The role of Spot Virtual Machines in the big data processing

Feb 06,2023 by Meghali Gupta
Spot Virtual Machines
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What are Spot Virtual machines?

Spot Virtual Machines (VMs) are additional computing capacities in the cloud provided by cloud service providers like us – Cyfuture Cloud at discounted prices.

These virtual machines are referred to as “Spot” because customers can bid for the additional capacity like a commodity in a spot market. If the bid price is higher than the current spot price, the customer’s request is fulfilled, and the customer can use the spare capacity for as long as their bid price is higher than the spot price. 

If the spot price rises above the customer’s bid price, the spot instance is terminated, and the customer must find another source of computing capacity.

Well-known cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure offer Spot Virtual Machines to their users. 

In this article, we will see how Spot Virtual Machines work and the role of spot virtual machines in big data processing.

How Do Spot Virtual Machines Work?

Spot Virtual Machines (VMs) are a cost-effective solution for running workloads on the cloud, made available by the cloud service providers. They can be purchased at a lower price than on-demand instances in exchange for being subject to interruption when the cloud service provider needs the capacity back. 

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Here is a step-by-step explanation of how Spot Virtual Machines (VMs) work:

  • In the first step user specifies a bid price, which is the maximum amount they are willing to pay for the instance per hour.
  • After that, a cloud service provider continually monitors the demand for its computing resources and will allocate spare capacity to spot instances.
  • The cost of the spot price often fluctuates due to supply and demand. If the spot price is higher than the bid price set by the user, the instance launch will not occur.
  • If the spot price is lower than or equal to the user’s bid price, the instance is launched and runs until the spot price exceeds the bid price or until the instance is terminated.
  • Cloud service providers may terminate spot instances if they require the capacity for on-demand instances or if the spot price surpasses the user’s bid price. Before the instance is ended, the user will be given a two-minute notice to save their data and shut down smoothly.
  • Lastly, the user can relaunch the instance when the spot price drops again or launch a new spot instance with a different bid price.

How are Spot Virtual Machines beneficial for Big Data processing?

Spot Virtual Machines (VMs) play a vital role in the big data processing. They provide an efficient and cost-effective way to handle the increasing volume of data organizations generate and collect. 

A massive amount of data are processed in parallel across many nodes in big data processing. Spot VMs enable organizations to take advantage of excess computing capacity at discounted prices, making them an attractive option for big data processing workloads

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Feature

Description

Cost Savings

Spot VMs allow users to bid on unused EC2 instances and receive discounts compared to on-demand instances, reducing the cost of running large, resource-intensive data processing jobs.

Compatibility

Spot VMs offer the same capabilities and compatibility as on-demand instances, allowing users to leverage existing big data tools and frameworks.

Scalability Spot VMs can be easily scaled up or down as needed, providing the ability to efficiently process large amounts of data.
Flexibility Users have the flexibility to bid on different instance types and sizes as needed, providing the ability to optimize for performance and cost.

Potential Risks and limitations

When using Spot VMs for big data processing, it is important to understand the potential risks and limitations. 

One of the main risks is that the instance can be terminated if the bid price falls below the current market price. This can result in data loss or processing disruptions, which can significantly impact an organization’s ability to operate effectively. 

To mitigate this risk, organizations can implement failover strategies, such as using multiple Spot VMs in different availability zones or using a combination of Spot VMs and On-Demand instances to provide more stability.

What terms need to be considered before the creation of Spot Virtual Machines?

It’s important to carefully evaluate several factors before creating spot virtual machines. These factors determine whether Spot Instances is the right choice for your workload. 

  • Availability: The availability of spot instances varies and may not be guaranteed.
  • Cost: Spot instances can be significantly cheaper than On-Demand instances, but their prices can fluctuate based on supply and demand.
  • Interruptions: Spot instances can be interrupted by AWS with two minutes of notification if the current spot price exceeds the spot instance bid price.
  • Auto Scaling: Using Auto Scaling with spot instances can help mitigate the risk of interruptions.
  • Data Persistence: Data on a spot instance is not guaranteed to persist after interruption.
  • Application Architecture: The application architecture should be able to handle interruptions and the loss of data on a spot instance.
  • Region: The availability of spot instances can vary by region, so selecting the right region for your use case is important.
  • Spot fleet: Spot fleet is a feature that lets you launch multiple spot instances across different instance types, Availability Zones, and subnets in a single request.
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Conclusion

Spot Virtual Machines are essential in big data processing as they offer an economical solution for managing large amounts of data generated and collected by organizations. With the ability to scale flexibly and cost savings, they provide an attractive option for organizations seeking to optimize their data processing operations and minimize expenses.

 

However, it is important to understand the potential risks and limitations associated with using Spot VMs and with implementing failover strategies to ensure the stability and reliability of big data processing workloads.

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