Service Clustering for Autonomic Clouds Using Random Forest

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Managing and optimising cloud services is one of the main challenges faced by industry and academia. A possible solution is resorting to self-management, as fostered by autonomic computing. However, the abstraction layer provided by cloud computing obfuscates several details of the provided services, which, in turn, hinders the effectiveness of autonomic managers. Data-driven approaches, particularly those relying on service clustering based on machine learning techniques, can assist the autonomic management and support decisions concerning, for example, the scheduling and deployment of services. One aspect that complicates this approach is that the information provided by the monitoring contains both continuous (e.g. CPU load) and categorical (e.g. VM instance type) data. Current approaches treat this problem in a heuristic fashion. This paper, instead, proposes an approach, which uses all kinds of data and learns in a data-driven fashion the similarities and resource usage patterns among the services. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. Using datasets from real-world clouds, we demonstrate the superiority of our solution with respect to others and validate the accuracy of the on-line prediction. Moreover, to show the applicability of our approach, we devise a service scheduler that uses the notion of similarity among services and evaluate it in a cloud test-bed.

Data & Analytics

Service Clustering for Autonomic
Clouds Using Random Forest
Rafael Brundo Uriarte
IMT Lucca
Sotirios Tsaftaris Francesco Tiezzi
IMT Lucca University of Camerino
CCGrid - 7th May 2015 - Shenzhen, China

Contents
1 Introduction
2 Requirements and Existing Solutions
3 RF+PAM
4 Evaluation
5 Conclusions
Uriarte, Tsaftaris and Tiezzi 1/29

Introduction
Introduction Uriarte, Tsaftaris and Tiezzi 2/29

Cloud Computing
Everything-as-a-Service
Dynamism
Heterogeneity
Virtualization
Large-Scale
Introduction Uriarte, Tsaftaris and Tiezzi 3/29

Autonomic Computing
Introduction Uriarte, Tsaftaris and Tiezzi 4/29

Autonomic Clouds
Restricted Knowledge
Approaches to alleviate the problem:
Machine Learning
Service Clustering
Introduction Uriarte, Tsaftaris and Tiezzi 5/29

Applications in the Domain
Anomalous Behaviour Detection
Service Scheduling
Application Proﬁling
SLA Risk Assessment
Introduction Uriarte, Tsaftaris and Tiezzi 6/29

Requirements and Existing Solutions
Requirements and Existing Solutions Uriarte, Tsaftaris and Tiezzi 7/29

Requirements
Characteristics Requirements
Security, Heterogeneity,
Dynamism
Mixed Types of
Features
Large-Scale, Dynamism On-line Prediction
Large-Scale, Multi-Agent
Loosely-Coupled
Parallelism
Heterogeneity
Large Number of
Features
Requirements and Existing Solutions Uriarte, Tsaftaris and Tiezzi 8/29

Existing Approaches
Solutions which handle mixed data types usually are not
scalable (e.g. HClustream)
Expert intervention is not feasible due to the dynamism
Distance Metric Learning Approaches require labelled data
or are computationally expensive.
Requirements and Existing Solutions Uriarte, Tsaftaris and Tiezzi 9/29

RF+PAM
RF+PAM Uriarte, Tsaftaris and Tiezzi 10/29

Random Forest
Mixed Features
Large Number of Features
Eﬃcient and Scales Well
Easily Parallelizable
RF+PAM Uriarte, Tsaftaris and Tiezzi 11/29

Random Forest
Clustering with Random Forest
Originally Developed for Classiﬁcation
On-Line Random Forest
Intrinsic Measure of Similarity
Clustering Algorithm (e.g. PAM)
RF+PAM Uriarte, Tsaftaris and Tiezzi 12/29

Similarity Using RF: Criteria
RF+PAM Uriarte, Tsaftaris and Tiezzi 13/29

Problems
Similarity Matrix (Big Memory Footprint)
Re-cluster on Every New Observation
RF+PAM Uriarte, Tsaftaris and Tiezzi 14/29

Solution: RF+PAM
Oﬀ-line Training and On-line Prediction
Similarity Learning and Standard Clustering
RF+PAM Uriarte, Tsaftaris and Tiezzi 15/29

Solution: RF+PAM
Build Forest, Calculate Similarities, Cluster, Select
the medoids and Store the references of the leaves.
RF+PAM Uriarte, Tsaftaris and Tiezzi 16/29

Solution: RF+PAM
Parse service and Assign the cluster of the most
similar medoid to it.
RF+PAM Uriarte, Tsaftaris and Tiezzi 17/29

Evaluation
Evaluation Uriarte, Tsaftaris and Tiezzi 18/29

Experiments
1. Cluster Quality
2. On-Line Prediction
3. Use Case
Evaluation Uriarte, Tsaftaris and Tiezzi 19/29

Cluster Quality
Clustering quality compared to 2 other
approaches (same dataset)
Better results in all criteria
Connectivity - Connectedness of the clusters
Dunn Index - Cluster density and Separation
Silhouette - Conﬁdence in the assignment
Evaluation Uriarte, Tsaftaris and Tiezzi 20/29

On-line Prediction
On-Line vs Batch Mode
K-Fold Cross-Validation
Compared the Adjusted Rand Index (ARI) for 2
datasets:
Monitoring data of Google’s production
clouds - 12500 servers
Requests of a grid of the Dutch Universities
Research Testbed (DAS-2) - 200 servers
Evaluation Uriarte, Tsaftaris and Tiezzi 21/29

Results: ARI
K Google DAS-2
100 0.81 (0.32) 0.70 (0.23)
50 0.75 (0.19) 0.68 (0.17)
20 0.73 (0.09) 0.67 (0.11)
10 0.70 (0.06) 0.63 (0.09)
5 0.69 (0.05) 0.61 (0.07)
Evaluation Uriarte, Tsaftaris and Tiezzi 22/29

Use Case
Schedules according to the Dissimilarity
Similar services separated
Algorithms:
1. Random
2. Dissimilarity
3. Isolated
Evaluation Uriarte, Tsaftaris and Tiezzi 23/29

Use Case
9 VMs
Arrival Rates
Types of Service
Services’ SLA
Evaluation Uriarte, Tsaftaris and Tiezzi 24/29

Results
Evaluation Uriarte, Tsaftaris and Tiezzi 25/29

Conclusions
Conclusions Uriarte, Tsaftaris and Tiezzi 26/29

Summary
We propose RF+PAM to alleviate the problem
of limited knowledge in AC
Validated RF+PAM with 3 Experiments
Scheduling Algorithm
Conclusions Uriarte, Tsaftaris and Tiezzi 27/29

Future Works
More Use Cases
Better Implementation
Conclusions Uriarte, Tsaftaris and Tiezzi 28/29

Thank you!
Questions?
Rafael Brundo Uriarte
rafael.uriarte@gmail.com
Conclusions Uriarte, Tsaftaris and Tiezzi 29/29

Prune Trees
Parsing is very fast and eﬃcient
Prune requires analysis (time consuming)
Conclusions Uriarte, Tsaftaris and Tiezzi 29/29

Retraining
Ratio of predictions/training services (user deﬁned):
Parallel training
Trade-oﬀ between updating/prediction
Other solutions:
Dissimilarity to Medoids
On-line Clustering (Current Limitations and
Prediction Speed)
Conclusions Uriarte, Tsaftaris and Tiezzi 29/29

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毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

End-to-end pipeline agility - Berlin Buzzwords 2024

Lars Albertsson

We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines. A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more. A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream. Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.

一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理

nuttdpt

毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Challenges of Nation Building-1.pptx with more important

Sm321

一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理

g4dpvqap0

毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样

apvysm8

原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Global Situational Awareness of A.I. and where its headed

vikram sood

You can see the future first in San Francisco. Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum. The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war. Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change. Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride. Let me tell you what we see.

一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理

nyfuhyz

毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Intelligence supported media monitoring in veterinary medicine

AndrzejJarynowski

My burning issue is homelessness K.C.M.O.

rwarrenll

Palo Alto Cortex XDR presentation .......

Sachin Paul

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...

Aggregage

DSSML24_tspann_CodelessGenerativeAIPipelines

Timothy Spann

Codeless Generative AI Pipelines (GenAI with Milvus) https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience. Timothy Spann https://www.youtube.com/@FLaNK-Stack https://medium.com/@tspann https://www.datainmotion.dev/ milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge

Population Growth in Bataan: The effects of population growth around rural pl...

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Service Clustering for Autonomic Clouds Using Random Forest

1. Service Clustering for Autonomic Clouds Using Random Forest Rafael Brundo Uriarte IMT Lucca Sotirios Tsaftaris Francesco Tiezzi IMT Lucca University of Camerino CCGrid - 7th May 2015 - Shenzhen, China

2. Contents 1 Introduction 2 Requirements and Existing Solutions 3 RF+PAM 4 Evaluation 5 Conclusions Uriarte, Tsaftaris and Tiezzi 1/29

3. Introduction Introduction Uriarte, Tsaftaris and Tiezzi 2/29

4. Cloud Computing Everything-as-a-Service Dynamism Heterogeneity Virtualization Large-Scale Introduction Uriarte, Tsaftaris and Tiezzi 3/29

5. Autonomic Computing Introduction Uriarte, Tsaftaris and Tiezzi 4/29

6. Autonomic Clouds Restricted Knowledge Approaches to alleviate the problem: Machine Learning Service Clustering Introduction Uriarte, Tsaftaris and Tiezzi 5/29

7. Applications in the Domain Anomalous Behaviour Detection Service Scheduling Application Proﬁling SLA Risk Assessment Introduction Uriarte, Tsaftaris and Tiezzi 6/29

8. Requirements and Existing Solutions Requirements and Existing Solutions Uriarte, Tsaftaris and Tiezzi 7/29

9. Requirements Characteristics Requirements Security, Heterogeneity, Dynamism Mixed Types of Features Large-Scale, Dynamism On-line Prediction Large-Scale, Multi-Agent Loosely-Coupled Parallelism Heterogeneity Large Number of Features Requirements and Existing Solutions Uriarte, Tsaftaris and Tiezzi 8/29

10. Existing Approaches Solutions which handle mixed data types usually are not scalable (e.g. HClustream) Expert intervention is not feasible due to the dynamism Distance Metric Learning Approaches require labelled data or are computationally expensive. Requirements and Existing Solutions Uriarte, Tsaftaris and Tiezzi 9/29

11. RF+PAM RF+PAM Uriarte, Tsaftaris and Tiezzi 10/29

12. Random Forest Mixed Features Large Number of Features Eﬃcient and Scales Well Easily Parallelizable RF+PAM Uriarte, Tsaftaris and Tiezzi 11/29

13. Random Forest Clustering with Random Forest Originally Developed for Classiﬁcation On-Line Random Forest Intrinsic Measure of Similarity Clustering Algorithm (e.g. PAM) RF+PAM Uriarte, Tsaftaris and Tiezzi 12/29

14. Similarity Using RF: Criteria RF+PAM Uriarte, Tsaftaris and Tiezzi 13/29

15. Problems Similarity Matrix (Big Memory Footprint) Re-cluster on Every New Observation RF+PAM Uriarte, Tsaftaris and Tiezzi 14/29

16. Solution: RF+PAM Oﬀ-line Training and On-line Prediction Similarity Learning and Standard Clustering RF+PAM Uriarte, Tsaftaris and Tiezzi 15/29

17. Solution: RF+PAM Build Forest, Calculate Similarities, Cluster, Select the medoids and Store the references of the leaves. RF+PAM Uriarte, Tsaftaris and Tiezzi 16/29

18. Solution: RF+PAM Parse service and Assign the cluster of the most similar medoid to it. RF+PAM Uriarte, Tsaftaris and Tiezzi 17/29

19. Evaluation Evaluation Uriarte, Tsaftaris and Tiezzi 18/29

20. Experiments 1. Cluster Quality 2. On-Line Prediction 3. Use Case Evaluation Uriarte, Tsaftaris and Tiezzi 19/29

21. Cluster Quality Clustering quality compared to 2 other approaches (same dataset) Better results in all criteria Connectivity - Connectedness of the clusters Dunn Index - Cluster density and Separation Silhouette - Conﬁdence in the assignment Evaluation Uriarte, Tsaftaris and Tiezzi 20/29

22. On-line Prediction On-Line vs Batch Mode K-Fold Cross-Validation Compared the Adjusted Rand Index (ARI) for 2 datasets: Monitoring data of Google’s production clouds - 12500 servers Requests of a grid of the Dutch Universities Research Testbed (DAS-2) - 200 servers Evaluation Uriarte, Tsaftaris and Tiezzi 21/29

23. Results: ARI K Google DAS-2 100 0.81 (0.32) 0.70 (0.23) 50 0.75 (0.19) 0.68 (0.17) 20 0.73 (0.09) 0.67 (0.11) 10 0.70 (0.06) 0.63 (0.09) 5 0.69 (0.05) 0.61 (0.07) Evaluation Uriarte, Tsaftaris and Tiezzi 22/29

24. Use Case Schedules according to the Dissimilarity Similar services separated Algorithms: 1. Random 2. Dissimilarity 3. Isolated Evaluation Uriarte, Tsaftaris and Tiezzi 23/29

25. Use Case 9 VMs Arrival Rates Types of Service Services’ SLA Evaluation Uriarte, Tsaftaris and Tiezzi 24/29

26. Results Evaluation Uriarte, Tsaftaris and Tiezzi 25/29

27. Conclusions Conclusions Uriarte, Tsaftaris and Tiezzi 26/29

28. Summary We propose RF+PAM to alleviate the problem of limited knowledge in AC Validated RF+PAM with 3 Experiments Scheduling Algorithm Conclusions Uriarte, Tsaftaris and Tiezzi 27/29

29. Future Works More Use Cases Better Implementation Conclusions Uriarte, Tsaftaris and Tiezzi 28/29

30. Thank you! Questions? Rafael Brundo Uriarte rafael.uriarte@gmail.com Conclusions Uriarte, Tsaftaris and Tiezzi 29/29

31. Prune Trees Parsing is very fast and eﬃcient Prune requires analysis (time consuming) Conclusions Uriarte, Tsaftaris and Tiezzi 29/29

32. Retraining Ratio of predictions/training services (user deﬁned): Parallel training Trade-oﬀ between updating/prediction Other solutions: Dissimilarity to Medoids On-line Clustering (Current Limitations and Prediction Speed) Conclusions Uriarte, Tsaftaris and Tiezzi 29/29

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