I recently hosted a panel with AWS, Amgen, Ardigen, and Seqera on running omics workflows in the cloud. 


Here were my key takeaways from the talk: 

You can watch the full video here: 

<iframe width="560" height="315" src="https://www.youtube.com/embed/zQyh7LF6_h4?si=4QVkjvxM0E6Kj5Oq" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

## Running omics workflows on the cloud is about understanding what tradeoffs you’re making. 

<iframe width="560" height="315" src="https://www.youtube.com/embed/T_keBXj_iuQ?si=KhA4UWL9n5647YkQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

“For example if you're running a production pipeline where you just need to keep it churning, but you don't need to have the results immediately, you can wait and deal with retries. You might also not necessarily need the latest and greatest hardware. 

However, if you're in an R&D activity, the time of your developers is more expensive than the time of the machines. 

So you need to be able to have your developers progress very rapidly, be able to iterate extremely rapidly, because the cost you're paying the humans who are operating machines is higher, and also the cost of the opportunity cost of needing to define the solution faster than maybe your competitors.

What's more important?” 

Getting the answer fast? Getting to a product fast? Or do you have just something that's running in your background?” 


## When going full cloud, it’s important to understand what kinds of CPUs you need to for your pipeline.

<iframe width="560" height="315" src="https://www.youtube.com/embed/D1o36dL5tGQ?si=gEsVaUN5zSuENptI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

“Depending on the shape and size of the data and the kind of scientific algorithm that you're running, there are going to be different computational requirements.

In some cases you might want a beefy VM with a lot of compute, a lot of memory. Sometimes you just, it's going to be a small operation that's just cheap, but it's more disk bound. 

So there's some very different shapes and sizes of machine that might be appropriate.” 


## Case Study: How the Broad Institute leverages spot instances to reduce costs

<iframe width="560" height="315" src="https://www.youtube.com/embed/-OEZnDIB0fs?si=BJTFvermcyj8DUcf" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

The Broad Institute saved ~90% on costs going from their first implementation to their final pipeline. By leveraging tools like Spot instances, they were able to take their costs from $85 per whole genome to $5. 

“When Broad's original genomic pipeline was migrated from on prem to cloud, the initial, completely naive implementation was about, I want to say $85 per whole genome. With some basic optimization, they very quickly brought that down to $45 per genome, which is almost half. 

Three to six months of further investment, (today it would take much less time because we have all these tools), we took it down to $5 a genome.

And if you think about the cost reduction there, that was huge. Nowadays you can use those kinds of approaches. You don't have to do six months of R&D because a lot of the tools exist. But you can really make a really big difference from the naive implementation to an optimized implementation.”



## Ready to Learn More? 
Spot instances are a great way to use spare capacity in the cloud at 60 to 80% cheaper. 

If you're curious about seamlessly running your research and other types of computational workloads on spot instances, feel free to [book a chat with me](https://calendly.com/jing-memverge/30-minute-general-discussion?month=2023-12&date=2023-12-05). 


## More about Jing
I'm Jing, I run GTM for our memory snapshot product at MemVerge.

<iframe width="560" height="315" src="https://www.youtube.com/embed/gW2uf5-LEYM?si=-ND4_wi-YSGYxGAb" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

We protect your workloads from being taken away or reclaimed by the cloud provider, even when they're not done running. And we are automatically finding you additional instances where you can continue to run your work to completion.

I love helping teams who may not have the resources to conduct their computationally intensive research locally or may not feel fully comfortable migrating their omics workflows to the cloud. 

I post regularly about running omics workflows on spot instances and in the cloud. Check out my writings on [LinkedIn](https://www.linkedin.com/in/jingxie/).

Vast crypto mining data centers, consuming electricity at the rate of a small country while simultaneously discharging heat into the air, are now common pop culture references, resulting in a push to reduce crypto mining’s power requirements. For example, in 2022 [Ethereum](https://ethereum.org/en/roadmap/merge/) officially switched to a Proof-of-Stake (PoS) consensus mechanism as a more energy-efficient way (energy consumption decreased by about 99 percent) to validate transactions and add new blocks to the blockchain.

The power consumption of conventional data centers worldwide is comparable to crypto mining data centers, so the crypto mining image tends to envelop all data centers. Yet the two scenarios are very different. It is not possible to change a single algorithm to reduce the power consumption of a data center supporting a wide array of applications. In most cases, the biggest reduction in the carbon footprint associated with general purpose computing (web site hosting, database transactions, machine learning, and so on) can be achieved by moving from on-premises to the cloud.

![](https://baywatch-api.memverge.com/media/upload/202403/240310180436734115.png)


Source: [Ethereum](https://blog.ethereum.org/2021/05/18/country-power-no-more)



## The Benefits of Sharing Resources

The risk of trivializing the complexity of carbon footprint analysis notwithstanding, consider a simple analogy. Suppose your grocery store only sells milk in one-gallon jugs. Each week, you buy a gallon of milk. You consume half a gallon and by the end of the week, the remaining half gallon has soured, so you throw it out. Carbon footprint generated: one gallon of milk and one plastic jug.

It turns out your neighbor has the same pattern. You decide to buy one gallon of milk and share it. Carbon footprint generated per person: a half gallon of milk and half a plastic jug. That is a 50 percent reduction. The analogy can be extended further: suppose you switch from dairy to soy milk whose production releases lower levels of carbon emissions. That’s like switching to an energy source with lower carbon intensity. This is the cloud computing model.

![](https://baywatch-api.memverge.com/media/upload/202403/240310180503991053.jpeg)


Source: [Statista](https://www.statista.com/chart/22659/cows-milk-plant-milk-sustainability/)

## Operational and Embedded Emissions

Hyperscale Cloud Service Providers (CSPs) have invested vast sums in optimizing power and heat distribution in their data centers so that roughly 90 percent of the electricity drawn is used to power IT equipment directly. By using electricity from renewable energy sources and purchasing carbon offset credits, the hyperscale CSPs ([Google Cloud](https://www.google.com/about/datacenters/cleanenergy/), [AWS](https://sustainability.aboutamazon.com/climate-solutions/renewable-energy?energyType=true), and [Azure](https://azure.microsoft.com/en-us/explore/global-infrastructure/sustainability)) claim that they are either carbon neutral for their electrical power today or will get there soon. Furthermore, much of the heat is [captured](https://www.weforum.org/agenda/2022/08/sustainable-data-centre-heating/). So even though not all the power consumed in every data center is from renewable sources, the focus is shifting from [operational emissions](https://www.cloudcarbonfootprint.org/docs/methodology/#summary) to other generators of carbon emissions. 

### Carbon Credits: Do they work?

Carbon offset credits are a market-based mechanism for reducing global emissions. If a company cannot reduce its greenhouse gas emissions, it pays for the equivalent amount of greenhouse gas to be removed from (or not added to) the atmosphere. The challenge is in implementation because the quality of carbon offset credits varies. Poor quality carbon offsets ["over-credit"](https://climate.mit.edu/explainers/carbon-offsets) the impact, that is, the reduction in emissions is less than promised. Metrics, such as [additionality](https://www.offsetguide.org/high-quality-offsets/additionality/), [immediacy](https://mitsloan.mit.edu/ideas-made-to-matter/how-to-choose-carbon-offsets-actually-cut-emissions), and [leakage](https://doi.org/10.1016/j.oneear.2023.05.024), are used to verify whether the offset works as promised.

## Reducing Embedded Emissions

CSPs purchase servers, storage arrays and other IT equipment to stock their data centers. To the CSP, these are sources of embedded emissions ([Scope 3](https://ghgprotocol.org/scope-3-calculation-guidance-2)), that is, the manufacture and transport of this equipment has an associated carbon footprint. Utilization is the key metric in apportioning the embedded emissions to end users – just like the volume of milk consumed compared to the size of the milk jug.

A typical on-premises data center may achieve server utilization of around [20 percent](https://www.nrdc.org/energy/files/data-center-efficiency-assessment-IP.pdf). To consume all the compute cycles that a single server (running at 100 percent) could provide, the organization would need to purchase five servers. In contrast, server utilization in a CSP is at least [50 percent](https://aws.amazon.com/blogs/aws/cloud-computing-server-utilization-the-environment/), so the CSP would only need to purchase two servers. That is a 60 percent reduction in embedded emissions, although the power consumption of a server does rise as utilization increases.

## Managing VM Resources to Reduce Carbon Footprint

A modern data center server has, aside from power supplies, network interfaces, and storage devices, multiple CPU slots and multiple memory slots for each CPU slot. Each CPU comprises physical processing units (cores). For example, Intel’s third-generation Xeon Scalable processor, codenamed [Ice Lake](https://www.hpcwire.com/2021/04/06/intel-launches-10nm-ice-lake-datacenter-cpu-with-up-to-40-cores/), can contain up to 40 cores. The enabling technology for sharing server resources among end users is the hypervisor, which virtualizes the physical components into (ephemeral) virtual machines (VMs) of varying capacities (measured in terms of cores and memory).

The key to reducing carbon footprint (including embedded emissions if we assume operational emissions [[GoClimate](https://www.goclimate.com/blog/the-carbon-footprint-of-servers/), [Amazon](https://sustainability.aboutamazon.com/products-services/the-cloud?energyType=true)] are zero or close to zero in hyperscale CSPs) is managing VM resources to maximize utilization. That is, size the VM for the job it needs to execute and release the resources when they are no longer needed so that other users have access to the resources. Sophisticated checkpoint/restore mechanisms (like Memory Machine Cloud's WaveRider feature) can optimize VM instances even more – by migrating jobs in real time as resource demands change while the job is executing. If resources are idle, but locked up (not available to other users), the CSP is forced to deploy additional servers and incur additional carbon footprint. 

Suppose, for example, that server utilization increases from 50 to 65 percent because of intelligent VM management. This would decrease the carbon footprint (associated with the entire server) by 23 percent. For the end user who is tracking the Scope 3 emissions attributable to running a workload on a cloud server, the effect of continually rightsizing the VM as the workload executes can be significant. For example, if without rightsizing, an end user consumes 20 percent of a server’s resources and with rightsizing the end user consumes 10 percent of the server’s resources (averaged over the run), the embedded emissions attributable to the end user are reduced by 50 percent.

![](https://baywatch-api.memverge.com/media/upload/202403/240310180008526778.png)

Source: [Benefits of Virtualization](https://cs181cloudstorage.weebly.com/benefits-of-virtualization.html)

## Conclusion

Modern hyperscale data centers are engineering marvels that are key components in reducing global carbon emissions, but underneath is an old-fashioned idea: take only what you need and if you have extra, share it with your neighbors. It’ll save you money as well.

## What is AppCapsule and what problems does it solve? 
AppCapsule creates a snapshot of all the data in memory, and all the application states required to restore the application at this moment in time, as well as the data on storage. With AppCapsule, we can transport the application runtime across different machines. 

Three problems that AppCapsule can help to solve - 
1. **Spot** reclaim - If there is a spot eviction event, Memory Machine Cloud creates an AppCapsule, and moves it to another spot instance, where the job will continue. That means, you can save cost running jobs on spot instance, while keeping all your job progress and does not need to run from the beginning all over again. A.k.a. *SpotSurfer*.
2. **OOM** (Out Of Memory) - Memory Machine Cloud detects if OOM is about to happen and if so, creates an AppCapsule, and moves it to another instance to avoid the application crash as well as have the job continue running.
3. **Right-sizing** - vertical scaling down to avoid over-provisioning or up to provide more resources to the application as it is needed for shorter wall-time and possibly lower total cost. You can trigger either manually by using float cli command / OpCenter Web GUI or automatically done by Memory Machine Cloud (a.k.a. *WaveRider*).
 
![](https://baywatch-api.memverge.com/media/upload/202312/231220190505523032.png)

## What is AppCapsule++ and what benefits does it bring? 
Three enhancements and benefits that AppCapsule++ brings to the users - 

1. Only saving **delta** - instead of saving a full snapshot, AppCapsule++ only saves the difference (memory change) since the previous snapshot. This way, the time required for the final snapshot is minimal.
2. **Adaptive** trigger - instead of snapshotting in a fixed, pre-determined interval, AppCapsule++ automatically and intelligently checks the memory change and take a snapshot when it reaches a threshold, which is calculated based on the cloud service provider’s pre-emption window. The benefit is no need to guess the fixed interval for periodic snapshot.
3. **Asynchronous offloading** - instead of synchronous snapshotting which will pause the application, AppCapsule++ does asynchronous data offloading, which decreases the time that the application is frozen. The benefit is lower impact to job completion wall-time. 

Below is an overall comparison table of AppCapsule and AppCapsule++
| Comparison | AppCapsule | AppCapsule++ |
| - | - | - |
| **Snapshot content** | Full snapshot | Delta snapshot |
| **Snapshot trigger** | Can be - Periodic done by MMCloud, or - Manual done by user’s script | Adaptive | 
| **Configuration** | Pre-determined interval for periodic snapshot | No special configuration | 
| **Application completion wall-time** | Impact higher (Synchronous) | Impact lower (Asynchronous) | 
| **Storage overhead** | Relatively lower (only need to save final snapshot to restore) | Relatively higher (need to save all delta snapshots to restore) | 
| **Usage** | `--dumpMode full` | `--dumpMode incremental` |


## Benchmark comparison 
Below is the benchmark experiment result running on AWS comparing AppCapsule and AppCapsule++. The experiment is based on 50GB memory change in terms of 60GB, 120GB, 240GB, and 480GB application memory usage, and how much time in seconds did AppCapsule and AppCapsule++ take to take the final snapshot. 

Three observations from this benchmark - 

1. For AppCapsule, as application memory usage grows, the snapshot time also **increases**. The reason is that AppCapsule takes a complete snapshot every time.
2. For AppCapsule++, as application memory usage grows, the snapshot time **stays more or less the same**. The reason is that AppCapsule++ takes a delta snapshot, which is triggered when the amount of change in memory reaches a certain threshold.
3. Considering using spot instance on AWS, which has 2-minute pre-emption time window, AppCapsule can only support relatively small workload (in this experiment for example, 60GB since the snapshot time is 109 seconds). AppCapsule will not be able to support bigger workload (in this experiment for example, 120GB needs 250 seconds to finish a snapshot which is way over the 2-minute time window). However, since AppCapsule++ only takes delta snapshot, the snapshot time is consistent with memory change, not the application memory usage. 
![AppCapsule vs AppCapsule++](https://baywatch-api.memverge.com/media/upload/202312/231220185252866864.png)


More detailed information in table format below: 
| Experiment No. | Experiment Variable | AppCapsule | AppCapsule++ |
| - | - | - | - |
| 1 | Application memory usage: 60G Application memory change: 50G | 109s r5.2xlarge/gp3 | 91s r5.2xlarge/gp3 |
| 2 | Application memory usage: 120G Application memory change: 50G | 250s r5.4xlarge/io1 | 102s r5.4xlarge/io1 |
| 3 | Application memory usage: 240G Application memory change: 50G | 533s r5.8xlarge/io1 | 104s r5.8xlarge/io1 |
| 4 | Application memory usage: 480G Application memory change: 50G | 1548s r5.16xlarge/io1 | 80s r5.16xlarge/io1 |

## Call to action - save your spending and time 
What is the characteristic of your running job and environment? AppCapsule++ is available now with Memory Machine Cloud 2.4 Goa release. Please reach out or leave comments below and we would love to chat and help!

Anyway you look at it, the Chinese mitten crab (also known as the Shanghai hairy crab because of the setae on its claws) has something to offer. For the chef, a seasonal delicacy — full of sweetness and umami flavors. To the aquaculturalist, an important crop and revenue source. This crab is a marvel to biologists — it can live in freshwater and saltwater? Regenerate limbs? Really?

For the geneticist, the challenge is to trace these phylogenetic behaviors (behaviors typical of the species) back to the genome and transcriptome level.

![](https://baywatch-api.memverge.com/media/upload/202311/231127195706586901.jpeg)

## Eriocheir sinensis

The Chinese mitten crab (*Eriocheir sinensis*) is native to China where it is found in lakes and rivers. In Europe and North America, it is considered an invasive species. The mitten crab spends most of its life in freshwater although the reproductive phase occurs in brackish water and in seawater (the species is catadromous). The change in salinity requires a complicated mechanism (osmoregulation) to maintain the ionic balance in its cells.

The first discovery of Chinese mitten crabs in North America occurred in the San Francisco Bay in [1992](https://wildlife.ca.gov/Conservation/Invasives/Species/Mitten-Crab). Since then, specimens have been found in the Chesapeake Bay, Hudson River, and the Great Lakes. They have been found all over Europe — in the Thames River, the Tagus River, the Danube, and even in the Venice lagoon, although finding an isolated specimen does not mean that a permanent population has established itself. Mitten crabs have a high reproductive rate and can withstand physiological stress (such as starvation and dessication) which makes them a formidable invasive species whose burrows undermine river bank stability. It's a mystery why no Chinese mitten crabs have been seen in California since [2010](https://www.smithsonianmag.com/blogs/smithsonian-environmental-research-center/2018/06/05/wanted-chinese-mitten-crabs/).

Beyond its culinary, agricultural, and environmental impacts, the Chinese mitten crab is worthy of study because of its ability to [regenerate](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481118/) an entire limb (as long as it can molt). Regeneration capability is stronger in inverterbrates than in vertebrates where it seems to have declined. Birds and  mammals both demonstrate some cell and tissue regeneration capability, so it is an interesting topic of medical research to see if regeneration ability can be stimulated.

It is no surprise, then, that *Eriocheir sinensis* has been studied extensively, in particular, to discover the gene expressions that underly its physiological and behavioral traits.  The first genome sequencing, assembly, and annotation of the Chinese mitten crab was reported in [2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4730596/) and the current reference assembly was posted in [2022](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_024679095.1/). The haploid [chromosome number](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4814232/) is 73 (2n=146) and the genome size is about 1.27 Gb, which puts it the high end of chromosome number among [arthropods](https://www.nature.com/articles/s41467-021-22604-3) (inverterbrates with exoskeletons), although there is a large percentage of repetitive elements. For comparison, the haploid chromosome number for humans is 23 and the haploid genome has about 3.1 Gb.

Nextflow and MMCloud
--------------------

So, what has this to do with Memory Machine Cloud (MMCloud)? RNA-seq is a popular computational pipeline for analysing gene expression and transcription activation using data generated by next-generation sequencing (NGS) machines from Illumina, PacBio, Oxford Nanopore Technologies, and others. A complicated pipeline, like RNA-seq, requires a workflow manager, such as Nextflow, to schedule and manage the individual tasks in the pipeline.


In Nextflow terminology, each task is assigned to an "executor," which is a complete environment for running that step in the analysis. By attaching the [nf-float](https://github.com/MemVerge/nf-float/blob/master/README.md) plugin to a workflow, Nextflow can use MMCloud as an "executor." From an MMCloud point of view, the execution task that Nextflow assigns to it is an independent job that it runs just like any other batch job. For the Nextflow user, the user experience remains the same while benefiting from all the MMCloud features, such as SpotSurfer, WaveRider, and WaveWatcher.

MMCloud release 2.3.3 introduced a POSIX-compliant, distributed file system that allows Nextflow pipelines to use a high-capacity storage service (such as AWS S3) as a high-performance file system. For complicated pipelines where individual tasks share a file system for writing and reading intermediate data, the performance of the file system has a dramatic impact. 

Running a Nextflow pipeline on MMCloud is straightforward. To demonstrate using AWS EC2 compute instances, we ran [nf-core/rnaseq](https://nf-co.re/rnaseq/3.13.2) on experimental *Eriocheir sinensis* sequence data available from the [European Nucleotide Archive](https://www.ebi.ac.uk/ena/browser/text-search?query=Eriocheir%20sinensis) and the [National Library of Medicine](https://www.ncbi.nlm.nih.gov/datasets/genome/?taxon=95602).

The nf-float plugin requires a configuration file, in which you specify parameters that determine the execution environment. One parameter is the VM creation policy. For this run, we chose "SpotFirst", which means that MMCloud tries to start a spot instance. If, after three attempts, a spot instance does not start, MMCloud starts an on-demand instance. If AWS reclaims the spot instance that a job is running on, MMCloud moves the job to a new instance and follows the same policy - try for a spot instance first and go to an on-demand instance if not successful.

## RNA-Seq Results

At the end of the successful run, Nextflow displayed the following message, which shows that 170 tasks (jobs) completed with no failures.

`-[nf-core/rnaseq] Pipeline completed successfully -`
`Completed at: 25-Nov-2023 10:42:32`
`Duration    : 6h 14m 7s`
`CPU hours   : 123.3`
`Succeeded   : 170`

Included in the output of nf-core/rnaseq is [MultiQC](https://multiqc.info/), a reporting tool that summarizes (in html format) the statistics and quality metrics obtained from multiple analysis modules and data sets. MultiQC is helpful in checking the results of a pipeline run for errors and anomalies.

Examples of the output from MultiQC for the *Eriocheir sinensis* run are shown in the figures that follow.

![](https://baywatch-api.memverge.com/media/upload/202311/231127200149299985.png)

![](https://baywatch-api.memverge.com/media/upload/202311/231127200212828822.png)


## Conclusion

The Chinese mitten crab is a fascinating bundle of surprises. There is no doubt that the species will continue to receive attention - on menus, in departments of environmental conservation, and as the subject of a range of "Omics" studies (for example, [transcriptomics](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328174/)). As researchers become aware of the power of MMCloud, many of those Nextflow pipelines will run on MMCloud in AWS, Google Cloud, or AliCloud.

For the residents of Shanghai, however, the annual appearance of the Shanghai hairy crab is always an eagerly anticipated milestone.

### What is application observability and why it is important

Application observability gains insights into the behavior of software applications utilizing the information from logs, metrics and traces. The goals include but not limited to

- identify bottlenecks and optimize performance
- identify issues and troubleshoot problems quickly
- identify resource requirements and determine the most appropriate corresponding infrastructure

### What is Memory Machine Cloud WaveWatcher and how it can help

The Memory Machine Cloud WaveWatcher service builds on its ability to track CPU, memory, network, storage (size, throughput, and IO) used by a ‘job’ (could be an application, a workflow, or a pipeline). Take the below WaveWatcher result of a Sentieon WGS pipeline as an example:

![WaveWatcher for Sentieon WGS Pipeline](https://baywatch-api.memverge.com/media/upload/202311/231120214738149768.png)

We can see how this pipeline can be characterized into different steps, each with corresponding ‘behaviors.’ The resources the steps consume are shown in the table below:
| Step | Function | Observability - Memory Usage | Observability - CPU Usage | Observability - Network | Observability - Storage Usage | Observability - Storage IO | What can / should we do? |
| - | - | - | - | - | - | - | - |
1 | Validate license and download reference and sample data | extremely low | extremely low | extensive | increasing | extensive | Use a less memory and CPU intensive but network and storage optimized instance |
2 | BWA-MEM | continuously growing | extremely high | none | increasing | medium | Use a memory and CPU optimized instance and larger instance
3 | Dedup | dynamically changing | dynamically changing | zero | dynamically changing | dynamically changing | Use a CPU optimized instance and larger instance |
4 | Variant calling, filtering and genotyping | high | extremely high | zero | retain | zero | Use a memory and CPU optimized instance and medium instance

### Go beyond observability - Memory Machine Cloud WaveRider

It is helpful to gain observability but the ultimate question is - what can be improved, and can it be automated? The data from WaveWatcher can then be used by the WaveRider service to automatically right-size resources, keep the application / pipeline / workflow state, and reduce CO2 footprint, hence to

- avoid under-provision which may result application performance bottleneck or even OOM, and
- avoid over-provision which spends unnecessary money and CO2 footprint.

More detailed Sentieon WGS pipeline benchmark can be found [here](https://www.mmcloud.io/blog/sentieon-genomics-tools-boosting-performance-wit-3).

### Three pillars of Memory Machine Cloud - observability (WaveWatcher), right-sizing (WaveRider), and job automation (Float)

Last but not the least, how to automate the whole process with minimum human intervention? Just like the three pillars of observability (logs, metrics and traces), there are three pillars for Memory Machine Cloud - job automation (Float), observability (WaveWatcher), and right-sizing (WaveRider).

- The user submits a job (for example, a bioinformatics analysis pipeline) via Float CLI or GUI.
- Memory Machine Cloud WaveWatcher automatically observes the behavior including the resources needed
- Memory Machine Cloud WaveRider automatically right-sizes / vertical scaling based on WaveWatcher’s observation supporting stateful job

These three pillars make a complete cloud automation solution removing the hassle of integrations among multiple solutions.

### Comparison of Memory Machine Cloud with modern solutions

Below is a summary table of comparison of Memory Machine Cloud with modern solutions, where Memory Machine Cloud provides an all-in-one solution.
| Category | Comparison | MemVerge MMCloud | Grafana | Datadog | Spot by NetApp | YellowDog | AWS CloudWatch | AWS Compute Optimizer | AWS Batch |
|:----------:|:---------------------------------------------------------------:|:------------------:|:----------:|:----------:|:--------------:|:---------------:|:--------------:|:---------------------:|:----------:|
| Feature | Observability | Yes (WaveWatcher) | Yes | Yes | Yes | Yes | Yes | Yes | No |
| Feature | Automatic right-sizing stateless application | Yes (WaveRider) | No | No | Yes | Yes | No | No | No |
| Feature | Automatic right-sizing stateful, non-fault-tolerant application | Yes (WaveRider) | No | No | No | No | No | No | No |
| Feature | Job automation | Yes (Float) | No | No | No | Yes | No | No | Yes |
| Deployment | On-prem version | Yes | Yes | No | No | Yes | No | No | No |
| Pricing | Pricing | All-in-one license | By license | By service | By license | Basic + add-ons | By service | Opt-in | By service |

\*note - more detailed comparisons with Spot by NetApp can be found [here](https://www.mmcloud.io/blog/memory-machine-cloud-vs-spot-by-netapp)

### Call for action - save your spending and time

Do you need to run stateful, non-fault-tolerant workloads or pipelines on the cloud? Would you like to benefit from automatic right-sizing and vertical scaling and reduce both cloud cost and job running hours without worrying about integrations among multiple solutions?  [Please try Memory Machine Cloud for free](https://www.mmcloud.io/customer/register).

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.

In the previous <a href="https://www.mmcloud.io/blog/surfzone-using-memory-machine-cloud-to-prevent-clo" rel="noopener noreferrer" target="_blank" style="color: var(--ds-link, #0052CC); background-color: rgb(255, 255, 255);">SurfZone blog</a>, we talked about some real-world examples of cloud budget overruns and their painful consequences. We also mentioned the limitations of existing solutions and how SurfZone can overcome them by providing timely enforcement while saving interim progress.In this blog, we describe the user journeys of both the Cloud Admin and the Cloud User. <h3>Typical user journey</h3>A typical high-level user journey takes 3 steps -<ol><li data-list="ordered">Cloud Admin defines quota group</li><li data-list="ordered">Cloud Admin applies quota group to Cloud User and Cloud Users' submitted jobs will automatically be enforced by the policy of the given quota group.</li><li data-list="ordered">Cloud Admin and Cloud User get notifications when Cloud User's consumption reaches the quota limit or any quota-related actions occur.</li></ol><img src="https://baywatch-api.memverge.com/media/upload/202310/231023012347452899.jpeg"> Now let’s take a closer look at each of the steps along with Memory MachineTM Cloud OpCenter Web GUI.Step 1 - Cloud Admin or Principal Investigator defines a quota group (see Figure 1.1) as follows, and views list of created quota groups (see Figure 1.2).<ol><li data-list="ordered" class="ql-indent-1">What is the quota type you would like to monitor (for example, cost)? What is the quota limit?</li><li data-list="ordered" class="ql-indent-1">What is the usage threshold for notification?</li><li data-list="ordered" class="ql-indent-1">What action should be taken when the usage exceeds the quota? Would it be</li><li data-list="ordered" class="ql-indent-2">Cancel the job, or</li><li data-list="ordered" class="ql-indent-2">Suspend the job (where you can enable auto resume)</li><li data-list="ordered" class="ql-indent-1">What action should be taken when additional quota is applied (for example, auto-resume)?</li></ol><img src="https://baywatch-api.memverge.com/media/upload/202310/231019182008359212.png">Figure 1.1. Cloud Admin adds/edits SurfZone&nbsp;<img src="https://baywatch-api.memverge.com/media/upload/202310/231019182028234291.png">Figure 1.2. Cloud Admin views the list of SurfZone&nbsp;Step 2 - Cloud Admin applies the quota group to a Cloud User (see Figure 2.1 and Figure 2.2) - the Cloud User's jobs are automatically subject to the enforcement mechanisms defined by the quota group (see Figure 2.3).<img src="https://baywatch-api.memverge.com/media/upload/202310/231019182141403006.png">Figure 2.1. Cloud Admin applies SurfZone to Cloud User&nbsp;<img src="https://baywatch-api.memverge.com/media/upload/202310/231019182152185529.png">Figure 2.2. Cloud Admin views the list of Cloud User including quota and usage&nbsp;<img src="https://baywatch-api.memverge.com/media/upload/202310/231019182400294309.png">Figure 2.3. Cloud Admin views the list of Job including their status&nbsp;Step 3 - Cloud Admin (or Principal Investigator) and Cloud User get notifications when user's consumption reaches the quota limit or any quota-related actions occur.<img src="https://baywatch-api.memverge.com/media/upload/202310/231019182429989309.png">Figure 3.1. Cloud Admin gets notifications on users whose quota usage above the configured threshold <img src="https://baywatch-api.memverge.com/media/upload/202310/231019182439198049.png">Figure 3.2. Cloud User views his/her own quota and usage&nbsp;<h3> </h3><h3>What’s next?</h3>What are your thoughts on this feature?If you would like to be on the waitlist to preview this feature, leave your contact information in the comments below.<a href="https://www.mmcloud.io/" rel="noopener noreferrer" target="_blank" style="color: var(--ds-link, #0052CC); background-color: rgb(255, 255, 255);">Memory Machine Cloud by MemVerge</a>

<h3>Who should read this blog? </h3>If you are a Cloud Admin or a Principal Investigator and you are concerned about your team inadvertently exceeding their cloud budget, reading this blog is worth your time (3-minutes).Real-world examples of cloud budget overrunsHere are real-world exampl﻿es of cloud budget overruns...and the painful consequences.In a <a href="http://www.rj-robbins.com/recommended/videos/airi-2021/" rel="noopener noreferrer" target="_blank">presentation</a> on cloud computing in grant-funded research environments, Robert J. Robbins explains how the success of projects can be jeopardized by unexpected (and accidental) expenses. Here are examples.<ol><li data-list="bullet">An expense of $20,000 incurred because a researcher forgot to shut down an AWS instance after completing an analysis - the instance continued to run at $7 an hour for months.</li><li data-list="bullet">An expense of $72,000 incurred because of exponential auto-scaling caused by inserting small tests into product code - the company said they almost went bankrupt.</li></ol> Why aren’t there solutions for this serious problem?Limitations of existing solutionsThere are solutions that monitor cloud cost and can stop running instances, but there are two critical limitations.<ol><li data-list="bullet">Not timely - AWS Budgets, for example, can track cost and usage, but updates typically occur at 8–12 hour intervals. The cloud budget overrun may happen before the next update.</li><li data-list="bullet">Interim progress lost - even if you are alerted when the cloud budget is exceeded and you stop all running instances, interim progress made by unfinished jobs is lost. This means not only the money but the time has been spent and wasted.</li></ol> Is there a single solution to overcome both of these two critical limitations?SurfZone: Memory Machine Cloud's solutionMemory Machine Cloud's SurfZone overcomes both limitations.<ol><li data-list="bullet">Timely - SurfZone provides real-time enforcement for a user's cloud spend (default update interval is one hour but can be configured to a shorter or longer interval).</li><li data-list="bullet">Interim progress saved - When a user reaches the cloud budget limit, SurfZone checkpoints the current application state and puts the application into sleep mode. The application automatically resumes when additional budget is applied.</li></ol> Typical user journeyA typical user journey resembles the following.<ol><li data-list="ordered">Cloud Admin or Principal Investigator defines a quota (budget) group as follows.</li><li data-list="ordered" class="ql-indent-1">What is the quota type you would like to monitor (for example, cost)?</li><li data-list="ordered" class="ql-indent-1">What action should be taken when the usage exceeds the quota (for example, suspend or terminate the job)?</li><li data-list="ordered" class="ql-indent-1">What action should be taken when additional quota is applied (for example, auto-resume)?</li><li data-list="ordered">Cloud Admin or Principal Investigator applies the quota group to a user - the user's jobs are automatically subject to the enforcement mechanisms defined by the quota group.</li><li data-list="ordered">Cloud Admin (or Principal Investigator) and user get notifications when user's spending reaches the quota limit or any quota-related actions occur.</li></ol> What’s next?In a subsequent blog, we’ll take a deeper dive into the user interface that supports SurfZone.What do you think of this feature? Does it work for you or your organization? If you would like to preview this feature, leave your contact information in the comments below.<a href="https://www.mmcloud.io/" rel="noopener noreferrer" target="_blank" style="background-color: var(--ds-surface-raised,white); color: var(--ds-link, #0052CC);"><img src="https://www.mmcloud.io/favicon.ico">Memory Machine Cloud by MemVerge</a>

<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>

<h2>Cloud’s promise unfulfilled</h2><img src="https://miro.medium.com/v2/resize:fit:1400/1*jCza7dtD08vfltfQw2XhYw.png" height="207" width="700">Recent news suggest that the growth of cloud adoption is encountering serious impedance. There are even organizations repatriating compute resources back to on-premises data centers, and cloud cost seems to be the culprit.Indeed, a 2 CPU (24 core)/48 GB RAM server sells for ~$10,000. On AWS, a 24 vCPU / 48 GB RAM VM costs ~$1.18/hr. If you continuously ran the VM for 1 year, you would have paid $10,195 and could have bought the server hardware. Since the typical useful life of server hardware is 4–5 years, you would’ve overpaid 4–5 times for the server. Of course in a more rigorous comparison we need to consider operational costs but I think we all agree that continuously running the cloud server the same way as an on-prem server is costly.The key word, of course, is “continuously.” To reap the benefit of the cloud you need to use it efficiently, i.e., releasing resources that you are not using, and using resources that match the needs of your applications.<h2>Several challenges stand in the way of efficient cloud usage in the enterprise:</h2><h2>App owner access to the cloud</h2>The team that manages the cloud resources, i.e., the cloud admin team, is not the team that is running the applications, i.e., the application owners: the developers, the analysts, the scientists, et al. The application users often need to wait hours or days to gain access to cloud resources, and they don’t have the tools or the discipline to release the resources as quickly as possible.<h2>App owner flying blind</h2>The application owners may not know the most appropriate machine size to use for their applications. Often, they do not have the tools necessary to view the resource utilization of the application. They only know their cloud bill is high, their application seems to take a long time to complete, they suffer out-of-memory errors, and their application crashes and must be restarted.<h2>Forced to provision for peak usage</h2>Even when the application owner understands the application, it is often difficult to pick one machine size because most applications have peaks and troughs in their resource utilizations. If one picks the machine size that can handle the peak utilization, it often means the machine is under-utilized most of the time. If one sacrifices performance by under provisioning, then the application may crash (OOM) or otherwise run very slowly.<h2>The serverless model for complex, stateful apps</h2>When we set out to build a product for the cloud, leveraging the fruits of our research in Big Memory Computing, we asked ourselves, “Can we build software that enable all applications, including stateful apps, to be serverless?” That means application owners can focus on their subject areas and never need to worry about management of servers or other cloud resources, nor depend on another team to do so.Stateful applications should be able to take advantage of the elasticity of the cloud and consume resources most appropriate for it, even when the resource utilization is bursty. Serverless computing services, such as AWS Lambda, have proven that the serverless model is highly desirable for cloud native microservices. But how do we enable complex applications, even stateful applications, to run serverless?<h2>Decoupling complex, stateful applications from servers</h2><img src="https://miro.medium.com/v2/resize:fit:1400/0*ETvWyn5e8XFOeJGV.png" height="354" width="700">Application runtime is “chained” to serversRecent news suggest that the growth of cloud adoption is encountering serious impedance. There are even organizations repatriating compute resources back to on-premises data centers, and cloud cost seems to be the culprit.Indeed, a 2 CPU (24 core)/48 GB RAM server sells for ~$10,000. On AWS, a 24 vCPU / 48 GB RAM VM costs ~$1.18/hr. If you continuously ran the VM for 1 year, you would have paid $10,195 and could have bought the server hardware. Since the typical useful life of server hardware is 4–5 years, you would’ve overpaid 4–5 times for the server. Of course in a more rigorous comparison we need to consider operational costs but I think we all agree that continuously running the cloud server the same way as an on-prem server is costly.The key word, of course, is “continuously.” To reap the benefit of the cloud you need to use it efficiently, i.e., releasing resources that you are not using, and using resources that match the needs of your applications.<h2>AppCapsule technology makes it possible</h2>We invented a solution for decoupling stateful apps from servers with AppCapsule technology. A stateful application is one whose execution depends on the data stored in memory. Because memory is not persistent, when the server hardware reboots or changes, the data in memory is lost, and the application must start from the beginning again. Our AppCapsule creates a snapshot of all the data in memory, and all the application states required to restore the application to this moment in time, as well as the data on storage. With AppCapsule, we can transport the application runtime across different machines, even different clouds.<img src="https://miro.medium.com/v2/resize:fit:1400/0*X_gS1OCoelC4ImVA.png" height="303" width="700">AppCapsule SnapshotArmed with this technology, we set out to build control components that natively integrates with the clouds, and the user interface designed to serve the application users, and voilà, we have Memory Machine Cloud, the cloud automation platform that delivers the Serverless experience for most applications.<h2>The Floating App</h2>The internal engineering codename for Memory Machine Cloud is “Float,” because when you launch your application using Memory Machine, your application is floating on the cloud, untethered to the server it is running on. For that reason, our CLI commands all start with the keyword “float.”It’s pretty amazing what you can do when your application is floating. The first use case we tackled was running long, stateful jobs, such as genomic sequencing, on spot instances. Spot instances offer the most economical way to tap into the vast compute resources in the public cloud, but because the CSP can evict your workload on spot with a very short notice period, they are not suitable for these jobs. That is, unless you use Memory Machine Cloud.When you launch your genomic sequencing job using Memory Machine, you simply specify the spec of the machine, and that you want to leverage spot instances. Memory Machine takes care of finding the spot instances, deploying your containers, mapping your storage, and launching your job. If there is a spot eviction event, Memory Machine creates an AppCapsule, and moves it to another spot instance, where the job will continue. In fact, the application is unaware of the floating event. This use case alone, we have found, consistently saves our users more than 60% in compute cost.Submitting a job with the Float GUI<img src="https://miro.medium.com/v2/resize:fit:1400/0*yALS-Fy-kHvybR0h.png" height="502" width="700">Launching your job in the Cloud takes &lt;1 minuteAnother use case is rightsizing. Often an application’s resource utilization curve has peaks and valleys over time. That is problematic for picking the appropriate machine capacity for the application. However short-lived, the peak memory usage typically becomes the minimum memory capacity for the machine, lest the application will experience an OOM and crash. That also means that during the majority of the execution, the machine is underutilized.By making the application runtime float on the cloud VM, Memory Machine Cloud has a great solution for this problem. It constantly monitors the resource utilization of the application, and when it detects under- or over- utilization conditions, it can “float” the application to a more appropriate machine. This becomes especially handy when the user uses a workflow manager, such as NextFlow or Cromwell, to kickoff hundreds and thousands of jobs, and it is virtually impossible to manually pick the most appropriate machine types for each of the jobs.Monitoring resource utilization and “floating” the app to a more appropriate VM<img src="https://miro.medium.com/v2/resize:fit:1400/0*xs9uaSJDYLK_n3m2.png" height="361" width="700">WaveWatcher shows how jobs automatically “surf” to the most appropriate instance type<h2>The post-server era</h2>In the hardware world, we are seeing tremendous progress in the deconstruction of servers, composable CPU, Storage, and most recently, memory. Those innovations will manifest themselves in the public cloud soon. There is a need and an opportunity to build the automation layer that enables application users and IT operators to transition into “post-server” mode TODAY, not only for cloud native applications, but for all applications. That is the mission of Memory Machine Cloud.<iframe class="ql-video ql-align-center" frameborder="0" allowfullscreen="true" src="https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fwww.youtube.com%2Fembed%2F2juNvP3BbK8%3Ffeature%3Doembed&amp;display_name=YouTube&amp;url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3D2juNvP3BbK8&amp;image=https%3A%2F%2Fi.ytimg.com%2Fvi%2F2juNvP3BbK8%2Fhqdefault.jpg&amp;key=a19fcc184b9711e1b4764040d3dc5c07&amp;type=text%2Fhtml&amp;schema=youtube" height="480" width="854"></iframe>

Blog - Cloud Automation