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Advances, Systems and Applications

Table 4 Input parameters to configure the simulation environment and their relative values

From: A real-world inspired multi-strategy based negotiating system for cloud service market

Possible values

Input parameters

CRPA_favorable

Balanced

CRBA_favorable

Cloud Service Market Type: CSMT

- [12, 23] (CRBA_to_CRPA ratio)

{10:1,5:1,3:1}

{1:1}

{1:3.1:5.1:10}

3

Number of Physical Resource Type: R

i.e., CPU, Memory, Storage))

3

Number Of VM Types

Small (m = 1)

Medium (m = 2)

Large (m = 3)

Physical resource

Characteristics of the VM Types: VMm

Where m∈{1,2,3}- [38]

w11 = 1

w21 = 2

w31 = 4

CPU

w12 = 1.7

w22 = 3.75

w32 = 7.5

Memory (GB)

w13 = 160

w23 = 410

w33 = 850

Storage (GB)

[0,20]

Number of Requested VM Type m Instances by i’th Customer: no. Ireqmi

200

Number Of Maximum Available Instances Of Each VM Type:I

Resource provider [66 + α,80 + 80α]

Resource customer [1 + α,15 + 15α]

Initial Price: IP

(Randomly generate)- Inspired from [12, 23]

α=0 for VM1,1 for VM2 and 2 for VM3

Resource provider [1 + α,15 + 15α]

Resource customer [66 + α,80 + 80α]

Reserve Price: RP

(Randomly generate)- Inspired from [12, 23]

α=0 for VM1,1 for VM2 and 2 for VM3

Long

Moderate

Short

Negotiation Deadline

- Randomly generate (no. of rounds)

[12, 23]

50–100

40–50

20–40

Conservative

Linear

Conciliatory

Time Dependent Strategy [12, 23]: λ

3

1

1/3

Sparse

Moderate

Dense

Market Density [12]

Pgen = 0.25

Pgen = 0.5

Pgen = 1

Pgen: Probability of generating an agent per round

Low

Moderate

High

Probability of Generating an Opponent with Mutual_Behavior_Class in Name NLNH

Pmbc = 0.25

Pmbc = 0.5

Pmbc = 1

Pmbc: Probability of generating an opponent agent with NLNH mutual_behavior_class