The document discusses load balancing in distributed systems, highlighting its importance in optimizing resource utilization and improving user experience. It outlines various challenges such as dynamic workloads and fault tolerance, as well as common and advanced techniques for load balancing. Future trends include edge computing, energy efficiency, and AI integration for smarter allocation.

1. Load Balancing Techniques
in Distributed Systems
Exploring Challenges and Solutions
Abdulaziz Awwad 44108981
Abdulrahman Alhababi 44152204
Faisal Althubaity 44008941
Khalid Altamimi 44106881
Abdullah Alharthi 44102462
Supervised by
Dr. Alwaleed Alharbi
Distributed Systems - 483

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Outlines
 Introduction
 Importance of Load Balancing
 Challenges in Load Balancing
 Common Techniques
 Advanced Techniques
 Future Trends
 Conclusion

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Introduction
What is Load Balancing?
 Distributes workloads across servers to
prevent overload.
 Ensures efficiency, reliability, and scalability.
Why is it important?
 Prevents slow performance.
 Handles dynamic demands.
An example of cloud computing services is
AWS.
I personally used S3. And it’s wicked!

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Importance of Load Balancing
The benefits of utilizing a load balancing is:
 Optimized resource utilization.
 Improved user experience “no network
lag/delay”.
 Reduced downtime.
Notable applications of using load balancing:
 Cloud Computing (AWS, Azure).
 Web Application (like e-commerce websites)
 IoT Systems

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Challenges in Load Balancing
 Dynamic Workloads
 Traffic spikes during peak hours.
 Scalability
 Managing more nodes as systems grow.
 Fault Tolerance
 Handling server failures efficiently.
 Resource Differences
 Balancing tasks for servers with varying capacities.
 Geographical Distribution (CDNs)
 Minimizing latency in global system.

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Common Techniques
Static Load Balancing
 Round Robin
 Cyclically assigns tasks to servers.
 Weighted Round Robin
 Considers server capacity.
Dynamic Load Balancing
 Least Connections
 Sends tasks to the server with the fewest active
connections.
 Least Response Time
 Allocates tasks to the fastest server.

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Advanced Techniques
 Consistent Hashing: Maps tasks to nodes efficiently, minimizes disruption when
servers are added/removed.
 AI/ML-Based: Predicts traffic and optimizes allocation.
 Geographical Load Balancing: Routes tasks to the nearest server.

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Future Trends
 Edge Computing: Allocating tasks between cloud and edge nodes.
 Serverless Architectures: Adapting to dynamic environments.
 Energy Efficiency: Reducing server energy consumption.
 AI Integration: Predicting workloads for smarter allocation.

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Conclusion
We summarize in this presentation that:
 Load balancing ensures reliability and scalability in distributed systems.
 The techniques address dynamic workloads and scalability.
The future direction of load balancing:
 AI-driven solutions for load balancing.
 Energy-aware strategies.