Elascale Poster
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Elascale is a comprehensive autoscaling and monitoring solution that leverages both virtual machines and containers. It adjusts both microservices and macroservices using Docker Swarm for clustering microservices and Docker Machine for provisioning macroservices. Metrics are collected using Beats and consolidated in Elasticsearch, while insights are provided in Kibana. The autoscaling engine uses metrics like CPU and memory utilization to scale services up and down according to a replication factor formula to maintain performance as workload increases. Elascale was tested on an IoT application deployed in a smart city testbed.
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![Elascale: A Comprehensive Autoscaling and Monitoring Solution
Rajsimman Ravichandiran, Hamzeh Khazaei, Byungchul Park, Hadi Bannazadeh and Alberto Leon-Garcia
Electrical and Computer Engineering Department, University of Toronto, Canada
Autoscalability: A required feature for today’s cloud
software systems
Both Virtual Machines (VMs) and containers are
leveraged to offer solutions as micro/macroservices
Scaling algorithms should support scalability for both
VMs and containers
Elascale adjusts both micro and macroservices [1]
Container: isolated process on host and shares kernel
space; does not contain full OS
Microservice: application components that provide
single service
Macroservice: worker nodes that host multiple
services
Docker Swarm: tool for clustering and scheduling
distributed microservices
Docker Machine: tool for provisioning and managing
macroservices across backend clouds
✧ Utilizes Docker Swarm for clustering and scheduling
services
✧ Uses Docker Machine to provision and manage
macroservices (Fig. 1)
✧ Beats (Metricbeat and Dockbeat) collect performance
metrics of micro/macroservices (Fig. 2)
✧ HTTPS encrypted communication channels to prevent
information leakage
✧ NGINX reverse proxy conceal Elasticsearch and
Kibana
✧ Elasticsearch consolidates statistics
✧ Informed insights from Kibana using adhoc queries
and visualizations
✧ UI to view dashboard and change configurations
✧ Source code on GitHub [2]
Architecture Views
1. Hamzeh Khazaei, Rajsimman Ravichandiran, Byungchul Park, Hadi Bannazadeh, Ali
Tizghadam and Alberto Leon-Garcia. Elascale: Autoscaling and Monitoring as a
Service. Accepted in the 27th Annual International Conference on Computer Science and
Software Engineering (CASCON), Sept 2017.
2. R. Ravichandiran, Elascale_scripts, (2017), GitHub repository,
https://github.com/RajsimmanRavi/Elascale_secure
Sample IoT Application
References:
Experimental Evaluation
Figure 1: Layered view
Figure 2: Component view
Figure 3: Sample IoT application deployed in SAVI Testbed
Elascale Architecture
Introduction Elascale Autoscaling Engine
✧ General-purpose, cloud and application agnostic monitoring and
autoscaling solution
✧ Autoscaling algorithms use statistics to scale up/down
micro/macroservices
✧ 𝒇 = 𝜶 ∙ 𝒄𝒑𝒖 𝒖𝒕𝒊𝒍 + 𝜷 ∙ 𝒎𝒆𝒎 𝒖𝒕𝒊𝒍 + 𝜸 ∙ 𝒏𝒆𝒕 𝒖𝒕𝒊𝒍 + 𝝀 ∙
𝒓𝒆𝒑 𝒇𝒂𝒄
𝒕
𝒓𝒆𝒑 𝒇𝒂𝒄
𝒄
✧ 𝒓𝒆𝒑 𝒇𝒂𝒄
𝒕
- target replication factor , 𝒓𝒆𝒑 𝒇𝒂𝒄
𝒄
- current replication factor
✧ 𝜶 + 𝜷 + 𝜸 + 𝝀 = 𝟏
Background
✧ Deployed as Docker services on swarm nodes
✧ Three layers containing different microservices (Fig. 3)
✧ Aggregate-sensor: virtual sensors and Kafka aggregator
✧ Edge-cloud: stream processor collects from aggregator, preprocesses and
sends to Core-cloud
✧ Core-cloud: Cassandra database stores data
✧ Experiment: If more sensors deployed, stream processor CPU
utilization increases
✧ Elascale automatically scales microservice to handle workload](https://image.slidesharecdn.com/hamzeh-casconelascaleposterupdated-191205203535/85/Elascale-Poster-1-320.jpg)
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Slides built to upskill and enable internal team and/or partners on foundational infra skills to work in a containerized world.
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Virtualization in Distributed System: A Brief Overview
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The “Virtualization and Cloud Computing” is a recent buzzword in the digital world. Behind this fancy
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Though the “Virtualization and Cloud Computing are recent but the idea of centralizing computation and
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services increases the complexity of back-end technologies also increased.
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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8.Isolation of pure cultures and preservation of cultures.pdf
Isolation of pure culture, its various method.
Alternate Wetting and Drying - Climate Smart Agriculture
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PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024. Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...
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Randomised Optimisation Algorithms in DAPHNE
Slides from talk:
Aleš Zamuda: Randomised Optimisation Algorithms in DAPHNE .
Austrian-Slovenian HPC Meeting 2024 – ASHPC24, Seeblickhotel Grundlsee in Austria, 10–13 June 2024
https://ashpc.eu/
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(UAM毕业证书)马德里自治大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/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。
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
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二.真实使馆公证(即留学回国人员证明,不成功不收费)
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四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
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◇申请留学生创业基金
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
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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
The cost of acquiring information by natural selection
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
11.1 Role of physical biological in deterioration of grains.pdf
Storagedeteriorationisanyformoflossinquantityandqualityofbio-materials.
Themajorcausesofdeteriorationinstorage
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•Biological
•Mechanical
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Storageonlypreservesquality.Itneverimprovesquality.
Itisadvisabletostartstoragewithqualityfoodproduct.Productwithinitialpoorqualityquicklydepreciates
Applied Science: Thermodynamics, Laws & Methodology.pdf
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.Compexometric titration/Chelatorphy titration/chelating titration
Classification
Metal ion ion indicators
Masking and demasking reagents
Estimation of Magnisium sulphate
Calcium gluconate
Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types-
1.Direct Titration
2.Back Titration
3.Replacement Titration
4.Indirect Titration
Masking agent, Demasking agents
formation of complex
comparition between masking and demasking agents,
Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators,
Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators
Instrumental Techniques-Photometry
Potentiometry
Miscellaneous methods.
Complex titration with EDTA.
在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/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。
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- 1. Elascale: A Comprehensive Autoscaling and Monitoring Solution Rajsimman Ravichandiran, Hamzeh Khazaei, Byungchul Park, Hadi Bannazadeh and Alberto Leon-Garcia Electrical and Computer Engineering Department, University of Toronto, Canada Autoscalability: A required feature for today’s cloud software systems Both Virtual Machines (VMs) and containers are leveraged to offer solutions as micro/macroservices Scaling algorithms should support scalability for both VMs and containers Elascale adjusts both micro and macroservices [1] Container: isolated process on host and shares kernel space; does not contain full OS Microservice: application components that provide single service Macroservice: worker nodes that host multiple services Docker Swarm: tool for clustering and scheduling distributed microservices Docker Machine: tool for provisioning and managing macroservices across backend clouds ✧ Utilizes Docker Swarm for clustering and scheduling services ✧ Uses Docker Machine to provision and manage macroservices (Fig. 1) ✧ Beats (Metricbeat and Dockbeat) collect performance metrics of micro/macroservices (Fig. 2) ✧ HTTPS encrypted communication channels to prevent information leakage ✧ NGINX reverse proxy conceal Elasticsearch and Kibana ✧ Elasticsearch consolidates statistics ✧ Informed insights from Kibana using adhoc queries and visualizations ✧ UI to view dashboard and change configurations ✧ Source code on GitHub [2] Architecture Views 1. Hamzeh Khazaei, Rajsimman Ravichandiran, Byungchul Park, Hadi Bannazadeh, Ali Tizghadam and Alberto Leon-Garcia. Elascale: Autoscaling and Monitoring as a Service. Accepted in the 27th Annual International Conference on Computer Science and Software Engineering (CASCON), Sept 2017. 2. R. Ravichandiran, Elascale_scripts, (2017), GitHub repository, https://github.com/RajsimmanRavi/Elascale_secure Sample IoT Application References: Experimental Evaluation Figure 1: Layered view Figure 2: Component view Figure 3: Sample IoT application deployed in SAVI Testbed Elascale Architecture Introduction Elascale Autoscaling Engine ✧ General-purpose, cloud and application agnostic monitoring and autoscaling solution ✧ Autoscaling algorithms use statistics to scale up/down micro/macroservices ✧ 𝒇 = 𝜶 ∙ 𝒄𝒑𝒖 𝒖𝒕𝒊𝒍 + 𝜷 ∙ 𝒎𝒆𝒎 𝒖𝒕𝒊𝒍 + 𝜸 ∙ 𝒏𝒆𝒕 𝒖𝒕𝒊𝒍 + 𝝀 ∙ 𝒓𝒆𝒑 𝒇𝒂𝒄 𝒕 𝒓𝒆𝒑 𝒇𝒂𝒄 𝒄 ✧ 𝒓𝒆𝒑 𝒇𝒂𝒄 𝒕 - target replication factor , 𝒓𝒆𝒑 𝒇𝒂𝒄 𝒄 - current replication factor ✧ 𝜶 + 𝜷 + 𝜸 + 𝝀 = 𝟏 Background ✧ Deployed as Docker services on swarm nodes ✧ Three layers containing different microservices (Fig. 3) ✧ Aggregate-sensor: virtual sensors and Kafka aggregator ✧ Edge-cloud: stream processor collects from aggregator, preprocesses and sends to Core-cloud ✧ Core-cloud: Cassandra database stores data ✧ Experiment: If more sensors deployed, stream processor CPU utilization increases ✧ Elascale automatically scales microservice to handle workload