Defining computational resources prices based onthe expectations equilibrium of consumers and

providers from a Desktop Grid

GOIS, L. A.Department of Computer Science

UTFPR, Ponta Grossa, Parana

Email: gois@utfpr.edu.br

CASELLA, G. B.Department of Computer Science

UTFPR, Ponta Grossa, Parana

Email: gabrielbcasella@gmail.com

ISHIKAWA, E. C. M.Department of Computer Science

UTFPR, Ponta Grossa, Parana

Email: eishikawa@utfpr.edu.br

Abstract—This paper proposes a dynamic approach for priceadjustment of computational resources ruled by a Desktop Grid.The adopted strategies are based on the supply and demand lawcommonly applied in economical markets. Thus, allows one todynamically set the prices according to the previous requestedconsumer’s services and supplier’s submitted resources. Thetesting analysis results show how the equilibrium expectationfrom the consumer and service providers are preponderant inthe processes of obtaining fairer values in their negotiations. Thereached individual satisfactions established by the well succeedsharing are appraised, in order to endure it or not in the gridmarket based on its influences.

Index Terms—Grid Computing; supply and demand; marketgrid users expectations;

I. INTRODUCTION

The growing utilization of independent and interconnected

personal computers, as well as the big number of private local

area network, make possible new computational paradigms to

be solved in distributed software applications. Nowadays, the

Internet plays a big role for researchers because it is made

by totally heterogeneous systems, not strongly connected, ge-

ographically dispersed and a big number of idle computers at

specific times [1]. Locally, these resources can be aggregated

to build a cluster machine that can solve problems faster due

to its power to distribute tasks.

However, it is known by a fact that certain computer

applications can not be solved with these cluster anymore as

they need more resources than restrictedly available. The use

of geographically dispersed computational power open new

doors to the new high processing power model. To make

this possible, responsibilities like tasks synchronization, data

transfer, communication protocols, message passing, security

issues, money rewards, and others, need to be taken care.

The grid computing [2] is a feature that explore the poten-

tialities of interconnected computers, allowing users to develop

applications with high computational demand. This approach

differs from the usual distributed computing by coordinated

allowing the sharing of large demand resources - as processors,

memories, storage devices - without the need of complex

systems to manage this common resources.

Although Desktop Grids [3], [4] are considered as a grid

they have exclusive characteristics. For instance, larger number

of personal computers connected through the Internet and dif-

ferent ways of sharing and managing the available resources.

The problem to well manage this is to grant the right tools to

the user, so the users can exactly set in what, to whom and

which conditions the hardware is shared.

Many research lines look for solving this situation. Some

of them are based on micro-economical concepts to keep

environments fair between using and providing computational

power within all grid users [5], [6]. These study areas aim

for modeling virtual markets of computational resources based

on financial reward as it happens on traditional economical

models.

The problem with this idea is to model a management

program that handles the shared resources [7] in a manner

that every participant gets the most benefit available. This

is possible through the price that the resources are sold by

a provider and the price paid by a client, respectively being

the highest sold and the lowest paid [8], [9]. The equilibrium

between these two points is the topic that needs to be studied

to permit one to create and maintain a virtual market like this.

This paper objective is to define a mechanism that discovers

the minimum and maximum computational price applicable

as long as whether new resources should be available or not.

This mechanism is based in the micro-economic principles

that command the usual supply and demand market allowing

one to set-up a model to meet the expectations required by

a provider and a client, motivating and maintaining them to

keep their business in the Desktop Grid.

II. MODELING THE DYNAMICAL PRICE ADJUSTMENT

Having in mind the needs in a ordinal supply and demand

market, the proposed mechanism to adjust the prices of a

computational market (based on CPU, Memory, Task time,

etc.) build on top of a Desktop Grid is the following:

• d: Market Director;

• C: Cooperative;

• m: resources manager

• c: resources consumer;

2014 28th International Conference on Advanced Information Networking and Applications Workshops

978-1-4799-2652-7/14 $31.00 © 2014 IEEE

DOI 10.1109/WAINA.2014.76

305

2014 28th International Conference on Advanced Information Networking and Applications Workshops

978-1-4799-2652-7/14 $31.00 © 2014 IEEE

DOI 10.1109/WAINA.2014.76

305

• p: resources provider

• r: resource ’r’ (CPU, Memory, Hard Drive, etc.) from a

resource provider ’p’;

• s: order service number ’s’ from an consumer ’c’.

So, p = {r1, ..., rTR}, being TR as the

total shared resources by the provider. Also,

Ci = {mi, p1, ..., pTP , c1, ..., cTc} where TP is the

total of providers and Tc the total of consumers affiliated

to the cooperative Ci controlled by the manager mi, with iranging from i = (1..TC) and d = {C1, ..., CTC} where TCis defined by the number of cooperatives controlled by the

market director. In this pattern, is known that Ca ∩ Cb = {}to any a �= b.

To set-up the prices for the cooperative manager, each

provider gets a price based on the hardware performance and

resource’s time availability. On the other hand, when a service

is submitted to a manager by an consumer, the latter expects

to get the highest price available by its resources.

As follows, Pcurrent is used to set the actual price to sell a

provider’s resource, that can be modified in the time t based

on the previous value t− 1 using the formula:

Pcurrentr,p(t) = Pcurrentr,p(t− 1) + ΔPp. (1)

In the same way, the expected value VM by an consumer

when buying a resource can be modified in the time t based

on the previous value t− 1 by:

VMr,c(t) = VMr,c(t− 1) + ΔPc (2)

where ΔP value sets how much a price will be incremented

or decremented.

To make ΔP worthwhile to meet the supply and demand

chain, it is defined based on the past resources utilization by

a provider p or by an consumer c, thus

ΔPp = α(u(t)− uP )× Pr,p(t− 1) (3)

ΔPc = β(uC − u(t))× VMr,c(t− 1) (4)

where α and β are the coefficient that control how the price

varies in addition to uP and uC, interpreted as the participa-

tion index from a provider (equation 5) or from an consumer

(equation 6) in relation to the cooperative neighborhood, such

that

uPp(t) =

(t∑

i=t0

Ri,p

)/

(t∑

i=t0

Ri

)(5)

where uP is the participation index of a provider p at the time

t, while Ri is an available and used resource from time t0 to

t. Also,

uCc(t) =

(t∑

i=t0

Si,c

)/

(t∑

i=t0

Si

)(6)

where uC is the participation index of an consumer c at time

t, while Si is a requested and successful done service at the

interval (t0, t)In general, a very small participation index shows that the

provider is fine with the low usage of its resources, or the client

Fig. 1. Price expectation of a resource (VMr,c) based on the supplyavailability

is also satisfied with the low execution rate of its requests. In

addition, whether the index is high, the both are offering or

requesting more resources by cause of being happy with the

market.

Equation 3 uses u(t) to describe the relationship of how

much resources were bought by the requests made from an

consumer at the interval (t0, t), while the equation 4 uses this

index as the relationship between the amount of resources sold

and the amount of resources offered at the interval (t0, t).Therefore,

u(t) =

(t∑

i=t0

n(i)

)/

(t∑

i=t0

N(i)

)(7)

being n the total of bought and sold resources in the time

interval (t0, t), and N the total number of resources requested

or offered in (t0, t) time interval.

By the supply and demand law, when the availability from

a resource drops, its price increases, in the meantime a price

decrease when the availability of a resource increase [5]. So,

equation 7 provides this knowledge to the prices adjustment

mechanism. Figure 1 shows the resource price reaction by the

consumer’s point of view.

The larger the u(t) value to the consumer is, the larger is the

offering of a resource at the time t. This strategy lowers the

price expectation (VMr,c) through equation 1. The β value

sets whether the adjustment is aggressive (β = 0.1) or not

(β = 0.9).

Figure 2 shows how a resource price acts in the provider’s

point of view.

As bigger is u(t) value to the provider, bigger is the need

for the resource at time t. This awareness changes how the

manager controls the price (Pr,m) to the provider’s resource,

using equation 1, while α sets whether the adjustment is

aggressive (α = 0.1) or not (α = 0.9).

When the consumers submit their services to the manager,

they also need to specify how much of resources is necessary

along with expectation price to be paid. Then, the manager

looks for the required resources and handle the business

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Fig. 2. Price changing of a resource (Pp,m) based on the demand availability

transactions. However, a transaction is made in time t whether

VMr,c(t) ≤ Pactualr,p(t). Pactualr,p(t) is used to define the

transaction price.

III. TESTING THE PROPOSED APPROACH

To test the proposed mechanism, a simulation is made with

one cooperative and fifty computers of different processing

power acting as providers, consumers or both. In these tests

the available resources from the providers are set with the

initial price of ten and thirty credits, updating them as the

need of these services increases or decreases managed by the

cooperative manager.

In these tests, each provider makes available one CPU

and each consumer requests one service. At each moment,

based on the workload needed and idle resources, the software

simulator starts one consumer by creating one task, or it uses

one resource and starts one provider. Three types of market

situations are used in the cooperative:

• Market Equilibrium (ME): Approximately the same num-

ber of services being offered and requested;

• Market Surplus (MS): Larger number of services being

offered;

• Market Shortage (MR): Larger number of services being

requested.

A. A Influence of α and β on Prices Change

The reason for these tests is to see in practice how con-

sumers and providers set the expected and selling price,

respectively, aside as the mechanism efficiency for using the

resources and successfully allocating the tasks based on the αand β variation for the three former markets. The uC and uPparameters are configured as 0.9 to all clients and providers,

meaning that they have the same weight participation in the

cooperative.

The average transaction price is based on the actual α and βvalues in the three market scenarios. As seen on figure 3, the

low prices can be noticed in the MR market. In this scenario,

it has more sellers than consumers, resulting in low prices

because of the shortage number of services being offered.

However, in the MS scenario, the average transaction price

Fig. 3. Average transaction price on different market types based on the αand β fluctuation

Fig. 4. Service allocation on each market scenario

increases from the initial state, when it has more buyers then

sellers, e.g. when α = 0.1 and β = 0.75 or α = β = 0.75. This

happens as there is more services offered than needed. Finally,

at the ME and MR scenarios, as the supply and demand are

similar, the prices are more stabilized then the MS scenario.

In the MS setup the high α and β values increase the trans-

action price at an average of 160 credits. This is expected as

there is a resource shortage, making the consumers to increase

the expectation price to be paid, succeeding in an aggressive

and competitive price strategy. Requiring an estimative price

from the client before a service is allocated is the key to solve

this issue.

B. Performance of the Price Adjustment Mechanism

The system efficiency is measured in the three market

scenarios with one value for α and other, that can be equal,

for β. The efficiency is given by the successful number of

allocated services, utilized resources in each situation and the

average queue time for a service or resource. The results are

shown in the pictures 4 and 5

The tests are based on well succeeded provider transactions,

and the total used resources from all requested.

In the market equilibrium, the allocation percentage is in

general 90%, except when α and β are too low. To α = 0.1and β = 0.1 the allocated percentage dropped to 75% making

the consumers and providers to decrease their prices as the

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Fig. 5. Used resources on each market scenario

Fig. 6. Average time to allocate resources by a client service (Logarithmscale on axis y)

cooperative has lower activity and there is more resources

available.

On the MR scenario, a high success allocation is seen, in

most cases near 100%. However just 25% from the resources

being offered are used by these services, due to the highly

offered resources by the cooperative. A shared fact among the

three market strategies is the high number of resources and

services allocations when α = 0.75

In the case of a high number of consumers, if the providers

increase their prices, the latter can earn more and still have a

successful allocation rate. This is proved on the MS scenario

where the resources allocation reached 100%, while the ser-

vices allocation achieved 25%. These results are accomplished

by reason of the high number of submitted services to the

cooperative. The optimal resource usage is achieved with high

β values, as β = 0.75.

Figures 6 and 7 show the amount of time waited to find

resources to start a service. Also, the time to a resource to be

used is shown on figure 7.

As seen, in the MR scenario, to a provider have its resources

allocated, it waits four more time then an consumer does. This

is expected as the number of available resources is bigger then

the number of services to be submitted.

On the other hand, in the MS scenario, the typical expected

time to one allocate consumer’s resources is five times greater

Fig. 7. Average time to provider’s resources be allocated by a service(Logarithm scale on axis y)

then providers allocate them as an effect of the rare available

resources. However, on a MR scenario using α = 0.75 a small

consumer transaction time is required. Also, a small provider

transaction time is needed when β = 0.75 in the MS scenario.

These configuration parameters are consistent with the high

resources utilization and high number of submitted services.

Considering the ME scenario, no average time difference is

noticed to providers and consumers. Nonetheless, the average

transaction time is bigger for both sides, using α = 0.1 and

β = 0.1 by the reason of the low price changing by the

providers and consumers, increasing processing time and the

number of requests.

By the results seen it is possible to conclude that the high

level of success to submit a service, to have high resources

utilization and low transaction time, apart of any market

scenario, is caused by the large values of α and β. So, the

idea is how consumers and providers are able to recognize the

current market scenario.

The solution to solve this problem is to constantly analyze

the price evolution from the consumers and providers based on

the resource’s offer and demand. For example, when the prices

are increasing, the consumers detect the resources shortage in

the cooperative, based on the changes of the market’s buying

and selling numbers, and change their strategies, assigning

β with higher values and submitting their services after

reevaluating their price expectation. On the other hand, as the

prices decrease, emphasizing a low level of resources being

used, the providers change their strategies to make α smaller.

C. Adapting the Level of Participation of Consumers andProviders

The next tests show how consumers and providers adapt

their strategies to setup a price based on each role played inside

the cooperative. If one of the former shares more resources

than submit services, this one is known by the cooperative

manager as an active provider or inactive consumer. In this

way, this characteristic needs to be represented and available

to the cooperative manager.

As already stated by 5 and 6, uP and uC set how much a

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Fig. 8. Negotiations made between providers and consumers

Fig. 9. Typical transaction time by participation level into the cooperative

provider or an consumer contribute to the cooperative between

a time period. In this way, the following tests show how

much these parameters are influencing the negotiation of the

resources. The tests are made on a well shared market with

uP = uC and α = β = 0.75.

The successful transactions are analyzed considering differ-

ent values to uP and uC. These results stated on figure 8,

reveal that the number of completed services and the quantity

of used resources are proportional with the participation index

from providers and consumers. As bigger these indexes values

are, larger is the number of transactions realized by the

cooperative manager, therefore the satisfaction from the market

users increases.

Another fact analyzed by having higher uP and uC are

lower average waiting time per transaction.

Increasing the participation index allows the cooperative

users to have higher priority on their activities, resulting

on requests and resources being allocated faster as figure

9 reports. This is due to the optimization realized by the

cooperative manager by looking for the best pairs to match for

a transaction, in contrast to the times analyzed at the figures

6 and 7.

IV. SUMMARY

In high demanding markets, the manager sets strategies to

block new resources into the cooperative, keeping the market

equilibrium. The proposed mechanism is modeled by three

market conditions: high availability of resources, high services

availability and similar quantity of resources and services.

The approach used is to have four variables set by providers

and consumers: α, β, uP and uC. The first two handle how

respectively the provider and the consumer set the increasing

or decreasing price strategy based on the present market. The

last two respectively show how much the provider and the

consumer are active compared to the total of transactions done

by the cooperative manager.

The discussed tests identify that markets with a high number

of resources are more influenced by α, leading the cooperative

manager to decrease the prices used by the providers and use

a conservative strategy to increase α value. However, markets

highly affected by β are the ones with multiple services avail-

able, bringing the consumers to use a conservative strategy to

β and decrease their price expectation. Scenarios with market

equilibrium are similar affected by these parameters and as

higher is the participation level, greater are the benefits and

smaller are the resources or services waiting time.

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