Mega Data Centers Have A Load Balancing Problem. Can ML Help?

Data Centers and AI: Businesses rely on AI/ML solutions to balance the load of Huge Data Centers.

“In the near future, the number of data centres are expected to grow significantly in size and with size comes load balancing challenges.” 

The data centre sector is booming as the central government and Reserve Bank of India require that players maintain their data in India. In an interview with reporters, Honeywell Building Technologies Asia President, Ashish Modi said that there will be an over four-fold increase in India’s data-centre capacity by FY26. He said that the capacity of data centres in India will expand by four or five times in the next four or five years. He further noted that “In India, we have around 600 megawatts of capacity, and currently, capital for roughly 1,000 megawatts of capacity has been committed. Another 1350 megawatt of capacity is nearing completion; when the two are combined, we expect a 4x increase in data centre capacity in the next four or five years”.

There are four major criteria for increasing a data centre’s efficiency.

  • Load Balancing,
  • Power management,
  • Equipment management, and
  • Security

Load balancing especially is key as the number of data centres are expected to grow significantly in size and with size comes load balancing challenges.

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Load Balancing – A significant challenge

In load balancing, network traffic is distributed across many servers. As a result, no single server gets overloaded. Through the even distribution of tasks, load balancing increases the responsiveness of the application. As a result, users get easier access to apps and websites. It has been recommended in recent years that a new networking architecture called software-defined network (SDN) should be used for managing DCNs. 

Deep Q learning(DNQ) has been proven to be quite handy for improving both load balancing strategies and routing using reinforcement learning and deep neural networks. It outperformed methods such as shortest route, round-robin, and ANN load balancing methods in terms of increasing network usage and reducing latency. Whereas, SDSNLB is another method that uses multi-path routing technologies to minimise the wireless sensor network’s maximum link usage. To implement an effective and flexible traffic scheduling, the SDSNLB approach takes use of the global perspective of the wireless sensor network provided by software-defined networks.

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Let’s look at some of the research contributions to data centre load balancing. The table below provides an overview of several intelligent load balancing techniques in software-defined networking (SDN).

Deep neural networks andQ-learning (DNQ) With varying loads, the packet loss rate is reduced.Intelligent methods were employed in DNQ to perform these three functions: path selection, key nodes, and flow forecasting.
Switch Migration basedDecision-Making (SMDM) Responsive time, load distribution, and migration costs are all factors to consider.The major goal of SMDM is the process of choosing a master controller that can improve load balancing factor. A switch will be picked for migration based on its low cost.
Adaptive Load BalancingScheme Throughput and loss rateThis is a new adaptive approach for load balancing in data centres that employ SDN.
Self-Adaptive Load Balancing(SALB) Throughput testing, load balancing time, bandwidth usage, and loss rate are all factors to consider.The goal of the SALB system is to guarantee that load balancing and device distance are both considered effective.
Double Deep Q Network based VNFPlacement Algorithm (DDQN-VNFPA) Path delay, VNFI running time, VNFI count, and VNFI usage ratioThis is a customized algorithm created with the use of obtained data to guarantee that network performance is optimised.When it comes to measures like latency, throughput, and load balancing, this method improves network performance.
Fuzzy Synthetic EvaluationMechanism (FSEM) Dynamically choose the best pathThe SDN controller may use its global view of the network and implement flow-handling by sending network flow to those flow pathways under the OpenFlow switches.
Software-Defined Sensor NetworkLoad Balancing (SDSNLB) algorithm Throughput, link load jitterSDSNLB’s major goal is to improve functionality by allocating network traffic to multiple flow routes.The goal is to establish a balanced multipath bandwidth allocation for the most efficient and productive use of network resources.

An Overview of Different Intelligent Load Balancing Strategies in SDN

As listed above, there are many different approaches available for load balancing and many are part of active research. 

Data centres are engines of the internet. Any latency can bring down power plants, your favourite streaming sites and can lead to irreparable losses. This makes every aspect of data centre strategies equally important. Thanks to deep learning techniques, the data centres now have a greater edge at being run more efficiently.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Dr. Nivash Jeevanandam
Nivash holds a doctorate in information technology and has been a research associate at a university and a development engineer in the IT industry. Data science and machine learning excite him.

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