On-Demand vs Spot Instances for HPC: Cost Analysis

When choosing between on-demand and spot instances for High-Performance Computing (HPC), the decision boils down to balancing cost and reliability:

• On-demand instances: Higher cost, but offer predictable pricing and uninterrupted availability. Ideal for critical workloads like simulations, research, and client-facing tasks where downtime isn't acceptable.
• Spot instances: Up to 90% cheaper, but come with the risk of sudden interruptions. Best for fault-tolerant tasks like batch processing or jobs with checkpointing systems.

Key Takeaways:

1. Cost: Spot instances can save 30–50% annually for UK businesses, sometimes up to £120,000.
2. Reliability: On-demand instances guarantee uptime, crucial for sensitive or time-critical operations.
3. Hybrid Strategy: Combining both types can optimise costs while maintaining reliability for critical tasks.

Quick Comparison:

For UK businesses, a mixed approach often works best: use on-demand for stability and spot for savings. Tools like checkpointing and automation can help manage interruptions effectively.

AWS re:Invent 2021 - Risk calculations using HPC and Spot Instances with Morgan Stanley

On-Demand Instances for HPC

On-demand instances are the go-to option for high-performance computing (HPC) workloads when reliability and predictability are non-negotiable. These instances offer immediate access to computational power without requiring long-term commitments, making them a key choice for organisations that prioritise stability. Let’s delve into the defining features of on-demand instances and why they remain a staple for HPC.

Fixed Pricing Structure

On-demand instances operate on a straightforward pay-as-you-go model, charging users a fixed hourly rate for each instance type. This pricing remains stable, regardless of fluctuations in market demand, which simplifies budgeting and eliminates unexpected costs. However, this convenience comes with a higher price tag compared to spot instances offering similar hardware.

The fixed pricing model is especially beneficial for finance teams, allowing them to forecast HPC project expenses accurately. With no surprise price spikes, organisations can maintain tight control over budgets and ensure smooth client billing processes.

Guaranteed Availability

One of the standout advantages of on-demand instances is their uninterrupted performance. Once deployed, these instances run continuously, making them ideal for HPC tasks that require consistent uptime over extended periods.

This reliability is crucial for applications like weather modelling, molecular dynamics simulations, and finite element analysis, where interruptions could lead to wasted computing time and delayed results. For UK organisations handling sensitive data or operating under strict compliance standards, guaranteed availability is often a must. Such reliability ensures research deadlines are met and regulatory requirements are upheld without compromise.

Common Use Cases

On-demand instances are the backbone for production-level HPC workloads, particularly those that are business-critical. Financial institutions running risk models, pharmaceutical companies conducting drug discovery, and engineering firms performing structural analysis all depend on the steady performance of these instances.

They’re also invaluable for academic institutions working against grant deadlines, companies preparing regulatory submissions, or organisations tackling emergencies. In these scenarios, uninterrupted access to computational resources is non-negotiable.

Client-facing services also benefit significantly. Consulting firms providing computational solutions, software-as-a-service providers running complex algorithms, and research organisations supporting multiple clients rely on the predictability of on-demand instances to meet service level agreements.

Additionally, the pay-as-you-go model offers flexibility, enabling organisations to scale resources up or down instantly based on workload demands. This is particularly useful for projects with fluctuating compute needs or unpredictable durations. Unlike spot instances, which can be more dynamic and less predictable, on-demand instances deliver consistent performance - a critical factor for many HPC applications. This contrast sets the stage for the next section, where we explore the characteristics of spot instances.

Spot Instances for HPC

Spot instances offer a cost-effective alternative for HPC (High-Performance Computing), trading guaranteed availability for significant savings. Unlike on-demand instances, which come with fixed pricing and assured availability, spot instances operate on a market-based pricing model. For organisations aiming to stretch their HPC budgets while maintaining computational power, understanding how spot instances work is essential.

Variable Pricing Model

Spot instances rely on a dynamic pricing system where costs fluctuate based on supply and demand. This model can provide savings of up to 90% compared to on-demand pricing.

Here’s how it works: users set a maximum bid price per hour for an instance. If the market price exceeds this bid, the instance may be terminated with minimal notice. For UK businesses operating on tight budgets, these savings can make a huge difference. Strategies like using spot instances have been shown to cut overall cloud expenses by 30–50% while maintaining or even boosting performance [1]. Real-world examples show annual savings of £120,000 [1]. However, because of the unpredictable nature of spot pricing, organisations need to plan their finances flexibly to balance the potential cost reductions with the risk of price spikes.

This pricing model does come with a trade-off: the risk of sudden instance termination, as explained below.

Instance Interruption Risk

The nature of spot pricing means there’s always a risk of an instance being interrupted. Cloud providers typically give just 30 seconds to 2 minutes’ notice before terminating an instance, either due to capacity demands or market prices exceeding the user’s bid.

This poses challenges for tasks requiring uninterrupted processing. For instance, a computational fluid dynamics simulation running over several hours could be cut short, forcing a restart and additional data recovery. To mitigate these risks, workloads should be designed with fault-tolerance measures like regular checkpointing, state-saving mechanisms, and automated restarts. These strategies ensure that even if an interruption occurs, progress isn’t entirely lost.

Best Use Cases

The key to using spot instances effectively lies in balancing cost savings with reliability. They’re best suited for workloads that can handle interruptions and resume processing seamlessly. Batch processing tasks and jobs with robust checkpointing systems are excellent candidates for spot deployment.

A hybrid strategy - using spot instances for fault-tolerant tasks while reserving on-demand instances for critical operations - can help organisations maximise savings without sacrificing reliability.

For UK organisations exploring spot instances for HPC, Hokstad Consulting offers expertise in cloud cost engineering. Their services can help reduce infrastructure costs by 30–50%, all while maintaining performance and reliability [1]. With a focus on DevOps transformation and strategic cloud migration, they ensure cost savings are achieved without compromising operational efficiency.

Cost Comparison: On-Demand vs Spot Instances

When it comes to high-performance computing (HPC), understanding the cost implications of choosing between on-demand and spot instances is crucial. Real-world pricing data reveals that while spot instances can offer substantial savings, they come with trade-offs that affect budget predictability and operational planning. This section builds on earlier discussions about HPC instance trade-offs, putting numbers to the potential savings.

Hourly Rate Comparison

The difference in hourly rates between on-demand and spot instances is striking, particularly for common HPC workloads. Spot pricing in the UK often provides steep discounts, as shown in the table below:

For standard compute instances, often used for general HPC tasks, savings can reach up to 80%. High-memory instances, which are essential for data-heavy computations, typically see around 75% savings. GPU instances, commonly used for tasks like machine learning, also offer similar discounts under normal conditions. It’s important to note that spot pricing can fluctuate based on demand and availability, but these figures demonstrate the potential cost advantages.

Monthly Cost Examples

To better understand the financial impact, let’s look at monthly costs for running instances continuously over 30 days.

For example, a research team using 10 high-memory instances non-stop would see the following costs:

• On-demand cost:
  10 × £0.60 × 24 × 30 = £4,320

• Spot cost:
  10 × £0.15 × 24 × 30 = £1,080

This results in a massive saving of £3,240, or 75%.

Now, consider a GPU-intensive workload, such as training machine learning models, with 5 GPU instances running continuously for a month:

• On-demand cost:
  5 × £2.25 × 24 × 30 = £8,100

• Spot cost:
  5 × £0.45 × 24 × 30 = £1,620

Here, the savings amount to £6,480, highlighting the cost benefits of spot instances. These calculations assume uninterrupted operation, though in practice, spot instances may experience interruptions. Even with occasional interruptions, the cost advantage remains significant.

Cost Savings Analysis

Several factors influence the overall savings from using spot instances. The ability of a workload to tolerate interruptions is key - applications designed to handle disruptions without losing progress can maximise the benefits of spot pricing.

Market conditions also play a role. During peak demand, spot prices can rise, reducing the savings. For example, while standard compute and high-memory instances often achieve high discounts, GPU instances might see savings drop to around 50% when demand spikes.

Geographic availability within the UK is another consideration. Spreading workloads across multiple availability zones can improve cost stability and reduce the risk of interruptions, though this requires careful planning.

Many organisations use spot instances for tasks like batch processing and data analysis, where fault tolerance is built into the workflow. By combining spot and on-demand instances strategically, businesses can strike a balance between cost efficiency and reliability. For those navigating these complexities, professional cloud cost management services can offer valuable guidance, helping UK organisations optimise their HPC spending while maintaining operational effectiveness.

Performance and Trade-Offs

When it comes to spot instances, the conversation often starts with cost savings. But there's more to the story - performance and operational trade-offs are just as crucial. Choosing between on-demand and spot instances isn't just about the budget; it directly impacts how High-Performance Computing (HPC) workloads perform, scale, and are managed. To make the right choice, organisations need to weigh these factors carefully.

How Interruptions Affect HPC Workflows

The biggest challenge with spot instances is their unpredictable availability. For HPC workflows, this can lead to significant disruptions. Imagine running a 12-hour computational fluid dynamics simulation, only to have it terminated just before completion. Without proper safeguards, such interruptions could mean starting over from scratch, wasting both time and resources.

This is where checkpointing becomes critical. By saving the state of a process at regular intervals, you can minimise the impact of interruptions. However, there's a trade-off. Frequent checkpointing lowers the risk of losing progress but can slow down I/O-intensive workloads. On the other hand, less frequent checkpointing reduces overhead but increases the chance of losing substantial work if an instance is interrupted.

Another effective strategy is using multi-replica applications. This approach involves running multiple instances of the same task across different availability zones. If one instance is interrupted, the others can continue, ensuring progress isn't entirely lost. This method works particularly well for problems that can be broken into smaller, independent tasks, often referred to as embarrassingly parallel problems.

While addressing interruptions is key to keeping workflows on track, the way HPC environments scale also varies significantly between on-demand and spot instances.

Scaling and Flexibility Differences

Scaling is another area where on-demand and spot instances differ, each offering its own set of pros and cons for HPC workloads. On-demand instances are all about predictability. When you need extra compute power, you can launch instances immediately, knowing they'll stay available until you decide to terminate them. This reliability simplifies capacity planning. For example, if your budget allows for 100 high-memory instances each month, you can run them continuously without worrying about unexpected interruptions disrupting your schedule. Plus, the management overhead is minimal since these instances operate consistently.

Spot instances, on the other hand, allow for more aggressive scaling but come with added complexity. During periods of low demand, you might secure far more compute capacity than your budget would typically allow under on-demand pricing. However, this flexibility is tied to availability constraints. Spot capacity varies depending on factors like instance type, availability zone, and market conditions.

Managing spot instances effectively requires robust automation tools. These tools monitor instance health, detect interruptions, and reschedule tasks automatically, ensuring job orchestration across different instance types. The management complexity doesn't stop there. Organisations need to track metrics like interruption rates, job completion times, and resource utilisation to decide when and how to use spot instances effectively.

Cost management also becomes more intricate with spot instances. Continuous monitoring is essential to avoid unexpected cost spikes, especially during periods of high demand. For organisations without the resources or expertise to handle these challenges, professional services can be a game-changer. Firms like Hokstad Consulting specialise in optimising cloud infrastructure and costs, helping UK businesses strike the right balance between performance and budget. They offer tailored strategies, combining smart instance selection with automated management systems to meet HPC demands efficiently.

Choosing the Right Instance Type for HPC

Selecting the right instance type for High-Performance Computing (HPC) involves finding a balance between cost and reliability. The decision largely hinges on your workload's importance, how well it handles interruptions, and its scalability requirements. Whether you choose on-demand, spot, or a combination of both depends on these factors.

Key Decision Factors

One of the most critical considerations is how interruptions might affect your workload. For example, if you're running mission-critical simulations where downtime could lead to significant costs or delays, on-demand instances are your safest bet. These are ideal for urgent engineering tasks or research projects that cannot tolerate delays.

On the other hand, if your workloads can easily recover from interruptions - like batch jobs or Monte Carlo simulations with built-in checkpointing - spot instances offer a more cost-effective solution. However, keep in mind that while spot instances can save you up to 90% [3], there are potential hidden costs. Frequent interruptions could mean lost compute time and added management overhead, which might offset the initial savings.

Scalability is another important factor. If your workload requires rapid scaling during peak times, spot instances may not always be available in the quantities you need. In such cases, on-demand instances provide the reliability and immediate availability necessary for unpredictable scaling demands.

Mixed Instance Approaches

A hybrid model, combining on-demand and spot instances, is a popular choice for many organisations. This strategy pairs reliable on-demand master nodes with cost-efficient spot worker nodes to achieve a balance between performance and savings.

This approach works particularly well for tasks that can be broken into independent units, such as rendering jobs, parameter sweeps, or large-scale data processing. For instance, rendering farms can use spot instances for non-critical tasks while reserving on-demand instances for essential operations.

Another effective tactic is scheduling workloads based on instance availability. For example, you could run development and testing tasks on spot instances during off-peak hours - when interruption rates are generally lower - and switch to on-demand instances for production runs or time-sensitive jobs.

The success of a mixed approach hinges on robust automation and monitoring systems. These systems should handle tasks like launching instances, performing health checks, and redistributing workloads seamlessly. With the right infrastructure, you can achieve significant savings without sacrificing reliability.

Professional HPC Cost Support

For those looking to optimise their HPC costs further, professional support can make a huge difference. Hokstad Consulting, for example, specialises in cloud cost engineering and has helped numerous UK businesses streamline their HPC expenses.

Our proven optimization strategies reduce your cloud spending by 30-50% while improving performance through right-sizing, automation, and smart resource allocation. [2]

Their services go beyond just selecting the right instance types. They offer comprehensive cloud cost audits, develop tailored strategies to match workload patterns, and implement automation tools that dynamically allocate resources. For HPC users, this might mean setting up advanced scheduling systems that decide between instance types based on job priority, deadlines, and current spot pricing.

The results are impressive. One SaaS company saved £98,000 annually after optimisation, while an e-commerce site saw a 50% performance boost alongside a 30% cost reduction. Another tech startup reduced deployment times from six hours to just 20 minutes, and one client achieved a 95% drop in infrastructure-related downtime.

What makes Hokstad Consulting particularly appealing for UK businesses is their no-savings, no-fee model. This risk-free approach means you only pay if measurable savings are delivered. Their ongoing monitoring ensures your HPC infrastructure remains efficient as your needs evolve.

For organisations seeking to implement these strategies, expert guidance is readily available.

Conclusion

This analysis brings together the trade-offs between cost and reliability when choosing HPC instance types. Striking the right balance is essential. On-demand instances stand out for their guaranteed availability and fixed pricing, making them indispensable for critical simulations and time-sensitive research. On the other hand, spot instances can slash costs by 50–90%, though they come with the risk of interruptions that may disrupt longer tasks.

A hybrid approach often proves to be the most effective. Many organisations in the UK have found success by blending on-demand and spot instances. This strategy leverages the reliability of on-demand instances for crucial operations, while using spot instances for fault-tolerant tasks to achieve significant cost savings without compromising stability. These findings align with the comparisons discussed earlier.

Cost optimisation doesn’t have to come at the expense of performance or reliability. In fact, case studies show that careful cloud cost engineering - through techniques like right-sizing, automation, and strategic resource allocation - can cut expenses by 30–50%, all while enhancing performance [2].

When selecting instances, focus on three key factors: workload criticality, tolerance for interruptions, and scalability needs. Spot instances are ideal for tasks that can handle interruptions via checkpointing or are easily divided into smaller parts. For continuous, high-priority workloads where downtime carries heavy consequences, on-demand instances remain the safer option.

It’s essential to revisit and refine your instance strategy as your workloads and requirements evolve. For tailored advice on optimising your HPC infrastructure, Hokstad Consulting offers expert guidance to help you maintain a cost-effective and reliable cloud environment.

FAQs