I know I haven't posted anything in a while here. This has a lot to do with the way I tend to write blogposts. Most of the time they become a lot longer than I first anticipate (such as now). Most of the time, I tend to have many ideas; and writing all of those down takes a lot of time.
I do believe however, that I should revive this blog since blogging is a great way to communicate latest findings fast. When keeping it up to date regularly, it also provides a good overview of the progression that is made. Additionally, my thesis does focus more on the 'ideas part' than on the 'implementation part'. Obviously, writing those ideas down is a good way to reflect on them.
Thus, my new year resolution is to update this blog far more frequently with smaller, but informative blogposts.
Since I haven't posted anything for about 2 months, I thought it was a good idea to create a small overview of the things I have been doing up until now (although nothing appeared on this blog, I have of course continued working on my thesis).
Chronological overview of the past months
A short update on where we are today
As you can see in the overview, I mention the term CRA a few times. The idea of the using the Continous Double Auction as the market mechanism has been somewhat altered to what I like to call a 'Continuous Reverse Auction' (or CRA for short). The idea behind the CRA is to use a Reverse Auction (which relates to some of the ideas I already discussed in a previous blogpost (http://thesisjorisroovers.blogspot.com/2010/10/component-based-offers-and-custom.html) in a continuous way. Rather than having several bidding phases for a single bid (such as in a Reverse Auction), providers will now only provide a single price for a single bid (when a bid comes in, the market contacts all of the providers to ask for a price). A bid is matched against the cheapest offer for that bid. The price lowering effect that classic Reverse Auction tends to result in, is achieved by having a lot of bids (and matches) at after each other. By publishing anonymized matches to a kind of public billboard, providers will have the tendency to lower their prices in order to 'win' more matches. This means that in time providers will tend to show incentive compatible behavior.
The main benefits of the CRA are that it is continuous (matches happen in real-time), and allows great matching flexibility. The limits of what this model can match is defined by the limits of the used bidding language (in our case, the used constraint mechanism), which is a nice feature to have.
However, when working with the Continuous Reverse Auctions, consumers are price takers (compared to the CDA, when they also influence the price of the match). A disadvantage of this is that maximal market efficiency is not reached. That is, it might be that there are consumers that are willing to pay more for a match than the CRA will ask since they attach more value to it. Although this benefits the consumer in this case, the CRA market clearly is not able to provide an optimal clearing here.
However, in the current market situation; it is difficult to come up with a mechanism that does provider maximum effiency. This is primarily caused by the fact that currently cloud providers claim to provide 'unlimited' capacity, which suggest that resources are never scarce and that fluctuations in demand do not affect the value the provider attaches to its remaining (unused) capacity. This is obviously not true (this idea goes against every demand-supply based market principle); but at the moment, this is what providers try to sell consumers. As long as providers keep using this principle to price their offers (that is, an offer is always priced the same, independently of demand. Additionally consumers that are prepared to pay more than other consumers are not treated differently), maximum market efficiency is not a achievable goal. Note that although Amazon's spot instances do have hourly fluctuating prices according to demand, it does not have admission preferences among consumers.
However, you can ask yourself whether maximum market efficiency is a desirable property in all cases. I believe that in a Business-to-Consumer environment, this is not neccesarily the case. An end-user is probably less interested in always providing a price when wanting to use a utility (even if advanced price discovery tools are available). Bidding/Procurement is more the task of a broker (as is the case in e.g. the electricity market), who later on uses seperate mechanisms (average price, flat fee, day/night tariffs, ...) to bill its clients.
I believe that the CRA model contains some good ideas in the case where the consumers are price takers (as is currently the case in the cloud ecosystem). When consumers also need to influence the prices, we need more a hybrid between CDA and CRA in which brokers take the roll of consumers. In such model, the brokers no longer simple take the price of the cheapest provider; but instead they will submit bids. The bid of the broker will be matched against the offer of the provider that made the cheapest offer. By doing this, we achieve maximum market efficiency for that single bid. When wanting to achieve maximum efficieny among multiple bids, the CRA model (or any other continuous model, such as the CDA, for that matter) can no longer be used since multiple bids then need to be compared with each other. This makes the clearing market a lot more complex. Additionality, since it is impossible in a real-time ecosystem (such as a cloud market) to know whether a better bid or offer will arrive or not, such systems will always need to use heuristics to approximate maximum market efficiency.
The CRA model still needs some more elaboration, and some questions remain to be answered (price discovery stays a big issue), but I think that in terms of flexibility it already passed a first test.
I will continue to work out some of the details in the coming weeks.
So, with this medium-sized post, I updated the blog with the most salient ideas behind the CRA model. More detailed information can be found in the pointers provided by the overview document embedded above.
I'll be writing a post about my plans for the coming weeks later today.
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