REDEFINING CLOUD-NATIVE PERFORMANCE: A TECHNICAL EVALUATION OF MICROSOFT AZURE’S COBALT 100 ARM-BASED VIRTUAL MACHINES
DOI:
https://doi.org/10.34218/IJCET_16_02_035Keywords:
Cloud-native Performance, Arm-based Virtual Machines, Azure Cobalt 100, Virtualization Benchmarking, Cloud Computing Efficiency, Sustainable ComputingAbstract
Cloud computing stands to dramatically change the paradigm with the enablement of Arm based architectures, shifting performance, power efficiency and cost optimization possibilities. A new, customized Arm-based virtual machine (VM) called Cobalt 100, recently announced by Microsoft Azure, and promises to rewire cloud-native computing for workloads of today. This work provides an in-depth performance comparison between x86-based Azure VM instances and the new Cobalt 100 VMs. Our approach is to use industry standard benchmarking suites (like Geekbench 6, SPEC CPU 2017, and Sysbench) on Arm based Cobalt 100 and x86 based Dv5 VMs. We also measure real-world application workloads (web servers, NGINX, and databases, MySQL, microservices in Kubernetes cluster) as well. Performance is evaluated in terms of CPU throughput, memory bandwidth, energy efficiency, cost/performance, and application latency. Results show Cobalt 100 VMs providing 40% higher price-performance and 60% better CPU performance on CPU-intensive workloads than Dv5 series. For webservers and database hosting Cobalt 100 showed 15–25% lower latency and 30% less power consumption, the perfect solution for your cloud native, black carbon free deployment! This benchmark proves the potential of Arm architecture in enterprise cloud, and it positions Cobalt 100 as a proven alternative to traditional x86 VMs for developers and organizations seeking optimal performance and sustainability for next generation of cloud-native applications.
References
L. Pons, J. Petit, J. Gómez, M. Sahuquillo, "A modular approach to build a hardware testbed for cloud resource management research," The Journal of Supercomputing, vol. 80, no. 8, pp. 10552–10583, Dec. 2023, doi: 10.1007/s11227-023-05856-2.
R. Grant, S. Hammond, J. Laros, M. Levenhagen, S. Olivier, K. Pedretti, L. Ward, and A. Younge, "Enabling power measurement and control on Astra: The first petascale Arm supercomputer," Concurrency and Computation: Practice and Experience, vol. 35, no. 15, e7303, Sep. 2022, doi: 10.1002/cpe.7303.
I. Afanasyev and D. Lichmanov, "Evaluating the performance of Kunpeng 920 processors on modern HPC applications," in Parallel Computing Technologies, vol. 1336, pp. 301–321, Springer, 2021, doi: 10.1007/978-3-030-86359-3_23.
D. Xie, Y. Hu, and L. Qin, "An Evaluation of Serverless Computing on x86 and ARM Platforms: Performance and Design Implications," in Proc. 2021 IEEE 14th Int. Conf. Cloud Computing (CLOUD), Chicago, IL, USA, pp. 313–321, Sep. 2021, doi: 10.1109/CLOUD53861.2021.00045.
B. Brank, S. Nassyr, F. Pouyan, and D. Pleiter, "Porting Applications to Arm-based Processors," in Proc. 2020 IEEE Int. Conf. Cluster Computing (CLUSTER), Kobe, Japan, pp. 559–566, Sep. 2020, doi: 10.1109/CLUSTER49012.2020.00079.
D. Yokoyama, B. Schulze, F. Borges, and G. McEvoy, "The survey on ARM processors for HPC," The Journal of Supercomputing, vol. 76, no. 10, pp. 8026–8051, Oct. 2019, doi: 10.1007/s11227-019-02911-9.
A. Jackson, A. Turner, M. Weiland, et al., "Evaluating the Arm ecosystem for high performance computing," Concurrency and Computation: Practice and Experience, vol. 31, no. 16, pp. e5094, Apr. 2019.
J. Maqbool, S. Oh, and G. C. Fox, “Evaluating ARM HPC clusters for scientific workloads,” Concurrency and Computation: Practice and Experience, vol. 27, no. 17, pp. 5390–5410, Jul. 2015, doi: https://doi.org/10.1002/cpe.3602.
J. Noor, M. B. Faysal, M. S. Amin, B. Tabassum, T. R. Khan, and T. Rahman, “Kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review,” High-Confidence Computing, p. 100276, Dec. 2024, doi: https://doi.org/10.1016/j.hcc.2024.100276.
X. Xu, A. Cui, H. Deng, et al., "virtCCA: Virtualized Arm Confidential Compute Architecture with TrustZone," arXiv preprint, arXiv:2306.00587, Jun. 2023.
S. Sridhara, A. Das, A. Paverd, et al., "ACAI: Protecting Accelerator Execution with Arm Confidential Computing Architecture," arXiv preprint, arXiv:2305.01859, May 2023.
Dakić, V.; Mršić, L.; Kunić, Z.; Đambić, G. Evaluating ARM and RISC-V Architectures for High-Performance Computing with Docker and Kubernetes. Electronics 2024, 13, 3494. https://doi.org/10.3390/electronics13173494
K. Gupta and T. Sharma, “Changing Trends in Computer Architecture : A Comprehensive Analysis of ARM and x86 Processors,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 7, no. 3, pp. 619–631, Jun. 2021, doi: https://doi.org/10.32628/cseit2173188.
F. Mantovani et al., “Performance and energy consumption of HPC workloads on a cluster based on Arm ThunderX2 CPU,” Future Generation Computer Systems, vol. 112, pp. 800–818, Nov. 2020, doi: https://doi.org/10.1016/j.future.2020.06.033.
Gupta NAshiwal RBrank BPeddoju SPleiter D(2020)Performance Evaluation of ParalleX Execution model on Arm-based Platforms2020 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER49012.2020.00080567-575Online publication date: Sep-2020
L. Xu, Z. Wang, and W. Chen, "The Study and Evaluation of ARM-Based Mobile Virtualization," Journal of Sensors, vol. 2015, Article ID 310308, pp. 1–12, 2015.
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