Optimising performance: Load balancing techniques for multi-cloud environments

Introduction

As organisations increasingly adopt multi-cloud strategies, the role of effective load balancing becomes indispensable. It was projected that the global load balancer market would reach $5.0 billion by 2023, up from $2.6 billion in 2018. This noteworthy expansion underscores the growing significance of load balancers in online services.

Multi-cloud environments ensure traffic is spread efficiently. It works across multiple cloud platforms, boosting reliability and performance. In this article, we explore various global server load balancing techniques, their importance in multi-cloud environments, and advanced strategies such as DNS-based, Layer 4, and Layer 7 load balancing, alongside dynamic scaling and health checks.

Understanding load balancing techniques

Distribution of computational workloads between two or more computers can be achieved with the help of Load balancing. Load balancing is a common practice on the Internet to divide network traffic among several servers. A load balancer is an application or program that manages this. These load balancers match a request from a user to a particular server when it comes in, and they do this for every request. Using a variety of methods, load balancers decide which server will handle each request. 

The two main types of load-balancing techniques are dynamic load balancing and static load balancing.

Static load balancing assigns preset tasks or resources to the system without considering real-time fluctuations. It includes methods such as Source IP Hash, Weighted Round-Robin, and Round-Robin.

Making judgements in real time about how to split up incoming network traffic or the computational burden among several servers or resources is known as dynamic load balancing. This strategy uses algorithms such as the Least Response Time Method and the Least Connection Method.

Importance of load balancing in multi-cloud environments

Load balancing is an essential technique in multi-cloud computing to optimise resource utilisation. It ensures that no single resource is overburdened with traffic. In order to improve performance, availability, and scalability, it divides workloads over several computing resources, including servers, virtual machines, and containers.

Load balancing in cloud computing can be applied at many layers, such as the database, application, and network layers. Additionally, it offers high availability and fault tolerance to manage traffic spikes or server outages, and it aids in the scalability of applications on demand.

Tata Communications stands at the forefront of powering hyper-connected ecosystems through a host of services, ranging from cloud connectivity to cyber security.

Traditional load balancing vs. Multi-cloud load balancing

Balancing loads is vital for any online service. It wants high availability and performance. Cloud computing has become common. Traditional load balancing is now outdated. This has led to the rise of multi-cloud load balancing. But what is the difference between the two, and which one should you choose? Let's explore.

Traditional load balancing	Multi-cloud load balancing
Traditional load balancing is an on-premises solution. It uses hardware or software devices to spread traffic across many servers. This ensures that no one server is overloaded. In this setup, the load balancer is a gateway to the backend servers. It sends requests to each server round-robin.	Multi-cloud load balancing is a cloud-based solution. It uses the resources of a cloud provider, such as AWS or Azure. It distributes traffic across many servers or devices.
The primary advantage of traditional load balancing is its control over the infrastructure. The IT team can easily monitor and manage load-balancing hardware and software. This is an advantage in some situations.	The primary advantage of multi-cloud load balancing is scalability. Multi-Cloud load balancing services can scale up or down to meet the traffic demands. They can handle sudden traffic spikes or changing traffic patterns.
Traditional load balancers have limited space. So, businesses must often update or replace them as they grow.	You can set up Multi-cloud load balancing services in minutes. They are quicker and more efficient to manage than traditional load balancers.
Traditional load balancing requires significant upfront investments in hardware. It also requires ongoing maintenance and software licensing costs.	Multi-cloud load balancing services remove the need for a significant upfront investment. They reduce operational costs by cutting the need for hardware or software licences.
They depend on networks. Traditional load balancing can be prone to outages or hardware failures. This makes it hard to keep reliable services.	The multi-cloud load balancing architecture is highly available and distributed across multiple data centers, which ensures reliable services.

Global load balancing strategies

It's essential to follow these established principles. They will guarantee reliability, scalability, and security for your global load balancing in production.

DNS-based load balancing

DNS is often called the Internet's phonebook. It translates website domains into IP addresses. An IP address is a long number. Servers use it to identify websites and any Internet-connected device. DNS translates domain names to IP addresses. This process is called DNS resolution. DNS saves people from memorising long number sequences. They use them to access websites and applications.

DNS-based load balancing is a specific type. It distributes traffic among different servers by using the DNS. In order to accomplish this, it responds to DNS queries with various IP addresses. In response to a DNS query, load balancers can select which IP address to share based on a variety of criteria or techniques. One of the most common DNS load-balancing techniques is called round-robin DNS.

Layer 4 load balancing

Layer-4 load balancing works at the OSI (Open Systems Interconnection) model's transport layer. This layer is mainly responsible for end-to-end communication. Layer-4 load balancers make decisions based on data from the transport layer. They route traffic based on network-level data. They do not inspect the content of the data packets.

Layer 7 load balancing

Layer-7 load balancing works at the application layer of the OSI model. This layer provides network services to end-users. It includes protocols like HTTP, HTTPS, and SMTP.

So, Layer-7 load balancers make routing decisions. They use application-specific data. This includes the content of the data packets, HTTP headers, URLs, and cookies. This allows Layer-7 load balancing to distribute traffic more intelligently. It is context-aware because the load balancer knows the application's structure well.

Load balancing algorithms

To spread incoming network traffic among several servers, load balancing methods are necessary. There are numerous load-balancing algorithms, each with unique properties. 

Round robin load balancing algorithm

The Round Robin algorithm is a simple static load-balancing approach in which requests are distributed across the servers sequentially or rotationally. It is simple to construct, but it does not account for the existing load on a server, therefore there is a possibility that one server would receive a large number of requests and become overwhelmed.

Weighted round robin load balancing algorithm

The Weighted round-robin algorithm is also a static load-balancing approach similar to the round-robin technique. The only distinction is that each resource in a list is assigned a weighted score. The weighted score determines which servers receive the request.

Servers with higher weights are given a more significant proportion of the requests.

Source IP hash load balancing algorithm

The source IP hash load balancing algorithm is a method used in network load balancing to distribute incoming requests among servers based on the hash value of the source IP address. This technique ensures that requests coming from the same IP address are continually routed to the same server.

Least connection method load balancing algorithm

The Least Connections algorithm is a dynamic load-balancing technique that routes incoming requests to the server with the fewest active connections. The goal is to distribute incoming workloads to reduce the existing load on each server, with an equal number of connections across all available resources.

Least response time method load balancing algorithm

The Least Response method is a dynamic load-balancing approach that aims to minimise response times by directing new requests to the server with the quickest response time. 

It considers the servers' historical performance to decide where to route incoming requests, optimising for faster processing.

Dynamic scaling and auto-scaling

Dynamic scaling adjusts the capacity of your Auto Scaling group in response to changing traffic patterns. It makes sure that your application can handle varying workloads efficiently. Auto-scaling dynamically allocates computational resources based on workload demands. As user needs fluctuate, the number of active servers in a server farm or pool automatically varies. It maintains optimal performance, availability, and cost efficiency by adjusting resources up or down as needed. The most significant advantage of auto-scaling is its ability to scale up from a few servers to hundreds or even thousands almost instantly.

Amazon EC2 Auto Scaling supports several dynamic scaling policies:

• Target tracking scaling: This policy increases or decreases the group's capacity based on a CloudWatch metric and a target value. Think of it like adjusting your thermostat—you set a desired temperature, and the system maintains it automatically.
• Step scaling: Here, the group's capacity changes based on predefined step adjustments triggered by alarm breaches. The adjustments vary depending on the alarm size.
• Simple scaling: This policy directly adjusts the group's capacity by a fixed amount, with a cooldown period between scaling activities.
• Recommendation: Using target tracking scaling policies with metrics that change inversely proportional to group capacity (e.g., average CPU utilisation or request count per target) is ideal. This ensures accurate proportional scaling events and adapts to load changes, such as seasonal variations.

Remember that dynamic scaling and auto-scaling are essential for maintaining performance, managing costs, and ensuring seamless user experiences. 

Health checks and failover mechanisms

A health check monitors the availability and health of your endpoints and API Gateways. Gravitee includes a built-in health check mechanism that allows you to create global health check configurations. Check out the interactive UI exploration or the text descriptions to learn more.

Failover is a mechanism to ensure the high availability and reliability of APIs by redirecting incoming traffic to a secondary server or backup system in the event of a primary server failure. Gravitee includes built-in failover mechanisms and capabilities. Check out the interactive UI exploration or the text descriptions to learn more.

Monitoring and optimisation of load balancers

Monitoring the network is vital to an administrator's job since it helps prevent critical parts and devices from going down. To avoid problems, the load should be evenly distributed between servers when client devices make numerous requests. Here's where a load balancer comes in.

As the demand for their services grows, load balancers become vital components of their network infrastructures. For companies, server failures or delays in serving clients can be disastrous. In online retail, for example, any website problem can be harmful. This is especially true during busy times like holidays or sales events. Problems can cause lost sales and harm the brand's reputation. They also make customers angry.

Therefore, monitoring and multi-cloud optimisation of load balancers are needed to manage traffic flow and maintain the availability of services.

Conclusion

Effective load balancing techniques are crucial in multi-cloud environments to ensure optimal performance, reliability, and resource utilisation. By distributing traffic evenly across cloud platforms, businesses can enhance system uptime, reduce latency, and improve the overall user experience. Tata Communications’  hybrid Multi Cloud Solutions empower businesses to seamlessly manage traffic across different clouds, optimising both cost and performance. Embracing advanced load balancing strategies helps organisations enhance operational agility and meet the growing demands of dynamic cloud environments. Start a free live demo today to experience streamlined cloud management.