App Engine's Price Shock and the New Web App Equilibrium

App Engine's Price Shock

The Google App Engine blog announced that Google's App Engine is moving out of beta this year. According to the blog this means that App Engine will have to become financially attractive to Google, and therefore the corresponding upward revision in pricing. The details are intricate and affect customers (app developers) differently based on what services they use; the takeaway is that there is a reduction in the free-tier plan, a fixed monthly subscription fee, and a switch from billing based on CPU cycles to billing based on CPU-instance uptime. Now this has made several app developers unhappy because they feel slighted by Google changing the pricing structure, so much, so late. Google is trying to assuage developer anger, but I am guessing that Google App Engine stands to loose many apps and developers given these higher pricing structures.

Anyway, going back to the 3 highlights of the pricing change, lets see how it improves App Engine's business plan.

1. Reduction of the free-tier - Google claims that the free tier was eating into App Engine resources because there are many non-paying apps that run just fine using these free resources. Google is out to trim the free tier, leaving just enough to still win over developers who want to experiment with the App Engine, but no more. If an app is seriously deployed, it needs to pay for App Engine real estate. Many apps running on the App Engine are back-ends to iOS and Facebook apps. Why should Google be bank-rolling apps that benefit those platforms? App Engine's early adopter phase is over and freebies can end. 
2. Fixed $9 monthly subscription fee - The App Engine has been a fantastic success with more than 100K deployed apps. Even if 10% of them can be converted into  monthly subscriptions then the App Engine will have an evergreen revenue stream (many developers have complained that they would end up pay much more than $9pm given the new pricing model). 
3. CPU-instance time billing - Google wants customers to pay for availability (keeping their web apps running 24 x 7) rather than the amount of computation used by the apps. Since web apps usually remain online throughout, it makes business sense for Google to charge for this 24x7 CPU-instance time. CPU-cycles usage can be little and far-between given the eccentric product life-cycle, usage variability, and popularity of apps. It makes perfect sense for Google to start charging for availability rather than CPU cycles. 

Off course these facts are not lost on other cloud providers such as Amazon's AWS service who have always charged per CPU-instance and offer lean free tiers as well.

The Equilibrium Shift

I don't expect developers to shun the App Engine (or other cloud services for that matter) given this and future price increases. Its much more expensive, both in terms of capex and opex, to achieve a cloud provider's level of service availability and convenience for small and mid-sized app shops. But these price changes will affect the technology and architecture decisions of app developers, here is how:

1. Simplification of server-side logic, and richer client side logic - Clever web app design can off-load CPU cycles to web-browsers, more so with the advent of sophisticated client-side Javascript libraries and HTML5. I expect web app developers to aggressively move more processing into the user browser. This has the added benefit of more responsive web applications, for example HTML5's local data store can be used to store user data and shave-off network latency.
2. Multi-threaded server-side architecture - Charging for instances would push app developers toward utilizing instances more efficiently, for example, by adopting multi-threaded programming approaches. There is already talk about App Engine developers shunning Python (inherently weak in multi-threaded functionality) and moving to Java.
3. Space-CPU time trade-off - Storage space continues to be relatively cheap in the cloud. I expect developers to store a lot more application state rather than having to compute it again at a later time. For example, instead of using traditional RDBMS databases (e.g. MySQL), app developers may start looking at simple nosql alternatives like couchDB, which are instead optimized to store multiple indexes and views based on common read patterns. 
4. QoS as a service - App developers will also move toward higher workload thresholds in their load-balancing algorithms, meaning that users may see web apps slowing down as new CPU instances are more sparingly fired up during times of greater demand. I expect app developers to start charging users for differentiated QoS in apps. After all, there is no free lunch, and lunch just got a whole lot more expensive.
5. Availability as a service - Does it make sense for an app to keep its back-end running (CPU instances online) in the dead of night because 0.1% of its users are insomniacs? I think that some web apps  may start experimenting with the breaking the unwritten rule of 24x7 web uptime, or at least charging more for the privilege of using these services outside waking hours. Off course, different time-zones complicate this idea.

In the end, money matters. Its going to be interesting to see the market affecting cloud-deployed web app architecture decisions of the future.

H.264 vs. WebM

And so it begins. The battle between the H.264 and WebM video codec.

Google's On2 acquisition and the subsequent open-sourcing of the VP8 video codec has created a formidable competitor for H.264. Formidable not because WebM is technically superior to H.264 but because now there is a free alternative to the proprietary and licensed H.264. WebM is free, underwritten by Google, and a proven web-video delivery veteren -after all, Adobe Flash has used On2's codecs for web video delivery over the years.

There are several things going for H.264. First, it is entrenched in several video delivery formats and standards. For example, Bluray uses H.264 to encode video. Millions of Bluray players will become obselete if WebM is used instead of H.264. My two cents are that this wont really happen, instead, newer players will incorporate the possibility of decoding WebM video also. Even as I write this I am aware of several hardware manufacturers who are incorporating the WebM video decoders into their ASIC hardware.  But I am not assuming that things like the Bluray standard will be changed, on the contrary, there are other emerging media delivery and storage standards that have been frozen with H.264 being selected as the codec of choice. Standards take years to change or deploy and its very unlikely that they can suddenly adopt WebM instead of H.264.

In the mid-term WebM will defeat H.264 where there is a (easily replacable) software decoder and soft-media. By soft-media I mean video that is not burnt onto read-only media like Bluray disks but instead exists, say, in the form of a web-downloadable video on a server's hard-disk. The economic compulsion of having to pay the H.264 licensing body per-video download and per decoder shipped compared to the free (as in air) WebM alternative shall edge out the former. I suspect web-video delivery platforms like You-tube will lead the charge because (1) The number of videos being downloaded are huge and, (2) Their average revenue per video is miniscule, and each WebM download instead of H.264 download saves a few cents in licensing fee.

 A black-knight for the time-frame question will be the innovation in H.264 vs. that in WebM. If open-sourcing WebM has the desired effect of creating a better and more innovative codec in the future then WebM could gain on H.264 faster. But I am sure that the H.264 camp won't be sitting on their palms all this while! Video codecs use advanced algorithms and developing such concepts needs big investments (R&D). Will backers of WebM bring that kind of investment to the table in the interest of improving WebM when there is no direct revenue coming back to them?

Another thing that is going for WebM is the push toward virtualization in consumer electronics (away from the conventional ASIC approach) in the coming years. This means that future hardware (such as future Bluray players) may be capable of running multiple upgradable decoders rather than being tied to a specific ASIC implementing a specific decoding algorithm for a specific codec. That may just break the hardware dominance of H.264 over WebM. As a consumer I would prefer to hedge my bets and buy a virtualization-capable decoder rather than being tied into one video codec via an ASIC decoder.

Parallelizing & Multiprocessing Commands Using Python

My computer has multiple processor cores. That means I could speed up scripts by running some of their tasks in parallel. I have written up a simple Python script that uses the Multiprocessing library to take a list of jobs (each is a unix command string) and then executes them on a specified number of independent processes. These processes are created only once and act as a pool of "workers" which undertake a job, submit the result of the computation, and then undertake another job (if available in the job queue). The script ends when there are no more jobs in the job queue.
This approach is useful when (1) You have a multi-processor/multicore CPU. (2) Your tasks are CPU intensive. (3) You are reasonably sure that the jobs are not internally parallelized to take advantage of multiple CPUs. In my case, I had two directories full of numerically-named image (.ppm) files whose PSNR's had to be compared using the pnmpsnr utility. Computing PSNR is a computationally intensive task. Running the comparisons serially (single process) was significantly slower than adopting a multiprocess approach.
The code below should get you started on parallelizing your computationally intensive script. You can download the script from here.

#! /usr/bin/env python
# Sachin Agarwal, Google, Twitter: sachinkagarwal, Web: http://sites.google.com/site/sachinkagarwal/ 
# November 2010
# Using Python to execute a bunch of job strings on multiple processors and print out the results of the jobs in the order they were listed in the job list (e.g. serially).
# Partly adapted from http://jeetworks.org/node/81


#These are needed by the multiprocessing scheduler
from multiprocessing import Queue
import multiprocessing
import commands
import sys

#These are specific to my jobs requirement
import os
import re
 
def RunCommand (fullCmd):
    try:
        return commands.getoutput(fullCmd)
    except:
        return "Error executing command %s" %(fullCmd)

        
class Worker(multiprocessing.Process):
 
    def __init__(self,
            work_queue,
            result_queue,
          ):
        # base class initialization
        multiprocessing.Process.__init__(self)
        self.work_queue = work_queue
        self.result_queue = result_queue
        self.kill_received = False
 
    def run(self):
        while (not (self.kill_received)) and (self.work_queue.empty()==False):
            try:
                job = self.work_queue.get_nowait()
            except:
                break

            (jobid,runCmd) = job
            rtnVal = (jobid,RunCommand(runCmd))
            self.result_queue.put(rtnVal)

            
def execute(jobs, num_processes=2):
    # load up work queue
    work_queue = multiprocessing.Queue()
    for job in jobs:
        work_queue.put(job)
 
    # create a queue to pass to workers to store the results
    result_queue = multiprocessing.Queue()
 
    # spawn workers
    worker = []
    for i in range(num_processes):
        worker.append(Worker(work_queue, result_queue))
        worker[i].start()
    
    # collect the results from the queue
    results = []
    while len(results) < len(jobs): #Beware - if a job hangs, then the whole program will hang
        result = result_queue.get()
        results.append(result)
    results.sort() # The tuples in result are sorted according to the first element - the jobid
    return (results) 

 
#MAIN 
if __name__ == "__main__":
    
    import time #Code to measure time
    starttime = time.time() #Code to measure time
    
   
    jobs = [] #List of jobs strings to execute
    jobid = 0#Ordering of results in the results list returned

    #Code to generate my job strings. Generate your own, or load joblist into the jobs[] list from a text file
    lagFactor = 5
    ppmDir1 = sys.argv[1]
    ppmDir2 = sys.argv[2]
    decNumRe = re.compile(u"((\d+)\.(\d+))")
    ctr = 0
    for root, dirs, files in os.walk(ppmDir1):
        numFiles = len(files)
        files.sort()
        fNameLen = len(files[0].split('.')[0])
        for fName in files:
            for offset in range(0,lagFactor):
                fNumber = int(fName.split('.')[0])
                targetFile =  '%0*d' % (fNameLen, max(fNumber-offset,1))
                targetPath = ppmDir2+'/'+targetFile+'.ppm'
                origPath = ppmDir1+'/'+fName
                fullCmd = "pnmpsnr "+origPath+' '+targetPath  #Linux command to execute
                jobs.append((jobid,fullCmd)) # Append to joblist
                jobid = jobid+1

    # run
    numProcesses = 2
    if len(sys.argv) == 3:
        numProcesses = int(sys.argv[1])
    results = execute(jobs,numProcesses) #job list and number of worker processes
    
    #Code to print out results as needed by me. Change this to suit your own need
    # dump results
    ctr = 0
    for r in results:
        (jobid, cmdop) = r  
        if jobid % lagFactor == 0:
            print
            print jobid/lagFactor,
        print '\t',
         
        try:
            print cmdop.split()[10],
        except:
            print "Err",
        ctr = ctr+1
    print

    print "Time taken = %f" %(time.time()-starttime) #Code to measure time

My Grocery Store is a Mobile Operator

My grocery store sells generic versions of bottled water, soap, breakfast cereal, butter, milk and mobile voice/Internet service. Now thats quite remarkable considering Rewe, the German grocery store chain I am alluding to, doesn't really have a history in the German telecommunications market. What they do have are 15445 stores across Europe that can stock up prepaid SIM cards branded "ja! Mobil" (the name comes from their generic in-store brand). Their physical presence and the mind space ja! occupies drives their business model. If shoppers can drink ja! branded generic cola then they could as well use ja!-branded mobile voice/Internet service.

The innovation here is the marketing possibility offered by Rewe grocery stores (instead of any technical innovation). Rewe has partnered with T-Mobile in Germany to implement its ja! branded "mobile operator". T-Mobile provides a white-label technical platform and Rewe simply brands it "ja! mobile". T-Mobile wins because it gets to sell its service at a discount to lower-paying market segments without putting off the premium T-Mobile customers, Rewe makes a neat profit by leveraging the ja! brand, and the customer wins by getting a discounted service from the best mobile operator of Germany, minus the T-Mobile brand.

I was looking at ja! mobile pricing. There are various flavors of pre-paid and flat-rate plans, although the focus seems to be on pre-paid plans that require no long-term contract and can be dispensed at Rewe's check-out counters. Depending on a customer's typical usage, s/he can can trade-off get a discounted subset of services from among the services offered - SMS, MMS, in-network calling, fixed-line calls, data etc. Interestingly, customer support is not free. Its a little like the contemporary airline business where everything from customer service to carry-on baggage can become a chargeable add-on rather than part of the product. Customers need to be mindful of what their money is buying them before assuming that things like customer service or technical support is part of the product.

Brick-and-mortar stores also sell iTunes gift cards and Facebook credit nowadays. Dell and Amazon partner with Best Buy to sell computers and Kindle e-books respectively. There are interesting business opportunities for anyone who can funnel real customers and subscribers (read: money) into the virtual/communications world. Very real profits await those brick-and-mortar outfits who can build bridges between technology companies and customers, even if they are just plain-Jane grocery stores!

Fancy Vertical Handover: A victim of REST?

There has been a ton of research, standardization work, and development around Vertical Handover - the ability to change the underlying network access without disturbing the overlying communication protocol (TCP or application) sessions. The simplest example is when a user moves from a Wifi zone (e.g. office) to a 3G zone (outdoors). A seamless handover hides the underlying rewiring of the access and lets the user continue using the device as if nothing changed. Vertical handovers have quickly graduated from laboratory quirk to mainstream occurance, with Wifi-enabled smart-phones switching between access technologies multiple times daily.

But the vertical handover on my smart-phone doesn't really preserve the underlying TCP session and yet works pretty well. Why? Because most of the apps on my phone use REST-ful protocols like HTTP, XML-RPC, or SOAP. That means they are, in theory, stateless. In fact, a TCP connection is created and torn down for every message exchange between the service server and the client. Sometimes TCP connections linger on to improve efficiency (carrying multiple request-response mesages between the client and service server), but a discontinuity in the TCP connection is not catastrophic.  I simply see my smart-phone negotiate a new connection with the new access (3G or Wifi) and then my app keeps working as if nothing has changed.

All that talk about preserving TCP connections across access technologies was much ado about nothing!

Mobile Video Calling: Can Tango Tango?

Tango is a newly launched mobile-to-mobile video calling application for iPhone and Android devices. Tango enables smart-phone owners to see each other in addition to speaking with each other during a Voice Over IP (voip) conversation. Many smart-phones come with front facing cameras, ostensibly for video calling, and Tango enables people to use these cameras during a voip call. Think of mobile video calling when you want to see your expat pet doing silly tricks on video (or for beach and boardroom voyeurism).

But, as Walter Mossberg's Tango's review in WSJ reports, the  quality of Tango's video call leaves a lot to be desired. I came across a video on Gizmodo's website showing Tango in action. The verdict is that Tango's performance is way below expectation. In fact, Tango's video frame-rate seemed to be approximately 1 frame per second in the Gizmodo video (and not the "high quality video mobile calling service" as the company's press release claims).

Make no mistake, achieving even 1 frame-per-second video+voice is no small feat. Tango's engineers have packed a real-time video+voice encoder/decoder into a smart-phone and have managed to trasmit/receive two parallel audio/video streams over Wifi (they also claim high quality video calls over 3G but lets not give Tango all the benefit of doubt :-) ). On top of this, achieving this for both the Android and iPhone platforms and for dozens of smart-phone models is admirable.

Frankly, I am not surprised by Tango's dismal video frame rate - resource bottlenecks such as smart-phone hardware, software/OS, network bandwidth and latency have to be overcome before an acceptable double digit frame-rate is achieved. But what surprised me was the poor voice quality: the Tango call sounded a lot like those cheap international calling cards I used to make international calls from the US  many years ago. Terrible sound quality. I wonder why Tango engineers didn't trade more video quality (or even cut out video entirely when resources were scarce) and spend resources on improving voice? Voice over IP for mobile phones is a solved problem - Skype and the umpteen number of mobile SIP voip clients got audio to work well even on older smart-phones. Why couldn't Tango?

Tango is an over-the-top application, meaning that it runs over the best-effort (ordinary) Internet. I mention this here because the alternative, 3G telecom-operator-supported video calling, uses a dedicated network channel to ensure call quality assurance. But a Tango call will be carried over the same pipes as plain web traffic, making the video/voice call quality dependent on what else is being transmitted during the call. Telecom-supported 3G video calling is also much more energy (battery) efficient  than Tango.Why? Because in order to remain signed-into Tango to receive calls, the smart-phone has to periodically send "I-am-alive" messages to the Tango server. This means that a TCP or UDP socket is always active (or repeatedly created and and torn-down), effectively disabling the smart-phone's built-on power-saving sleep function. Offcourse, telecom supported 3G video calling costs money, but it is technically superior to Tango or any other over-the-top mobile video calling system.

But this is not about Telecom vs. Internet applications. This is about the use-case. Video calling was touted as one of the big use-cases for 3G Telecom networks (and 4G too?). 3G standards support video calling and so there is hardware acceleration, network resource reservation, optimized audio/video codecs, and cross-phone/OS support for video calling on every modern smart-phone. But apart from the cost of making 3G video calls, is their something else that relegated video calling to its sad never-used status in phones? Yes there is. Video calling has simply not been accepted as a viable form of mass communication in our society, and remains to-date, a quirky add-on. When was the last time you  placed a video call?

When Internet telephony (voip) arrived it quickly replaced circuit-switched calling. With mobile video calling, even if Tango can eventually fix its technical/engineering limitations, there is nothing to replace! Sadly, the mobile video calling use-case was still-born from the beginning.

Android Device Chatter with the Google Mother Ship

Parts of this post were moved into a formal research study (click link below).


https://sites.google.com/site/sachinkagarwal/home/publications-talks/gis-2011-infocom-2011

Microsoft Kin: RIP == Social Networks:RIP (?)

Microsoft Kin 04/2010-06/2010

Microsoft is phasing out its social-network/cloud storage-heavy Kin smart phone just 2 months after launch. This embarrassing report from CNN claims that the Microsoft+Verizon Kin sold less than 10000 units in the two months. RIP Kin.

I never got around to using the Kin, but apparently the market didn't see the justification for the expensive data plan (>=$29 p.m.) tagged on to the Kin by Verizon. The market was supposed to be teens looking to stay connected via social networks, but they did not bite into the insanely high data-plan tariff. Social networking, it seems, is not worth that much to them. How much is it worth anyway?

Lets not belittle the effort Microsoft put into this device - as a product the Kin was fully functional and seemed to do the things you would expect from this sort of device - Internet social networking, cloud storage and syncing of users' data, a built in Zune player, sleek design, etc. And at under $100 (with a data plan) it had a low entry barrier too. It seems like all the pieces were there but the Kin machine never got off the ground.

I don't know if the lack of a credible app store spelt the end for the Kin. What I do know is that social networking apps completely failed to drive sales. Next time someone uses social networking as the use-case for a device or service that is supposed to make money, say - Kin!

Untitled Poem

Among many other things, my father taught me how to read and write English. Everything I've ever written starts with what he taught me. Now as he lies dying of cancer, I wrote this for him. Say a prayer for him.


All of my thoughts
Like river drops
Together making up me
Like a river that flows
Until it throws
Fresh into the salty sea

All rivers meet that end
No matter what they pretend
Or how many bends they make
And so it will be
With every drop inside me
No matter what path I take

So you may ask
The point of the task
To meander toward the salty end
But don't we all know
Drops become vapor and snow
From which new rivers descend

Plug and play internal HDDs, literally!

I just saw this contraption on a colleagues desk. As you can see, a 3.5" HDD is literally plugged into the dock as if it were some super-sized memory card . Well, thats exactly what it is. The dock also has ports for USB keys, SD cards, and probably a few other formats.

Interesting to see the form factor difference between the SD slot and the 3.5" HDD slot. Flash memory capacity is quickly catching up with HDD capacity (the latter's lead has shrunk to only about ~10x). HDDs are endangered species!

India's Broadband Future

Ajit Balakrishnan, CEO, Rediff gave a keynote in IIT Delhi earlier today. His talk suggested that Indian telecommunication operators and the government should not be concentrating at delivering niche multi-Mbps broadband services but should instead concentrate on delivering reasonably good service (100s of kbps) to a larger population. Ajit flashed a slide which showed that 86% of 3G users use their smartphones to access their email, a relatively low bandwidth application, but only 6% use 3G to download and watch videos. Ajit's point was to recognize the importance of broadband as an "always on" connection rather than a high-bandwidth connection in India.

There is a analogue in India's history to this choice that Indian telecommunication operators and the government has to make. The government of India created top notch higher education institutes - IITs, RECs, and IIMs - in the 1950s (after Indian independence). It spends tens of thousands of dollars per year on each student enrolled in these institutes, arguably at the expense of thousands of primary education schools in backward areas of the country. The thinking at the time of creation of these institutes was that this creme de la creme would catalyze the growth of industry and technology in the country. Similarly, it may be theorized that by providing high-speed Internet connectivity, early adoptors will drive applications and create demand in the general population to upgrade their connectivity.

Countries like China or South Korea concentrated on their primary education institutions rather than creating world-class higher education institutes. It is safe to say that both these countries are significantly ahead of India, measured via any human development index. But does this analogy suggest that India should concentrate on democratization of (relatively low speed) broadband rather than creating small pockets of high speed broadband?

I think that the market forces will decide the balance between broadband services in India. The ARPU on low-speed broadband may not exceed $5, but this will be compensated via large volumes. I also believe that low-speed broadband will be served via wireless in India. With mobile phones outpacing fixed line connections by a 12:1 ratio in the country, there is limited scope for technologies like DSL to be widely deployed. Fortunately, 3G, LTE and Wimax are nicely poised to fill in for the lack of fixed line infrastructure in India. As for the niche multi-Mbps broadband, I expect FTTX being deployed in highly urbanized areas where western ARPUs (10s of dollars) are possible.

Thermal imaging cameras at Bangalore airport!

Photo: Thermal imaging for Swine Flu screening at Bangalore International Airport

Arriving on an international flight at the Bangalore International airport, I was surprised to see two thermal imaging cameras. Each camera was looking at arriving passengers and visually marking those who had an elevated body temperature, in order to discern people who may be suffering from Swine Flu. These cameras are sensitive to IR heat radiation in the body temperature range. The cameras work by mapping temperature readings into a colormap that visually depicts body temperature. The video images produced by the cameras looked eerily similar to the IR images that the alien saw in the Predator movie series!

As compared to conventional body temperature measurements via thermometers, this real-time technique makes it possible for a medical officer to screen many more people. I wonder why these systems are not installed in other world airports.

Multiprocessing vs. Network I/O

I've been reading up on Python's (v2.6 and above) multiprocessing module. While multiprocessing has been around for a long time, simplified libraries like this multiprocessing module may spur even casual programmers to consider parallelism in their programs. My feeling is that if issues like inter-process communication, synchronization among processes, and deadlock avoidance are dealt with painlessly, then many non-professional programmers would feel confident enough to load up CPUs with programs with multiple processes to speed things up. Moreover, given that multiple CPU cores are becoming the norm rather than the exception on commodity hardware, there is a real incentive to eventually switch to multiprocessing.

What will this switch in program design mean for network data I/O? Will average users end up opening and using more network connections on average? Web browser tabs are a good example of multiple threads or processes. When modern browsers fire up they often connect to several websites saved from the previous session. I conjecture that multiple tabs fill up the network's queue faster than was possible with single core CPUs. Although Network I/O is much slower than CPU bandwidth (data rate at which CPUs process say, HTML), there is a point beyond which a single core CPU becomes the bottle-neck (e.g. firing a dozen browser tabs). But multiple cores remove this limitation and drive network I/O to its physical (or traffic-shaped) limits. I plan to measure this interplay between multiprocessing and network I/O. Watch this space!

Free airport Wifi as a marketing tool

Google is offering free Wifi in 47 US airports during the holiday season The idea is to flash a few web pages marketing Google's software and services to users in return for free Wifi service. According to this CNN article, Google is not the only company to do so - apparently Lexus and Ebay have also implemented similar ideas, or intend do so in the near future.

Free service is probably going to bring a torrent of airport Wifi users online - probably many more than the current number of (paying) users. Given that Wifi Internet channel space is a shared resource, it will be interesting to see how airport Wifi scales with the up-tick in usage. I just hope that the service doesn't deteriorate so much that the sponsoring companys' well-meaning message is lost to disgruntled users. And I do hope that engineers running these Wifi access points have done the networking provisioning Math beforehand.

Now the economics. The sponsoring company (Google) is probably going to pay a lot less than the retail price of airport Wifi connectivity. Why? Because the sheer volume of users will be much higher than when users have to pay individually. I think that the payment will include a fixed component depending on the number of access points participating in the service, and a variable component depending on the number of users accessing the service.

Lets assume that an average airport has about 20 accessible Wifi access points. Each access point can support (with any reasonable quality of service) about 10 concurrent users. If the airport is busy for, say, 12 hours in a day, and further say that we assume an average utilization of 50% of the total capacity of the access points, then we have (per day)

10 * 20 * 12 * 0.5 = 1200 hours of usage per day per airport.

I would assume that the sponsoring company (Google) would pay about $5000 per day as a fixed cost and then about $1 per hour usage. This brings the daily total cost per airport for the sponsoring company to $5000+$1200 = $6200.

So for 47 airports and 50 holiday season days, we are looking at a bill of about

6200 * 47 * 50 = $14.57m

That's not a bad deal for a big company like Google, considering the number of eyeballs they will capture. Lets say a user uses the free Wifi for 30 minutes on average. So, we are looking at about 12*10*20/(1/2) = 4800 users per airport, per day. That works out to over 11m users in the 47 airports over the 50 day holiday period. Even if we assume that most people make round trips and therefore use the Wifi connection 2 times, Google can still reach about 5.5 million unique users! No too bad for the $15 million spent.

And I haven't even started counting the goodwill ROI bonus for playing Santa during holiday season! Nifty nifty marketing.

Call for action! Powering down PCs

I've been playing with the idea of building a PC application that measures a computer's idle time. The idea is to gently convince users to suspend or power-down their PCs when they are not being utilised. I strongly believe that if PCs are optimally powered down, then many users could cut energy consumption (and hence also save on energy bills). Powering down battery-powered laptops will also increase the longevity of batteries and thereby decrease toxic battery waste in landfills.

As an example of where the possible savings may be, above is a pie chart showing my own PC usage over the past few working days. As you can see, there is ample scope to power down/suspend PCs when they are idle.

If you want to contribute time to this project (coding/web page/translation into other languages/spreading the word), feel free to contact me. If not, then do suspend your PC every time you are away for more than a few minutes :-).