What is load distribution?
The distribution of computational tasks, data, and resources across several system nodes or components in distributed systems is known as load distribution. The main goal of load distribution is to achieve efficient resource utilization, enhance system performance, and ensure fault tolerance in case of node failures. To understand how load distribution works, let’s explore some scenarios where load distribution improves system efficiency.
Nodes overloaded by requests
When a node is overloaded by requests, it means that a specific node is handling the majority of user requests. It happens when the traffic increases and the nodes that respond to the requests are burdened with the requests.
In the illustration above, two nodes, Node 1 and Node 2, are responsible for the system's request management. Most user requests are directed to Node 1, which is now responsible for fetching data from data stores and generating responses to user requests. This puts a lot of load on Node 1, and the request-response time is compromised.
Since Node 2 has to respond to fewer user requests, it is not under load and can efficiently manage the requests directed toward it.
Excessive data retrieval from a data store
Consider a system where the data regarding Cryptocurrency prices and Cryptocurrency gain/loss percentage reside in the same data store. Assume that most users request the system for Cryptocurrency prices and gain/loss percentages, which reside in the same data store. Since the data resides in a specific data store, that data store is responsible for the majority of the read and write operations that are taking place in the system. The other data stores, which store data not accessed frequently, are not utilizing their resources to the full extent.
To balance the load in the system, we can distribute the data on data stores to ensure that none of the nodes face excessive traffic.
The solution to distribute the load
We can improve the efficiency of Node 1 by distributing the requests evenly across the nodes.
We can achieve this by updating the algorithms used to manage user requests. In the illustration above, we are reducing the load on Node 1 by redirecting some of the requests that were previously directed toward Node 1 to Node 2.
Where is load balancing crucial?
The following are common examples where load distribution is a very important component:
Round-robin DNS
The domain name system (DNS) can be seen as a phonebook for the internet. DNS resolves the domain names to their respective IP addresses. When a client requests a website (e.g., www.educative.io), requests are forwarded to the DNS resolver, and the DNS resolver responds with an IP address of the responsible server from their respective authoritative servers.
A website might be hosted on more than one server; in that case, the authoritative servers use the round-robin technique, where incoming traffic is distributed across multiple servers in a rotational manner. The illustration below depicts how round-robin works:
The illustration above shows how the round-robin algorithm is used to resolve DNS queries and respond to client requests. There are three clients that are requesting for xyz.com and the requests are forwarded to the DNS resolver which then forwards it to the respective authoritative server. The authoritative server contains the name server records for the domain and responds with a name server record using the round-robin algorithm.
Reverse proxy servers
Reverse proxy servers are intermediaries that sit between clients and backend servers. Unlike traditional forward proxies that handle requests from clients to the internet, they distribute incoming client requests across multiple backend servers to improve performance and reliability.
The illustration below depicts how reverse proxy servers work:
The illustration above shows how reverse proxy works. We operate with a single frontend server, linking to several backend servers, each capable of handling various responsibilities. backend server 1 may oversee the website’s core backend while backend server 2 could handle machine learning and AI components, and backend server 3 might manage customer support features.
Benefits of load distribution
The purpose of load distribution is to efficiently utilize the resources and increase the availability of the system and fault tolerance. The key aspects of load distribution are:
Balancing workload: Load distribution aims to prevent any single node or component from becoming overloaded with excessive computational tasks or data requests. By distributing the workload evenly, we can avoid bottlenecks and optimize system efficiency.
Resource allocation: It involves allocating system resources such as CPU, memory, storage, and network bandwidth to different nodes based on their capabilities and the current demand. Effective resource allocation is essential for maintaining system stability and performance.
Scalability: Load distribution supports system scalability by allowing new nodes or components to be added to the network easily. This ensures that the system can handle increased workloads as demands grow.
Fault tolerance: Distributing tasks and data across multiple nodes enhances fault tolerance. If one node fails or becomes unavailable, the system can continue functioning with reduced performance degradation, as other nodes can take over the load.
Data distribution: In distributed databases and storage systems, load distribution may also refer to distributing data across multiple storage nodes or partitions. This helps enhance data availability, reduce access latency, and improve overall system performance.
Addressing these key aspects, load distribution contributes to the efficient operation of distributed systems, ensuring a balance in resource usage and maintaining system resilience.
Challenges with load distribution
Load distribution in distributed systems can present several challenges, including:
Increased complexity: Balancing incoming requests across numerous servers evenly to maximize resource utilization and avoid overwhelming individual servers. Implementing effective load-balancing algorithms can be complex, requiring ongoing monitoring and adjustment.
Networking challenges: Balancing the workload among multiple nodes adds network communication overhead, potentially causing delays in processing requests. Managing network latency effectively while distributing loads efficiently poses a significant challenge, especially in systems spread across various geographic locations.
Adapting to dynamic workloads: In distributed systems, workload fluctuations arise from factors like changing user demand, seasonal variations, or sudden traffic surges. Adjusting load distribution methods to handle these dynamic workloads and scale resources appropriately to uphold system performance and availability is crucial.
Implementing fault tolerance: Distributed systems are prone to failures, including node failures, network partitions, and communication errors. Ensuring fault tolerance in load distribution mechanisms requires robust error handling, redundancy, and failover mechanisms to maintain the system’s reliability and availability in the case of failures.
Data consistency: Maintaining data consistency and coherence is challenging in distributed systems where data is replicated across multiple nodes for load distribution. Achieving this while distributing data in real-time requires well-designed replication protocols and coordination mechanisms.
Ensuring scalability: Scaling load distribution mechanisms to accommodate increasing workloads and resource demands becomes increasingly challenging as the system grows in size and complexity. Ensuring scalability requires architectural design considerations, such as decoupling components, horizontal scaling, and efficient resource allocation.
Conclusion
In conclusion, load distribution in distributed systems involves balancing workload, allocating resources effectively, supporting scalability, enhancing fault tolerance, and distributing data across nodes. By addressing these key aspects, load distribution ensures efficient resource utilization, optimal system performance, and resilience in the face of failures.
