QoS-Aware Task Allocation and Scheduling in Three-Tier

Cloud-Fog-IoT Architecture Using Double Auction

Nikita Joshi and Sanjay Srivastava

Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India

Keywords:

Internet of Things, Fog Computing, Task Scheduling, Perishable Resources, Double Auction, Competitive

Bidding, Truthful Bidding.

Abstract:

There is a lot of time-sensitive data provided by IoT applications that needs to be analysed. A Fog-Integrated

Cloud Architecture is available to process these data. Due to the QoS requirements of IoT applications, the

perishable nature of fog cloud resources, and the competition among users and service providers, task alloca-

tion in such an architecture is challenging. In this paper, we propose a competitive bidding (CompBid) strategy

and a QoS-based task allocation and scheduling (QoTAS) algorithm using double auctions that aim to max-

imize user and service provider proﬁt while also satisfying QoS requirements. A remote patient monitoring

system is used to compare QoTAS performance to that of two previous studies, DPDA and MADA. In both

the truthful and CompBid strategies, QoTAS achieves a higher task allocation ratio and resource utilization

than DPDA and MADA. It has 89% more system utility than DPDA and 54% more user utility than MADA.

Furthermore, the CompBid strategy increases QoTAS system utility by 25%.

1 INTRODUCTION

The beneﬁts of various use cases of the Internet of

Things (IoT) have resulted in a signiﬁcant increment

in IoT adoption. IoT applications generate large

amount of time-critical data, that should be processed

in a timely manner. Internet Database Connector

(IDC) forecast estimated total of 41.6 billion con-

nected IoT devices or “things” will generate 79.4

zettabytes (ZB) of data by 2025 (IoT, 2022). How-

ever, IoT devices cannot process all these data be-

cause of resource and energy constraints. Processing

of this enormous volume of data typically takes place

at remote cloud data centres (Suh et al., 2011).

Cloud servers have sufﬁcient resources to process

IoT data. However, the delay between cloud and IoT

devices may not be acceptable by critical IoT appli-

cations such as healthcare and Vehicular Adhoc Net-

work(VANET). Also, IoT applications generate large

amount of data which requires hundreds of Gbps net-

work bandwidth, which is more than trafﬁc capacity

of WAN in use. IoT applications need context-aware

computing and communication in order to have intel-

ligent interactions. Contextual data can include things

like the user’s location, actions, neighbours’ gadgets,

the time of day, etc. To deal with such IoT appli-

cation requirements, fog computing concept is intro-

duced in which network devices between IoT devices

and cloud server are used for preprocessing, ﬁltering

and compressing the data.

In three tier Cloud-Fog-IoT architecture, IoT Ser-

vice Providers (IoSPs) outsource the task to a Cloud

Service Providers(CSPs) as shown in Figure 1. Fog

Service Providers (FSPs) are registered with CSP.

CSP allocates appropriate FSP to each IoSP. When

allocating resources in such a distributed architecture,

monetary cost must be considered. The cost of com-

puting resources in edge computing, like cloud com-

puting, is determined by their availability and usage

(Jin et al., 2015).

According to J. Weinman, “fogonomics” is a

branch of study that focuses on the economic aspects

impacting the architecture of the fog computing sys-

tem (Weinman, 2017). Fogonomics discusses a va-

riety of pricing techniques, such as static price and

dynamic price. In such a setting, dynamic pricing en-

courages healthy competition among FSPs, which is

advantageous for both IoSPs and FSPs. However, dy-

namic pricing makes it challenging for FSPs to deter-

mine resource prices and for IoSPs to establish bud-

gets dependent on the status of the market (Baranwal

et al., 2018). The challenges of dynamic pricing are

often overcome with the help of many modiﬁcations

of the auction dynamic pricing method (Joshi and Sri-

Joshi, N. and Srivastava, S.

QoS-Aware Task Allocation and Scheduling in Three-Tier Cloud-Fog-IoT Architecture Using Double Auction.

DOI: 10.5220/0011967400003488

In Proceedings of the 13th Inter national Conference on Cloud Computing and Services Science (CLOSER 2023), pages 253-260

ISBN: 978-989-758-650-7; ISSN: 2184-5042

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

253

Figure 1: Fog IoT architecture.

vastava, 2019).

Money should not be the only consideration when

allocating resources in this three-tiered design. Other

factors to take into account include the QoS needs of

IoT applications and the perishable nature of cloud

and fog resources, which means they cannot be reused

as double resources if not allocated at a time. It is

possible that the FSP bids high in the in an effort to

increase its proﬁt, and no IoSP will be able to match

that bid. It ultimately results in those resources not

being auctioned in that round. These resources are

not eligible for double usage in the following round.

Additionally, if FSP places a low bid, its proﬁt gets af-

fected. As a result, there is a trade-off between proﬁt

and a loss from unsold resources. This problem has

been addressed in our work.

We propose a competitive bidding strategy

(CompBid) in this paper to maximize the beneﬁt of

competition among IoSPs and FSPs. QoS-based Task

Allocation and Scheduling (QoTAS) is proposed to

maximize resource utilization and proﬁt of all mar-

ket agents (IoSPs/FSPs/CSP) while taking perishabil-

ity of resources into account at FSPs. A remote pa-

tient monitoring (Verma and Sood, 2018) application

is used to compare QoTAS performance to that of pre-

vious studies DPDA (Joshi and Srivastava, 2020) and

MADA (Peng et al., 2020).

The structure of this paper is as follows:Section 2

discusses related work. The system model and prob-

lem statement are explained in Section 3. The task

allocation algorithm is explained in Section 4. Sec-

tion 5 discusses the simulation results, and Section 6

discusses the conclusions.

2 RELATED WORK

In a distributed Cloud-Fog-IoT architecture, IoSPs,

FSPs, and CSPs share resources by purchasing them

from one another. Each has its own utility function

based on the cost and payment received. When con-

sidering task allocation using both QoS requirements

and monetary costs, the literature divides this problem

into two subproblems. These are the allocation and

price determination problems. The allocation prob-

lem meets QoS requirements, while the price determi-

nation problem seeks to maximize proﬁt for all mar-

ket agents such as FSP, IoSP and CSP. Literature is

divided into three categories depending on the agents’

bidding strategies: forward auction-based allocation,

reverse auction-based allocation, and double auction-

based allocation.

Forward Auction-Based Allocation: In a forward auc-

tion, buyers submit bids, and the seller allocates re-

sources to the highest paying buyer. (Zu et al., 2019)

proposed an ascending bid auction for task ofﬂoad-

ing problem in fog computing using KKT conditions.

(Zhang et al., 2019) presented a Parking Vehicle As-

sistance (PVA) in VANET using forward auction.

Forward auction is appropriate when there are many

competing buyers but only one seller or when there is

no competition among sellers. In our scenario, how-

ever, multiple sellers compete with one another to sell

their resources for the highest possible proﬁt. Com-

petitive bidding strategy is proposed in this work.

Reverse Auction-Based Allocation: In a reverse auc-

tion, buyers choose the seller with the lowest asking

price after competing sellers compete to sell their re-

sources. Reverse auction was utilized by (Guo et al.,

2020) to select the target fog nodes and convey the

data from the source to the destination fog nodes.

Minimizing overall energy use was the goal. For

the IoT fog, (Aggarwal et al., 2021) suggest reverse

auction-based resource allocation, which also satisﬁes

the QoS criteria in IoT applications. This technique

only allows service providers to submit bids. As a re-

sult, the seller is unable to proﬁt from the competition

among buyers.

Double Auction-Based Allocation: Buyers and sell-

ers submit bids simultaneously in a double auction

process, and the auctioneer matches the right buyers

and sellers by employing various methods of price de-

termination. (Joshi and Srivastava, 2020) proposed a

VCG-inspired price determination algorithm. How-

ever, in some cases, VCG may result in negative util-

ity for the broker. CSP serves as a broker in our case.

As a result, using CSP may have a negative impact.

(Peng et al., 2020) has proposed a resource alloca-

tion method that creates a bipartite graph of users and

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254

fog nodes. Edges in the graph are weighted based on

QoS requirements. Allocation is then performed us-

ing maximum matching of a bipartite graph. The ex-

tended McAfee (Yang et al., 2011) method ﬁnds the

ﬁnal price for buyers and sellers. Matching is used

to ﬁnd allocation, so an FSP can serve only one user,

even if some of its resources can satisfy the needs of

other users. In an actual situation, one FSP can satisfy

more than one IoSP.

All of the studies discussed thus far allocate all of

the resources required by the task in a single round,

resulting in a scarcity of resources in that round,

which can only satisfy a limited number of tasks.

Furthermore, none of them have taken into account

the perishable nature of resources when allocating re-

sources. (Miyashita, 2014) and (Saﬁanowska et al.,

2017) have proposed a bidding strategy for perishable

resource sellers. The allocation algorithm in both of

these works lacks a unique allocation technique for

allocating maximum resources to avoid loss due to

perishability. To avoid loss due to perishability, our

algorithm employs a novel resource allocation tech-

nique.

Furthermore, bidding strategy is an important

component of the auction mechanism. In a double

auction mechanism, the strategy must converge to a

point where both parties make the most proﬁt while

meeting each other’s needs.(Chowdhury et al., 2018)

and (Thavikulwat and Pillutla, 2008) have proposed

a bidding strategy for periodic double auctions where

bids are cleared at regular intervals. It is appropriate

for our architecture. However, in this bidding strat-

egy, they are capable of wasting a few initial rounds

in which the buyer may not receive any resources and

the seller may not sell anything in order to ﬁnd the

equilibrium point. However, because our resources

are perishable, we must ensure that resources are al-

located from the ﬁrst round itself. To the best of our

knowledge, no bidding strategy is proposed for the

fog market.

3 PROBLEM FORMULATION

We consider a three-tier architecture,in which there

are N IoSPs in the IoT layer, , M FSPs in the Fog

layer, and one CSP in the Cloud layer. FSPs com-

municate with one another via a mesh network. It is

assumed that IoSPs receive virtualized services from

FSPs (Zhang et al., 2017). As a result, IoSPs can only

connect to FSPs via CSP. Also, time is divided into

rounds. Allocation occurs at the start of each round.

IoSPs and FSPs register with CSP, as shown in Figure

2. CSP sends summary report of the previous round

to all registered IoSPs. When the IoSP requires re-

sources, it sends a request to the CSP. A single IoSP

can send multiple task requests. IoSPs send their bid

for the task, CSP communicates with FSPs and as-

signs the best FSP to IoSP. IoSP sends input data to

FSP, who performs the required task and returns the

results to IoSP. Following task completion, IoSP sub-

mits a QoS report; CSP calculates the ﬁnal payment

for FSPs and IoSPs.

Figure 2: System model.

Resources are represented as Virtual Machines

(VMs). Each VM has a set of ﬁxed resources such as

memory, network bandwidth, and computing power.

Each FSP provides a varying level of QoS service.

When resources are limited, higher QoS level VMs

are prioritised. It is assumed that the valuation of

VMs becomes zero at the end of each round.

Each FSP registers with the CSP by sending fog

information I

= {A

, b

} where A

is the number

of available VMs at j

FSP and b

is bid for x

QoS level. Because CSP stores this information, it

is only necessary to send it once during FSP registra-

tion. If IoSP wants to execute a task, it sends task

requirements to CSP at the beginning of the round.

task requirement of i

IoSP for k

task contains

i,k

= {λ

i,k

, d

i,k

, D

i,k

, s

i,k

} where λ

i,k

is required num-

ber of VMs, d

i,k

is deadline,D

i,k

is data size, s

i,k

is re-

quired QoS level at fog devices. At the beginning of

each round t CSP sends a summary report (S

). Using

this report IoSP calculate its bid b

i,k

using competi-

tive bidding strategy(CompBid) proposed in the next

section and send it to CSP.

1. Maximization of IoSPs utility: IoSP wishes to

maximize its utility by adjusting the price paid

to CSP. Constraint (1.1) indicates that the task

must be completed before the deadline. Con-

QoS-Aware Task Allocation and Scheduling in Three-Tier Cloud-Fog-IoT Architecture Using Double Auction

255

straint (1.2) states that each task can only be as-

signed to one FSP, implying that the task cannot

be divided into multiple sub-tasks. (1.3) denotes

that each IoSP pays less than its valuation, result-

ing in a positive utility.

Subproblem-1: Maximization of IoSP utility

argmax

i,k

∑

i=1

(1.0)

Subject to T

i,k

≤ d

i,k

, ∀i∀k (1.1)

i,k, j

∈ {0, 1} (1.2)

i,k

< v

i,k

, ∀i (1.3)

2. Maximization of FSP utility: Each FSP seeks to

maximize utility by varying the price and number

of allocated VMs (a

). Constraint (2.1) states that

FSP may not allocate more VMs than are avail-

able. Constraint (2.2) states that each FSP must

be priced higher than its maintenance cost in or-

der to be utility positive.

Subproblem-2: Maximization of FSP utility

argmax

∑

j=1

(2.0)

Subject to a

≤ A

, ∀ j (2.1)

> E

, ∀ j (2.2)

3. Maximization of CSP utility: CSP wants to max-

imize its utility by matching IoSPs and FSPs and

determining their prices. The requirement in con-

straint (3.1) states that IoSPs’ payments must ex-

ceed the price that must be paid to FSPs. Thus, its

utility is positive.

Subproblem-3: Maximization of CSP utility

argmax

i,k, j

i,k

(3.0)

Subject to

∑

i=1

i,k

≥

∑

j=1

(3.1)

These subproblems are interdependent. IoSPs want

to maximize their utility by lowering the price to be

paid. However, because of this, it may not receive any

resources at all. Furthermore, the utility of CSP and

FSP may decrease as they receive less payment from

IoSP. FSP wishes to maximize its utility by charging

higher fees, which may reduce its total allocated re-

sources as well as IoSP and CSP utility. This is an ex-

ample of a joint optimization problem. It is NP-hard

to maximize the utility of all three nodes at the same

time(Zhang et al., 2017). Furthermore, perishable na-

ture of cloud-fog resources makes this problem more

difﬁcult.

4 PROPOSED SOLUTION

We propose a heuristic-based solution that has two

components namely Competitive bidding (CompBid)

strategy and QoS-based Task Allocation and Schedul-

ing (QoTAS).

4.1 Competitive Bidding (CompBid)

To maximize service providers’ beneﬁts as a result

of competition among IoSPs, an IoSP’s bid must in-

crease in conjunction with an increase in the demand

supply ratio, also known as normalised load. To put

this logic into action, we design a bidding strategy in

which CSP shares a summary report at the start of

each round (t). Which contains S

= {n

t−1

, c

t−1

, n

represents normalized load of t

round and c

t−1

average VM price of the previous round for y

QoS

level. Each IoSP has a summary report published by

CSP as an input, as well as its own aggressiveness

factor (β

). Each IoSP determines its own bid for QoS

level y using

i,k

= min(B

+ β

t−1

, v

i,k

) (1)

Here, B

is the base price, which should be equal to

or greater than the VM’s maintenance cost. β

can be

any value between 0 and 1. The greater the value of

, the more aggressive the IoSP and the higher the

bid.

4.2 QoS-Based Task Allocation and

Scheduling (QoTAS)

We propose a double auction-based algorithm for

QoS-based Task Allocation and Scheduling (Qo-

TAS) in commercial three-tier Architecture. Algo-

rithm 1 depicts the steps of the QoTAS algorithm. In

the ﬁrst step, we identify FSP that meets QoS require-

ments for each task. The ﬁnal winner and price are de-

termined in the second step based on the bids of ISPs

and FSPs. Because VMs are perishable resources, in

the third step, if there are tasks with eligible FSPs

based on QoS requirements that did not win due to a

lower bid, we assign VMs of those FSPs to the IoSPs

with a penalty.

Algorithm 1: QoS-based Task Allocation and

Scheduling (QoTAS).

1: Find eligible FSP for each task(Algorithm 2)

2: Price and ﬁnal winner determination (Algorithm 3)

3: Allocation of remaining VMs (Algorithm 4)

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4.2.1 Find Eligible FSP for Each Task

In this algorithm, we try to ﬁnd an eligible FSP for

each task that has the minimum service delay for

the task. We also schedule VMs based on resource

scarcity on each FSP for load balancing and efﬁcient

VM utilization. In the ﬁrst step, we identify candi-

date FSPs for each task. Candidate FSP is an FSP

who is capable of completing tasks before the dead-

line. We may receive a more than one candidate FSPs

for each task. Then, using Algorithm 2, we ﬁlter them

to ﬁnd the most eligible FSP for each task. To dis-

tribute VMs with load balancing so that each FSP re-

ceives an equivalent number of tasks. It ﬁrst computes

each FSP’s resource scarcity using Eq. (2), which is

the ratio of VM demand based on the candidate FSP

list to total available VMs on a speciﬁc FSP.

∑

i=1

∑

k=1

i, j,k

i,k

(2)

Where w

i, j,k

is one if j

FSP is candidate FSP for k

task of i

IoSP. The minimum penalty p

min

i,k

for a ser-

vice delay of d

i,k+1

is calculated for all tasks. It is a

penalty if this task is completed one unit of time af-

ter its deadline. Following that, all FSPs are sorted

in ascending order of r

, with the FSPs with the low-

est resource scarcity being allocated ﬁrst. Tasks, on

the other hand, are prioritised using a penalty system.

The greater the minimum penalty, the more important

that task. As a result, for All tasks, the descending

order of p

min

i,k

is used.

Each task in the sorted list is compared against

each FSP in the sorted list. If w

i, j,k

= 1, task schedul-

ing is done. We provide a task list, fog information,

and candidate FSPs for each task as input to Algo-

rithm 2. For each task, the algorithm returns eligible

FSP.

4.2.2 Price Determination

Following the identiﬁcation of eligible FSPs for each

task, we must determine the ﬁnal winners and the ﬁnal

price to be paid by ISPs, as well as the ﬁnal payment

to be received by FSPs based on their bid. For double

auctions, there are numerous winner determination

and pricing methods available. We map the require-

ment of our problem statement with terms of auction

theory to ﬁnd the best method for our scenario; from

that exercise it is concluded that, we need Individ-

ual rationality (IR), Weekly budget baance (WBB),

Truthull (TF) and Allocative efﬁcient (AE) pricing

mechanism. We compared k-DA, VCG, Trade reduc-

tion, McAfee and extended McAfee techniques and

concluded that extended McAfee (Peng et al., 2020)

is most suitable pricing mechanism for our model.

Algorithm 2: Find eligible FSP.

Input: Task list, Fog info(I), Candidate list

Output: Eligible Task-FSP pair

1: for Each FSP in Fog info do

2: Calculate r

using Eq. (2)

3: Sort FSPs in ascending order of r

4: for Each Task do

5: Calculate p

min

i,k

6: Sort Task in descending order of p

min

i,k

7: for Each Task in sorted Task list do

8: for Each FSP in sorted Fog info do

9: if FSP is candidate FSP for Task then

10: if Deadline of task is one round then

11: Allocate all required VMs in one round

12: else if Deadline(d

i,k

) is less than r

then

13: Allocate VMs equally till d

i,k

rounds

14: else

15: Allocate VMs equally till r

rounds

However, we have a bid of IoSPs for different QoS

levels so we can’t not allocate them altogether. Algo-

rithm 3 shows solution to handle it.

Algorithm 3: Winner and price determination.

1: Create a group of task requests for each QoS level

2: We have the price of all FSPs for each QoS level

3: Apply extended McAfee for each group and ﬁnd

winners and price

4.2.3 Allocation of Remaining VMs

We may not keep VMs idle if IoSPs’ bid is less than

the required cost since they are perishable. However,

if we allocate the same VMs at a lower price, users

may always bid lower to maximize their utility. In re-

sponse to this situation, we propose algorithm 4 where

we are allocating lesser VMs than demanded and QoS

satisaction is not guaranteed.

Algorithm 4: Allocation of remaining VMs.

Input: Task list, Fog info, Eligible Task FSP pair,

Winner pairs

Output: Updated winner pairs

1: for Each Task in Task list do

2: for Each FSP in Fog info do

3: if FSP is eligible for Task but not winner then

4: Declare that Task FSP pair as winner

5: Price to be paid is equal to bid of IoSP

6: Allocate γ percentage of required VMs

with QoS level one

QoS-Aware Task Allocation and Scheduling in Three-Tier Cloud-Fog-IoT Architecture Using Double Auction

257

5 SIMULATION AND RESULTS

A remote patient monitoring case study is used to

compare the performance of QoTAS in truthful bid-

ding and CompBid strategy with Deadline and Prior-

ity enabled Double Auction (DPDA) (Joshi and Sri-

vastava, 2020) and Multi Attribute Double Auction

(MADA) (Peng et al., 2020).

5.1 Simulation Setup

There are four types of tasks in remote patient mon-

itoring. Namely, client module task, pre-processing

task, abnormality task, and prediction task. Out of

that client module task, pre-processing task, abnor-

mality task are processed on fog nodes (Mahmud

et al., 2022). This task request can be generated by a

variety of patients. The monitoring period and dead-

line for processing collected data differs depending

on the criticality of the patient (Kumar et al., 2008).

We assume that each patient’s monitoring period, also

known as task generation period, is a multiple of the

auction period. As a result, at the start of each auction

round, requests from all patients are received.

We implement a scenario in which there are 30

patients, 6 FSPs and one CSP. We consider two tasks

here, preprocessing and abnormality detection, be-

cause they are expected to be performed on fog.

The resource requirements for both of these tasks are

listed in (Mahmud et al., 2022). Total simulation time

is 500s and allocation is performed at every 30s. Sim-

ulation parameters are shown in Table 1. Here,λ

rep-

resents VM requirement of preprocessing task and λ

represents VM requirement of abnormality detection

task (Mahmud et al., 2022)

Table 1: Simulation parameters.

Parameter Value

N(250,50)

i,k

N(500,100)

N(250,50)

N(0,1)

The patients are classiﬁed in four groups here

based on their criticality(sensitivity) as shown in Ta-

ble 2.

Table 2: Patient information.

Class % of patient Periodicity Deadline Sensitivity

0 10% 30s 10s 3

1 20% 60s 30s 3

2 30% 120s 60s 2

3 40% 240s 90s 1

The delay between the FSP and the ISP is mod-

elled in Netsim by simulating real-time scenarios like

congestion, link down, and packet loss, and then used

it in Python code for allocation. After this setup, we

have compare the following parameters in the alloca-

tion mechanism:

1. Task Allocation Ratio(TA): It is ratio of number

of tasks successfully executed and number of task

requests accepted by CSP.

2. Resource Utilization (RU): It is the ratio of total

VMs allocated and total VMs available.

3. Average User Utility(UU): It is the ratio of aver-

age IoSP utility per task and task valuation for all

assigned tasks.

4. System Utility (SU): It is the sum of the average

FSP utility(FU) and the average CSP utility(CU).

Where FU is the ratio of FSP utility per task to

task maintenance cost. CU is the ratio of the CSP

utility per task and the IoSP valuation for that task.

5.2 Results

We measure QoTAS performance using two bidding

strategies: truthful bidding and competitive bidding.

Normalized load (NL) is changed from 0 to 1 with

step size 0.1.

5.2.1 Truthful Biding Strategy

We assume that FSP and IoSP both bid their true task

valuations. In this bidding strategy, agents do not con-

sider market scenarios such as resource demand and

other agents’ bids. As shown in Figure 3(a), MADA’s

TA is the lowest because it uses a matching algorithm

for VM allocation, allowing it to allocate only one

task per FSP. Also, because of that its TA decreases

as NL increase. DPDA has more TA than MADA be-

cause each FSP can be assigned multiple tasks. Qo-

TAS has the highest TA because it assigns resources

to tasks in multiple rounds if necessary. It also em-

ploys extended McAffee, which allocates the maxi-

mum number of FSP and IoSP pairs. Furthermore,

QoTAS allocates remaining resources at a low cost

as part of perishable allocation, so QoTAS has the

highest TA. Overall, QoTAS has 54% more TA than

DPDA and 69% more than MADA.

Because more resources are used when more tasks

are assigned, the resource utilization (RU) of QoTAS

is maximum and the RU of MADA is minimum as

shown in Figure 3(b). Also, for MADA, RU remains

constant since it always allocated constant number of

tasks which is equal to number of FSPs. Overall, Qo-

TAS has 63% more RU than DPDA and 75% more

than MADA.

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258

(a) Truthful bidding: NL vs. TA. (b) Truthful bidding: NL vs. RU. (c) Truthful bidding: NL vs. UU.

(d) Truthful bidding: NL vs. SU. (e) CompBid: NL vs. UU. (f) CompBid: NL vs. SU.

Figure 3: Comparision of QoTAS with DPDA and MADA.

As shown in Figure 3(c), the UU of VCG is the

highest because in VCG, buyers pay the seller’s ask at

the breakeven point. In contrast, in extended McAfee,

buyers pay the buyer’s bid at the break even point.

Because the seller’s ask at the breakeven point is al-

ways less than the buyer’s bid, buyers must pay less

in VCG. In our case, IoSPs are the buyers. QoTAS

has a higher UU than MADA because it completes

more tasks after accepting, avoiding penalties and in-

creasing UU. Oerall, DPDA has 59% more UU than

QoTAS and QoTAS has 54% more than MADA.

As shown in Figure 3(d), the SU of DPDA is min-

imum because it employs VCG, and in VCG auction-

eer has negative utility because it accepts less pay-

ment from buyers and gives more payment to sell-

ers, necessitating the addition of its own funds. In

our case, CSP is an auctioneer, so while DPDA has

the highest FSP utility , it has the lowest CSP util-

ity, making its total system utility the lowest of all

three. MADA has more SU than QoTAS because,

due to perishable allocation in QoTAS, it must sell re-

sources at a lower price than FSP’s ask, so CSP must

add money from its own pocket to pay FSP. This re-

duces CU and, eventually, SU. Overall, QoTAS has

89% more SU than DPDA and 26% less than MADA.

5.2.2 Competitive Bidding Strategy

In this section, we use CompBid strategy in which

IoSPs increased their bid as resource demand in-

creased. TA and RU has same behaviour as truth-

ful biddding since total number of tasks allocated de-

pends on available resources and QoS requirements.

As illustrated in Figure 3(e), the comparative be-

haviour of DPDA, MADA, and QoTAS is identical

to the Truthull bidding strategy depicted in 3(c). The

only difference is that the UU of MADA and QoTAS

decreases as load increases because IoSPs must pay

more because they are expected to pay the lowest bid

of all IoSPs and bid increases as load increases. UU

for DPDA remains constant because in VCG IoSP

pays seller’s ask, which is constant here. Overall,

DPDA has 78% more UU than QoTAS and QoTAS

has 20% more than MADA.

As illustrated in Figure 3(f), the SU of MADA and

QoTAS increases as load increases due to higher IoSP

bids. For DPDA, SU remains constant because the bid

of IoSP increases as the load increases. This causes

CSP to pay more to FSP, lowering CU and increasing

FU, but the SU is the sum of both, keeping SU con-

stant. Overall, QoTAS has 90% more SU than DPDA

and 12% more than MADA.

As a conclusion, QoTAS increase achieves more

TA and RU than DPDA and MADA in both bidding

strategy. Also, it has more SU than DPDA and more

UU than MADA in both truthful and CompBid strat-

egy. Also, it is observed that CompBid strategy in-

creases SU of QoTAS by 25% with some reduction in

UU.

6 CONCLUSION

Tasks with deadlines are generated by IoT applica-

tions. This task can be handled by cloud-fog service

providers. Task allocation and scheduling are difﬁcult

in commercial cloud and fog architecture. To solve

this problem, a multi-attribute double auction is used.

The perishable nature of cloud-fog VMs motivates

QoS-Aware Task Allocation and Scheduling in Three-Tier Cloud-Fog-IoT Architecture Using Double Auction

259

allocation of the maximum number of VMs in each

round. The algorithm for allocating the remaining

VMs after the auction is proposed. To take advantage

of competition among users and service providers, a

competitive bidding strategy is proposed. To com-

pare QoTAS with existing studies MADA (Peng et al.,

2020) and DPDA (Joshi and Srivastava, 2020) remote

patient monitoring tasks are used. In both the truth-

ful and competitive bidding scenarios, QoTAS outper-

forms DPDA and MADA in both bidding strategies.

It has more 89% SU than DPDA and more 54% UU

than MADA. Furthermore, the CompBid strategy in-

creases SU of QoTAS by 25% while decreasing UU.

REFERENCES

(2022). IoT data. https://www.businesswire.com/news/ho

me/20190618005012/en/The-Growth-in-Connected

-IoT-Devices-is-Expected-to-Generate-79.4ZB-of

-Data-in-2025-According-to-a-New-IDC-Forecast.

[Online; accessed 18-03-2022].

Aggarwal, A., Kumar, N., Vidyarthi, D. P., and Buyya,

R. (2021). Fog-integrated cloud architecture en-

abled multi-attribute combinatorial reverse auctioning

framework. Simulation Modelling Practice and The-

ory, 109:102307.

Baranwal, G., Kumar, D., Raza, Z., and Vidyarthi, D. P.

(2018). Auction based resource provisioning in cloud

computing. Springer.

Chowdhury, M. M. P., Kiekintveld, C., Tran, S., and Yeoh,

W. (2018). Bidding in periodic double auctions using

heuristics and dynamic monte carlo tree search. In

IJCAI, pages 166–172.

Guo, Y., Saito, T., Oma, R., Nakamura, S., Enokido, T.,

and Takizawa, M. (2020). Distributed approach to

fog computing with auction method. In International

Conference on Advanced Information Networking and

Applications, pages 268–275. Springer.

Jin, A.-L., Song, W., Wang, P., Niyato, D., and Ju, P. (2015).

Auction mechanisms toward efﬁcient resource sharing

for cloudlets in mobile cloud computing. IEEE Trans-

actions on Services Computing, 9(6):895–909.

Joshi, N. and Srivastava, S. (2019). Task allocation in three

tier fog iot architecture for patient monitoring sys-

tem using stackelberg game and matching algorithm.

In 2019 IEEE International Conference on Advanced

Networks and Telecommunications Systems (ANTS),

pages 1–6. IEEE.

Joshi, N. and Srivastava, S. (2020). Auction-based deadline

and priority enabled resource allocation in fog-iot ar-

chitecture. In 2020 Futuristic Trends in Network and

Communication Technologies(FTNCT). Springer.

Kumar, S., Kambhatla, K., Hu, F., Lifson, M., and Xiao,

Y. (2008). Ubiquitous computing for remote cardiac

patient monitoring: a survey. International journal of

telemedicine and applications, 2008.

Mahmud, R., Pallewatta, S., Goudarzi, M., and Buyya, R.

(2022). Ifogsim2: An extended ifogsim simulator for

mobility, clustering, and microservice management in

edge and fog computing environments. Journal of

Systems and Software, 190:111351.

Miyashita, K. (2014). Online double auction mechanism

for perishable goods. Electronic Commerce Research

and Applications, 13(5):355–367.

Peng, X., Ota, K., and Dong, M. (2020). Multiattribute-

based double auction toward resource allocation in ve-

hicular fog computing. IEEE Internet of Things Jour-

nal, 7(4):3094–3103.

Saﬁanowska, M. B., Gdowski, R., and Huang, C.

(2017). Preventing market collapse in auctions

for perishable internet of things resources. Inter-

national Journal of Distributed Sensor Networks,

13(11):1550147717743693.

Suh, M.-k., Chen, C.-A., Woodbridge, J., Tu, M. K., Kim,

J. I., Nahapetian, A., Evangelista, L. S., and Sar-

rafzadeh, M. (2011). A remote patient monitoring

system for congestive heart failure. Journal of med-

ical systems, 35(5):1165–1179.

Thavikulwat, P. and Pillutla, S. (2008). Pure and mixed

strategies for programmed trading in a periodic dou-

ble auction. International Journal of Applied Decision

Sciences, 1(3):338–358.

Verma, P. and Sood, S. K. (2018). Fog assisted-iot enabled

patient health monitoring in smart homes. IEEE Inter-

net of Things Journal.

Weinman, J. (2017). Fogonomics — the strategic, eco-

nomic, and ﬁnancial aspects of the cloud. In 2017

IEEE 41st Annual Computer Software and Applica-

tions Conference (COMPSAC), volume 1, pages 705–

705.

Yang, D., Fang, X., and Xue, G. (2011). Truthful auction

for cooperative communications. In Proceedings of

the Twelfth ACM International Symposium on Mobile

Ad Hoc Networking and Computing, pages 1–10.

Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, F. R., and Han,

Z. (2017). Computing resource allocation in three-tier

iot fog networks: A joint optimization approach com-

bining stackelberg game and matching. IEEE Internet

of Things Journal, 4(5):1204–1215.

Zhang, Y., Wang, C.-Y., and Wei, H.-Y. (2019). Parking

reservation auction for parked vehicle assistance in ve-

hicular fog computing. IEEE Transactions on Vehicu-

lar Technology, 68(4):3126–3139.

Zu, Y., Shen, F., Yan, F., Yang, Y., Zhang, Y., Bu, Z., and

Shen, L. (2019). An auction-based mechanism for

task ofﬂoading in fog networks. In 2019 IEEE 30th

Annual International Symposium on Personal, Indoor

and Mobile Radio Communications (PIMRC), pages

1–6. IEEE.

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