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Efficient Offloading and Task Scheduling in Internet of
Things-Cloud-Fog Environment
Table of content
1
Introduction
Problem Definition
Related Work
System Model
Proposed Methods
Simulation & Results
Conclusions & Future works
Publication
Introduction / Background
2
Task Offloading in IoT-Cloud-Fog
Environment
 In the IoT ecosystem, Fog Computing (FC) extends cloud services to the network
edge, enabling faster data processing near the source without requiring high
bandwidth.
 By combining FC and cloud computing, IoT systems achieve reduced latency,
improved efficiency, and lower costs while still utilizing cloud resources when
necessary.
 Fog computing benefits real-time systems by providing high-speed internet
connectivity, but its limited resources present challenges in meeting dynamic
real-time demands.
 A key issue in fog environments is the optimal assignment of tasks to fog nodes
to ensure efficient resource utilization and performance.
Background /Cont.
3
IoT-Cloud-Fog offloading refers to the process of distributing computational tasks
across IoT devices, fog nodes, and cloud servers to optimize performance, reduce
latency, and enhance resource utilization. In this paradigm:
Background /Cont.
4
 IoT devices collect and generate data, often with limited processing power,
and may offload tasks to nearby fog nodes for faster processing.
 Fog nodes are positioned closer to IoT devices and provide intermediate
computing resources, reducing latency and bandwidth usage compared to
cloud servers.
 Cloud servers handle more complex and resource-intensive tasks that fog
nodes cannot process, providing scalable storage and computational power
when needed.
Effective offloading strategies improve task execution time, energy efficiency,
and overall system responsiveness in IoT-fog-cloud environments.
Introduction / Scientific Workflow
5
A scientific workflow is a series of computational or data manipulation steps
designed to achieve a specific scientific objective.
 A workflow application is modeled as a Directed Acyclic Graph (DAG), where
tasks (vertices) are represented by T, and task dependencies (edges) by E.
 Each edge indicates the data transfer between tasks, with di,j representing the
size of output data from task ti to task tj. Task tj can only begin after ti is
completed.
 Starting tasks have no parent, and ending tasks have no child. Tasks at the same
level can run concurrently.
 In an IoT-cloud-fog environment, workflow application offloading and
scheduling aim to assign tasks to different computing resources, optimizing for
completion time (makespan), energy consumption, and overall cost.
Introduction / Scientific Workflow
6
This study uses three scientific workflow applications: Montage, Epigenomics, and
CyberShake.
 Montage creates customized sky
mosaics for astronomical data.
 CyberShake evaluates earthquake
hazards for the Southern California
Earthquake Center.
 Epigenomics automates genome
sequence handling, in collaboration
with the USC Epigenome Center
and the Pegasus Team.
Introduction / Offloading
7
Offloading involves deciding where a task or set of tasks should be processed either
locally, on a fog node, or on remote cloud servers. Steps:
Task Identification
 Identify which tasks can or need to be offloaded from a local
device (IoT, fog node) to more powerful cloud resources.
Resource Evaluation
 Evaluate the available resources such as local fog nodes or
cloud infrastructure. This includes checking parameters like
latency, bandwidth, computation power, and energy
consumption.
Decision Making
 Determine whether to offload the task to the fog (nearby) or
cloud (remote) based on factors like response time, resource
availability, energy efficiency, or processing capacity.
Offload Execution
 Transfer the task (and necessary data) to the selected resource
(fog or cloud) for processing.
Task Completion
 Retrieve the results from the cloud or fog resource and return it
to the originating device.
Introduction / Workflow Scheduling
8
We concerned in our research on workflow scheduling because offloading
decides the location of task execution, while task scheduling manages the
timing and sequencing of task execution across available resources, and using
both offloading and workflow scheduling together offers a comprehensive
approach to optimizing task execution in cloud-fog computing environments.
Here are the combined advantages:
➢ Optimized Resource Utilization
➢ Enhanced Performance and Reduced Latency
➢ Cost, and Energy Efficiency
➢ Improved Quality of Service
➢ Comprehensive Task Management
Introduction / Challenge
9
: Complex Decision-Making: Deciding
• Resource Allocation and
Management
where to offload tasks (fog, cloud, or local)
and scheduling them efficiently involves
complex decision-making processes that
must consider multiple factors such as
resource availability, task requirements, and
performance metrics.
: Resource Heterogeneity: Different
resources (fog nodes, cloud servers, etc.)
have varying capabilities, costs, and
constraints, making it challenging to match
tasks with the most appropriate resources.
Introduction / Motivation and Objectives
10
The main Motivation of this article lies in optimizing performance, resource
utilization, cost, and energy efficiency, while enhancing scalability and quality of
service.
➢ From this motivation, we identify the main objectives of this thesis as follow:
o Optimize Task Execution: by reducing the total completion time.
o Achieve Cost Efficiency: by offloading tasks to cost-effective resources and
schedule them to optimize resource usage and reduce costs.
o Improve Energy Efficiency: Optimize task scheduling to minimize energy
usage and leverage local or fog resources to save energy compared to cloudbased solutions.
o Enhance System Scalability.
Introduction / Problem Definition
11
The problem definition of cloud-fog computing involves addressing the
complexities of integrating and managing resources across cloud and fog
environments, optimizing task execution, resource allocation, cost, and energy
efficiency, and ensuring high performance, scalability, and security.
• Integration of Cloud and Fog Computing
• Resource Management and Allocation
• Task Offloading and Scheduling
• The existing problem of application-awareness
• Cost, and Energy Efficiency
Related Work / The baseline method
12
The Simple offloading algorithm
proposed in FogWorkflowSim is a
straightforward approach that
primarily considers task deadlines
and execution times when making
offloading decisions. It prioritizes
meeting deadlines over energy
consumption.
Simple offloading / Cont.
13
 The algorithm prioritizes meeting deadlines over energy
consumption.
 It calculates execution times on different devices based on factors
like network bandwidth and device capabilities.
 The offloading decision is made based on the task's deadline and the
calculated execution times.
 The algorithm is relatively simple and easy to implement.
Simple offloading / Limitations
14
The limitations of Simple offloading:
➢ The algorithm may not be optimal in scenarios where energy consumption is
a critical factor.
➢ It does not consider other factors such as task complexity or resource
availability.
This simple offloading algorithm provides a baseline for understanding the
concept of offloading in IoT-fog-cloud environments. More advanced
algorithms may incorporate additional factors and optimization techniques to
improve performance and energy efficiency.
Related Work / Cont.
15
We have deduced the methods used in this article from the following:
1. The idea of this paper “An energy consumption oriented offloading
algorithm for fog computing”, and Simple offloading strategy
proposed in ‘‘FogWorkflowSim: An automated simulation toolkit
for workflow performance evaluation in fog computing,’’
2. The requirements of real time applications.
System Model / Architecture
16
 The system model in FogWorkflowSim is a
three-tier architecture consisting of:
1. End devices (EDs): These are IoT devices or
mobile devices that generate tasks and may
execute some tasks locally. They have limited
computational resources and may offload
tasks to fog nodes or cloud servers.
2. Fog nodes: These are intermediate devices
located at the network edge. They have more
computational resources than end devices but
less than cloud servers. Fog nodes can process
tasks locally or offload them to cloud servers.
3. Cloud servers: These are powerful servers
located in data centers. They have the highest
computational resources and can handle
complex tasks.
System Model / Assumption
17
❖ Deterministic task execution times.
❖ Tasks can be offloaded between any of the three tiers.
❖ Perfect knowledge of network conditions.
❖ The network bandwidth and latency between devices are known and constant.
❖ The cost of offloading tasks to fog nodes and cloud servers is known.
 The system model also includes parameters for:
• Task execution times on different devices.
• Network bandwidth between devices.
• Energy consumption of devices.
• Cost of computation and communication.
Proposed Methods
18
Three offloading strategies are proposed in this article
They are formulated as an optimization problem that aims to minimize the
total execution time, cost, and energy consumption.
Goal:
❖ develop an offloading algorithm that optimizes execution time, cost, and
energy consumption.
❖ create methods that can effectively handle diverse scenarios, especially
real-time applications.
They are formulated as an optimization problem that aims to minimize the
total execution time, cost, and energy consumption.
Latency Centric Offloading (LCO)
19
LCO is an offloading strategy that prioritizes minimizing task completion
time (latency). It aims to offload tasks to the tier (fog or cloud) that can
execute them most quickly, even if it means incurring higher costs or energy
consumption. This strategy is particularly suitable for applications that have
strict latency requirements, such as real-time video streaming or online
gaming.
Decision-Making Process:
❖ Calculate execution times: For each task, calculate the estimated execution
time on the end device, fog node, and cloud server.
❖ Select the fastest tier: Choose the tier with the shortest estimated execution
time for the task.
❖ Offload if necessary: If the fastest tier is not the end device, offload the
task to that tier.
LCO / Cont.
20
Advantages of LCO:
➢ Low latency: Ensures that tasks are
executed
quickly,
meeting
the
requirements
of
latency-sensitive
applications.
➢ Simple implementation: The decisionmaking process is straightforward and
easy to implement.
Disadvantages of LCO:
➢ High energy consumption: Offloading
tasks to fog or cloud servers can
consume more energy than executing
them locally on the end device
Energy Based Offloading (EBO)
21
EBO is an offloading strategy that prioritizes energy conservation on IoT
devices. It aims to offload tasks to the tier (fog or cloud) that can execute
them with the least energy consumption, even if it means sacrificing some
latency or incurring higher costs. This strategy is particularly suitable for
applications where battery life or energy efficiency is a critical concern.
Decision-Making Process:
❖ Calculate energy consumption: For each task, calculate the estimated
energy consumption on the end device, fog node, and cloud server.
❖ Select the most energy-efficient tier: Choose the tier with the lowest
estimated energy consumption for the task.
❖ Offload if necessary: If the most energy-efficient tier is not the end device,
offload the task to that tier.
EBO / Cont.
22
Advantages of EBO:
➢ Energy conservation: Helps to prolong the
battery life of IoT devices and reduce
energy costs.
➢ Reduced environmental impact: Contributes
to a more sustainable and environmentally
friendly computing environment.
Disadvantages of EBO:
➢ Increased latency: Offloading tasks to fog
or cloud servers may increase their
execution time, leading to higher latency.
➢ Higher costs: Offloading tasks to fog or
cloud servers can incur higher costs due to
data
transmission
and
computation
expenses.
Efficient Offloading (EO)
23
EO is an offloading strategy that aims to strike a balance between task completion time, energy
consumption, and cost. It leverages the hybrid IoT-fog-cloud architecture to exploit the cost differentials
between different resource types.
Decision-Making Process:
❖ Calculate execution times and energy consumption: For each task, calculate the estimated execution
time and energy consumption on the end device, fog node, and cloud server.
❖ Normalize execution times and energy consumption: Normalize the execution times and energy
consumption values to a common scale.
❖ Calculate fitness function: Combine the normalized execution time and energy consumption values
into a fitness function.
❖ Select the optimal tier: Choose the tier with the lowest fitness value. This tier represents the best
balance between execution time and energy consumption for the task.
❖ Offload if necessary: If the optimal tier is not the end device, offload the task to that tier.
EO / Cont.
24
Advantages of EO:
➢ Balanced approach: Considers both
execution
time
and
energy
consumption, providing a more
comprehensive optimization.
➢ Improved efficiency: Can achieve
better overall system performance
by effectively utilizing resources.
Disadvantages of EO:
➢ Complexity: The decision-making
process is more complex than LCO
and EBO.
Task Scheduling using Genetic Algorithm
25
Integration with a genetic algorithm (GA) for task scheduling in IoT-fog-cloud environments. The goal is to
optimize the overall system performance by considering both offloading decisions and task scheduling.
Integration Approach
1.Offloading Decision:
1. EO, EBO, or LCO: Apply one of the proposed offloading strategies to determine the optimal tier for
each task based on the desired objectives (latency, energy, or balance).
2.Task Scheduling:
1. GA: Use a GA to generate a population of potential task schedules. Each individual in the population
represents a specific allocation of tasks to available resources.
2. Fitness Function: Define a fitness function that evaluates the quality of each schedule based on the
chosen objectives. The fitness function can incorporate factors such as makespan, cost, and energy
consumption.
3. Genetic Operators: Apply genetic operators like selection, crossover, and mutation to generate new
individuals and explore the solution space.
3.Iterative Process:
1. Repeat the above steps for multiple generations until a satisfactory solution is found or a termination
criterion is met.
Simulation & Results/ SW &HW Configuration
26
➢ Operating system: windows 10 Enterprise.
➢ The FogWorkflowSim simulator runs on the Eclipse Java
IDE.
➢ Processor: Intel Core. I7- CPU @ 2.80GHZ..
➢ Installed memory (RAM): 16.00 GB.
➢ System type: 64-bit Operating System, x64-based processor.
Simulation & Results/ Simulation Parameters
27
Parameters
End device
Fog VM
Cloud VM
Number of servers/devices
10
6
3
Processing rate (MIPS)
1000
1300
1600
Task execution cost ($)
0
0.48
0.96
Communication cost ($)
0
0.01
0.02
Working power (MW)
700
800
1600
Idle power (MW)
30
40
1300
Uplink bandwidth (Mbps)
20
10
1
Downlink bandwidth (Mbps)
40
10
10
28
Simulation & Results / Performance Metrics
Comparing the results of our proposed method with the Simple offloading
strategy in terms of:
 Total completion time (makespan).
 Average energy consumption.
 Total cost.
Simulation & Results / Makespan
29
Simulation & Results / Cost
30
Simulation & Results / Energy Consumption
31
Conclusions
32
➢ This study proposes three strategies: LCO, EBO, and EO as offloading strategies in IoT-fogcloud environments, with a focus on real-time applications.
➢ LCO is best for time-sensitive tasks but can be more energy-intensive.
➢ EBO focuses on energy efficiency, which is beneficial for long-term and computationally
demanding tasks.
➢ EO seeks a balanced trade-off between time and energy, suited for using resources more
effectively.
➢ This work employs genetic algorithms for task scheduling because they excel at handling
complex solution spaces and dynamic situations.
➢ Combining offloading techniques with task scheduling algorithms provides efficient task
execution.
➢ Overall, these strategies contribute to the optimization and reliability of IoT-fog-cloud
systems in different environments, offering specified solutions for different task
requirements and environmental constraints.
Future Works
33
▪ Future work could focus on incorporating additional
workflow types and increasing the number of tasks in each
workflow.
▪ Investigating the impact of dynamic resource provisioning
and adaptive offloading strategies could also be explored.
Publication
34
 Gamal, M., Awad, S., Abdel-Kader, R. F., & Abd El Salam, K. (2024).
Efficient offloading and task scheduling in internet of things-cloud-fog
environment. International Journal of Electrical and Computer
Engineering (IJECE), 14(4), 4445-4455.
35
Thank you