based on detected process behavior:

I/O-bound processes: Smaller quantum (they don't use it anyway)
CPU-bound processes: Larger quantum (efficiency)
Interactive processes: Priority access, small quantum

This is implemented in MLFQ schedulers, which we'll cover in depth later.

Strategy 3: Time-of-Day Adjustment:

Some systems adjust quantum based on expected usage patterns:

Business hours: Smaller quantum for interactive responsiveness
Nights/weekends: Larger quantum for batch throughput

This is common in mainframe environments with predictable workload cycles.

Strategy 4: User-Preference Based:

Allow administrators or users to influence scheduling:

nice values affect priority and potentially quantum
Cgroup configurations can specify scheduling latency targets
Real-time processes can request specific timing guarantees

Linux CFS: Beyond Fixed Quantum

Linux's Completely Fair Scheduler (CFS) replaces fixed quantum with 'virtual runtime' tracking. Each process accumulates virtual runtime as it runs, and the scheduler always picks the process with lowest virtual runtime. The effective 'quantum' is derived from sched_latency / num_runnable, typically yielding 4-20ms slices dynamically.

Quantum and System Performance

The choice of quantum has cascading effects throughout the system, affecting metrics beyond just scheduling.

Cache Performance:

Modern CPUs rely heavily on cache locality. When a process runs, it loads its working set into L1/L2/L3 caches. A context switch may evict this data:

Small quantum: Frequent switches prevent cache warming; each process starts 'cold'
Large quantum: Processes warm caches and run efficiently before switching

The cache effect can dominate raw switch time. A 1μs switch that causes 100μs of cache refill effectively costs 100μs.

TLB (Translation Lookaside Buffer) Effects:

Each process has its own page tables. Context switches typically flush or tag-switch the TLB:

TLB miss penalty: 10-100 cycles per miss
Working set may require 100-1000 TLB entries
Refilling TLB can cost 1,000-100,000 cycles (1-100μs)

Memory Bandwidth:

Context switches cause memory traffic for:

Saving/restoring register state (~1KB)
Cache line refills (~KB to MB depending on working set)
Page table walks for TLB misses

With many switches, memory bandwidth is consumed by overhead rather than productive work.

Hidden Costs of Context Switches
Component	Direct Cost	Indirect Cost	Total Impact
Register Save/Restore	~1μs	Negligible	~1μs
Scheduler Execution	~1-5μs	Negligible	~1-5μs
L1 Cache Warmup	N/A	5-20μs	5-20μs
L2 Cache Warmup	N/A	10-50μs	10-50μs
L3 Cache (if missed)	N/A	20-100μs	20-100μs
TLB Refill	N/A	10-50μs	10-50μs
Branch Predictor	N/A	5-20μs	5-20μs
Typical Total	5μs	50-100μs	55-105μs

The Hidden Overhead Multiplier

Benchmarks that measure only direct switch costs (saving/restoring registers) dramatically underestimate true overhead. The cache/TLB effects often add 10-50× to the direct cost. When tuning quantum, consider effective overhead, not just raw switch time.

Power Consumption:

Context switches affect power efficiency:

More switches mean more memory access (power-hungry)
Caches operate efficiently when hits are high (switching hurts this)
CPU cannot enter deep sleep states with frequent timer interrupts

For battery-powered devices, larger quanta can improve battery life by reducing overhead and enabling deeper sleep states between work periods.

Real-Time Latency:

In systems with real-time requirements, the quantum affects worst-case latency:

$$Worst\ Case\ Latency = (n - 1) \times Q + interrupt\ latency$$

For real-time systems requiring < 10ms latency with 10 processes, Q must be < 1ms—but this conflicts with efficiency. This is why real-time systems often use priority-based scheduling instead of pure Round Robin.

Practical Guidelines for Quantum Selection

Based on our comprehensive analysis, here are actionable guidelines for selecting time quanta:

Decision Framework

•Step 1: Profile Your Workload — Measure actual CPU burst distributions using performance tools. Identify the 80th percentile burst time.
•Step 2: Measure Your Switch Cost — Benchmark context switch time including cache effects on your specific hardware. Tools: lat_ctx from lmbench, custom microbenchmarks.
•Step 3: Apply the 80% Rule — Set quantum to cover ~80% of bursts without preemption. This typically yields Q = 8-20ms for interactive workloads.
•Step 4: Apply the 10× Rule — Ensure Q ≥ 10× effective switch cost. With 50μs effective switch time, Q ≥ 0.5ms (easily satisfied for interactive).
•Step 5: Consider Response Requirements — If max response time T_max is constrained: Q ≤ T_max / (n-1) where n is expected concurrency.
•Step 6: Test Under Load — Validate chosen quantum under realistic load. Measure actual response times and throughput, then adjust.

Quick Reference: Quantum Selection by Use Case
Use Case	Recommended Q	Rationale
Desktop/Workstation	10-15ms	Balance interactive responsiveness with efficiency
Web Server	5-10ms	Low latency for request handling
Database Server	5-10ms	Quick response to queries
HPC/Batch Processing	50-100ms	Maximize throughput, latency unimportant
Embedded/IoT	20-50ms	Balance responsiveness with power efficiency
Gaming	4-8ms	Minimize input latency
Real-Time (soft)	2-5ms	Meet latency deadlines
Virtualization Host	10-20ms per VM	Fair sharing among VMs

When in Doubt

If you cannot measure your workload specifically, start with 10ms quantum. This value has stood the test of time across diverse systems and provides a reasonable balance for most general-purpose workloads. Tune from there based on observed behavior.

Summary: Time Quantum Selection

We have thoroughly explored the critical decision of time quantum selection—the tuning parameter that determines whether Round Robin provides responsive interactivity or degenerates into overhead-laden thrashing.

Key Takeaways

•The Fundamental Tradeoff: Small quantum = responsive but high overhead; large quantum = efficient but sluggish. There is no free lunch.
•CPU Utilization Formula: U = Q / (Q + S). With S = 0.5ms, Q = 10ms yields 95% utilization; Q = 1ms yields only 67%.
•The 80% Rule: Set quantum to cover ~80% of CPU bursts without preemption, typically 8-20ms for interactive workloads.
•Burst Distribution Matters: Most bursts are short (I/O-bound), so reasonable quanta let most processes complete without preemption.
•Hidden Overhead: Cache/TLB effects often add 10-50× to direct switch cost. Consider effective overhead, not raw switch time.
•Process Type Sensitivity: I/O-bound processes are quantum-insensitive; CPU-bound strongly prefer larger quanta.
•Adaptive Strategies: Modern systems dynamically adjust quantum based on load (Q = target_latency / num_runnable).
•Default Recommendation: 10-20ms for general-purpose systems; tune from there based on profiling.

Looking Ahead:

While careful quantum selection improves Round Robin, fundamental limitations remain—particularly for mixed workloads where I/O-bound and CPU-bound processes have conflicting needs. The next page introduces Multi-Level Queues, which address this by maintaining separate queues with different scheduling policies for different process types.

Page Complete

You now possess comprehensive knowledge of time quantum selection—the mathematics, tradeoffs, empirical guidelines, and adaptive strategies. This understanding enables you to tune scheduling for optimal performance in any workload scenario.