by Simon Firth
Airlines, utilities, hotels, Internet service providers: These are vastly different
industries with vastly different product offerings, yet all share a key problem:
managing peak demand.
In each case, customers typically want more computer processing
power, or tickets, or electricity at some times of the
year (or day, or week, or month) than others.
Some of these times can be predicted, but some can't.
So suppliers worry about having adequate resources to meet
peak demand. They'd like to be able to plan ahead.
For customers, the problem is reverse – will the
resources they need be available at peak periods and, if
so, at how high a price?
Both would be a lot happier, says HP Senior Fellow Bernardo
Huberman, if it was possible to structure prices so that
demand for any particular product evens out.
That can be done, Huberman argues.
To accomplish it, he says, "one needs to design new
mechanisms for reservations that induce users to reveal
how likely it is that they will use the resources they
A reservations system allows people to pay up front to
keep a resource (say an airline ticket, or computing power
in a data center) available to them at some point in the
future. The price they pay to hold that resource for peak
times is less than if they bought it at the time they actually
“But the problem with reservation systems,” says
Huberman, “is, if they are not structured well, people
can strategically 'game' them.” People can lie about
how likely they are to use a resource and get a better
price than if they revealed their true intentions.
Or worse, they could not use it at all, with the consequent
cost to the provider, who cannot sell that reservation
to someone else.
Huberman, who directs the HP Information Dynamics Lab,
and researchers Fang Wu and Li Zhang recently published
a new model for a market-based resource allocation system
that they believe is ‘game-proof’.
The inspiration for their work was the desire to manage
peaks in demand for IT resources. But they believe their
model could be applied to any industry that faces the same
problem of uneven demand and thus requires a reservation
“What we came up with,” says Huberman, “is
what we call 'truth-telling' reservations. It’s a
pricing structure for reservations that forces people to
reveal the true likelihood of their using that resource
in the future.”
Under such a structure, the more likely you are to use
whatever you are reserving, the less you pay for the reservation
as a non-refundable down payment. But there is also a sliding
scale of penalties for not using the resource. The more
likely you said it was that you would use the resource
(for which you got a price break) the more you pay for
The two scales set it up so you will always pay less by
being honest about your likelihood of using the resource
than if you either wait and end up having to pay peak prices,
or if you lie to get a price break and then have to pay
a higher penalty when you don’t use it.
Under this kind of system, argues Huberman, buyers get
a better price for covering their future -- but still uncertain
-- resource requirements, while suppliers get predictable
demand. An online merchant that reserves IT time, for example,
gets assurance that it will have the computing capacity
it requires to meet its own customers' needs during peak
The result: peak-time reservation prices are lower for
the buyer, and the supplier has an accurate (and therefore
profitable) way of knowing what demand is going to be.
For such a model to work well, however, buyers need to
be able to realistically predict what their demand for
any particular resource will be days, weeks, or even months
“We have a simulator tool that will do that,” says
Huberman. “It allows you to measure past usage and
predict how you would price in the future.”
This simulator, designed for data center customers, estimates
the cost of new reservations from a set of historical customer
data. It allows users to adjust the amount of CPU power,
memory and bandwidth they might need, and then be either
more aggressive or conservative about future demand.
All this, Huberman argues, provides “a powerful ‘what-if’ capability
to both the resource provider and the customer for estimating
outright costs and risks associated with fluctuations in
The HP team is hoping to trial their new reservation model
soon within HP Labs.
One candidate for such testing is the Labs’ new
virtualized market system for allocating resources in distributed
With this system, a customer pays for usage in a kind
of resource spot market. This allocates the cluster more
efficiently than the more common time-sharing model, and
it allows users to change allocations in seconds. Adding ‘truth-telling’ reservations
should only improve the resource-allocation system’s
power to efficiently allocate IT resources.
Huberman also hopes the model will find broader use.
For many industries, he says, the issue of reservations
Airplane tickets, hotel room reservations, even the financial
services, all need an efficient reservation system to manage
peak demand, he notes.
If Huberman, Wu and Zhang’s ideas are widely adopted,
expect changes to hit even the humble office cubicle.
“Today we are over-provisioning IT,” notes
Huberman. “I have a desktop. It’s incredibly
powerful, but half the time it’s not being used.
“It’s the same as the difference between having
access to any airplane for free and having to pay a ticket
when you really need to fly,” he explains, suggesting
we should all have budgets for the compute time we use
“Markets are good,” Huberman adds, “because
they force you to express your preference by paying a price.”