High-performance computing (HPC) traditionally involves clusters of compute servers deployed in dedicated facilities. In recent years, demands for scale and performance have pushed HPC from on-premises to the cloud - a transition that offers new opportunities as well as significant hurdles.

On the positive side, the cloud offers virtually unlimited on-demand computing and storage resources because cloud platforms provide elastic capacity that can grow (and shrink) dynamically when resource demands change.

There are economic benefits to moving to the cloud, although not always. A stable workload that runs 24x7x365 for several years may be cheaper to run on dedicated hardware (cloud service providers have responded to this case by offering bare-metal instances). The pay-as-you-go model eliminates upfront capital expenses and avoids the costs associated with unused compute cycles. The range of compute instance types available in the cloud allows users to pay only for what they need, when they need it.

However, running applications in public clouds introduces new challenges. Each cloud service provider has an extensive catalog of services, and each service has a wide array of features. Without the knowledge required to configure and optimize cloud services, the researcher may encounter unpredictable or unsatisfactory application performance, unexpected costs, or unnecessary security risks.

This is where Memverge’s Memory Machine Cloud (MMCloud) can help. MMCloud is an easy-to-use cloud automation platform designed for scientific computing and data science. MMCloud simplifies the deployment of data-intensive pipelines and interactive computing applications in the cloud through automation and containerization.

Containers are the key technology that enables application mobility and automates the deployment of applications, at scale, in the cloud. The container includes all the software dependencies and runtime libraries needed to run the application in a reproducible way. But container deployment requires a cloud orchestration platform to schedule, instantiate, and manage containers. MMCloud provides an intuitive GUI (as well as a CLI) that allows the non-cloud specialist to deploy complex workflows as containerized applications in the cloud.

There are many reasons why scientists rely on MMCloud to enhance their research capabilities. Here are the top five reasons why you should choose MMCloud as the platform to run your cloud workloads.

**1. Seamless Expansion of High-performance Computing to the Cloud**

Hybrid cloud (a combination of on-premises and cloud resources) is a popular architecture for HPC because of its flexibility and scalability. When demand for computing resources exceeds what is available locally (on-premises), applications can “burst” to the cloud.

MMCloud effectively augments the local HPC environment with public cloud by enabling the user to launch jobs in the cloud using the same familiar HPC job managers such as SLURM and PBS. This way, scientists can operate in an efficient hybrid environment. Containerization and automation are a core feature of MMCloud. MMCloud automates deployment of containerized applications into the clouds, and it automatically deletes cloud resources after a job completes, removing the risk of unattended resources adding to the monthly cloud bill. Interactive sessions can be hibernated and restarted when needed again.

![Memory Machine Cloud HPC Bridge extends HPC into the cloud](https://baywatch-api.memverge.com/media/upload/202312/231207200802821427.jpeg)


Figure 1 MMCloud expands HPC to clouds

**2. Insightful Monitoring of Cloud Workloads**

Some users are reluctant to move workloads to the cloud because of a concern that they will have limited insight into, and control over, how their applications run in the cloud. Will the job complete in a shorter or longer time? Are the resources over- or under-provisioned?

Visibility into workload performance and resource utilization is critical when operating in the cloud. Granular monitoring of resource usage and costs can identify opportunities for optimization through rightsizing, shutting down idle resources or shifting workloads based on the current prices of compute instances. While cloud service providers do offer monitoring tools, these tools are not always easy to use and often come with additional costs.

MMCloud provides a comprehensive solution with advanced monitoring and metering capabilities, ensuring that scientists have a detailed understanding how their workloads run. CostReporter gives you a summary how much you spend on the jobs running in the cloud. WaveWatcher makes it easy to see the real-time consumption of resources including CPU, memory, storage, and network transfer speeds. For example, an application may exceed its storage limits or crash because of insufficient resources. Issues like these can be identified using MMCloud’s graphical interface, making troubleshooting much more straightforward.

MMCloud generates event and performance logs at both the job and host (worker node) level. Logs can be viewed in real-time to track the progress of a job as it executes, or downloaded after a job completes to identify issues or errors. The monitoring features enable researchers to fine-tune their environments for maximum efficiency.

![MMCloud WaveWatcher provides insights to cloud workloads](https://baywatch-api.memverge.com/media/upload/202311/231121040115584000.png)

Figure 2 MMCloud WaveWatcher provides insights to cloud workloads

**3. Reliable Spot Instances**

Almost all service providers offer spot Instances as a way for users to purchase excess computing capacity at a significantly lower price than on-demand instances (discounts can be as much as 90%). The trade-off is unpredictable availability. The cloud service provider can reclaim these instances with nominal warning (usually two minutes or less). Spot instances are ideal for stateless applications and can be used in stateful, batch-oriented pipelines if the workflow manager can requeue any failed jobs, although the total cost and wall clock time become unpredictable.

MMCloud's SpotSurfer feature allows stateful applications to always run to completion by "surfing" to a new VM instance if the underlying Spot Instance is reclaimed. Customers can realize the cost savings of spot instances without the risk of job interruptions. The technology that makes SpotSurfer possible is AppCapsule. AppCapsule creates a snapshot of the in-memory state as well as file system data. AppCapsule is triggered automatically when the cloud service provider signals that it is reclaiming the spot instance. Job execution pauses and then resumes on a new spot instance.

For interactive sessions, MMCloud provides a reverse proxy to ensure that each interactive session appears to clients as an IP address that does not change even if the session moves to a different virtual machine with a different IP address.
![MMCloud SpotSurfer protects workloads on spot instances](https://baywatch-api.memverge.com/media/upload/202311/231121040319580845.png)

Figure 3 MMCloud SpotSurfer protects workloads on spot instances

**4. Cost Control**

Cost management is a major concern for researchers, many of whom are grant-funded with strict budget constraints. While cloud services can be cost-effective, they can also lead to unpredictable expenses if not managed carefully. Institutions must develop new financial governance models to control cloud spending, ensuring that costs align with research budgets. Managing costs at cloud scale demands continuous monitoring of cloud resource usage to guard against runaway expenses. MMCloud’s Job Cost Reporting tools allow for precise budgeting, and the SurfZone feature provides real-time enforcement of limits on cloud spending.

Lifecycle management of cloud-based workloads can be critical for cost control. MMCloud provides control over of resources, for example, by enforcing automated schedules for job startup and shutdown, and maximum job execution times. This level of control is essential for managing costs in group settings where multiple users may run concurrent experiments. There are actions that individual users can take, for example, canceling jobs, moving jobs to smaller (cheaper) instances, or suspending interactive sessions. These actions can result in saving cost and can be used for avoiding idle charges.

Performance can be critical for cost control too. When jobs run on the spot instances, the slower jobs in data pipelines have a higher chance of getting reclaimed and the time and expense penalty for starting over might be huge. MMCloud’s integration with third-party high-performance file systems has improved the efficiency of file operation and has shown a significant advantage for Nextflow workflows.

WaveRadar, an interactive dashboard created on mmcloud.io is a free FinOps tool for viewing historical spot EC2 prices across all major AWS regions. WaveRadar lets you identify the regions offering the lowest rates for a given instance or instance family.

**5. Rightsizing Cloud Resources**

Compute instances available from cloud service providers range in size from one vCPU and 500 MB memory to 192 vCPUs and 1.5 TB memory (and even more in some specialized instances). Choosing the optimal cloud resources for scientific workloads is a complex task. Sizing for the peak load is safe but inefficient. Sizing for the average load reduces the cost but can lead to unacceptable performance or even out-of-memory (OOM) errors.

MMCloud's WaveRider feature simplifies this process by removing the need to select a single instance for each task. WaveRider provides the capability to migrate running jobs to optimally sized virtual machines based on their real-time resource utilization.

Job migration can be initiated in three ways: manually, driven by policy, or programmatically. At any time, you can manually migrate a job to a specific instance type, or to an instance whose capacities you define. With a rules-based policy, WaveRider monitors the resource utilization of your workload. If the upper threshold for CPU or memory utilization is crossed for a specified interval, the job is migrated to a virtual machine that has more virtual CPUs or more memory. Similarly, if the lower threshold is crossed and utilization remains low for a specified interval, the job is moved to a smaller virtual machine. For jobs with a well-known resource usage profile, job migration can be programmed into the job script so that the job migrates at predetermined stages in the execution.

Rightsizing has a positive effect on the optimal allocation and sharing of cloud resources. The optimization of using cloud resources has a profound impact on environmental sustainability. Users who proactively release underutilized resources allow the cloud service provider to increase server utilization. Fewer servers running at higher utilization lowers operational as well as embedded emissions. In a world increasingly conscious of climate change, optimizing cloud resource usage is a step towards reducing the overall carbon footprint of the IT industry. MMCloud provides a carbon emissions dashboard so that users become aware of the impact of how they run their jobs in the cloud.

![MMCloud Carbon Emission Monitor](https://baywatch-api.memverge.com/media/upload/202311/231121040303736482.png)

Figure 4 MMCloud Carbon Emission Monitor

**Conclusion**

Moving HPC workloads to the cloud, or bursting workloads to the cloud when needed, has benefits in terms of cost, scalability, efficiency, and sustainability. Challenges in usability and cost control in the cloud have prevented users from taking advantage of the cloud or a hybrid cloud architecture.

MMCloud is a powerful, yet easy to use, platform that facilitates the use of cloud computing by the research community. MMCloud is cloud-agnostic and is supported on AWS, Google Cloud, and AliCloud today. A free 30-day, no obligation trial is available. Register at [https://mmcloud.io/customer/register](https://mmcloud.io/customer/register) today.

<h2>Variability in AWS EC2 Spot Instance Discounts</h2><h2>Executive Summary</h2>AWS customers can reduce cloud costs by using <a href="https://www.mmcloud.io" rel="noopener noreferrer" target="_blank">Memory Machine Cloud</a> to deploy their containerized applications on spot instances (EC2 instances offered at a discount to on-demand instances). <a href="https://www.mmcloud.io/blog/waveradar-see-and-understand-ec2-spot-price-histor" rel="noopener noreferrer" target="_blank">WaveRadar</a> is a useful (free) online tool for examining historical spot instance prices and discounts. For a given EC2 instance type, the discount depends on several parameters. The analysis reported here, using data from WaveRadar filtered for spot instances available in the US, shows that discounts vary strongly with region, instance family, and CPU vendor. Other factors, such as instance size and CPU generation, also impact discounts but the effects are less.Generally, discounts are smallest in us-east-1 and us-west-2, and greatest in us-east-2 and us-west-1. However, not all EC2 types are available as spot instances in us-west-1. AMD-powered instances generally have smaller discounts than Intel-powered instances although the Intel discounts are more variable. If maximizing discount is the only selection criterion, then deploying Intel-based instances in us-east-2 is recommended. In practice, there are additional factors that affect cost and performance, such as networking and data transfer.Discounts for memory-optimized instances are larger than discounts for compute-optimized instances with discounts for general-purpose instances in between.The current inventory of spot instances, and their reclaim probabilities, are additional factors that may influence spot instance selection. Detailed data on inventory and reclaim probability is not available, so those aspects are not discussed here.<h2>AWS Compute Instances</h2>Elastic Compute Cloud (EC2) is the cloud service that provides virtual machines to AWS customers. EC2 instances are available in different types where each type is identified by a name (for example, c6i.4xlarge) and each type represents a configuration of physical CPU (for example, Intel Xeon 8375C), virtual CPUs (for example, 16 vCPUs), and memory (for example, 32 GiB).EC2 instance types are grouped into families, including:<ol><li data-list="bullet">General purpose</li><li data-list="bullet">Compute optimized</li><li data-list="bullet">Memory optimized</li></ol> Most EC2 instances follow the same naming convention.<code>[family][generation][CPU-vendor]</code>.<code>[size]</code>largewhere<ol><li data-list="bullet"><code>[family]</code> is a letter to indicate general purpose, compute-optimized, or memory-optimized</li><li data-list="bullet"><code>[generation]</code> is an integer</li><li data-list="bullet"><code>[CPU-vendor]</code> is a letter to indicate Intel, AMD, or Graviton</li><li data-list="bullet"><code>[size]</code> is a string that identifies a combination of number of vCPUs and memory</li></ol> For example, r6i.4xlarge is a memory-optimized, 6th generation, Intel-powered, instance with 16 vCPUs and 128 GiB of memory.EC2 instances are distributed geographically among the regions where AWS locates its data centers. Within a region, EC2 instances are distributed among availability zones, which are logically separate networks that provide fault isolation. Currently, AWS operates 102 availability zones within 32 geographic regions around the world.<h2>Price Lists</h2>EC2 instances are billed in one-second increments, although the prices are displayed on the AWS website as hourly rates. There are three price lists that apply to EC2 instances.<ol><li data-list="bullet">On-demand prices apply to instances that are configured without prior reservations, can run for any length of time, and carry no long term commitments.</li><li data-list="bullet">Reserve prices apply to instances that are associated with commitments to pay for a minimum number of EC2 instances for a fixed period (1 or 3 years) with constraints on the type and location of each instance.</li><li data-list="bullet">Spot prices apply to EC2 instances that are only available because AWS has excess capacity at that time. Spot prices vary as supply and demand change.</li></ol> For a given EC2 instance type, the on-demand price is the highest and the spot price is the lowest (the reserve price is in between).<h2>EC2 Price Data</h2>AWS provides a Price List API to programmatically retrieve information on AWS products and pricing. By querying the AWS Price List API multiple times a day and inserting the results into a database, WaveRadar assembles a historical record of on-demand and spot instance prices for all EC2 instances in all regions.On-demand prices change slowly — prices changes only occur at the start of each month and prices may remain unchanged for several months. Spot prices are volatile but in practice, changes are only visible when viewed in a three month window (you can view EC2 price history from your AWS management console).EC2 instances are available with a choice of operating system: a generic, unsupported Linux version (labeled Linux/UNIX or AWS Linux), two supported Linux versions (SUSE and Red Hat), and Windows. Of the four operating systems, Linux/UNIX (AWS Linux) has the lowest price (Windows has the highest).<h2>The Ideal Search</h2>The search for the optimal EC2 instance to run a job is a complex task. A user might not know the peak resource requirements (CPU clock speed, number of vCPUs, and memory size) for a job or how to map resource requirements to an EC2 instance at the best price. In a simple case, a user may ask: for a given EC2 instance type, where can I get the lowest price for this type as a spot instance? A more complex query may search for optimal price performance, that is, the best price per vCPU, or per GiB, adjusted for processor generation.AWS offers spot instances at a discount as a way to monetize excess EC2 capacity. That means that not every instance type is available as a spot instance at any given time, and any spot instance can be reclaimed with nominal warning. The ideal search would include the current inventory of every spot instance type to allow the user to estimate the probabilities of reclaim events. Inventory information is not available via the AWS API, so discounts must be used as an indirect indicator.<h2>Simplifying Assumptions</h2>The studies described in this report focus on finding the largest discounts on spot instances configured with Linux/UNIX (AWS Linux) in the regions that AWS operates in the continental United States.<h2>Variability in Discounts</h2>All functions in <a href="https://www.mmcloud.io" rel="noopener noreferrer" target="_blank">Memory Machine Cloud</a> (MMCloud) are controlled by the Operations Center (OpCenter). One OpCenter can deploy many jobs in a region but one OpCenter cannot span regions. Although an MMCloud customer could deploy OpCenters in multiple regions, it is more common to deploy one OpCenter and run all jobs in a single region. An OpCenter is deployed in one availability zone but jobs are deployed in all availability zones in the region.Containers deployed by the OpCenter run on EC2 instances supported by Intel or AMD processors. As Intel and AMD develop new generations of processors, AWS makes new generations of EC2 families available. For example, the C5 family of EC2 instances runs on 2nd generation Intel Xeon Scalable Processors (Cascade Lake) or 1st generation Intel Xeon Platinum 8000 series (Skylake) processors. The C6i family runs on 3rd generation Intel Xeon Scalable processors and the C7i family runs on 4th Generation Intel Xeon Scalable processors. Later generations of processor offer better price performance than earlier generations. A similar pattern applies to AMD-based families, for example, C5a and C6a, or R5a, R6a, and R7a.With this background, we identify potential sources of variability in spot prices (and hence, a path towards finding the largest discounts), namely,<ol><li data-list="bullet">Region (us-east-1, us-east-2, us-west-1, or us-west-2)</li><li data-list="bullet">EC2 instance family (M, C or R)</li><li data-list="bullet">EC2 instance family generation (5, 6 or 7)</li><li data-list="bullet">CPU vendor (AMD or Intel)</li></ol> To preview the data, we survey the discounts for each EC2 instance family aggregated across all regions (see Figure 1). Figure 1 shows that the majority of the discounts fall between 20% and 75% (multiply by 100 to convert the discount to a percentage), with C instances towards the lower end, R instances towards the upper end, and M instances in the middle.<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211145336885.png">Figure 1. Discounts for M, C, and R instance families<h2>Discount Variation by Region</h2>To investigate how discount varies by region, we hold the instance family (M, C, or R), generation (5), and CPU vendor (a denotes AMD) constant while querying discounts in each region. The results for the M5a family, the C5a family, and R5a family are in shown in Figures 2 to 4, respectively. Multiply the discount by 100 to convert to a percentage.<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211241021492.png">Figure 2. Discounts for M5a family of instances by region<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211309865110.png">Figure 3. Discounts for C5a family of instances by region<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211340069740.png">Figure 4. Discounts for R5a family of instances by regionThe results show that, although there are outliers, discounts in us-east-1 and us-west-2 are similar whereas discounts in us-east-2 and us-west-1 are similar. The same pattern is seen for Intel-powered instances.For the remaining queries, we treat us-east-1 and us-west-2 as a single group, and us-east-2 and us-west-1 as a single group.<h2>Discount Variation by EC2 size</h2>To investigate how discount varies with instance size, we hold the instance family (M, C, or R), generation (5, 6, or 7), and CPU vendor (a denotes AMD and i denotes Intel) constant while querying discounts for each <code>[size]</code>. The results for the M5a family, the C5a family, and R5a family are in shown in Figures 5 to 7, respectively.<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211409811895.png">Figure 5. Discounts for M5a family of instances by size<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211430566036.png">Figure 6. Discounts for C5a family of instances by size<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211457040980.png">Figure 7. Discounts for R5a family of instances by sizeThe results show that, although there are outliers in the C5a family, discounts do not vary greatly from one instance size to another in the same family. Discounts do vary between families: the R5a family has the greatest discounts, the C5a family has the lowest discounts, and the M5a family is in the middle.For the remaining queries, we treat large, xlarge, 2xlarge...48xlarge instances within a family as a single group.<h2>Discount Variation by CPU vendor</h2>For most of the EC2 families, there is an Intel-powered version and an AMD-powered version of every instance. To investigate whether discounts vary by CPU vendor, we repeat the queries behind Figures 5, 6, and 7 for the Intel-powered versions. Compared to the equivalent AMD-powered instances, we see that discounts for Intel-powered instances are larger and more variable. The detailed results are shown in the Appendix ( see Figures A1, A2, and A3).If all the generation 5 family types are aggregated into separate Intel- and AMD-powered groups, the results show that on average Intel-powered instances are offered at higher discounts than AMD-powered instances (see Figure 8).<img src="https://baywatch-api.memverge.com/media/upload/202310/231010211935867992.png">Figure 8. Discounts for generation "5" instances by CPU vendor<h2>Discount Variation by Generation</h2>To investigate how discounts change as newer processors become available, we compare CPU generations while keeping the group of families constant, for example, we group {M5a, C5a, R5a} into one group and compare that group's average discount with the {M6a, C6a, R6a} group's average discount and the {M7a, C7a, R7a} group's average discount. The results for AMD-powered instances are shown in Figure 9 and the results for Intel-powered instances are shown in Figure 10.<img src="https://baywatch-api.memverge.com/media/upload/202310/231010212037208725.png">Figure 9. Discounts for groups of families by CPU generation (AMD)<img src="https://baywatch-api.memverge.com/media/upload/202310/231010212115384416.png">Figure 10. Discounts for groups of families by CPU generation (Intel)The results for the AMD-powered group show that the latest generation (7) has the largest discounts. As expected, the Intel results are more variable. Although the average discount for the latest generation (7) is lower, there are outliers that offer larger discounts than either generation 5 or 6.<h2>Conclusion</h2>AWS customers can reduce cloud costs by using <a href="https://www.mmcloud.io" rel="noopener noreferrer" target="_blank">Memory Machine Cloud</a> to deploy their containerized applications on spot instances, which are offered at a discount to on-demand instances. For a given EC2 instance type, the discount depends on a number of parameters. The analysis reported here shows that discounts vary strongly with region, instance family, and CPU vendor. Other factors, such as instance size and CPU generation, also impact discounts but the effect is less.Future analysis will investigate other factors (such as availability zone and day of week) and interactions between factors, for example, instance size and CPU generation.<a href="https://www.mmcloud.io/blog/waveradar-see-and-understand-ec2-spot-price-histor" rel="noopener noreferrer" target="_blank">WaveRadar</a> can be used to explore the data further.<h2>Appendix</h2>To investigate how discount varies with Intel-powered instance size, we hold the instance family (M, C, or R) and generation (5, 6, or 7) constant while querying discounts for each <code>[size]</code>. The results for the M5 family, the C5 family, and R5 family are in shown in Figures A1 to A3, respectively.<img src="https://baywatch-api.memverge.com/media/upload/202310/231010212152522850.png">Figure A1. Discounts for M5 (Intel) family of instances by size<img src="https://baywatch-api.memverge.com/media/upload/202310/231010212223876009.png">Figure A2. Discounts for C5 (Intel) family of instances by size<img src="https://baywatch-api.memverge.com/media/upload/202310/231010212244122999.png">Figure A3. Discounts for R5 (Intel) family of instances by size

<h2>Introducing WaveRadar</h2><h3> </h3>There aren't really any great tools for understanding and comparing historical Spot EC2 prices for the 600+ instances available on AWS. To solve this, we created WaveRadar, an interactive dashboard for viewing Spot EC2 prices across all major AWS regions. WaveRadar lets you easily identify the regions offering the lowest $/hour for a given instance or instance family. It can also help you quickly compare the relative price performance $/core and $/memory of different EC2 instances at recent and historical Spot market prices. Use WaveRadar as a free FinOps tool to make more informed decisions around cost optimization, just sign up for a free account via the steps below. To access WaveRadar just navigate to the <a href="https://www.mmcloud.io/aws-spot-pricing" rel="noopener noreferrer" target="_blank" style="background-color: rgb(255, 255, 255);">dashboard_link</a>. <h3>Screenshots of WaveRadar</h3>region optimizer tab<img src="https://hackmd.io/_uploads/HysT_bWkT.png"> visualize/download data tab<img src="https://hackmd.io/_uploads/rJQxY-ZyT.png"><h3> </h3><h3> </h3><h3>Example use cases</h3>Filter/sort the tabulator in the region optimizer tab to find the right instance and region:<ol><li data-list="bullet">Finding the cheapest region for t3.medium spot instance on Sep 12 (3x difference) : <code>lowest recent spot price ($0.0125) at us-east-2b / highest recent spot price ($0.0404) at il-central-1a</code></li><li data-list="bullet">Finding the cheapest region for t3.medium on-demand instance on Sep 12 (1.5x difference) : <code>lowest on-demand price ($0.0416) at us-east-2b / highest on-demand price ($0.0672) at sa-east-1c</code></li><li data-list="bullet">Find a cheap instance with 4 CPUs and 16 GiB memory in us-west-1c on Sep 14 : <code>lowest recent spot price ($0.0480) for t4g.xlarge / highest recent spot price ($0.0662) for t2.xlarge </code></li></ol> Visualize and download spot price history in the visualize/download data tab:<ol><li data-list="bullet" class="ql-indent-1">Access historical spot price data from April to the current date for informed decision-making.</li></ol>

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