A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective
Abstract
:1. Introduction
- We elucidate the differences between the operation mode and execution paradigms of edge computing and cloud computing. We analyze each paradigm from multiple aspects, including deployment, distance, latency, computation power, and storage capacity.
- We explain the architecture of edge computing and its collaboration with the end user and the cloud. In addition, we illustrate the network architecture, which encompasses the end-user (things), edge, and cloud components. We also provide an explanation of each layer within this architecture. Moreover, we conduct a comprehensive review of the available computing resources within edge computing. Additionally, we distinguish and outline the distinctive characteristics associated with each resource type. Furthermore, we present advancements in 6G as an emerging technology and consequential impact on edge computing.
- We present a step-by-step explanation of the task scheduling procedure in edge computing and discuss why edge computing is considered a promising approach for offloading time-sensitive and data-sensitive applications.
- We explore the optimization perspectives and objectives presented in state-of-the-art papers on task scheduling and examine how each paper formulates the scheduling problem.
- We categorize the task scheduling techniques into two main categories, distinguished by their operation and execution mode. Moreover, we thoroughly examine each category, presenting a detailed discussion of their characteristics. Additionally, we clarify the advantages and disadvantages inherent in each technique. Furthermore, we construct a table that compares over fifty state-of-the-art works on task scheduling to each other, considering multiple parameters.
- We clarify which task-scheduling techniques appear promising for effectively scheduling time-sensitive applications.
2. Task Scheduling in Edge Computing
- Participants: the components of the network that collaborate on task execution, such as user, edge, and cloud, are the participants [35].
- Resources: edge computing components that provide a service in the network, such as communication resources, storage resources, caching resources, and computing resources [36].
- Methodology: different methods can be utilized to schedule tasks, including centralized and distributed [39].
- Computation offloading: Determining which tasks need to executed by edge computing.
- Resource allocation: Determining which of the edge computing server nodes is the most suitable for the task.
- User mobility: The task scheduling method should regularly check the presence of the end-user as the user might leave or join the covered area.
3. Method
3.1. Research Question
- RQ1: What techniques have been utilized for scheduling the tasks in edge computing?
- o
- Through resolving these investigations, a more comprehensive comprehension of different task scheduling methods can be achieved, facilitating an exploration of the advantages and disadvantages of each method to determine appropriate task scheduling approaches for time-sensitive applications.
- RQ2: What techniques are suitable for scheduling the tasks of time-sensitive applications?
- o
- Answering this inquiry would clarify which task-scheduling technique is more suitable for time-sensitive applications.
3.2. Inclusion Criteria
- IC1: Address the task and resource management on edge computing.
- IC2: Address the task scheduling challenge on edge computing.
- IC3: Address the implementation and development of time-sensitive applications in edge computing.
- IC4: Address task complexity in task scheduling to manage various tasks with different levels of complexity.
- IC5: Address resource availability of computational resources, including processors, memory, and storage, at edge devices into the task scheduling algorithm.
- IC6: Address latency requirements of tasks with strict latency requirements to ensure deadlines are met.
- IC7: Address the optimization of energy consumption in edge devices.
- IC8: Address the optimization of computation latency of edge devices in edge computing.
- IC9: Address security and privacy to ensure the confidentiality and integrity of the data being processed on edge devices.
- IC10: Address network bandwidth availability where data need to be transmitted in the edge network.
- IC11: Address users’ preferences, such as their desired service quality level or willingness to trade off performance for energy savings.
4. Network Architecture
4.1. Things Layer
4.2. Edge Layer
4.3. Cloud Layer
4.4. Network Resources
4.5. 6G Networks
5. Optimization Properties
5.1. Main Viewpoint
- End-user devices: the scheduling techniques consider optimizing parameters such as energy consumption, response time, or cost on the side of end-user devices.
- Edge servers: given the limited computational and storage resources of end-user devices, the proposed scheduling techniques aim to enhance the efficiency of edge servers. Specifically, these techniques strive to minimize energy consumption, improve resource utilization, and minimize costs.
- Hybrid: subsequent studies in this field have focused on hybrid scheduling techniques that optimize the parameters of both end-user devices and edge servers. These studies acknowledge that end-user devices have modest computational resources and explore the offloading of specific tasks to edge servers to improve the overall performance of the edge computing platform, including both end-user devices and edge servers.
5.2. Optimization Objective
6. RQ1: Centralized and Distributed Task Scheduling Techniques
6.1. Centralized Task Scheduling Technique
6.1.1. Convex Optimization
6.1.2. Approximation Algorithms
6.1.3. Heuristic
6.1.4. Metaheuristic
6.1.5. Machine Learning
6.2. Distributed Task Scheduling Techniques
6.2.1. Game Theory
6.2.2. Matching Theory
6.2.3. Auction
6.2.4. Distributed Machine Learning
7. RQ2: Scheduling Real-Time Embedded System Application Tasks
8. Challenges and Future Research Directions
8.1. Requirements of Realtime Systems
8.2. Dynamic Environments and Tasks Dependancy
8.3. Security and Privacy
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Ref. | Edge Computing | Resource and Task Management | Real-Time Perspective | Summary |
---|---|---|---|---|
[17] | Yes | No | No | Presents a classification of industrial aspects benefiting from IoT and edge computing. Proposes two real-world use cases that address urban smart living challenges and proposes a new architecture based on edge-IoT for e-healthcare. |
[18] | Yes | No | No | Explains the issues in the collaboration between edge computing and CPS, reviewing recent papers focusing on and classifying QoS optimization. |
[14] | Yes | No | No | Reviews the research on the collaboration between edge computing and healthcare applications, focusing on architecture and techniques. Discusses the challenges of healthcare applications in edge computing and provide an overview of all data operations. |
[6] | Yes | No | No | Investigates edge computing as a next-generation computing technology. Elaborates on how edge computing can reduce operating costs and enhance security. Analyzes the aspects of data transmission and communication within edge computing. |
[1] | Yes | No | No | Overview of edge computing architecture, applications, and security includes the analysis of potential security risks and vulnerabilities. Several protection methods are explored to mitigate security threats. |
[19] | Yes | No | No | Examines virtualization techniques in computation and networking resources and explore their deployment in edge computing. Investigates the relationship between virtualization techniques and the requirements of IoT services. |
[20] | Yes | No | No | Explains the definitions and core characteristics of edge computing and investigates different application scenarios. |
[21] | Yes | Yes | No | Research works on task offloading are analyzed from a stochastic perspective, and a taxonomy comprising Markov chains, Markov processes, and hidden Markov models is presented. |
[22] | Yes | Yes | No | Reviews recent research on VEC regarding different aspects, presents various VEC applications, and categorize them. |
[23] | Yes | Yes | No | Reviews the papers on resource management in edge computing, wherein different aspects of resource management are explained, including computation offloading, resource allocation, and resource provisioning. |
[24] | Yes | Yes | No | Examines various task scheduling methods in the context of edge computing and explores the relationship between these methods and their corresponding problem formulations. |
[25] | Yes | Yes | No | Reviews resource management methods suitable for cloud, edge, and fog environments. Proposes an assessment framework comprising measurements for resource management algorithms in edge computing. |
[26] | Yes | Yes | No | Reviews the research progress made in edge computing regarding the service placement problem (SPP). Categorizes the various methods employed for task scheduling and other aspects associated with SPP. |
[2] | Yes | Yes | No | Reviews recent research progress in task offloading techniques for edge computing. |
[27] | Yes | Yes | No | Reviews the progress made on energy-aware aspects of edge computing in different domains, including task management. |
[23] | Yes | Yes | No | Explains the edge computing architecture and its collaboration with different task scheduling algorithms and classify recent research on resource management in edge computing. Divides the scheduling algorithms based on their operation mode. |
[12] | Yes | Yes | No | Explains the collaboration between IIoT and edge computing, as well as the related research progress. Provides a review of the advancements achieved in various technical aspects of edge computing, including task scheduling. |
[28] | Yes | Yes | No | Provides an overview of the advancements in computation offloading and categorizes computation offloading models into different classes. Explains the fundamental concepts of computation offloading and discuss various methods utilized in it. |
[29] | Yes | Yes | No | Provides a taxonomy of recent task scheduling algorithms in edge/fog computing. |
[30] | Yes | Yes | No | Reviews the recent research progress of task scheduling algorithms in edge computing, categorizing them based on task dependency and the number of available servers. |
This paper | Yes | Yes | Yes | A comprehensive survey examines the recent progress in task scheduling algorithms. The algorithms are categorized based on their operation mode, problem formulation method, and their suitability for time-sensitive applications. |
Acronym | Definition |
---|---|
WAN | Wide Area Network |
QoS | Quality of Service |
IoT | Internet of Things |
QoE | Quality of Experience |
AR | Augmented Reality |
VR | Virtual Reality |
CAD | Connected and Autonomous Driving |
SLAM | Simultaneous Localization and Mapping |
RSU | Roadside Unit |
UAV | Unmanned Aerial Vehicle |
WBAN | Wireless Body Area Networks |
RQ | Research Question |
IC | Inclusion Criteria |
LTE | Long-Term Evolution |
Wi-Fi | Wireless Fidelity |
CPU | Central Processing Unit |
GPU | Graphics Processing Unit |
ASIC | Application Specific Integrated Circuit |
FPGA | Field Programmable Gate Array |
DAG | Directed Acyclic Graph |
XR | Extended Reality |
AI | Artificial Intelligence |
ML | Machine Learning |
ILP | Integer Linear Programming |
MILP | Mixed Integer Linear Programming |
MINLP | Mixed Integer Non-Linear Programming |
MDP | Markov Decision Process |
ADMM | Alternating Direction Method of Multipliers |
EDF | Earliest Deadline First |
FCFS | First Come First Serve |
NSGA | Non-dominated Sorting Genetic Algorithm |
MOWO | Multi-Objective Whale Optimization |
SLA | Service Level Agreement |
DRL | Deep Reinforcement Learning |
ASA | Simulated Annealing Approach |
DQN | Deep Q-learning Network |
FL | Federated Learning |
LSTM | Long Short-Term Memory |
MAML | Model-agnostic Meta-learning |
IIoT | Industrial Internet of Things |
IoV | Internet of Vehicles |
Characteristic | Cloud Computing | Edge Computing |
---|---|---|
Deployment | Centralized | Distributed |
Distance | High | Low |
Latency | High | Low |
Computation power | Ample | Limited |
Storage capacity | Ample | Limited |
Processor | Characteristic |
---|---|
GPU | High latency High power consumption High flexibility |
ASIC | Low latency Low power consumption Low flexibility |
FPGA | Low latency Low power consumption High flexibility |
Technique | Operation Manner | Advantages | Disadvantages |
---|---|---|---|
Convex optimization | Centralized |
|
|
Approximation | Centralized |
|
|
Heuristic methods | Centralized |
|
|
Meta-heuristic methods | Centralized |
|
|
Machine Learning | Centralized |
|
|
Game Theory | Distributed |
|
|
Matching Theory | Distributed |
|
|
Federated Learning | Distributed |
|
|
Reference | Main Viewpoint | Optimization Goal | Objective Number | Modeling Problem | Utilized Technique | Applicable for Real-Time Task Scheduling |
---|---|---|---|---|---|---|
[57] | edge servers | Energy | Single | MINLP | Convex optimization (Lyapunov technique) | No |
[58] | End-user devices | Privacy, Energy | Multiple | ILP | Convex optimization (Lyapunov technique) | No |
[59] | edge servers | Energy | Single | MINLP | Convex optimization (Lyapunov technique) | No |
[60] | edge servers | Time, Energy, Data transmission | Multiple | MINLP | Convex optimization (Lyapunov technique) | No |
[90] | End-user devices | Energy | Single | MILP | Meta-heuristic (genetic algorithm) | No |
[69] | End-user devices | Time | Single | MILP | Heuristic (EDF) | Yes |
[130] | End-user devices | QoE | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[77] | End-user devices | Time | Single | MIP | Meta-heuristic (genetic algorithm) | Yes |
[131] | End-user devices | Energy, Time, Cost | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[89] | edge servers | Energy | Multiple | MILP | Meta-heuristic (Whale Optimization Algorithm) | No |
[75] | edge servers | Energy, Time | Multiple | ILP | Meta-heuristic (genetic algorithm) | Yes |
[132] | End-user devices | Energy, Time | Multiple | ILP | Machine learning (deep reinforcement learning) | Yes |
[70] | edge servers | Energy | Single | MINLP | Heuristic (semi-greedy) | Yes |
[92] | Hybrid | Energy, Time | Multiple | MILP | Machine learning (deep learning) | No |
[91] | End-user devices | Energy | Single | MINLP | Machine learning (deep learning) | Yes |
[94] | End-user devices | Time | Single | MINLP | Machine learning (deep reinforcement learning) | No |
[71] | End-user devices | Time | Single | MILP | Heuristic (Greedy Algorithm) | Yes |
[76] | Hybrid | Time, Cost | Multiple | MINLP | Meta-heuristic (genetic algorithm) | No |
[133] | End-user devices | Energy, Time | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[134] | Hybrid | Time, Energy | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[135] | Hybrid | Energy, QoS | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[136] | End-user devices | Energy, Task finish ratio | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[61] | End-user devices | Time | Single | MINLP | Heuristic | No |
[63] | edge server | Time | Single | MILP | Heuristic | No |
[62] | edge server | Time, Cost | Multiple | MILP | Heuristic | No |
[64] | edge server | Energy, Time | Multiple | ILP | Heuristic (Lyapunov) | Yes |
[65] | edge server | Cost | Single | MILP | Approximation | No |
[73] | Hybrid | Energy | Single | MILP | Heuristic (variation of FCFS) | No |
[74] | edge server | Time, Energy | Multiple | MILP | Heuristic (Greedy Algorithm) | No |
[87] | edge server | Time, Energy, Cost | Multiple | MINLP | Meta-heuristic (Evolutionary Algorithm) | No |
[76] | Hybrid | Service Level Agreement | Multiple | MINLP | Meta-heuristic (Genetic Algorithm) | No |
[85] | End-user devices | Time | Single | ILP | Meta-heuristic (Ant colony) | No |
[86] | End-user devices | Energy | Single | MILP | Meta-heuristic (Ant colony) | No |
[88] | End-user devices | Energy | Single | MINLP | Meta-heuristic (Genetic algorithm + Particle swarm optimization) | No |
[78] | End-user devices | Time, Energy | Multiple | MILP | Meta-heuristic (NSGA-III) | No |
[72] | End-user devices | Time | Single | ILP | Heuristic (EDF) | Yes |
[59] | edge servers | Energy | Single | MINLP | Convex optimization (Lyapunov technique) | No |
[60] | edge servers | Time, Energy, Data transmission | Multiple | MINLP | Convex optimization (Lyapunov technique) | No |
[90] | End-user devices | Energy | Single | MILP | Meta-heuristic (genetic algorithm) | No |
[69] | End-user devices | Time | Single | MILP | Heuristic (EDF) | Yes |
[130] | End-user devices | QoE | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[77] | End-user devices | Time | Single | MIP | Meta-heuristic (genetic algorithm) | Yes |
[131] | End-user devices | Energy, Time, Cost | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[89] | edge servers | Energy | Multiple | MILP | Meta-heuristic (Whale Optimization Algorithm) | No |
[75] | edge servers | Energy, Time | Multiple | ILP | Meta-heuristic (genetic algorithm) | Yes |
[132] | End-user devices | Energy, Time | Multiple | ILP | Machine learning (deep reinforcement learning) | Yes |
[70] | edge servers | Energy | Single | MINLP | Heuristic (semi-greedy) | Yes |
[92] | Hybrid | Energy, Time | Multiple | MILP | Machine learning (deep learning) | No |
[91] | End-user devices | Energy | Single | MINLP | Machine learning (deep learning) | Yes |
[94] | End-user devices | Time | Single | MINLP | Machine learning (deep reinforcement learning) | No |
[71] | End-user devices | Time | Single | MILP | Heuristic (Greedy Algorithm) | Yes |
[76] | Hybrid | Time, Cost | Multiple | MINLP | Meta-heuristic (genetic algorithm) | No |
[85] | End-user devices | Time | Single | ILP | Meta-heuristic (Ant colony) | No |
[86] | End-user devices | Energy | Single | MILP | Meta-heuristic (Ant colony) | No |
[88] | End-user devices | Energy | Single | MINLP | Meta-heuristic (Genetic algorithm + Particle swarm optimization) | No |
[78] | End-user devices | Time, Energy | Multiple | MILP | Meta-heuristic (NSGA-III) | No |
[72] | End-user devices | Time | Single | ILP | Heuristic (EDF) | Yes |
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Avan, A.; Azim, A.; Mahmoud, Q.H. A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective. Electronics 2023, 12, 2599. https://doi.org/10.3390/electronics12122599
Avan A, Azim A, Mahmoud QH. A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective. Electronics. 2023; 12(12):2599. https://doi.org/10.3390/electronics12122599
Chicago/Turabian StyleAvan, Amin, Akramul Azim, and Qusay H. Mahmoud. 2023. "A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective" Electronics 12, no. 12: 2599. https://doi.org/10.3390/electronics12122599
APA StyleAvan, A., Azim, A., & Mahmoud, Q. H. (2023). A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective. Electronics, 12(12), 2599. https://doi.org/10.3390/electronics12122599