Cheapest Cloud Vm Provider – Some departments in your company don’t need cloud computing resources to handle high-performance tasks, right? Because Google has created a service plan just for such needs. Google launched Preemptible Virtual Machine, a new cloud service that allows the use of computing resources at a low cost. The offer is suitable for low-priority workloads and may, therefore, be interrupted.
The search giant introduced a new cloud platform that cost 70% less than the same default setting in Calculation Engine. Preemptible Virtual Machine can run cheaply, about $0.01 per instance/hour. The cheapest VM costs per hour can range from $0.03 per hour, to $0.11 per hour or more. The problem is that VMs can crash when you need them or experience peak periods.
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The company argues, however, that the offer (in beta) better serves a variety of computational tasks. The company cites, for example, a critical workflow that can be distributed among multiple virtual machines. However, it would be a bad idea to adopt an analysis, modeling, and simulation approach that requires high computing power and instant responses.
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To provide the service, Google will use free capacity in its data center. When there is high demand and Google needs more resources, the virtual machines involved in the Preemptible Compute Engine VMs are recalled and interrupt the current processing. Users receive a 30 second notice period, which should be enough to save your job. Google said No Preemptible VM can run for more than 24 hours straight.
According to Google’s post, “all machine types are billed at a minimum rate of 10 minutes. For example, if you run your instance for 2 minutes, you’ll be billed for 10 minutes of usage. After 10 minutes, instances are billed in 1-minute increments, rolling to the nearest minute.For example, a model that lives for 11.25 minutes will be billed for 12 minutes of usage.
According to Google, there are many that use cloud computing and a pricing structure to compute large, but short-term jobs. It includes video encoding, rendering of visual effects and calculations based on large amounts of information, such as in data analysis, modeling and genetics.
The solution is similar to that of Spot Instances offered by Amazon Web Services (AWS). AWS models vary in price. Google has a fixed cost whereas the competitor’s price varies based on demand.
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Market leader AWS regularly lowers their cloud prices. The company is facing fierce competition from Google and Microsoft to maintain its leadership in cloud computing and is trying to convince more developers to adopt its solution with lower prices, more hardware versions, and more advanced technologies. Microsoft, on the other hand, continued to be a serious threat to Amazon’s dominance in the market.
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Today, Google announced the planned introduction of their new set of “Tau” VMs, or T2D, in their Google Compute Engine VM offerings. The devices feature AMD’s new Milan processors – a welcome addition to Google’s offerings.
The main news of today’s announcement however was not Milan, but the reality of what Google is doing in terms of vCPUs, how this affects performance, and its consequences in the cloud service provider space – especially in the context of the new Arm server CPU competition. .
Starting with the most important data feature that Google is presenting today, is that the new GCP Tau VMs show a significant performance advantage over competitor offerings from AWS and Azure. VM comparison details are posted here:
Google’s SPECrate2017_int method largely mimics our in-house test unit usage based on flags (a few differences such as LTO and splitter integration), but the most important figure comes from compiler exposure, with Google stating a +56% gain The most powerful Graviton2 for AWS comes from the AOCC run. They further reveal that the GCC implementation achieves a performance gain of +25%, which explains some features:
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Note that we also tested with GCC using -O3, but we saw better performance with -Ofast on all machines tested. Interestingly, while we saw a 56% SPECrate®2017_int_base performance improvement on t2d-standard-32 over m6g.8xlarge when we used AMD’s optimization compiler, which can take advantage of AMD’s architecture, we also saw a 25. % improvement of performance on t2d-standard-32 on m6g.8xlarge when using GCC 11.1 with the above benchmarks for both machines.
With this 25% figure in mind, we can go back to our own data tested inside the Graviton2 as well as AMD Milan’s recent quality tested for rough space where things stand:
Google isn’t revealing any details on the type of SKU they’re testing, however we have data for a 64-core and 32-core vCPU on the Graviton2, getting estimated scores of 169.9 and 97.8 with scores of 2.65 and 2.16 per thread. Our internal numbers for the AMD EPYC 7763 (64 core 280W) CPU show an estimated benchmark score of 255 and 1.99 per thread using SMT, and a speed of 219 and 3.43 per thread for 128 threads and 64 threads running per socket. Reducing the score based on the count of 32 threads – according to what Google says here as vCPU for the T2D example, will bring us to a score of 63.8 with SMT, or 109.8 without SMT. A 32 thread SMT operation will make the Graviton2 less efficient, however a non-SMT operation will be +12 times more efficient. We estimate that actual scores in a 32-vCPU environment with less load on the remaining SoC would be higher, and this would match the quoted performance gain of +25.
And here’s the big surprise of today’s announcement: for Google’s new Milan performance figures to make sense, it is
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It means they are using instances with vCPU counts that actually match the actual core count – which has a big impact on comparing and contrasting performance between instances of the same vCPU count.
In particular, because Google focuses on the comparison of Graviton2 in AWS, I see this as a direct attack and response to the claims of Amazon and Arm cloud performance metrics related to VMs and a certain number of vCPUs. Indeed, even when we reviewed Graviton2 last year, we noted this difference that when comparing VM cloud versions with x86 cloud versions that have SMT, and where vCPU basically means you get a logical core instead of a physical core. , unlike Arm’s new Graviton2 events. In fact, we’ve been comparing Arm CPUs with double core counts against x86 incumbents for the same instance sizes. In fact, this is still what Google is doing today when comparing a 32vCPU Milan Tau VM vs an Azure 32vCPU Cascade Lake VM – it’s a basic 32 vs 16 comparison, it’s the last version with SMT enabled.
Because Google is now essentially leveling the playing field against Arm’s Graviton2 VM instances with the same vCPU count, actually having the same number of physical cores available, means it has no issues competing in terms of performance with Arm’s competitor. , and it usually also beats other cloud provider options where the vCPU is still a logical SMT CPU.
Google offers a 32vCPU T2D instance with 128GB of RAM for USD 1.35 per hour, compared to an AWS m6g.8xlarge instance with 32vCPU and 128GB of RAM for USD 1.23 per hour. While Google’s use of AOCC to achieve higher performance levels compared to our GCC numbers plays a role, and Milan’s performance is good, the fact is that we now appear to be comparing the physical particles to the physical particles that make up the new Tau VM. special cases compared to AWS and Azure (logical end-to-end) versions.
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Overall, I applaud Google for this move, as releasing only a core component like vCPU until now was a total bust. In a sense, we also have to thank the new Arms race for finally shifting the ecosystem in bringing about what appears to be the beginning of the end of such.
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