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PSSC Labs Featured in IT Pro Portal: 7 hidden AWS Costs That Could Be Killing Your Budget

By marketing@site-a.com

Is your organization paying what it should for hosting with AWS?

The AWS Elastic Compute Cloud (EC2) service has many advantages, including easy scalability, pay-for-what-you-use, as-you-go pricing, and an enormous array of options and upgrades – so many that your AWS bill may become quite complicated. Have you been suffering from sticker shock but have no idea which of the literally thousands of line items on your invoice are the culprits? Here are seven hidden AWS costs that could be breaking the bank. 

1. Unused Instances 

Among the biggest contributors to inflated AWS bills are unused or underutilized EC2 instances, which result in your organization paying for resources it is not using. Be sure to terminate all instances as soon as you are finished using them; note that this applies separately, to each world region. Also make sure to monitor your EC2 usage data for low CPU usage, bandwidth, and I/O, which are red flags that may indicate underutilized servers that could be shut down. 

2. Unneeded Orphaned Snapshots 

Terminating an unused or underutilized EC2 instance isn’t enough. Even though the attached EBS volumes are automatically deleted along with the instance, your snapshots will remain stored on Amazon Simple Storage Service (S3), and you will continue to be charged monthly for them. Deleting these orphaned snapshots will save as much money as deleting the original EBS volume, so unless you’re certain you’ll need them again to create future EBS volumes, make sure to get rid of them. 

3. Unattached/Unused EBS Volumes 

It’s good practice to delete EBS volumes, except for root volumes, that are not attached to EC2 instances. Not only do these unattached volumes add charges to your AWS bill – whether they are being used or not – but they also pose cyber security risks to any sensitive data stored on them. Even when an EBS volume is attached to an EC2 instance, it’s billed for separately, so make sure to delete volumes you no longer need. Just make sure to back up the data first; once the EBS volume is deleted, the data is lost. 

4. Underutilizing Reserved Instances 

Many AWS users pay as they go and never consider purchasing reserved instances (RIs), which are pre-booked resources and capacity for a one- or three-year term. Because you are committing to pay for all of the hours during your term, your hourly rate is deeply discounted. RIs can save your organization a lot of money – if you actually use all of the time you’ve bought. Calculating your usage that far in advance can be difficult, and if your needs are lower than you anticipated, RIs can be a money pit. If you’ve bought RIs that you have no use for, consider selling them on the AWS Marketplace. 

5. Data Transfer Costs 

Most of the time – but not always – transferring data into EC2 is free, but transferring data out will always cost you. How much you will be charged depends on how much data is being transferred and where it is going, and these costs vary by Region. Moving data across services within the same Region is usually less expensive than moving data across services outside your Region, and some Regions have higher costs than others. To minimize data transfer costs, you must pick the least expensive route for your data to flow through, depending on what it is and where it is going; because of the wide variations in prices, this is easier said than done. 

6. Unused Elastic IP (EIP) Addresses 

EIPs are static different EC2 instances. They allow users to mask an instance or software failure by rapidly remapping the address to another instance in their account. AWS users are allotted one free EIP address with each running EC2 instance, but they are charged hourly if they attach additional EIPs to the same instance. Additionally, users are charged for any EIPs that are not associated with a running instance. As with instances, orphan snapshots, and EBS volumes, you should monitor your account for EIPs you are no longer using. 

7. Unused Elastic Load Balancers (ELBs) 

ELBs, which are commonly put in front of your web servers, automatically distribute incoming application traffic, scale resources to meet traffic demands, and are designed to keep a minimum number of EC2 instances running. You are charged monthly for each ELB, whether you’re using it or not, and per GB transferred. If any of your ELBs are not attached to back-end instances, consider registering instances or deleting them. In a similar vein, if an ELB is not attached to any healthy backend instances, consider troubleshooting the configuration or deleting it. Additionally, before you can terminate an EC2 instance, you must delete any ELBs associated with it. 

Other Hidden Costs 

Other potential “gotcha’s” that could be inflating your AWS bill include unused services started in AWS OpsWorks, unhealthy instances, and fees for excessive API calls. There are also numerous indirect costs associated with AWS and other cloud solutions in the form of performance, reliability, and cyber security problems. Misconfigured AWS servers were at fault for the recent data breaches at business associates of Verizon, the Republican National Committee, and private security firm TigerSwan. In February, numerous large websites were knocked offline due to an error by an employee at AWS, and the tech community recently expressed grave concerns about widespread chaos if AWS were to have another, larger failure, particularly since so many financial institutions rely on it. 

The Cloud Isn’t Always Cheaper 

Despite sticker shock and concerns about these hidden and indirect costs, many organizations continue to grumble and pay their AWS bill due to the misconception that cloud computing is always cheaper and more efficient than purchasing their own IT infrastructure. This is a myth. In many cases, an organization’s monthly AWS bill alone costs more than an in-house solution would. If your organization processes large amounts of data, it would probably be more cost-effective to purchase and maintain your own infrastructure. 

It’s not always necessary or beneficial to abandon the cloud completely. Many organizations would greatly benefit from a hybrid approach, where they use their own infrastructure for certain tasks and utilize cloud solutions when they need additional capacity. 

Don’t feel like you’re locked into paying AWS or another cloud provider forever. If you can’t seem to get your AWS bill down to a reasonable level, purchasing your own equipment is worth looking into. 

Source: http://www.itproportal.com/features/7-hidden-aws-costs-that-could-be-killing-your-budget/

Welcome to the Age of “Big Intelligence”

By marketing@site-a.com

Everyone knows the scenario – after years of development and advancements, machines imbued with Artificial Intelligence somehow become self-aware without the knowledge of their human creators and end up destroying humanity as we know it. It’s a crazy premise, but if you listen to Tesla and SpaceX CEO Elon Musk and other futurists, you’ll hear over and over that Big Intelligence is something to fear.

Science fiction movies have a habit of predicting doom and destruction, but the truth is we don’t have to worry about this dystopian world where AI becomes something man cannot control. True, there could be unpredictable social consequence much like those brought about by the rise of social media. But in terms of actual takeover and destruction, the odds are slim to none. Rather than fretting about killer robots, it’s time to realize that the AI revolution is actually the proliferation of “Big Intelligence” and its future is much more benign. Big Intelligence is where we are today in terms of automation, robotics and computing – and it bears little similarity to the sentient machines most people think of when they hear AI. What’s more, fear of AI shouldn’t hinder the legitimately useful work Big Intelligence can help complete.

AI is Big Intelligence in Disguise

Many companies like to tout the AI capabilities of their offerings, but much of the literature you’ll find is nothing more than marketing gimmick. The technology being commercialized or used in research is not really AI, but simply better programming and faster data crunching fueled by advancements in hardware. While futurists like to envision the all-knowing, self-aware machine, Artificial Intelligence or machine learning is not a substitute for human intelligence, and even the most advanced machines are far from substituting a human brain.

With the convergence of large datasets with faster computers and better code to process the data, Big Intelligence has progressed over the past decade due to real technology advancements that allow us to collect and process data at an ever increasing rate, and it’s what powers most AI platforms today. But the key here is not some technology that can replace human intelligence – rather, AI as it currently stands is a set of tools that merely help us better process, interpret, and understand the mass amounts of data companies gather from actual thinking humans. It can help businesses better predict their own and their customers’ needs, to better optimize and even conserve resources.

Hardware is Critical to Big Intelligence 

The only reason we are discussing the possibility of AI is due to recent advancements in computing performance. If the AI hardware could not process data in near real-time, things like self-driving cars, automated logistics centers, operating systems that are virtual and learn through association would still be figments of our imagination.  However, ultimately these so-called AI data still rely on constant data input from humans, without which they could not function.

Artificial Intelligence is a misnomer. Most of what we consider AI is really a high-performance computer (HPC) crunching a massive amount of data, which does not have intelligence the way a human brain does to conceptualize, reach its own conclusions and think for itself. What it can do is improve computing processes through automation, but the end result of most automated processes still require human supervision.

Computers can now process data in real time, but all that processing power is useless if people feed the machine bad data from the get go or don’t know what to do with all that information and analysis once they have it. Cognitive solutions that leverage AI can provide explanations, recommendations, and inform what future actions or outcomes might be required via their predictive nature, but it’s still a human who is feeding the beast.  We are still the “intelligence” behind AI – the artificial part is being able to crunch data at a scale and time-span humans can’t achieve.

The promise of AI has been around a long time, but never went anywhere because hardware could not sustain that much data analysis.  No one could capitalize on the concept. That is not the case today. Hardware has advanced to such a degree that for each new automation concept there is a company that builds the hardware necessary to realize the idea.  Super-fast multi-core processors or massive storage devices are tomorrow’s recycling candidate. Cloud computing, virtualization, faster processors – all make up the core technology of what we call AI.

In addition, the amount of data companies now churn out is almost unfathomable. Almost everything we touch is sending data to someone from smartphones, to the Internet, to online processes, to set-top boxes, and on and on. Technology is everywhere and every bit of it is a data source. At the convergence of all this Big Data and mock AI technology is nothing that resembles actual sentience – it’s simply Big Intelligence. We’ll continue to see it improve as the convergence of better programming through High Power Computing and Big Data and companies traditional working with one or the other begin to bring the two together in ever more creative applications.

We are living in a truly exciting time of data and high-performance computing. We now have the ability to take advantage of data at scale and analyze that data in real time, but we shouldn’t let the misnomer of AI scare us away from this pursuit. Let’s call Big Intelligence what it is and enjoy the power more data and advanced hardware has bestowed on the human race.

Optoro Case Study

By marketing@site-a.com

In the Optoro Case Study below, learn how Optoro slashed their costs by 50% through migrating from Amazon Web Services (AWS) to PSSC Labs’ Enterprise CloudOOP12000 server platform…while increasing performance, usability, and scalability.

“Unlike other providers, PSSC Labs provided the flexibility and knowledge needed to deliver Optoro with hardware that maximized performance without upselling of unnecessary upgrades” said Zach Dunn, Optoro’s Director of DevOps. “PSSC Labs delivered a complete solution with all necessary rack, power connection and out of band management tightly integrated into a simple-to-deploy datacenter environment.” 

Download our case study.

Optoro Ends Reliance on Public Cloud with the Help of PSSC Labs

By marketing@site-a.com

Enterprise CloudOOP 12000 server platform helps reduce cost nearly 50% compared to using Amazon Web Services (AWS) while increasing performance, usability, and scalability

LAKE FOREST, Calif., August 4, 2017 /PRWeb/ — PSSC Labs, a developer of custom High-Performance Computing (HPC) and Big Data computing solutions, today announced it has successfully helped reverse logistics platform Optoro migrate off Amazon Web Services (AWS) and create its own in-house infrastructure.

Optoro is the world’s leading reverse logistics platform, offering a superior end-to-end reverse logistics solution that helps retailers process, manage, and sell their returned and excess inventory. Optoro’s software platform helps retailers optimize the management of returned and excess inventory in a more efficient and cost-effective way, maximizing recovery value, enabling consumers to get great deals, and reducing environmental waste. Founded in 2010, Optoro has seen phenomenal growth as ecommerce continues to rise, and has therefore seen a steady rise in data infrastructure needs.

PSSC Labs worked with Optoro to completely build a self-hosted infrastructure from the ground up, emphasizing flexibility and maintainability, utilizing its CloudOOP 12000 Enterprise Big Data server platform connected via a 10 GigE network backbone. Performance benefits were maximized using only Flash SSD hard drives from Micron® and the latest Intel® Xeon® E5-2600 series processors.

PSSC Labs also worked extensively with Optoro to customize the solution so that they could continue running on an API infrastructure with a Joyent Triton platform. “Unlike other providers, PSSC Labs provided the flexibility and knowledge needed to deliver Optoro with hardware that maximized performance without upselling of unnecessary upgrades” said Zach Dunn, Optoro’s Director of DevOps. “PSSC Labs delivered a complete solution with all necessary rack, power connection and out of band management tightly integrated into a simple-to-deploy datacenter environment.”

Upon the completion of the migration, Optoro saw an immediate increase in the consistency and stability of performance as well as increase to storage capacity compared to AWS. The new self-hosted solution immediately halved Optoro’s costs as well as greatly increasing performance, and private infrastructure has also resulted in better visibility, with a clearer breakdown of usage for proper reporting and monitoring. Detailed information on Optoro’s migration from AWS will be available as a case study at https://www.site-a.com/.

The CloudOOP12000 is PSSC Lab’s unique platform for enterprise users. It offers 2x the density and up to 35% lower power draw than traditional manufacturers as well as a near 50% increase in data throughput performance. All CloudOOP 12000 server configurations receive service and support from PSSC Lab’s US based, expert in house engineers. Prices for a custom CloudOOP 12000 server start at $5000. For more information see https://pssclabs.losangeles.dev.buckupstudio.com/products/big-data-servers/.

Source: http://www.prweb.com/releases/2017/08/prweb14625720.htm

PSSC Labs Integrates Latest Intel Xeon Scalable Processors in its HPC Solutions

By marketing@site-a.com

Custom, high performance PowerServe Servers and PowerWulf Clusters now with next gen processing power available

LAKE FOREST, Calif., August 07, 2017 /PRWEB/ — PSSC Labs, a developer of custom HPC and Big Data computing solutions, today announced it now offers integration of Intel’s new Xeon® Scalable Processors in its PowerServe line of HPC servers and PowerWulf line of HPC clusters. The integration provides PSSC Labs customers with breakthrough technology, offering performance capable of handling cutting edge computing tasks including real-time analytics, virtualized infrastructure and high-performance computing.

In addition to advanced architecture, the new processors feature a rich suite of platform innovations for enhanced application performance including Intel AVX-512, Intel Mesh Architecture, Intel QuickAssist, Intel Optane SSDs, and Intel Omni-Path Fabric.

PSSC Labs’ PowerServe and PowerWulf HPC solutions offer reliable, flexible, high performance computing solutions for a variety of applications across government, academic, and commercial environments including: Design & Engineering, Life Sciences, Physical Science, Financial Services and Machine/Deep Learning.

“PSSC Labs endeavors to offer our customers the latest and best hardware options in our line of custom turn-key HPC servers and clusters,” said Alex Lesser, Executive Vice President of PSSC Labs. “The new Intel® Xeon® processors are a major advancement for enhanced performance on resource hungry computing tasks and we are happy to make this technology available to our customers.”

Every PowerWulf Server and PowerServe Cluster come with a three-year unlimited phone / email support package (additional year support available) with all support provided by a US-based team of experienced engineers. Configurations start at $2,495.

Intel® Xeon® Scalable Processor Features:

Advanced Architecture: new core microarchitecture, new on-die interconnects and memory controllers means optimized performance, reliability, security and manageability. The processor also offers better efficiency and lower energy costs as well as space efficiency.

Performance: The Intel® Xeon® Scalable Processors deliver an overall performance increase up to 1.65x versus the previous generation, and up to 5x OLTP warehouse workloads versus the current install base.

Scalability: Up to 28 cores and up to 6 terabytes of system memory, and can scale to support 2-socket – 8-socket systems.

Agility: Optimized computing, network, and storage performance on premise, through a network, or in the cloud. Optional network integration with Intel OmniPath Architecture.

Security: 3.1x performance improvement in cryptography performance compared to the previous generation. Application can now run with less than 1% overhead with data-at-rest encryption turned on. Intel Key Protection Technology delivers enhanced protection to security key attacks.

About PSSC Labs 
For technology powered visionaries with a passion for challenging the status quo, PSSC Labs is the answer for hand-crafted HPC and Big Data computing solutions that deliver relentless performance with the absolute lowest total cost of ownership. All products are designed and built at the company’s headquarters in Lake Forest, California. For more information, 949-380-7288, https://www.site-a.com, sales@site-a.com.

Source: http://www.prweb.com/releases/2017/08/prweb14575716.htm

PSSC Labs Featured in Forbes: The Cloud Vs. In-House Infrastructure: Deciding Which Is Best For Your Organization

By marketing@site-a.com

Fueled by mounting storage requirements, ease of use, automatic software updates and users’ thirst for limitless access and maximum flexibility, cloud computing has grown exponentially over the past several years. In fact, since 2009, spending on cloud computing has been growing at a rate that is 4.5 times faster than the rate of IT spending, and it’s expected to exceed six times the rate from 2015 to 2020. This is certainly because there are many situations in which utilizing the cloud makes sense.

The most obvious would be in a small startup operating on a lean budget where cash flow is tight. This is because cloud computing allows access to resources without large capital expenditures. The cloud can also be an option for enterprises of all sizes as it relates to disaster recovery. And the cloud excels at providing options for immediate capacity whereby companies can have separate instances for moderate burst capacity.

However, the cloud is not always superior to building in-house IT infrastructure. Cloud providers’ slick marketing materials gloss over the technology’s numerous drawbacks, such as skyrocketing fees, poor performance and cybersecurity issues. The decision between using a public cloud and building your own IT infrastructure is not so different than deciding between renting a workspace for your business or buying your own building; both decisions boil down to having total control (and responsibility) over your own environment versus depending on a landlord to provide an adequate workspace and fix problems quickly and adequately.

Beyond The Hype: Not All Clouds Have A Silver Lining

Some enterprises choose cloud services for their scalability; the typical pricing model for cloud computing is “pay for what you use,” with organizations having the option to buy more (or less) computing power as their needs change. This may not be as cost-effective as it sounds. Cloud computing can easily result in unexpected costs, especially when dealing with performance and security setbacks. Trading algorithm developer Deep Value found using Amazon’s EC2 service to be 380% more expensive than running its own internal cluster, noting that “For one-off peaks, EC2 makes sense, but given the ongoing nature of our simulated analysis, moving to our own data center is a very clear winner.”

Cloud security is another serious issue, especially because it is up to the enterprise — not the cloud provider — to properly configure certain cybersecurity settings. Improperly configured cloud security settings were at fault for the recent massive breach of voter data mined by a data analytics company that had been hired by the Republican National Committee.

Additionally, enterprises that use a public or shared cloud can experience a cyberattack or performance issue through no fault of their own. This past February, AWS customers experienced a widespread outage because AWS misconfigured something. For these reasons, enterprises running mission-critical applications with high-availability needs and compliance or regulatory requirements may want to think twice about using a public or shared cloud.

Public and shared clouds are also plagued by performance and reliability problems. Using a public cloud means potentially sharing a network with so-called “noisy neighbors” or users who hog resources. Further, since cloud service providers have servers in several dispersed locations, users may experience latency issues and are often forced to pay exorbitant fees to avoid them. In contrast with bare-metal hardware users, public cloud customers have limitations on resource availability and may struggle with application performance and data transfer rates.

When Is In-House Infrastructure A Better Option?

Several factors determine when it is better to deploy in-house infrastructure than to use the cloud. At the top of this list is the monthly cost. For some companies, the $30,000 monthly AWS bill exceeds the cost of an in-house solution. For other companies, the decision is tied to performance, reliability and security issues. Organizations that own their infrastructure have total control over their computing environments; if something goes wrong, or if an organization wants to implement new features, it can just call on its own staff to make the fixes or changes instead of having to depend on their cloud provider to do it. An in-house IT infrastructure is probably best for high-scale IT environments that process large amounts of data, especially if that data is constantly growing, and for companies that want maximum flexibility to make changes.

There are also competitive issues to consider. Giving a potential competitor insight into your business model, applications and customer base is a very risky endeavor. A recent Wired article outlined Dropbox’s “exodus” from Amazon. While cost was one of the reasons Dropbox cited for building its own infrastructure, so were concerns over Amazon’s foray into file-sharing services — Dropbox’s domain. Enterprises would do well to be cautious about the prospect of Amazon, which already competes in numerous industries, eventually offering services that will compete against those of its own cloud storage customers.

It Doesn’t Have To Be All Or Nothing

The decision of where to store data and run applications doesn’t have to be a strict matter of cloud vs. in-house. In some cases, the best solution is a combination of both (hybrid). For some enterprises with limited budgets, a public cloud is the most realistic choice. Other organizations may want to implement in-house servers to handle standard traffic and turn to the cloud for additional capacity.

However, organizations need to seriously consider their individual data needs beyond the “cloud first” hype. This includes prioritizing requirements for processing, performance, storage, security, data transfers and, of course, determining how much they are willing and able to spend. There is inherent value in building and owning in-house infrastructure instead of being at the mercy of a virtual landlord. Enterprises must shift their mindsets and view their IT departments as assets and business drivers rather than cost centers. An investment in appropriate IT infrastructure ultimately drives long-term profits that will support the growth of not only the IT department, but also the larger business of which it is a part.

Source: https://www.forbes.com/sites/forbestechcouncil/2017/07/25/the-cloud-vs-in-house-infrastructure-deciding-which-is-best-for-your-organization/#1251e6b420f6