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So I wanted to really find out how cheap the cloud was- but I got confused by the 23 kinds of instances than Amazon has http://aws.amazon.com/ec2/pricing/ and 15 kinds of instances at https://developers.google.com/compute/pricing.
or whether there is any price collusion between them ;)
Now Amazon has spot pricing so I can bid for prices as well (http://aws.amazon.com/ec2/purchasing-options/spot-instances/ ) and upto 60% off for reserved instances (http://aws.amazon.com/ec2/purchasing-options/reserved-instances/) but charges $2 for dedicated instances (which are not dedicated but pay as you go)
- $2 per hour – An additional fee is charged once per hour in which at least one Dedicated Instance of any type is running in a Region.
Google has sustained discounts ( will not offer Windows on the cloud though!)
The table below describes the discount at each usage level. These discounts apply for all instance types.
|Usage Level (% of month)||% at which incremental is charged||Example incremental rate (USD/per hour) for an n1-standard-1 instance|
|0%-25%||100% of base rate||$0.07|
|25%-50%||80% of base rate||$0.056|
|50%-75%||60% of base rate||$0.042|
|75%-100%||40% of base rate||$0.028|
Anyways- I tried to create this simple table to help me with it- after all hard disks are cheap- it is memory I want on the cloud !
Or maybe I am wrong and the cloud is not so cheap- or its just too complicated for someone to build a pricing calculator that can take in prices from all providers (Amazon, Azure, Google Compute) and show us the money!
|vCPU||RAM(GiB)||$ per Hour||Type -Linux Usage||Provider||Notes|
|t2.micro||1||1||$0.01||General Purpose – Current Generation||Amazon (North Virginia)||Amazon also has spot instances|
|t2.small||1||2||$0.03||General Purpose – Current Generation||Amazon (North Virginia)||that can lower prices|
|t2.medium||2||4||$0.05||General Purpose – Current Generation||Amazon (North Virginia)|
|m3.medium||1||3.75||$0.07||General Purpose – Current Generation||Amazon (North Virginia)|
|m3.large||2||7.5||$0.14||General Purpose – Current Generation||Amazon (North Virginia)|
|m3.xlarge||4||15||$0.28||General Purpose – Current Generation||Amazon (North Virginia)|
|m3.2xlarge||8||30||$0.56||General Purpose – Current Generation||Amazon (North Virginia)|
|c3.large||2||3.75||$0.11||Compute Optimized – Current Generation||Amazon (North Virginia)|
|c3.xlarge||4||7.5||$0.21||Compute Optimized – Current Generation||Amazon (North Virginia)|
|c3.2xlarge||8||15||$0.42||Compute Optimized – Current Generation||Amazon (North Virginia)|
|c3.4xlarge||16||30||$0.84||Compute Optimized – Current Generation||Amazon (North Virginia)|
|c3.8xlarge||32||60||$1.68||Compute Optimized – Current Generation||Amazon (North Virginia)|
|g2.2xlarge||8||15||$0.65||GPU Instances – Current Generation||Amazon (North Virginia)|
|r3.large||2||15||$0.18||Memory Optimized – Current Generation||Amazon (North Virginia)|
|r3.xlarge||4||30.5||$0.35||Memory Optimized – Current Generation||Amazon (North Virginia)|
|r3.2xlarge||8||61||$0.70||Memory Optimized – Current Generation||Amazon (North Virginia)|
|r3.4xlarge||16||122||$1.40||Memory Optimized – Current Generation||Amazon (North Virginia)|
|r3.8xlarge||32||244||$2.80||Memory Optimized – Current Generation||Amazon (North Virginia)|
|i2.xlarge||4||30.5||$0.85||Storage Optimized – Current Generation||Amazon (North Virginia)|
|i2.2xlarge||8||61||$1.71||Storage Optimized – Current Generation||Amazon (North Virginia)|
|i2.4xlarge||16||122||$3.41||Storage Optimized – Current Generation||Amazon (North Virginia)|
|i2.8xlarge||32||244||$6.82||Storage Optimized – Current Generation||Amazon (North Virginia)|
|hs1.8xlarge||16||117||$4.60||Storage Optimized – Current Generation||Amazon (North Virginia)|
|n1-standard-1||1||3.75||$0.07||Standard||Google -US||Google charges per minute|
|n1-standard-2||2||7.5||$0.14||Standard||Google -US||of usage (subject to minimum of 10 minutes)|
|n1-highmem-2||2||13||$0.16||High Memory||Google -US|
|n1-highmem-4||4||26||$0.33||High Memory||Google -US|
|n1-highmem-8||8||52||$0.66||High Memory||Google -US|
|n1-highmem-16||16||104||$1.31||High Memory||Google -US|
|n1-highcpu-2||2||1.8||$0.09||High CPU||Google -US|
|n1-highcpu-4||4||3.6||$0.18||High CPU||Google -US|
|n1-highcpu-8||8||7.2||$0.35||High CPU||Google -US|
|n1-highcpu-16||16||14.4||$0.70||High CPU||Google -US|
|f1-micro||1||0.6||$0.01||Shared Core||Google -US|
|g1-small||1||1.7||$0.04||Shared Core||Google -US|
From naming the algorithm after himself ( PageRank ?) to forsaking his professors at Stanford ( who legally own the rights to many intellectual property), to first learning under Eric Schmidt and then pushing him out on the pretense of a political appointment to never came, to the era of silent cooperation with the US Government, to collecting a lot of data by assessing the risk of litigation (especially mobile), and to push intellectual property rights between open source and patent rights, to massive expensive lobbying and now even sidelining his brother in arms- Larry Page has emerged as the most ruthless combination of business savvy and formidable technological skills since Bill Gates.
He now owns a representative sample of nearly all the data on video (Youtube) , email (Gmail), website analytics ( Google Analytics), search engine (Google.com), advertising clicks ( Adwords and Adsense), a majority of mobile phones (Android).
And he wants more. To collect data from your thermostat. Your glasses. His government will not file an anti trust case because of national security. As an extension of US foreign policy, he will lead protests against Chinese hackers, censorship and even abandon the market than comply with Chinese Law, but he will gladly pay fines and delete links to comply with European Law.
There are ways to make money that are not evil. But they do not teach what is evil or not, at Stanford. Not even to dropouts.
What is information asymmetry?
information asymmetry deals with the study of decisions in transactions where one party has more or better information than the other. This creates an imbalance of power in transactions which can sometimes cause the transactions to go awry, a kind of market failure in the worst case. Examples of this problem are adverse selection, moral hazard, and information monopoly
Adverse selection, anti-selection, or negative selection refers to a market process in which undesired results occur when buyers and sellers have asymmetric information (access to different information); the “bad” products or services are more likely to be selected.
The principal–agent problem or agency dilemma occurs when one person or entity (the “agent“) is able to make decisions that impact, or on behalf of, another person or entity: the “principal“. The dilemma exists because sometimes the agent is motivated to act in his own best interests rather than those of the principal.
Monopolies of knowledge arise when ruling classes maintain their political power through their control of key communications technologies. An example of this occurs in ancient Egypt where a complex writing system conferred a monopoly of knowledge on literate priests and scribes.
- This especially is true in enterprise software
- and online advertising and spam
- and commodities across the globe (oil spikes after iraq, oil slumps after heating oil data, climate data, or even releases from strategic reservoirs)
- and internet spying which may be for economic espionage or trade negotiations but are justified as looking for terrorists.
- and inflation in the developing and poor countries
- and lobbying in the developed and rich countries
People who enable information asymmetry are corrupted people, misled by their own greed and agent-employees in decisions that run counter to the principles when they founded their corporation.
Do you think information asymmetry is evil? Or do you think we should jump on the bandwagon and play the game. Click those ads, while we share your data with the government!
Is Game of Thrones more popular than Lost
Not quite. But its getting there!
Here is a short post in retrieving information from the Google+ API using R, and then analysing it.
To create an API key:
- Go to the Google Developers Console.
- Create or select a project.
- In the sidebar on the left, select APIs & auth.
- In the displayed list of APIs, find the Google+ API and set its status to ON.
- In the sidebar on the left, select Credentials.
- Create an API key by clicking Create New Key. Select the appropriate kind of key: Server key Then clickCreate.
and the R code
#install.packages("plusser") library(plusser) help(plusser) library(RCurl) options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl"))) setAPIkey('AIzaSyBtYqDsAtzp4FOS7FGbrc_n6mD-uJIOvcQ') myProfile=harvestProfile("+AjayOhri", parseFun = parseProfile) str(myProfile) myposts=harvestPage("+AjayOhri", parseFun = parsePost, results = 1, nextToken = NULL, cr = 1) str(myposts) head(myposts) plot(myposts$ti,myposts$nC) #number of comments plot(myposts$ti,myposts$nP) #number of likes or plus 1 plot(myposts$ti,myposts$nR) #number of reshares some screenshots and images You can also see the Rpubs document here http://rpubs.com/decisionstats2/plusser Now you can do text analysis and sentiment analysis on myposts$msg and do social media analysis on what makes people like what kind of content. For better results, use a google plus id (page or person) which has a lot of PUBLIC posts!
Add the words- free download and search engines will show you all the links for the download of the copyrighted material .
Now why would anyone want to use the word “free download” . Unless search engines come up with a better filter to commonly used keywords for copyright infringement- this loop hole will stay.
Use the chrome browser for convenient searching of “name of copyrighted material” + “free download”
Scroll down page of Google Search Results
Download using whatever that particular website does
Dont be Evil. Yup. That’s my friends
Use Image Search to write blog post ruing your poverty
I would like to write a thank you note to some of the people who helped make Decisionstats.com possible . We had a total of 150,644 views this year.For that, I have to thank you dear readers for putting up with me- it is now our seventh year.
I would like to thank Chris (of Mashape) for helping me with some of the interviews I wrote here .I did 26 interviews this year for Programmable Web and a total of 30+ articles including the interviews in 2013.
Of course- we have now reached 116 excellent interviews on Decisionstats.com alone ( see http://goo.gl/V6UsCG )I would like to thank each one of the interviewees who took precious time to fill out the questions.
Sponsors- I would like to thank Dr Eric Siegel ( individually as an author and as founder chair of www.pawcon.com ) , Nadja and Ingo (for Rapid-Miner) , Dr Jonathan ( for Datamind) , Chris M (for Statace.com ) , Gergely ( Author) and many more during all these six years who have kept us afloat and the servers warm in these days of cold reflection, including Gregory (of KDNuggets.com) and erstwhile AsterData founders.
Training Partners- I would like to thank Lovleen Bhatia ( of Edureka for giving me the opportunity to make http://www.edureka.in/r-for-analytics which now has 1721 learners as per http://www.edureka.in/)
I would also specially say Thank you to Jigsaw Academy for giving me the opportunity to create
the first affordable and quality R course in Asia http://analyticstraining.com/2013/jigsaw-completes-training-of-300-students-on-r/
These training courses including those by Datamind and Coursera remain a formidable and affordable alternative to many others catching up in the analytics education game in India ( an issue I wrote here)
Each and Everyone of my students (past and present) and Everyone in the #rstats and SAS-L community, including people who may have been left out.
Thank you sir, for helping me and Decisionstats.com !
Wish each one of you a very happy and Joyous Happy New Year and a great and prosperous 2014!