TheHingineer

 Queueing Models in Computer Networks 

 Queueing Model Kya Hota Hai?

Jab network par bahut saare data packets ek server ya router par aate hain, to wo line (queue) mein lag jaate hain. Queueing model help karta hai yeh samajhne mein ki:

  • Packets kitni speed se aa rahe hain (arrival rate)

  • Ek packet ko process karne mein kitna time lagta hai (service rate)

  • System mein kitna delay ho raha hai

  • Kitne servers available hain

  • Kya packet drop hote hain agar jagah nahi hai?

Socho ek ATM par log line mein lag rahe hain — yeh ek queue hai. Har aadmi ek packet ki tarah hai jo wait kar raha hai server (ATM machine) se serve hone ke liye.

 Important Terms:

Term Meaning (Hinglish)
λ (lambda) Kitne packets per second system mein aa rahe hain (arrival rate)
μ (mu) Ek server kitne packets per second serve kar sakta hai (service rate)
Queue Wo line jahan packets wait karte hain
Server Wo machine ya process jo packet ko handle karta hai
ρ (rho) Server ki busy hone ki percentage (utilization) – ρ = λ / μ

 Little’s Theorem – Super Simple Formula

Formula: L = λ × W

Symbol Meaning
L System mein average packets
λ Packets aane ki speed
W Har packet ka system mein rukne ka average time

 Example:

  • 5 packets/sec aa rahe hain (λ = 5)

  • Har packet 0.4 sec rukta hai (W = 0.4)

  • To system mein average 5 × 0.4 = 2 packets hamesha hote hain

 Common Queueing Models (Kendall’s Notation)

Queueing models ko likhne ka ek format hota hai:
A/S/c

  • A (Arrival) → kaise packets aate hain (M = exponential)

  • S (Service) → server kaise process karta hai (M ya G)

  • c (Servers) → kitne servers hain

M = “Memoryless” yaani exponential distribution (most common),
G = General (koi bhi distribution)

 M/M/1 Queue – Sabse Basic Aur Common Model

Feature Value
Arrival M (exponential)
Service M (exponential)
Servers 1

 Diagram:

Users → [Queue] → (1 Server) → Output

 Use Case:

  • Ek router ya printer jahan sabko line mein wait karna padta hai

 Example:

  • λ = 4/sec, μ = 5/sec

  • ρ = λ / μ = 4 / 5 = 0.8 → server 80% time busy hai

  • Delay = 1 / (μ – λ) = 1 second average waiting time

Agar ρ 1 se zyada ho gaya, to queue kabhi khatam nahi hogi!

 M/M/m Queue – Jab Ek Se Zyada Server Hain

Feature Value
Servers m (multiple)

 Diagram:

Users → [Queue] → (Server 1)
                  (Server 2)
                     ...
                  (Server m)

 Use Case:

  • Call centers (multiple agents)

  • Zomato kitchen jahan ek se zyada chef ho

 Example:

  • λ = 12/sec, μ = 4/sec, m = 4 servers

  • Total capacity = 4 × 4 = 16/sec

  • Utilization = 12 / 16 = 0.75 → servers 75% time busy

 M/M/∞ Queue – Unlimited Servers, No Waiting

Feature Value
Servers ∞ (infinite)

 Diagram:

Users → (Server 1)
        (Server 2)
           ...
        (Server ∞)

 Use Case:

  • Cloud-based apps jaise Google Search, ChatGPT etc.

  • Har user ko instantly ek server mil jaata hai

 Feature:

  • Koi queue nahi hoti, har request instantly serve hoti hai

 M/M/m/m Queue – Fixed Servers, No Queue, Request Drop

Feature Value
Servers m
Queue nahi hai
Blocking yes

 Diagram:

Users → (Server 1)
        (Server 2)
           ...
        (Server m)

If full → Request dropped

 Use Case:

  • Mobile networks jahan limited calling channels hote hain

  • Agar 4 calls chal rahi hain aur 5th aayi → busy tone

Isse Blocking Probability calculate karne ke liye Erlang B formula use hota hai

 M/G/1 Queue – Flexible Service Time, 1 Server

Feature Value
Arrival M
Service G (General)
Server 1

 Use Case:

  • Web server jahan kabhi chhoti file (image) aur kabhi badi file (video) serve hoti hai

  • Service time fix nahi hota

 Note:

  • Ye zyada realistic model hai, but complex calculations hoti hain


 Full Comparison Table 

Model Arrival Service Servers Queue Blocking Use Case
M/M/1 M M 1 Yes No Printer, single router
M/M/m M M m Yes No Call centers, load balancing
M/M/∞ M M No No Cloud computing
M/M/m/m M M m No Yes Mobile networks
M/G/1 M General 1 Yes No Realistic server model

 Summary 

Concept Easy Explanation
Queueing Model Request ki line aur server ka behavior model
Little’s Law L = λ × W – packets, arrival rate, aur delay ka relation
M/M/1 Ek server, exponential arrival/service
M/M/m Multiple servers
M/M/∞ Unlimited servers, zero waiting
M/M/m/m Fixed servers, request drop ho sakti hai
M/G/1 Flexible service time ke saath ek server

 Real Life Analogy Summary

Model Real Example
M/M/1 ATM machine
M/M/m Hospital with multiple doctors
M/M/∞ Search engine like Google
M/M/m/m Phone line with limited seats
M/G/1 Xerox shop with small and large jobs
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