devops in cloud computing research paper

A Model DevOps Framework for SaaS in the Cloud

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• First Online: 20 March 2021
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• Karina Ojo-Gonzalez   ORCID: orcid.org/0000-0002-8104-7606 17 ,
• Rene Prosper-Heredia   ORCID: orcid.org/0000-0002-0938-7809 17 ,
• Luis Dominguez-Quintero   ORCID: orcid.org/0000-0002-3309-6745 17 &
• Miguel Vargas-Lombardo   ORCID: orcid.org/0000-0002-2074-2939 17  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1307))

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The corporate organizational structure and culture should evolve in the development of software technology as the company grow; otherwise, it will present weaknesses that hinder the implementation of any software methodology. In this paper, the beneficial characteristics acquired by software products that have been developed under a DevOps conception have been identified, which has given DevOps a greater degree of relevance, promoting its study in greater detail, and assessing the growing interest of companies in having software as a quality that does not require additional costs for updating and maintenance. Additionally, the paper presents a systematic literature review, obtaining a conceptual schema of the relationship of DevOps with service-oriented software development, mitigating the lack of formalization of the concepts involved in each stage through an ontology. The result approximates a Model DevOps-based framework for SaaS development which can contribute as a guide for enterprises interested in the adoption of DevOps in their software production process for the first time.

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Acknowledgements

We are grateful for the support provided by the Science, Technology and Innovation National Secretariat of Panama (SENACYT), Scientific Master program TIC-UTP-FISC-2019, and to the National Research System (SNI-SENACYT) which one author is a member.

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Karina Ojo-Gonzalez, Rene Prosper-Heredia, Luis Dominguez-Quintero & Miguel Vargas-Lombardo

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Conceptualization, K.O, R.P, M.V.; methodology, K.O, R.P, M.V; formal analysis, K.O, R.P; research, K.O, R.P, M.V.; original-writing K.O, R.P, R.D, M.V.; writing–review and edition, K.O, M.V.; Corresponding author, M.V.

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Correspondence to Miguel Vargas-Lombardo .

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Ojo-Gonzalez, K., Prosper-Heredia, R., Dominguez-Quintero, L., Vargas-Lombardo, M. (2021). A Model DevOps Framework for SaaS in the Cloud. In: García, M.V., Fernández-Peña, F., Gordón-Gallegos, C. (eds) Advances and Applications in Computer Science, Electronics and Industrial Engineering. Advances in Intelligent Systems and Computing, vol 1307. Springer, Singapore. https://doi.org/10.1007/978-981-33-4565-2_3

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2023 State of DevOps Report

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AI + Machine Learning , Announcements , Azure AI , Azure AI Studio

Introducing Phi-3: Redefining what’s possible with SLMs

By Misha Bilenko Corporate Vice President, Microsoft GenAI

Posted on April 23, 2024 4 min read

• Tag: Copilot
• Tag: Generative AI

We are excited to introduce Phi-3, a family of open AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks. This release expands the selection of high-quality models for customers, offering more practical choices as they compose and build generative AI applications.

Starting today, Phi-3-mini , a 3.8B language model is available on Microsoft Azure AI Studio , Hugging Face , and Ollama . 

• Phi-3-mini is available in two context-length variants—4K and 128K tokens. It is the first model in its class to support a context window of up to 128K tokens, with little impact on quality.
• It is instruction-tuned, meaning that it’s trained to follow different types of instructions reflecting how people normally communicate. This ensures the model is ready to use out-of-the-box.
• It is available on Azure AI to take advantage of the deploy-eval-finetune toolchain, and is available on Ollama for developers to run locally on their laptops.
• It has been optimized for ONNX Runtime with support for Windows DirectML along with cross-platform support across graphics processing unit (GPU), CPU, and even mobile hardware.
• It is also available as an NVIDIA NIM microservice with a standard API interface that can be deployed anywhere. And has been optimized for NVIDIA GPUs . 

In the coming weeks, additional models will be added to Phi-3 family to offer customers even more flexibility across the quality-cost curve. Phi-3-small (7B) and Phi-3-medium (14B) will be available in the Azure AI model catalog and other model gardens shortly.   

Microsoft continues to offer the best models across the quality-cost curve and today’s Phi-3 release expands the selection of models with state-of-the-art small models.

Azure AI Studio

Phi-3-mini is now available

Groundbreaking performance at a small size

Phi-3 models significantly outperform language models of the same and larger sizes on key benchmarks (see benchmark numbers below, higher is better). Phi-3-mini does better than models twice its size, and Phi-3-small and Phi-3-medium outperform much larger models, including GPT-3.5T.  

All reported numbers are produced with the same pipeline to ensure that the numbers are comparable. As a result, these numbers may differ from other published numbers due to slight differences in the evaluation methodology. More details on benchmarks are provided in our technical paper . 

Note: Phi-3 models do not perform as well on factual knowledge benchmarks (such as TriviaQA) as the smaller model size results in less capacity to retain facts.  

Safety-first model design

Responsible ai principles

Phi-3 models were developed in accordance with the Microsoft Responsible AI Standard , which is a company-wide set of requirements based on the following six principles: accountability, transparency, fairness, reliability and safety, privacy and security, and inclusiveness. Phi-3 models underwent rigorous safety measurement and evaluation, red-teaming, sensitive use review, and adherence to security guidance to help ensure that these models are responsibly developed, tested, and deployed in alignment with Microsoft’s standards and best practices.  

Building on our prior work with Phi models (“ Textbooks Are All You Need ”), Phi-3 models are also trained using high-quality data. They were further improved with extensive safety post-training, including reinforcement learning from human feedback (RLHF), automated testing and evaluations across dozens of harm categories, and manual red-teaming. Our approach to safety training and evaluations are detailed in our technical paper , and we outline recommended uses and limitations in the model cards. See the model card collection .  

Unlocking new capabilities

Microsoft’s experience shipping copilots and enabling customers to transform their businesses with generative AI using Azure AI has highlighted the growing need for different-size models across the quality-cost curve for different tasks. Small language models, like Phi-3, are especially great for: 

• Resource constrained environments including on-device and offline inference scenarios.
• Latency bound scenarios where fast response times are critical.
• Cost constrained use cases, particularly those with simpler tasks.

For more on small language models, see our Microsoft Source Blog .

Thanks to their smaller size, Phi-3 models can be used in compute-limited inference environments. Phi-3-mini, in particular, can be used on-device, especially when further optimized with ONNX Runtime for cross-platform availability. The smaller size of Phi-3 models also makes fine-tuning or customization easier and more affordable. In addition, their lower computational needs make them a lower cost option with much better latency. The longer context window enables taking in and reasoning over large text content—documents, web pages, code, and more. Phi-3-mini demonstrates strong reasoning and logic capabilities, making it a good candidate for analytical tasks. 

Customers are already building solutions with Phi-3. One example where Phi-3 is already demonstrating value is in agriculture, where internet might not be readily accessible. Powerful small models like Phi-3 along with Microsoft copilot templates are available to farmers at the point of need and provide the additional benefit of running at reduced cost, making AI technologies even more accessible.  

ITC, a leading business conglomerate based in India, is leveraging Phi-3 as part of their continued collaboration with Microsoft on the copilot for Krishi Mitra, a farmer-facing app that reaches over a million farmers.

“ Our goal with the Krishi Mitra copilot is to improve efficiency while maintaining the accuracy of a large language model. We are excited to partner with Microsoft on using fine-tuned versions of Phi-3 to meet both our goals—efficiency and accuracy! ”    Saif Naik, Head of Technology, ITCMAARS

Originating in Microsoft Research, Phi models have been broadly used, with Phi-2 downloaded over 2 million times. The Phi series of models have achieved remarkable performance with strategic data curation and innovative scaling. Starting with Phi-1, a model used for Python coding, to Phi-1.5, enhancing reasoning and understanding, and then to Phi-2, a 2.7 billion-parameter model outperforming those up to 25 times its size in language comprehension. 1 Each iteration has leveraged high-quality training data and knowledge transfer techniques to challenge conventional scaling laws. 

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To experience Phi-3 for yourself, start with playing with the model on Azure AI Playground . You can also find the model on the Hugging Chat playground . Start building with and customizing Phi-3 for your scenarios using the  Azure AI Studio . Join us to learn more about Phi-3 during a special  live stream of the AI Show.  

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Vendor voice.

Alibaba Cloud details storage tech that's doubled its VMs per host

Using one disk as a write cache eases stresses created by manycore cpus.

Exclusive Alibaba Cloud has detailed the tech it developed to run local storage in its servers and bust bottlenecks created by new-generation manycore processors.

The tech was detailed last week in a paper titled "CSAL: the Next-Gen Local Disks for the Cloud" published in the April edition of Proceedings of the Nineteenth European Conference on Computer Systems. Eleven authors work at Alibaba Cloud, and another six work at Solidigm – Intel's old SSD business now mostly owned by SK hynix.

The paper sets the scene by reminding readers that cloud servers typically use local storage, and that local capacity determines how many VMs each cloudy host can handle. It then notes that modern manycore CPUs encourage clouds and users to run more VMs on each host.

The obvious way to run more VMs, the paper notes, is to pack cloud servers full of colossal hard disks, storage-class memory, or fast solid state disks. But hard disks have bandwidth limits, storage-class memory mostly failed (the paper mentions the Optane tech Intel snuffed), and fast SSDs have capacity problems and big price tags.

What's a cloud to do? Quad-level cell (QLC) SSDs are an obvious answer, the paper suggests, because they offer high capacity and decent prices.

Alibaba Cloud therefore tried QLC disk in three scenarios: as a drop in replacement for other disks, as part of a layered system alongside high-speed SSDs, and using the dm-zoned a kernel device mapper.

• French cloud Scaleway starts renting Alibaba's RISC-V SoC
• Alibaba Cloud cuts prices – hard – for multi-year commitments in mainland China
• Alibaba Cloud posts modest growth, mostly thanks to other Alibaba business units
• Beijing demands government apps must shed their bureaucratic skins

The paper explains that QLC failed as a drop-in replacement because of "the two levels of write amplification caused by device-level address mapping with Indirection Unit and NAND-level garbage collection."

A layered system that used a write-back cache to handle small writes in one SSD helped, but didn't match hard disk performance.

dm-zoned didn't help either, because under load it constantly needed to move data – which smashed performance.

Alibaba therefore devised the Cloud Storage Acceleration Layer (CSAL), which the paper explains sees the most recently used data stored in DRAM and swapped to a fast SSD, which also handles all incoming writes. When possible and sensible, data from that SSD is shunted into the QLC disk.

The paper explains CSAL's workings in sufficient detail that that even our storage-centric sibling site Blocks and Files might find its attention wavering.

The impact of CSAL on Alibaba Cloud ops is easier to understand and is outlined as follows:

Compared to last-gen HDD-based local disks (24× 2TB HDDs with a 48-core Xeon Cascade CPU), CSAL-ready servers (an 800GB HP-SSD and a 15.36TB QLC SSD with a 64-core Xeon Ice Lake CPU) can host twice more instances while achieving the same Service Level Objects.

That's a doubling of VM density from second-gen to third-gen Xeons, despite the extra 16 cores in the newer processor stressing storage more than the older silicon. Also, 64 is not double 48.

CSAL is in production across "thousands of Elastic Compute Service (ECS) nodes in Alibaba Cloud." Maybe you could run it too: Alibaba Cloud has open-sourced CSAL into the Storage Performance Development Kit .

Alibaba Cloud has racked up a few wins lately. After cutting prices , its homebrew Yitian 710 was recently rated the fastest Arm CPU in the cloud . We've also covered an in-house networking tool that slashed the number of personnel the Chinese concern needed to dedicate to troubleshooting, and research suggesting Alibaba Cloud's operations could be more efficient than Google's.

Which is great news for Chinese cloud users, who have no qualms about working with Alibaba Cloud. For the rest of us, the decision to consider Alibaba cloud is doubtless more complicated. ®

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