A Robust Adaptive Workload Orchestration in Pure Edge Computing

Zahra Safavifar

1 a

, Charafeddine Mechalikh

2 b

and Fatemeh Golpayegani

1 c

University College Dublin, School of Computer Science, Ireland

Laboratory of Artiﬁcial Intelligence and Information Technology (LINATI), University Kasdi Merbah, Ouargla, Algeria

Keywords:

Workload Orchestration, Reinforcement Learning, Pure Edge Computing, Adaptive Model, Robust Model.

Abstract:

Pure Edge computing (PEC) aims to bring cloud applications and services to the edge of the network to sup-

port the growing user demand for time-sensitive applications and data-driven computing. However, mobility

and limited computational capacity of edge devices pose challenges in supporting some urgent and computa-

tionally intensive tasks with strict response time demands. If the execution results of these tasks exceed the

deadline, they become worthless and can cause severe safety issues. Therefore, it is essential to ensure that

edge nodes complete as many latency-sensitive tasks as possible.

In this paper, we propose a Robust Adaptive Workload Orchestration (R-AdWOrch) model to minimize dead-

line misses and data loss by using priority deﬁnition and a reallocation strategy. The results show that R-

AdWOrch can minimize deadline misses of urgent tasks while minimizing the data loss of lower priority tasks

under all conditions.

1 INTRODUCTION

The rapid increase in actively connected Internet of

Things (IoT) devices and their applications create

massive data at the network’s edge. A recent report

estimates that connected devices will reach 38 billion

by 2025 (Analytics, 2019). The affordability and ex-

pansion of these devices have given rise to many IoT

applications including but not limited to connected

vehicles, smart cities, healthcare, security, surveil-

lance, and trafﬁc monitoring. ”Data Age” estimates

that 175 zettabytes of data will be generated every

year by 2025 of which 30% will need real-time pro-

cessing (Rao, 2021). While the cloud has huge re-

sources, it suffers from increased latency, inconsistent

connectivity, and a lack of real-time responsiveness.

As a response, a new paradigm of Edge Computing

has been introduced which involves the deployment

of servers (or small-scale data centers) near the edge

of the network. Increasing the number of such servers

by increasing the data produced at the edge is not a so-

lution. We need to shift toward Pure Edge Computing

(PEC) to minimize the use of high-capacity servers

and take advantage of currently available resources at

the edge devices. However, edge devices mobility and

https://orcid.org/0000-0001-8531-5068

https://orcid.org/0000-0003-2811-9903

https://orcid.org/0000-0002-3712-6550

their limited resources and energy may lead to the sit-

uation that available edge devices in the area cannot

support all user’s demands and increase failure.

IoT applications can involve private data, require

minimal latency, or produce a considerable volume

of data while requiring fast execution. The failure to

meet a critical deadline can lead to fatalities and sig-

niﬁcant losses (Uddin et al., 2021; Dai et al., 2019;

Khan, 2016). This highlights the importance of re-

source management and scheduling in edge comput-

ing where a mix of requests with various Service

Level Agreements (SLA) and deadlines should be

managed with resource-constraint devices. The liter-

ature proposes different approaches to accommodate

tasks with varying time sensitivity in Edge Comput-

ing. However, some dynamicity of the real-world

environment is ignored for the sake of simplifying

the problem. These simpliﬁcations include homo-

geneity of computing nodes and tasks (Dai et al.,

2019; Sharif et al., 2022), neglecting bandwidth ﬂuc-

tuation and its impact on delay (Sharif et al., 2022;

Fadahunsi et al., 2021; Dai et al., 2019), and disre-

garding the mobility of devices (Sharif et al., 2022;

Dai et al., 2019; Xu et al., 2020; Lee and Park, 2021;

Fadahunsi et al., 2021; Azizi et al., 2022). Moreover,

some of them assume simultaneous batch arrival of

resource requests or formulate the problem as an of-

ﬂine scheduling problem by assuming a prior knowl-

Safavifar, Z., Mechalikh, C. and Golpayegani, F.

A Robust Adaptive Workload Orchestration in Pure Edge Computing.

DOI: 10.5220/0011782500003393

In Proceedings of the 15th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2023) - Volume 2, pages 325-333

ISBN: 978-989-758-623-1; ISSN: 2184-433X

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

325

edge of task arrival times (Dai et al., 2019; Sharif

et al., 2022). Fault tolerance is another concern in cur-

rent distributed platforms where mission-critical IoT

applications are involved, and the target edge node

cannot function for any reason. Given the real-time

nature of IoT applications, not handling such failures

can cause a disaster.

Currently, edge computing hosts a variety of IoT

applications of which healthcare is attracting the at-

tention of both researchers and industry players. Edge

computing is being used to improve services in the

healthcare domain. Since data in these platforms is

related to human health safety, it can be very sensi-

tive to latency and network dynamics (e.g., changes in

available bandwidth) (Wang et al., 2017). Pure Edge

Computing, by taking advantage of available nearby

devices as a computational resource, supports low la-

tency services, which makes it a suitable option for

deploying healthcare platforms.

This paper applies workload orchestration in the

healthcare platforms of a nursing home, where elderly

people with varying health conditions are taken care

of by monitoring and analyzing their health signals.

This platform is facilitated by Pure Edge Computing

where there are heterogeneous mobile and stationery

edge devices such as smartphones, computers, wear-

ables, sensors, etc., and a private Edge server in the

area. As the generated tasks vary in urgency and pri-

ority, various Hard-Real-Time (HRT) tasks such as

monitoring vital signals of bedridden patients, Soft-

Real-Time (SRT) tasks such as processing camera

data, and Non-Real-Time (NRT) tasks such as histori-

cal data gathering are generated and must be handled.

Since healthcare applications are highly sensitive, and

because the PEC environment is highly unpredictable

and dynamic, a robust model is needed that can handle

the workload in different conditions, including emer-

gencies. It should be able to handle an environment

that is sparse with a few devices, or when there are

high loads of devices and requests.

This paper proposes a Robust Adaptive Workload

Orchestration (R-AdWOrch) that is designed based

on AdWOrch (Safavifar et al., 2021), which uses

Reinforcement Learning for workload orchestration.

The R-AdWOrch is able to function in various ran-

dom dynamics in the PEC. The goal is to minimize the

missing deadline of SRT tasks while meeting nearly

all HRT tasks in any condition, speciﬁcally in sparse

or crowded areas, by using a priority deﬁnition and a

reallocation strategy. Our contributions to this paper

are as follows:

• Designing a reallocation strategy that prevents

task failure when there are no available resources

or the resource cannot execute the assigned task

for any reason.

• Prioritizing execution of HRT tasks over other

types.

• Reshaping the reward function to minimize the

delay for HRT and SRT tasks.

The remainder of this paper is organized as fol-

lows, the PEC, Real-Time system, and AdWOrch are

over-viewed in Section 2. The problem and environ-

ment characteristics are described in Section 3. Task

ofﬂoading process using R-AdWOrch is presented in

Section 4. Section 5 evaluates the proposed model

through extensive simulations. Finally, Section 6 con-

cludes by giving an outline of the future directions of

this work.

2 BACKGROUND

2.1 Pure Edge Computing

Pure Edge Computing takes place at the bottom layer

of the network architecture. It aims to improve the

system’s performance by taking advantage of avail-

able resources at the edge of the network. Based on a

study conducted by Carnegie Mellon University, com-

puting at the extreme edge is more energy and latency

efﬁcient than computing in the distant cloud for cer-

tain applications (Drolia et al., 2013). However, PEC

is a highly decentralized and dynamic network of het-

erogeneous and resource-constrained devices that can

be mobile or stationary, which produce unpredictable

and ﬂuctuating workloads. IoT applications vary and

are requested at different time sensitivity. Therefore,

such an environment requires an orchestrator that can

adapt to different situations and dynamics.

2.2 Real-Time and Non-Real-Time

Systems

A real-time system is one whose basic speciﬁcation

and design correctness arguments must include its

ability to meet its timing constraints (Kim and Son,

1995). In contrast, non-real-time systems have no

strict deadlines, and their tasks can be failed and re-

peated if they are not complete. Three levels of time

sensitivity systems and tasks are deﬁned:

Hard-Real-Time (HRT), tasks have ﬁrm dead-

lines. Failure to meet these deadlines can cause se-

vere damage to the system or its environment, includ-

ing injury or even death. Aviation control, ﬁre alarm

system, and some subsystem of the health care system

are instances of hard real-time systems.

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

326

Figure 1: AdWOrch Components and task ofﬂoading ﬂow.

Soft-Real-Time (SRT), tasks have soft/ﬂexible

deadlines in which delays or failure in execution lead

to downgrading the Quality of Service. Video stream-

ing and online gaming are two examples of soft real-

time systems.

Non-Real-Time (NRT), tasks do not have re-

stricted deadlines, and the system can tolerate the fail-

ure of such tasks. Data collection for some appli-

cations, such as weather prediction and road mainte-

nance, can be categorized as non-real-time systems.

Real-world smart platforms usually contain a mix-

ture of different types of tasks which should be man-

aged based on priority.

2.3 AdWOrch

The Adaptive Workload Orchestration (AdWOrch)

model is developed based on the Fuzzy Decision

Tree (FDT) algorithm proposed in (Mechalikh et al.,

2020a).The base task orchestration algorithmis com-

plemented by a new resource selection strategy and

reward function.

Figure 1 shows the AdWOrch process and com-

ponents. First, the edge device sends an ofﬂoading

request to the central orchestrator. The orchestrator

gets all nearby resources’ characteristics from the in-

ventory and creates a state observation from the tasks’

attributes and resources’ characteristics and sends it

to the fuzziﬁer. After the fuzziﬁcation phase, the re-

sources are classiﬁed to estimate how much each re-

source is reliable for ofﬂoading the task using a de-

cision tree. A decision tree is created using the Q-

table that arises from the RL component. Then the

decision-maker selects a nearby device with sufﬁcient

reliability. After ofﬂoading the tasks to the destina-

tion, resource status in the inventory is updated si-

multaneously. The result is sent to the orchestrator,

which in turn sends it to the corresponding device. In

addition, when the result is returned, the device’s re-

source status in the inventory is updated again and the

task execution result is sent back to the RL compo-

nent. Finally, the RL component updates the Q-table

based on the received results by the reward function.

3 PROBLEM STATEMENT

This study aims to propose a robust and adaptive

workload orchestration model to minimize missed

soft real-time deadlines while meeting almost all hard

real-time deadlines and minimizing data loss due to

mobility or any other reasons to fail (i.e., insufﬁcient

resources, no available resources, and dead devices)

in a PEC environment.

The PEC environment is where resource-

constrained devices should accommodate a mix

of various tasks with different urgency levels and

SLA. Also, devices are heterogeneous, and both end

devices and resources can be stationary or mobile.

Mobile devices are able to enter and leave the area or

move within it effortlessly. Battery-powered devices

differ in their remaining battery level and locations.

3.1 Parameter Modelling

A centralized powerful RL-based orchestrator entity

allocates or reallocates tasks to the available resources

by considering the type of the tasks (i.e., HRT, SRT,

NRT) to minimize the delay and failure in the sys-

tems.

We proposed two tiers architecture in which edge

devices and sensors at the ﬁrst layer generate the

tasks and are utilized as the primary computational

resources. While for managing emergent conditions

in which devices at the edge cannot satisfy the re-

quirements of the user request, the Edge servers at

the second layer will facilitate the required resources

for HRT and SRT tasks. In this paper, the state, ac-

tion, and reward for the RL algorithm are deﬁned as

follows:

• State Representation. In AdWOrch, the state is

A Robust Adaptive Workload Orchestration in Pure Edge Computing

327

represented by two feature sets, tasks’ attributes

and resources’ characteristics at timestamp t. A

task’s attributes include latency, size, generator

mobility, and task type. Resources’ characteris-

tics include CPU utilization, CPU MIPS, mobility

behavior, and remaining battery. To improve the

learning process, instead of CPU utilization, we

consider the current load on the device, d

, which

is calculated by the queue length of tasks waiting

for execution on the device, d

, divided by the

number of device CPU cores, d

. See Equation.

= d

(1)

Instead of device capacity that was the MIPS size

and task size, the expected execution time, e

,is

considered which is calculated by the task size, t

divided by the MIPS size, d

. See Equation. 2.

= t

(2)

• Action Space. The action space A = {0,1} is

the set of all possible resource selection strategies

(i.e., indicating a speciﬁc resource that has been

selected for the task).

• Reward. The immediate reward the orchestrator

gets from the environment at a time step t after

taking action a is the success or failure status of

the task, which is represented as 1 or 0, respec-

tively.

4 MODEL DESIGN

A Robust Adaptive Workload Orchestration (R-

AdWOrch) model is proposed based on AdWOrch

model (Safavifar et al., 2021). It aims to achieve ro-

bustness along with adaptability in random and dy-

namic PEC environments which can accommodate

resource constraints in crowded or sparse areas. R-

AdWOrch introduces a central orchestrator that em-

ploys an RL-based algorithm that aims to minimize

the missing deadline for SRT tasks and meet nearly

all HRT tasks. Hence, it should handle different types

of task failure. There are ﬁve types of failure in the

system:

• Failure Due to Missing The Deadline: a task

cannot be completed before its deadline.

• Failure Due to Mobility: mobile devices can join

or leave the area effortlessly and not be able to

access one another to return the result during the

ofﬂoading process.

• Failure Due to Incompatible Hard-

ware/Software: various devices in the area

Figure 2: Task ofﬂoading process by R-AdWOrch.

have different hardware and software scopes.

Since tasks have different requirements in terms

of hardware and software, sometimes the allo-

cated device scope might not be compatible with

the task requirements. It causes the task to fail

due to insufﬁcient resources.

• Failure Due to no Available Resources: In the

PEC environment, a device might be isolated and

cannot ﬁnd nearby devices to ofﬂoad a task. So,

the task fails due to no available resources.

• Failure Due to a Dead Device: When a battery-

operated device runs out of energy and cannot ex-

ecutes ofﬂoaded tasks.

Figure 2 demonstrates how R-AdWOrch manages

different types of tasks (i.e., HRT, SRT, NRT) to min-

imize task failure for the above failure reasons. The

R-AdWOrch has added red components to AdWOrch.

When R-AdWOrch receives a task, it tries to ﬁnd a

proper resource from surrounding devices using the

AdWOrch orchestrator. If a proper resource is found,

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

328

Figure 3: Health care platform in a PEC environment.

the task will be sent to the resource. In the case of

no accessible resources, there are two possibilities:

when the task is an NRT task, it will fail immediately

due to the ”no available resources”. Otherwise, if it

is an HRT or SRT task, it will be sent to the Edge

server directly to prevent failure due to ”No avail-

able resources”. The R-AdWOrch ignores all NRT

tasks in this step for two reasons. i) When a disaster

happens, there are a few resources and limited band-

widths which should be mainly utilized for executing

Real-Time tasks. ii) If the Edge server and bandwidth

are overloaded with NRT tasks, it might lead to long

response time and therefore the failure of the HRT

task.

When an ofﬂoading destination is selected, the

task is ofﬂoaded to that device for execution. If it is

an HRT task, it will be a high priority to execute be-

fore all SRT and NRT tasks. Hence, it decreases the

chance of HRT tasks failure due to ”missing dead-

line”. Since a preemptive system is considered, the

NRT task execution is paused and returned back to

the queue to allow arrived HRT task to be executed

immediately. NRT and SRT tasks should wait in the

device queue for execution. After execution, the re-

sult returns to calculate the new q-value and update

the q-table (see 4.1). When a failed result is returned,

if the task is an SRT and failed for other reasons than

latency, such as incompatible hardware/software, mo-

bility, and dead device, it will be reallocated to the

Edge server to be completed before the deadline. Fi-

nally, the result returns after execution at the Edge

server.

Also, to avoid failure due to the dead device, all

SRT and NRT tasks will be reallocated when the bat-

tery level drops below a given threshold. Current HRT

tasks will still be executed but the orchestrator will

prevent ofﬂoading future tasks to this device (see Al-

gorithm 1).

4.1 Reward Function

Our goal is to minimize the SRT task failure rate while

meeting nearly all HRT deadlines. The AdWorch

(Safavifar et al., 2021) deﬁned a delayed penalty in

the reward function to minimize the failure due to

missing a deadline. This work adds more penalties

for HRT and SRT task failure as is shown in Equ. 3.

R(s,a) = R

− (w ∗ D

) (3)

= T

(4)

is the task’s execution result, which is in the

set of {0, 1}to indicate success or failure. The delay

penalty D

is calculated by dividing the delay time

by the task latency time T

(see Equation 4). The

value can exceed 1 but this rarely occurs. w is the

weight of the penalty which for the HRT task is 3, for

the SRT task is 1.5 and for the NRT tasks is 1.

5 PERFORMANCE EVALUATION

5.1 Simulation Settings

To evaluate the performance of R-AdWOrch in a pure

edge environment we use PureEdgeSim (Mechalikh

et al., 2020b). We simulate a smart nursing home

where senior citizens with different health conditions

live. Some are bedridden, while others are in bet-

ter condition and can move around and do everyday

tasks. Various healthcare sensors collect different vi-

tals and send the data for analysis and diagnosis. The

A Robust Adaptive Workload Orchestration in Pure Edge Computing

329

while running do

if ofﬂoading request (task,device) then

if remaining energy(device) < threshold then

if task ̸= HRT then

reallocate(task,INSUFFICIENT POWER);

/*reallocate due to insufﬁcient power*/

else

addToExecutionQueue(t);

end

Algorithm 1: Tasks reallocation for low battery edge de-

vices.

urgency of data differs; for example, the vitals of

a critically ill patient has a very high priority com-

pared with those of a healthy individual. In addition

to health sensors, other devices such as cameras are

used to record people’s apparent activities, and some

sensors collect historical data from the equipment and

environment. The characteristics of these devices are

summarized in Table 1.

An emergency in the mentioned smart nursing

home can happen when a telecommunication incident

happens. Half of the edge devices are disconnected

from the main network while still having access to

the internal network. Also, when for any reason the

number of people is increased, the system faces a high

workload. (see Table 3). A private Edge server is

in the area, and devices can communicate with it via

the main network. This server will have a comput-

ing capacity of 400 GIPS (Giga Instruction Per Sec-

ond). Monitoring vital signs of critically ill patients

are HRT tasks, while analyzing vitals from healthy in-

dividuals and processing camera data are SRT tasks.

The tasks of gathering historical data are considered

as NRT (see Table 2). There is a central orchestra-

tor that all devices can access via the internal network

and accesses the main network and the Edge server.

To evaluate the performance of the proposed

model, the results are compared to AdWOrch (Safav-

ifar et al., 2021). For each of the explained scenarios,

a simulation of 30 minutes is run 5 times.

5.2 Evaluation Metrics

We deﬁne two evaluation metrics. The task success

rate for each task type is calculated by the number of

successful tasks divided by the total number of gen-

erated tasks. The average delay time for each type

of task is calculated by dividing the total delay time

by the number of generated tasks. The total delay

time is calculated by summing up the total network

time, total execution time, and total waiting time. Fur-

thermore, we assessed how effective the reallocation

strategy is by calculating how many tasks are reallo-

cated due to low battery power, mobility, and incom-

patible resources.

5.3 Results and Discussion

Average Delay. Figure 4(a-c) shows the average de-

lay for each type of task under different workloads.

As can be seen, R-AdWOrch signiﬁcantly minimizes

the delay of HRT tasks compared to the baseline when

the density of devices is low (i.e., 50 devices) while

providing comparable results in all other cases. One

of the reasons for the reduction of the delay is the ex-

tra penalty that we deﬁned in the reward function for

HRT and SRT task failure. The RL component re-

ceives a heavily weighted penalty when an HRT task

is not completed within the expected time interval.

Moreover, HRT tasks are prioritized over non-HRT

in two ways: i) the HRT tasks will add to the front

of the queue; ii) when an HRT task arrives, if no free

computational resource is available, executing NRT

tasks are paused and resources are allocated to HRT

tasks. These allow fast execution even in resource-

constrained scenarios.

Success Rate. Figure 4d shows that R-AdWOrch

increases the success rate of HRT tasks compared to

the baseline. The reason is R-AdWOrch applies two

levels of prioritization to prevent HRT tasks from fail-

ing: i) by reassigning non-HRT tasks to other de-

vices when the remaining battery power is not enough

for executing all its remaining tasks; ii) as mentioned

in section 4.1, the delay penalty for the HRT tasks

is multiplied by 3 which causes the algorithm to be

more sensitive to HRT failures compared with SRT

and NRT tasks. Moreover, as can be seen in Figure

4e R-AdWOrch success rate for SRT tasks is signif-

icantly higher than the baseline. The main reason is

the allocation strategy that applies to SRT tasks when

they fail due to reasons other than latency. By reallo-

cating these tasks to the edge server, they can meet the

deadline and succeed. Figure 4f shows the NRT tasks

baseline outperforms the R-AdWOrch which is rea-

sonable because i) in R-AdWOrch due to the nature

of NRT tasks they have lower priority for execution;

ii) they do not send to the edge server when no nearby

edge devices are available for assigning and they will

be failed.

Figure 5 depicts the contribution of each of

the proposed methods (i.e., priority, reallocation,

SRT/HRT delay penalty, and edge server) in improv-

ing the task success rate. In emergency scenarios,

AdWOrch accounts for only 40% of the success rate

while the other 60% comes from R-AdWOrch im-

proving methods.

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

330

Table 1: Types of edge devices.

Types Laptops Smartphones Gateways Stationary sensors Mobile sensors

Generate tasks No Yes No Yes Yes

Ratio (%) 11 18 11 28 32

Mobility No Yes No No Yes

Speed (m/s) - 1.4 - - 1.4

Battery-powered Yes Yes No No Yes

Battery-capacity(Wh) 56.2 18.75 - - -

Idle energy consumption (W) 1.7 0.2 3.8 - -

Max energy consumption (W) 23.6 5 5.5 - -

CPU (GIPS) 110 25 16 - -

CPU cores 8 8 4 - -

(a) Average HRT delay (b) Average SRT delay (c) Average NRT delay

(d) HRT success rate (e) SRT success rate (f) NRT success rate

Figure 4: The simulation results for different scenarios.

Table 2: The types of applications.

Application types Latency(ms) Size (MI)

HRT 15 200

SRT 500 5000

NRT 30000 10000

Table 3: Scenarios proﬁle.

Number of devices HRT

SRT

NRT

50 135 135 270

100 135 135 270

300 135 135 270

Requested task for each device per minute.

Reallocation: Figure 6 gives the number of real-

located SRT tasks and their reallocation reasons. As

mentioned above, R-AdWOrch reallocates SRT tasks

for two reasons: i) to avoid data loss when an SRT

task is failed due to ”mobility” or ”incompatible hard-

Figure 5: The contribution of R-AdWOrch methods in the

success rate.

ware/software”; ii) to prevent task failure resulting

from a battery-powered device dead.

A Robust Adaptive Workload Orchestration in Pure Edge Computing

331

Figure 6: The contribution of reallocation reasons.

6 CONCLUSIONS

By considering the real-world characteristics of the

PEC, explained above, this paper proposed a Robust

Adaptive Workload Orchestration (R-AdWOrch) in a

PEC environment that can function in different situ-

ations and circumstances. It aims to meet nearly all

HRT tasks while minimizing the deadline missing of

SRT tasks. We applied our model for a healthcare

application and compared it with the baseline model

(AdWOrch). The results show that R-AdWOrch out-

performs AdWOrch in HRT and SRT success rates

and decreases the delay time for these task types.

R-AdWOrch uses a centralized orchestrator which

might be a single point of failure and is a risk for a

robust model. Designing a distributed network of or-

chestrators is part of our future work. Moreover, the

orchestration of dependent tasks is another challenge

in this area.

ACKNOWLEDGEMENTS

This publication has emanated from research sup-

ported in part by a grant from Science Foundation

Ireland under Grant number 18/CRT/6183. For the

purpose of Open Access, the author has applied a CC

BY public copyright license to any Author Accepted

Manuscript version arising from this submission.

REFERENCES

Analytics, S. (2019). “internet of things now numbers 22

billion devices but where is the revenue”. https://news

.strategyanalytics.com/press-releases/press-release-d

etails/2019/Strategy-Analytics-Internet-of-Things-N

ow-Numbers-22-Billion-Devices-But-Where-Is-The

-Revenue/.

Azizi, S., Shojafar, M., Abawajy, J., and Buyya, R. (2022).

Deadline-aware and energy-efﬁcient iot task schedul-

ing in fog computing systems: A semi-greedy ap-

proach. Journal of network and computer applica-

tions, 201:103333.

Dai, H., Zeng, X., Yu, Z., and Wang, T. (2019). A schedul-

ing algorithm for autonomous driving tasks on mobile

edge computing servers. Journal of Systems Architec-

ture, 94:14–23.

Drolia, U., Martins, R., Tan, J., Chheda, A., Sanghavi, M.,

Gandhi, R., and Narasimhan, P. (2013). The case for

mobile edge-clouds. In 2013 IEEE 10th International

Conference on Ubiquitous Intelligence and Comput-

ing and 2013 IEEE 10th International Conference on

Autonomic and Trusted Computing, pages 209–215.

IEEE.

Fadahunsi, O., Ma, Y., and Maheswaran, M. (2021). Edge

scheduling framework for real-time and non real-time

tasks. In Proceedings of the 36th Annual ACM Sym-

posium on Applied Computing, pages 719–728.

Khan, M. A. (2016). A survey of security issues for cloud

computing. Journal of network and computer appli-

cations, 71:11–29.

Kim, Y.-K. and Son, S. H. (1995). Predictability and con-

sistency in real-time database systems. Advances in

real-time systems, pages 509–531.

Lee, C. H. and Park, J. S. (2021). An sdn-based packet

scheduling scheme for transmitting emergency data

in mobile edge computing environments. Hum. Cent.

Comput. Inf. Sci, 11:28.

Mechalikh, C., Taktak, H., and Moussa, F. (2020a). A fuzzy

decision tree based tasks orchestration algorithm for

edge computing environments. In International Con-

ference on Advanced Information Networking and Ap-

plications, pages 193–203. Springer.

Mechalikh, C., Taktak, H., and Moussa, F. (2020b). Pureed-

gesim: A simulation framework for performance eval-

uation of cloud, edge and mist computing environ-

ments. Computer Science and Information Systems,

(00):42–42.

Rao, S. K. (2021). Data-driven business model innovation

for 6g. Journal of ICT Standardization, pages 405–

426.

Safavifar, Z., Ghanadbashi, S., and Golpayegani, F. (2021).

Adaptive workload orchestration in pure edge com-

puting: A reinforcement-learning model. In 2021

IEEE 33rd International Conference on Tools with Ar-

tiﬁcial Intelligence (ICTAI), pages 856–860. IEEE.

Sharif, Z., Jung, L. T., and Ayaz, M. (2022). Priority-based

resource allocation scheme for mobile edge comput-

ing. In 2022 2nd International Conference on Com-

puting and Information Technology (ICCIT), pages

138–143. IEEE.

Uddin, M., Ayaz, M., Mansour, A., Aggoune, e.-H. M.,

Sharif, Z., and Razzak, I. (2021). Cloud-connected

ﬂying edge computing for smart agriculture. Peer-to-

Peer Networking and Applications, 14(6):3405–3415.

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

332

Wang, H., Gong, J., Zhuang, Y., Shen, H., and Lach, J.

(2017). Healthedge: Task scheduling for edge com-

puting with health emergency and human behavior

consideration in smart homes. In 2017 IEEE Inter-

national Conference on Big Data (Big Data), pages

1213–1222. IEEE.

Xu, J., Hu, Z., and Zou, J. (2020). Computing ofﬂoading

and resource allocation algorithm based on game the-

ory for iot devices in mobile edge computing. Interna-

tional Journal of Innovative Computing, Information

and Control, 16(6):1895–1914.

A Robust Adaptive Workload Orchestration in Pure Edge Computing

333