Presentation for the paper titled- Evaluating and reducing cloud waste and cost—A data-driven case study from Azure workloads

1. Evaluating and reducing cloud waste and
cost—A data-driven case study from Azure
workloads
Brad Everman, Maxim Gao, Ziliang Zong
Sustainable Computing: Informatics and Systems 35 (2022)
Mehedi Hasan Raju

2. Outline
 Introduction
 Related Work
 Workload Analysis
 Cloud waste and cost analysis
 Metrics- CWP, CWI, CUS
 Limitations
 Conclusions
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3. Introduction
 Cloud waste is common when users provision resources beyond
what they need.
 The user behaviors in the cloud could provide viable solutions to
reduce cloud cost and waste.
 This paper addresses these concerns by conducting a
comprehensive analysis of the Microsoft Azure 2019 traces.
 A large portion of VMs are under-utilized or over-provisioned for
resources.
 Cloud Waste Points (CWP) for quantitatively evaluating the waste of
each VM.
 Categorizing VMs based on cloud resource utilization.
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4. Introduction (contd.)
 Cloud Waste Indicator (CWI) to classify Azure users as red, green,
and normal users, depending on their efficiency in utilizing cloud
resources.
 In addition, we introduce Cloud Utilization Score (CUS) to rank the
relative performance of Azure users in term of cloud waste.
 Lastly, we propose an algorithm to identify red VMs and
recommend lower priced VMs that can help users reduce cost
without compromising quality of service (QoS).
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5. Related works
 Studies related to cost reduction of large-scale cloud systems
 Survey on various techniques that can reduce the energy usage of
MapReduce in Hadoop systems [1]
 Analysis tool to measure energy consumption in cloud environments
based on different runtime tasks [2]
 a genetic-based optimization algorithm to reduce the energy cost of
cloud systems with phase change memory [3]
 an intra and inter-server smart task scheduling algorithm, which can
jointly optimize profit and energy when allocating jobs to datacenters [4]
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6. Related works (contd.)
 Three papers considered the Microsoft Azure cloud platform
directly.
 Design and implementation of Protean, which allocates VMs on the Azure
platform [5]
 Shahrad et al. focused specifically on Function as a Service (FaaS) in the
Azure system [6]
 The most relevant work was published by Cortez[7]
 It utilized the 2017 Azure data traces.
 discussed certain behaviors that can be used to predict future
behavior of VM workloads.
 However, their predictive model aimed to increase the utilization of
Azure from the system.
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7. Workload Analysis
 Microsoft Azure is a public cloud computing platform.
 We analyze the 2019 trace
 subset of applications running on Azure during July of 2019
 235 GB of data contained within 198 files
 30 consecutive days of VM readings
 ~2.7 M total VMs
 6,687 individual users
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8. Descriptive analysis
 How users utilize Azure cloud
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9. Descriptive analysis
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10. Descriptive analysis
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11. Cloud waste and cost analysis
 Azure Pricing Model
 VM is priced based on requested core count and memory size
 Factors – choice of operating systems, cloud services region, server types
etc.
 Information to apply complex pricing is missing in the Azure traces
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12. Cloud waste and cost analysis
 Assumptions
 Deployed VMs will run on Linux (CentOS or Ubuntu).
 VM will be deployed to the US-West (California) region.
 VM is general purpose, not CPU or Memory-optimized.
 Minimal storage is available for each VM.
 Users ‘‘pay as they go’’, and do not receive any discounts for pre-paying
nor volume purchasing.
 Interactive VMs costs 3.33x of the price of a Delay-Insensitive VMs
 based on the information from Google Cloud [8]
 Unknown categorized VMs as are considered Delay-Insensitive when
calculating price.
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13. Cloud waste and cost analysis
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 VM Cost Calculation
VM Cost = VM Lifetime * VM Price
• It was not provided in the Azure traces.
• To calculate the cost of each VM, we need to know the price of each VM
and its corresponding lifetime.
• VM Price
• VM Lifetime - the length of time in hours a VM exists, which is calculated
by the difference between the creation and deletion timestamps.
• Core Hours= VM lifetime * number of cores of that VM.
• Core hours are used to indicate the computation resources utilized
by a VM.

14. Cloud waste and cost analysis
 Green and red VMs
 10% is a very conservative threshold
 higher threshold will yield more cost savings
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15. Cloud waste and cost analysis
Why is the utilization so low ?
 Not enough work
 Lack of parallel computing
• sequential application cannot leverage multiple virtual cores
• requesting more cores for such applications would decrease
overall CPU utilization
 Improvement of hardware
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16. Cloud waste and cost analysis
 VM cloud waste points (CWP)
• CWP = VM lifetime * corresponding waste factor
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17. Cloud waste and cost analysis
 VM cloud waste points (CWP) distribution
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18. Cloud waste and cost analysis
 Cloud Waste Indicator (CWI)
• average CWP of all VMs deployed by an individual user
• Categorize users into three different groups—the green, normal, red
users
 To determine the delineation
• Remove users with less than 200 total core hours.
• Dropped 6,121 users (i.e. ∼92% of all users).
• calculate the CWI of each user then normalized their CWI.
• 𝐶𝑊𝐼𝑛𝑜𝑟𝑚 = (𝐶𝑊𝐼𝑖 − 𝑚𝑖𝑛(𝐶𝑊𝐼))/(𝑚𝑎𝑥(𝐶𝑊𝐼) − 𝑚𝑖𝑛(𝐶𝑊𝐼))
• CWInorm is a value between 0 and 1.
• CWInorm threshold
• green users- 0.01
• red users – 0.05
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19. Cloud waste and cost analysis
 Cloud utilization score (CUS)
 calculated by providing the percentile rank of their CWInorm in
comparison with other users in the cloud
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20. Cloud waste and cost analysis
 Recommendation algorithm
• It creates a red VMs list for each user.
• It calculates the CWP of all VMs in the list and sorts the VMs in the list in
descending order.
• Third, it sends recommendations to that user asking to migrate down the
top n number of VMs in the list by one level
• This algorithm repeats for all users.
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21. Cloud waste and cost analysis
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 Cost savings (from all red VMs)
• Original Cost: $61,595,170.23
• Total VMs: 2,695,548
• VMs with Savings: 1,369,364
• Percent of VMs with Savings: 51%
• New Cost: $39,341,202.17
• Total Savings: $22,253,968.06

22. Limitations
 Lack of information regarding the nature of jobs and applications
running on each VM
• could affect the quality of our recommendations.
 Lack of information about memory usage of each VM (in traces).
 Over-provisioning of memory
 Multiple assumptions
• assumptions made in pricing model
• assumptions on user behavior
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23. Conclusions
 Studying the user behaviors in the cloud, providing viable solution
to reduce cloud cost and waste
 comprehensive analysis of the Microsoft Azure 2019 traces
• VMs are underutilized or over-provisioned for resources
 Mitigate the cloud waste problem and save cost
 proposing some metrics – CWP, CWI, CUS
 Experimental results show that over $22 million savings can be
achieved.
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24. Reference
1. M. Alalawi, H. Daly, A survey on hadoop MapReduce energy efficient techniques for intensive workload, in: Proceedings of
the International Conference on Big Data and Internet of Thing, in: BDIOT2017, Association for Computing Machinery, New
York, NY, USA, 2017, pp. 62–66
2. F. Chen, J.-G. Schneider, Y. Yang, J. Grundy, Q. He, An energy consumption model and analysis tool for cloud computing
environments, in: 2012 First International Workshop on Green and Sustainable Software (GREENS), 2012, pp. 45–50
3. M. Qiu, Z. Ming, J. Li, K. Gai, Z. Zong, Phase-change memory optimization for green cloud with genetic algorithm, IEEE Trans.
Comput. 64 (12) (2015) 3528–3540
4. S. Mamun, A. Gilday, A. Singh, A. Ganguly, G. Merrett, X. Wang, B. AIHashimi, Intra- and inter-server smart task scheduling
for profit and energy optimization of HPC data centers, J. Low Power Electron. Appl. 10 (2020) 32
5. O. Hadary, L. Marshall, I. Menache, A. Pan, E.E. Greeff, D. Dion, S. Dorminey, S. Joshi, Y. Chen, M. Russinovich, T. Moscibroda,
Protean: VM allocation service at scale, in: 14th USENIX Symposium on Operating Systems Design and Implementation
(OSDI 20), USENIX Association, 2020, pp. 845–861
6. M. Shahrad, R. Fonseca, I. Goiri, G. Chaudhry, P. Batum, J. Cooke, E. Laureano, C. Tresness, M. Russinovich, R. Bianchini,
Serverless in the wild: Characterizing and optimizing the serverless workload at a large cloud provider, in: 2020 USENIX
Annual Technical Conference (USENIX ATC 20), USENIX Association, 2020, pp. 205–218
7. E. Cortez, A. Bonde, A. Muzio, M. Russinovich, M. Fontoura, R. Bianchini, Resource central: Understanding and predicting
workloads for improved resource management in large cloud platforms, in: Proceedings of the 26th Symposium on
Operating Systems Principles, in: SOSP ’17, Association for Computing Machinery, New York, NY, USA, 2017, pp. 153–167
8. Google, Google cloud pricing, 2021, https://cloud.google.com/pricing
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25. Questions?
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