Middleville Plastic Manufacturers

How to Find a Plastic Molding in Middleville ?

Whether the fabricator’s shop is large or small, the Ironworker is the backbone. The Ironworker isn’t a single machine; it is five machines united into an engineering wonder. It has much more versatility than most people would imagine. The five working sections that are involved in the make-up of this machine are a punch, a section shear, a bar shear, a plate shear, and a coper-notcher.

A number of the cheaper ironworkers are constructed to employ a fulcrum where the ram shakes back and forth, making the punch go into the succumb at a small angle. This normally leads to the erosion of the punch and succumb on the front rims. The higher quality machines integrate a ram which moves in a direct vertical line and utilizes modifiable gibs and guidebooks to guarantee a constant traveling path.

Mouldable Plastic

When you look for a End of Arm Tooling (EOAT)  that develop a Plastic Molding in Middleville, looks for experience and not only pricing.

That devotes more life to the tooling, and allows the punch to penetrate the succumb right in the middle in order to capitalize on the machine’s total tonnage.

When looking for a design house that designs a Plastic Molding in Middleville  don’t look just in Michigan , other States also have great providers.

Pneumatic Gripper

Real-life AWS infrastructure cost optimization strategy

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By Rod Vagg

ARM: A Quick Primer

ARM is a tricky beast to describe because it’s more than one thing. In common parlance, we use it to describe a CPU architecture, akin to x86 from Intel and AMD. The ARM name comes from its designer, ARM Holdings, but they don’t actually make the hardware, unlike Intel and AMD. ARM is primarily an intellectual property company which licenses their technology to manufacturers to form a vibrant ecosystem of processor and SoC (System on a Chip) products.

An ecosystem of manufacturers

Companies such as Samsung, Qualcomm, Broadcom and even AMD (traditionally known for their x86 products) license core CPU designs from ARM, largely made up of the “Cortex” range. A number of CPU design licensees release Cortex-based processors under their own branding, which is where you see familiar names such as the Qualcomm Snapdragon, the Samsung Exynos or Nvidia Tegra.

In addition, ARM offers an architectural license that gives licensees permission to design their own CPUs that fully comply with the ARM architecture to ensure instruction set architecture (ISA) compatibility. Companies such as Applied Micro and Cavium currently hold architectural licenses and are producing their own processor designs. Apple uses an architectural license to produce its Ax series of processors, including the A7 and A8 which power the current iPhone and iPad range.

The ARM architecture

Due to the compact nature of the ARM architecture, it has traditionally been used for small devices. ARM processor designs tend to focus on efficiency as their current primary uses are in devices where power draw is a major concern. Most smartphones and tablets in the market today are based around ARM processors and they are even showing up in laptops, with many of the current Chromebook range using ARM processors.

ARM’s architecture designs are broken up in to generational versions. The most common ARM architecture generation used in smartphones, tablets and other small computers today is ARMv7. For instance, the newest incarnation of the Raspberry Pi uses an ARMv7 processor, while the original Pi used an ARMv6 processor, the previous generation.

There’s a new generation that’s starting to roll out, ARMv8 and this represents a major shift in architecture design and also a shift in the commercial potential that ARM Holdings sees for its processors.

The HiKey development board from 96Boards using an HiSilicon Kirin 6220 eight-core ARMv8 Cortex-A53 CPU

Until now, ARM’s range of processors and architecture designs have been 32-bit, meaning they have limitations in their ability to scale to uses beyond small devices. But even our smartphones are starting to push up against the barriers that 32-bit processors present, most notably the limitations to the amount of RAM you can couple with the processor. ARMv8 is a new 64-bit design that alleviates the barriers presented by 32-bits. The ARM family of processors already reaches deep into the low-power and small-size end of the market (as demonstrated b the Cortex-M0+ pictured above), but with ARMv8, there is a new target: the server market.

ARM on the Server

The phenomenal success of the Raspberry Pi saw the dawn of a whole new class of computers gaining wide acceptance: “single-board computers”. There is now a huge range of products in this market, all vying for the attention of hobbyists and commercial users alike. Even Intel is in on the game with their low-power x86 incarnation, the Atom. The low cost and surprising versatility of these small computers have lead to some interesting new uses. DataStax likes to show off their 32-node Rasperry Pi Cassandra Cluster as a way to demonstrate the versatility of Cassandra but even more, it shows the potential uses that low-cost single-board computers can be put to. Online Labs have rolled out a new IaaS (Infrastructure as a Service) product named Scaleway based completely around ARMv7 servers and are finding strong interest from customers wanting smaller and simpler cloud infrastructure.

The DataStax demonstration 32-node Rasperry Pi Cassandra Cluster

miniNodes, another IaaS company, has jumped straight to ARMv8 in its offering by using early development ARMv8 boards. The University of Utah, in its contribution to the scientific computing cloud research project CloudLab, are rolling out a cluster of 315 HP Moonshot m400 cartridges, with which HP are claiming the title of “The World’s First Enterprise-ready 64-bit ARM Server”.

Also getting in on the ARMv8 hardware action is Gigabyte, Lenovo, Hyve Solutions, SoftIron, StackVelocity and E4 who specifically target HPC applications. As 2015 rolls on, expect a flourish of new hardware to appear, pushing us to rethink some traditional approaches.

The HP Moonshot m400 ARMv8 cartridge

The new ARMv8 processors are intended to further bridge the gap between traditional ARM uses and the new forms of server computers that there is an obvious demand for. Their low-power profile will mean that their natural target will still be smaller servers but we will likely see many cluster-style products come on to the market where many ARMv8 boards are combined into a unified cluster.

The Software Stack

Just as we are seeing shifts in the hardware market, with new demand for clusters of smaller servers rather than simply continuing to push at Moore’s Law to make servers ever-bigger, we are also seeing shifts in the traditional trajectory of the software stack. Monolithic applications are now viewed as both business and technical risks. SOA (Service Oriented Architecture) is the new best-practice with experimentation all the way down to micro-services. We’re in the midst of a great ‘unbundling’ in the software world.

While the JVM is right at the heart of the monolithic software stack and the tooling that surrounds it, Node, or server-side JavaScript, is arguably at the heart of the new SOA stack. Node’s small and nimble runtime profile along with its overriding culture of modularity make it a perfect fit for a transition to the composition of applications from smaller, focused, services.

There is an interesting intersection between the changes in the hardware market and the changes in best-practice software development. Smaller, more nimble software is perfectly suited to smaller, more nimble and low-power hardware. What’s more, Node’s development model encourages developers to think multi-process from the beginning because we know that without the crutch of threads, the only way we can scale our applications is to multiply the number of processes (have you ever noticed how you rarely hear Node developers talk about “sticky-sessions” while Java developers obsess about them?). This means that Node applications scale as easily across clusters of servers as they do within a single server. Not only does the Node development model buy you free scalability, it also buys you resilience by fitting better on larger numbers of smaller servers instead of smaller numbers of larger servers as you typically see in the JVM world (although, the typical Node application performance profile means that you need significantly less total hardware investment as well).

One of the common patterns that NodeSource encounters across the enterprise as companies start waking up to the potential that Node offers them is that they need to start rethinking their hardware needs. Typically, large companies will have a homogeneous production environment, with one or two types of server available for deploying applications. Commonly these are tuned to the needs of the JVM and other monolithic application stacks so there is a priority placed the on speed and size of each hardware unit. An average server might have 16 cores and 32G of RAM and be a perfect match for a JVM application that makes liberal use of threads and is a natural memory hog. Unfortunately, this doesn’t translate very well to Node, particularly on the memory side. So we see a lot of wasted hardware in these environments with architects exploring new ways to make use of all of the free RAM they now have available. This is not ideal from a cost perspective but understandable where Node is only at the beginning of its journey into these environments.

Node and ARM: A Perfect Match

As argued above, Node is a great fit for the changes occurring in the hardware stack:

  1. Node isn’t a resource hog, it’s at home in smaller environments with its low memory profile and single-threaded nature.
  2. Node is nimble; for example, we advise our clients to kill & quickly restart when their applications enter an unexpected-error state. You can’t do this with a runtime that takes minutes to properly start and warm-up.
  3. Node’s development model and culture is naturally SOA; if you’re building a large application and it’s not made up of small services then you’re doing Node wrong. Node applications are generally scalable by default.

Another important factor here is Node’s use of V8 as a JavaScript foundation. From its early days, the Chromium project has treated the ARM platform as one of its primary targets. Chrome is on every new Android-based phone and tablet and is obviously a foundational component of Chromebooks. V8 is already heavily optimized for ARM and is moving in lock-step with ARM because it’s in the interests of both ARM and Google to do so.

io.js, the community fork of Node.js, released its 1.0 earlier this year. ARM has been second-class for Node.js until now so we encouraged a new focus on ARM as a first-class platform target for the io.js project. ARM hardware has been a fixture in the io.js CI system from the beginning and the project has been shipping ARM binaries since 1.0. Today you can download both ARMv6 and ARMv7 optimized binaries for io.js releases and nightlies right from the downloads directory. Through this focus, io.js has even been able to feed patches back in V8 to fix and improve support for ARM.

Because io.js is using current V8 releases and we have made it clear that ARM as a platform with primary support, ARM Holdings has taken an interest in the project. It’s clear that they see similar synergies to us between Node and ARM hardware, particularly with their new focus on server use of their architecture. ARM has stated publicly that their goal is to carve out 20% of the server market with its new architecture within five years, up from less than 1% today.

ARMv6 and ARMv7 boards serving in the current io.js ARM test and build cluster

We have been working with ARM to get access to test hardware for the io.js CI system to bring the codebase up to scratch on the new ARMv8 architecture. The not-for-profit Linaro organization was set up by ARM and its partners to work on bringing better ARMv7 and ARMv8 support to open source software. The organization maintains a server cluster which the io.js project currently has access to for ARMv8 test hardware and has used this resource to understand and solve the technical hurdles involved. io.js is now shipping experimental 64-bit ARMv8 binaries in its nightly distribution channel. By the time single-board ARMv8 computers are available on the general market there will also be release builds of io.js available for use. Keep an eye on 96Boards, a project by Linaro, if you are interested in affordable ARMv8 hardware.

Getting Real

Of course, any embrace of the combination of smaller servers and Node for the enterprise is likely to be part of a longer, multi-year strategy. As of right now, Node adoption is still in the early stages at most companies that are choosing to embrace it. Their immediate concerns are more about the basic architecture questions relating to unbundling monolithic structures. As new SOA models emerge, questions about the optimization of hardware platforms will arise and it’s likely that ARM will be in serious consideration.

Aside from enterprise concerns, it’s clear that ARM at least has a future in new-style, low-cost cloud platforms that may be very attractive to start-ups and those of us who are looking for cheap hosting for our side-projects.

Node is still young, and adapting to a changing hardware landscape should be easy. Through io.js, Node’s future on ARM hardware is looking very positive. NodeSource will be keenly watching how the community and companies, both small and large, react to the new possibilities as they emerge.

Injection Moulding Machine Price

AWS recently announced its new per second billing for its EC2 instances and EBS volumes. This is perfect timing to talk about cost optimization. After a short intro we will guide you through some real world examples and best practices that we use at Teads to optimize our infrastructure costs.

The cloud computing opportunity and its traps

One of the advantages of cloud computing is its ability to fit the infrastructure to your needs, you only pay for what you really use. That is how most hyper growth startups have managed their incredible ascents.

Most companies migrating to the cloud embrace the “lift & shift” strategy, replicating what was once on premises.

You most likely won’t save a penny with this first step.

Main reasons being:

  • Your applications do not support elasticity yet,
  • Your applications rely on complex backend you need to migrate with (RabbitMQ, Cassandra, Galera clusters, etc.),
  • Your code relies on being executed in a known network environment and most likely uses NFS as distributed storage mechanism.

Once in the cloud, you need to “cloudify” your infrastructure.

Then, and only then, will you have access to virtually infinite computing power and storage.

Watch out, this apparent freedom can lead to very serious drifts: over provisioning, under optimizing your code or even forgetting to “turn off the lights” by letting that small PoC run more than necessary using that very nice r3.8xlarge instance.

Essentially, you have just replaced your need for capacity planning by a need for cost monitoring and optimization.

The dark side of cloud computing

At Teads we were “born in the cloud” and we are very happy about it.

One of our biggest pain today with our cloud providers is the complexity of their pricing.

It is designed to look very simple at the first glance (usually based on simple metrics like $/GB/month or $/hour or, more recently, $/second) but as you expand and go into a multi-region infrastructure mixing lots of products, you will have a hard time tracking the ever-growing cost of your cloud infrastructure.

For example, the cost of putting a file on S3 and serving it from there includes four different lines of billing:

  • Actual storage cost (80% of your bill)
  • Cost of the HTTP PUT request (2% of your bill)
  • Cost of the many HTTP GET requests (3% of your bill)
  • Cost of the data transfer (15% of your bill)

Our take on Cost Optimization

  • Focus on structural costs - Never block short term costs increase that would speed up the business, or enable a technical migration.
  • Everyone is responsible - Provide tooling to each team to make them autonomous on their cost optimization.

The limit of cost optimization for us is when it drives more complexity in the code and less agility in the future, for a limited ROI. 
This way of thinking also helps us to tackle cost optimisation in our day to day developments.

Overall we can extend this famous quote from Kent Beck:

“Make it work, make it right, make it fast” … and then cost efficient.

Billing Hygiene

It is of the utmost importance to keep a strict billing hygiene and know your daily spends.

In some cases, it will help you identify suspicious uptrends, like a service stuck in a loop and writing a huge volume of logs to S3 or a developer that left its test infrastructure up & running during a week-end.

You need to arm yourself with a detailed monitoring of your costs and spend time looking at it every day.

You have several options to do so, starting with AWS’s own tools:

  • Billing Dashboard, giving a high level view of your main costs (Amazon S3, Amazon EC2, etc.) and a rarely accurate forecast, at least for us. Overall, it’s not detailed enough to be of use for serious monitoring.
  • Detailed Billing Report, this feature has to be enabled in your account preferences. It sends you a daily gzipped .csv file containing one line per billable item since the beginning of the month (e.g., instance A sent X Mb of data on the Internet). 
    The detailed billing is an interesting source of data once you have added custom tags to your services so that you can group your costs by feature / application / part of your infrastructure. 
    Be aware that this file is accurate within a delay of approximately two days as it takes time for AWS to compute the files. 
    UPDATE (June ‘18) Detailed Billing is officially deprecated, use the Cost and Usage Report instead.
  • Trusted Advisor, available at the business and enterprise support level, also includes a cost section with interesting optimization insights.
Trusted Advisor cost section - Courtesy of AWS
  • Cost Explorer, an interesting tool since its update in august 2017. It can be used to quickly identify trends but it is still limited as you cannot build complete dashboards with it. It is mainly a reporting tool.
Example of a Cost Explorer report — AWS documentation

Then you have several other external options to monitor the costs of your infrastructure:

  • SaaS products like Cloudyn / Cloudhealth. These solutions are really well made and will tell you how to optimize your infrastructure. Their pricing model is based on a percentage of your annual AWS bill, not on the savings that the tools will help you make, which was a show stopper for us.
  • The open source project Ice, initially developed by Netflix for their own use. Recently, the leadership of this project was transferred to the french startup Teevity who is also offering a SaaS version for a fixed fee. This could be a great option as it also handles GCP and Azure.

Building our own monitoring solution

At Teads we decided to go DIY using the detailed billings files.

We built a small Lambda function that ingests the detailed billing file into Redshift every day. This tool helps us slice and dice our data along numerous dimensions to dive deeper into our costs. We also use it to spot suspicious usage uptrends, down to the service level.

This is an example of our daily dashboard built with chart.io, each color corresponds to a service we taggedWhen zoomed on a specific service, we can quickly figure out what is expensive

On top of that, we still use a spreadsheet to integrate the reservation upfronts in order to get a complete overview and the full daily costs.

Now that we have the data, how to optimize?

Here are the 5 pillars of our cost optimization strategy.

1 - Reserved Instances (RIs)

First things first, you need to reserve your instances. Technically speaking, RIs will only make sure that you have access to the reserved resources.

At Teads our reservation strategy is based on bi-annual reservation batches and we are also evaluating higher frequencies (3 to 4 batches per year).

The right frequency should be determined by the best compromise between flexibility (handling growth, having leaner financial streams) and the ability to manage the reservations efficiently. 
In the end, managing reservations is a time consuming task.

Reservation is mostly a financial tool, you commit to pay for resources during 1 or 3 years and get a discount over the on-demand price:

  • You have two types of reservations, standard or convertible. Convertible lets you change the instance family but comes with a smaller discount compared to standard (avg. 75% vs 54% for a convertible). They are the best option to leverage future instance families in the long run.
  • Reservations come with three different payment options: Full Upfront, Partial Upfront, and No Upfront. With partial and no upfront, you pay the remaining balance monthly over the term. We prefer partial upfront since the discount rate is really close to the full upfront one (e.g. 56% vs 55% for a convertible 3-year term with partial).
  • Don’t forget that you can reserve a lot of things and not only Amazon EC2 instances: Amazon RDS, Amazon Elasticache, Amazon Redshift, Amazon DynamoDB, etc.

2 - Optimize Amazon S3

The second source of optimization is the object management on S3. Storage is cheap and infinite, but it is not a valid reason to keep all your data there forever. Many companies do not clean their data on S3, even though several trivial mechanisms could be used:

The Object Lifecycle option enables you to set simple rules for objects in a bucket :

  • Infrequent Access Storage (IAS): for application logs, set the object storage class to Infrequent Access Storage after a few days. 
    IAS will cut the storage cost by a factor of two but comes with a higher cost for requests. 
    The main drawback of IAS is that it uses 128kb blocks to store data so if you want to store a lot of smaller objects it will end up more expensive than standard storage.
  • Glacier: Amazon Glacier is a very long term archiving service, also called cold storage. 
    Here is a nice article from Cloudability if you want to dig deeper into optimizing storage costs and compare the different options.

Also, don’t forget to set up a delete policy when you think you won’t need those files anymore.

Finally, enabling a VPC Endpoint for your Amazon S3 buckets will suppress the data transfer costs between Amazon S3 and your instances.

3 - Leverage the Spot market

Spot instances enables you to use AWS’s spare computing power at a heavily discounted price. This can be very interesting depending on your workloads.

Spot instances are bought using some sort of auction model, if your bid is above the spot market rate you will get the instance and only pay the market price. However these instances can be reclaimed if the market price exceeds your bid.

At Teads, we usually bid the on-demand price to be sure that we can get the instance. We only pay the “market” rate which gives us a rebate up to 90%.

It is worth noting that:

  • You get a 2 min termination notice before your spot is reclaimed but you need to look for it.
  • Spot Instances are easy to use for non critical batch workloads and interesting for data processing, it’s a very good match with Amazon Elastic Map Reduce.

4 - Data transfer

Back in the physical world, you were used to pay for the network link between your Data Center and the Internet.

Whatever data you sent through that link was free of charge.

In the cloud, data transfer can grow to become really expensive.

You are charged for data transfer from your services to the Internet but also in-between AWS Availability Zones.

This can quickly become an issue when using distributed systems like Kafka and Cassandra that need to be deployed in different zones to be highly available and constantly exchange over the network.

Some advice:

  • If you have instances communicating with each other, you should try to locate them in the same AZ
  • Use managed services like Amazon DynamoDB or Amazon RDS as their inter-AZ replication costs is built-in their pricing
  • If you serve more than a few hundred Terabytes per months you should discuss with your account manager
  • Use Amazon CloudFront (AWS’s CDN) as much as you can when serving static files. The data transfer out rates are cheaper from CloudFront and free between CloudFront and EC2 or S3.

5 - Unused infrastructure

With a growing infrastructure, you can rapidly forget to turn off unused and idle things:

  • Detached Elastic IPs (EIPs), they are free when attached to an EC2 instance but you have to pay for it if they are not.
  • The block stores (EBS) starting with the EC2 instances are preserved when you stop your instances. As you will rarely re-attach a root EBS volume you can delete them. Also, snapshots tend to pile up over time, you should also look into it.
  • A Load Balancer (ELB) with no traffic is easy to detect and obviously useless. Still, it will cost you ~20 $/month.
  • Instances with no network activity over the last week. In a cloud context it doesn’t make a lot of sense.

Trusted Advisor can help you in detecting these unnecessary expenses.

Key takeaways

Thank you for reading. This article was inspired by the talks I made during the #2 AWS Montpellier Meetup and Devops D-Day conference.

Devops D-Day 2017 — Marseille

If you like working on big cloud infrastructures and growth challenges, feel free to contact us, we are constantly looking for great teammates.

If you want to know more about Engineering at Teads:

About Teads Engineering
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You Can Find a EOAT in Middleville here:

 



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