Emerging Research Directions - Revision history

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2025-04-24T23:13:51Z

7.5.5 Latency and Throughput Considerations

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	===7.5.5 Latency and Throughput Considerations===		===7.5.5 Latency and Throughput Considerations===

			Latency and throughput are two pivotal metrics that directly affect the performance and reliability of edge data persistence systems. Their impact is especially critical in applications requiring real-time response and high data fidelity, such as autonomous vehicles, industrial automation, and AR/VR systems.

			Latency refers to the time delay from the generation of data to its availability for consumption or action. Edge computing reduces this delay by processing and storing data closer to the source, circumventing long cloud transmission cycles. However, resource constraints, task prioritization, and variable network conditions at the edge can still introduce latency. According to [1], designing adaptive reference architectures for highly uncertain environments is critical to minimizing latency and ensuring responsiveness.

			Throughput, by contrast, defines the volume of data processed or transmitted within a given time. With edge deployments supporting data-intensive applications like federated learning and predictive maintenance, maintaining high throughput is essential. Federated models, as discussed in [3], rely on consistent, rapid data handling across decentralized nodes without raw data sharing, further stressing throughput capabilities.

			Achieving a balance between low latency and high throughput presents architectural challenges. Caching strategies and lightweight local databases—such as SQLite and Redis—help accelerate data access and reduce I/O bottlenecks. Recent work in MQTT broker optimization using these tools demonstrates significant performance gains in edge scenarios [7]. Embedded databases like RocksDB and SQLite are also recognized for their fast read/write performance, particularly in constrained environments [9].

			Edge object storage, which bypasses hierarchical file systems in favor of flat, scalable models, supports high-throughput access to unstructured data across distributed systems [10]. Partial offloading and adaptive data compression are further techniques that reduce transmission load while preserving speed and data integrity.

			Data consistency protocols also impact throughput. Conflict-Free Replicated Data Types (CRDTs) [6] and replicated database systems [5] allow updates to propagate across distributed nodes without costly synchronization steps, maintaining system fluidity. These solutions enable high availability while tolerating intermittent connections and reducing coordination overhead.

			As edge systems continue to evolve, future research must address bottlenecks by integrating intelligent scheduling, data-aware caching, and predictive throughput modeling. Techniques like edge-aware sharding [11] and fault-tolerant consensus algorithms [2] are among promising directions to ensure edge persistence systems can scale effectively while delivering low-latency and high-throughput performance.

	===7.5.6 Fault Tolerance and Recovery===		===7.5.6 Fault Tolerance and Recovery===

141.215.209.23: /* 7.5 Data Persistence */

2025-04-24T22:42:46Z

7.5 Data Persistence

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	===7.5.7 Edge Persistence in Modern Frameworks===		===7.5.7 Edge Persistence in Modern Frameworks===

	'''Integration with Kubernetes and K3s'''		'''Integration with Kubernetes and K3s'''

			Modern edge computing frameworks increasingly rely on Kubernetes (K8s) and its lightweight derivative K3s to manage containerized workloads at the edge. These orchestration platforms allow edge nodes to operate with greater autonomy and reliability, particularly in disconnected or resource-constrained environments. Persistent Volumes (PVs) in Kubernetes facilitate stable, long-term data storage across container lifecycles, a key requirement for edge persistence [1][2]. K3s, designed specifically for edge deployments, reduces overhead while maintaining compatibility with standard Kubernetes storage classes, enabling efficient orchestration and data management across distributed edge clusters [3].

	'''Leveraging Persistent Volumes in Edge Clusters'''		'''Leveraging Persistent Volumes in Edge Clusters'''

			Persistent Volumes enable critical applications to maintain data integrity during container restarts, node failures, and network partitions. This is especially important in edge environments where connectivity is often intermittent and fault tolerance is paramount. Strategies such as replication, snapshotting, and dynamic provisioning have been adapted from core cloud infrastructure to support edge persistence more efficiently. Lightweight block storage (e.g., SQLite) and in-memory solutions (e.g., Redis) are often used to store state and cache frequently accessed data, thereby reducing latency and maintaining responsiveness [7].

	'''Edge DBs and Lightweight Orchestration Tools'''		'''Edge DBs and Lightweight Orchestration Tools'''

			Embedded and lightweight databases such as SQLite, DuckDB, and RocksDB are increasingly used in edge deployments due to their minimal resource requirements and robust offline capabilities [9]. These databases can seamlessly integrate with messaging systems like MQTT for real-time processing and data synchronization. To ensure consistency and eventual convergence, conflict-free replicated data types (CRDTs) have emerged as a popular model, allowing updates to propagate and resolve autonomously across distributed nodes [6]. In tandem, orchestration tools like K3s simplify cluster management while enabling operators to deploy, monitor, and update stateful services with minimal effort [3][7].

	===7.5.8 Conclusion===		===7.5.8 Conclusion===

			Edge data persistence has become a cornerstone of modern edge computing, enabling applications to operate reliably and independently even in disconnected or volatile conditions. With the integration of Kubernetes-based orchestration, persistent volume strategies, and lightweight embedded databases, developers can maintain consistency, reduce latency, and scale their edge solutions efficiently. Technologies like CRDTs, edge object storage, and federated consensus protocols further enhance fault tolerance and data availability, ensuring that edge systems remain robust, resilient, and secure across diverse deployment scenarios.

	=== References ===		=== References ===

Mattkwan at 14:34, 24 April 2025

2025-04-24T14:34:41Z

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	===7.1.5 Future Trends and Research Directions===		===7.1.5 Future Trends and Research Directions===
			====Computation migration====
	Much research is being done in this emerging field, but a significant push is being done within this topic in the fields of computation migration and task partitioning. That is, finding cooperation between edge devices and not only sharing tasks, but sharing parts within each task [5]. Dividing the task, determining the nature of the subtasks, and finding optimal offloading ratios between edge nodes is a key area of study.		Much research is being done in this emerging field, but a significant push is being done within this topic in the fields of computation migration and task partitioning. That is, finding cooperation between edge devices and not only sharing tasks, but sharing parts within each task [5]. Dividing the task, determining the nature of the subtasks, and finding optimal offloading ratios between edge nodes is a key area of study.

			====Green Computing====
	Another ongoing research direction is in green computing. That is, using renewable resources for electricity to power edge devices. This is an important research direction as computing usage grows in smart cities and more technically advanced societies, but it adds an additional consideration in the field of task and resource scheduling, since energy is consumed in collaboration models for both processing and transmission. Not only that, but the energy provided from renewables can be unstable, requiring dynamic setups to maintain uptime [6].		Another ongoing research direction is in green computing. That is, using renewable resources for electricity to power edge devices. This is an important research direction as computing usage grows in smart cities and more technically advanced societies, but it adds an additional consideration in the field of task and resource scheduling, since energy is consumed in collaboration models for both processing and transmission. Not only that, but the energy provided from renewables can be unstable, requiring dynamic setups to maintain uptime [6].

Mattkwan at 14:33, 24 April 2025

2025-04-24T14:33:58Z

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	===7.1.3 Scheduling Strategies and Algorithms===		===7.1.3 Scheduling Strategies and Algorithms===
			====Collaboration Manners====
	At a high level, the collaboration manner used in an edge computing architecture will determine the system's strengths and weaknesses. The various collaboration models are among devices (things), edge servers, and cloud infrastructure. Things-edge collaboration allows smart devices (things) to offload computation to nearby edge servers, reducing latency and conserving device energy [3]. This model is commonly used in scenarios where rapid responses are crucial, such as in autonomous vehicles or mobile applications. Things-edge-cloud collaboration extends this by leveraging both edge and cloud resources—tasks are dynamically split or redirected based on resource availability and performance goals, enabling scalability for complex, data-intensive applications like AI and 3D sensing for industrial IoT.		At a high level, the collaboration manner used in an edge computing architecture will determine the system's strengths and weaknesses. The various collaboration models are among devices (things), edge servers, and cloud infrastructure. Things-edge collaboration allows smart devices (things) to offload computation to nearby edge servers, reducing latency and conserving device energy [3]. This model is commonly used in scenarios where rapid responses are crucial, such as in autonomous vehicles or mobile applications. Things-edge-cloud collaboration extends this by leveraging both edge and cloud resources—tasks are dynamically split or redirected based on resource availability and performance goals, enabling scalability for complex, data-intensive applications like AI and 3D sensing for industrial IoT.

	Edge-edge and edge-cloud collaboration further enhance system flexibility and efficiency. In edge-edge collaboration, overloaded edge servers can offload tasks to other edge nodes, promoting better load balancing for QoS requirements. Meanwhile, edge-cloud collaboration enables cloud services to offload computation closer to the user. For example, video transcoding taking place on a home WiFi point instead of in the cloud layer [3].		Edge-edge and edge-cloud collaboration further enhance system flexibility and efficiency. In edge-edge collaboration, overloaded edge servers can offload tasks to other edge nodes, promoting better load balancing for QoS requirements. Meanwhile, edge-cloud collaboration enables cloud services to offload computation closer to the user. For example, video transcoding taking place on a home WiFi point instead of in the cloud layer [3].

			====Algorithms====
	The task/resource scheduling can be split between centralized methods vs. distributed methods. Examples of centralized methods include convex optimization, approximate algorithm, heuristic algorithm, and machine learning. Examples of distributed methods include game theory, matching theory, auction, federated learning, and blockchain. The effectivity of each method can be measured by the following performance indicators: latency, energy consumption, cost, utility, profit, and resource utilization [1].		The task/resource scheduling can be split between centralized methods vs. distributed methods. Examples of centralized methods include convex optimization, approximate algorithm, heuristic algorithm, and machine learning. Examples of distributed methods include game theory, matching theory, auction, federated learning, and blockchain. The effectivity of each method can be measured by the following performance indicators: latency, energy consumption, cost, utility, profit, and resource utilization [1].

Mattkwan at 14:32, 24 April 2025

2025-04-24T14:32:43Z

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	===7.1.2 Core Challenges in Scheduling===		===7.1.2 Core Challenges in Scheduling===
			====Dynamic, Real-time Environments====
	The main challenges in task and resource scheduling are primarily the heterogeneous and constrained edge resources that these types of systems work with, the dynamic workloads and real-time requirements that they are deployed in, and privacy and security concerns. On the first point, not only are the devices heterogenous and geographically distributed, the data volume and data attributes are also heterogenous. While challenging, resource/task scheduling can reduce the difficult effects of this aspect of edge computing.		The main challenges in task and resource scheduling are primarily the heterogeneous and constrained edge resources that these types of systems work with, the dynamic workloads and real-time requirements that they are deployed in, and privacy and security concerns. On the first point, not only are the devices heterogenous and geographically distributed, the data volume and data attributes are also heterogenous. While challenging, resource/task scheduling can reduce the difficult effects of this aspect of edge computing.

	In terms of the dynamic workloads and real-time requirements, the main complication stems from user mobility with their devices. Smartphones, connected vehicles (CVs), and other mobile devices are often moving through the world connecting to different nodes over various protocols. Per [2], strategies that determine offloading and cache decisions can be incorrect very quickly after deciding, or its users may move out of range, or into range of a separate setup.		In terms of the dynamic workloads and real-time requirements, the main complication stems from user mobility with their devices. Smartphones, connected vehicles (CVs), and other mobile devices are often moving through the world connecting to different nodes over various protocols. Per [2], strategies that determine offloading and cache decisions can be incorrect very quickly after deciding, or its users may move out of range, or into range of a separate setup.

			====Security and Privacy====
	For security and privacy, the main challenges are categorized as such: system-level, service-level, and data-level. System-level discusses the overall reliability of the edge system, whether by intrusion or malfunction. Service-level discusses user authentication/authorization and validation of offload nodes. Lastly, data-level discusses the trustworthiness and protection of the data as it passes between IoT devices and edge nodes, as the data can contain sensitive information.		For security and privacy, the main challenges are categorized as such: system-level, service-level, and data-level. System-level discusses the overall reliability of the edge system, whether by intrusion or malfunction. Service-level discusses user authentication/authorization and validation of offload nodes. Lastly, data-level discusses the trustworthiness and protection of the data as it passes between IoT devices and edge nodes, as the data can contain sensitive information.

			====Resource Allocation====
	A large portion of research on this topic is also done on resource allocation and computation offloading. Resource allocation focuses on efficiently distributing computing, communication, and storage resources to support offloaded tasks. Some studies consider single-resource allocation (e.g., just bandwidth or CPU cycles), while many optimize joint allocation of multiple resources (e.g., computing and communication). More comprehensive approaches also include caching strategies to reduce latency. Allocation decisions aim to balance energy use, service quality, and operational cost, often using advanced techniques like optimization algorithms or machine learning to dynamically adapt to changing workloads [2].		A large portion of research on this topic is also done on resource allocation and computation offloading. Resource allocation focuses on efficiently distributing computing, communication, and storage resources to support offloaded tasks. Some studies consider single-resource allocation (e.g., just bandwidth or CPU cycles), while many optimize joint allocation of multiple resources (e.g., computing and communication). More comprehensive approaches also include caching strategies to reduce latency. Allocation decisions aim to balance energy use, service quality, and operational cost, often using advanced techniques like optimization algorithms or machine learning to dynamically adapt to changing workloads [2].

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	[[File:Research techniques.png\|600px\|thumb\|center\| Diagram of research techniques in task/resource scheduling [1]]]		[[File:Research techniques.png\|600px\|thumb\|center\| Diagram of research techniques in task/resource scheduling [1]]]

			====Centralized Methods====
	Here, we will discuss these techniques in more detail. Convex optimization is widely used and well-studied for its mathematical rigor and ability to support sub-optimal yet efficient solutions, such as through Lyapunov optimization for online decision-making. However, its practicality in real-world deployments is limited by the complexity of solving certain problems in parallel. Simpler methods like approximate algorithms—including Markov Decision Processes and k-means clustering—offer more flexibility and ease of implementation but often suffer from local optima and unreliable performance. Similarly, heuristic algorithms, often based on greedy strategies, provide quick and practical solutions but may also fail to reach the global optimum. Machine learning techniques, especially deep learning, scale effectively with hardware and can model complex non-linear patterns, yet they introduce challenges in explainability and require intensive training [4].		Here, we will discuss these techniques in more detail. Convex optimization is widely used and well-studied for its mathematical rigor and ability to support sub-optimal yet efficient solutions, such as through Lyapunov optimization for online decision-making. However, its practicality in real-world deployments is limited by the complexity of solving certain problems in parallel. Simpler methods like approximate algorithms—including Markov Decision Processes and k-means clustering—offer more flexibility and ease of implementation but often suffer from local optima and unreliable performance. Similarly, heuristic algorithms, often based on greedy strategies, provide quick and practical solutions but may also fail to reach the global optimum. Machine learning techniques, especially deep learning, scale effectively with hardware and can model complex non-linear patterns, yet they introduce challenges in explainability and require intensive training [4].

			====Decentralized & Distributed Methods====
	There are several techniques in the decentralized and distributed methods side as well. Game theory models users as rational agents seeking equilibrium, providing practical yet possibly sub-optimal solutions. Matching theory is useful for binary offloading scenarios but struggles with more nuanced partial offloading. Auction-based models mirror economic systems, aligning task requests with resource availability. Meanwhile, federated learning addresses privacy and scalability by training models collaboratively across devices without sharing raw data, though it requires careful orchestration. Blockchain, another decentralized approach, ensures data integrity and trust in scheduling decisions but introduces significant latency, making it less suitable for real-time tasks. These decentralized strategies are increasingly critical as edge networks grow more heterogeneous and resource-constrained [4].		There are several techniques in the decentralized and distributed methods side as well. Game theory models users as rational agents seeking equilibrium, providing practical yet possibly sub-optimal solutions. Matching theory is useful for binary offloading scenarios but struggles with more nuanced partial offloading. Auction-based models mirror economic systems, aligning task requests with resource availability. Meanwhile, federated learning addresses privacy and scalability by training models collaboratively across devices without sharing raw data, though it requires careful orchestration. Blockchain, another decentralized approach, ensures data integrity and trust in scheduling decisions but introduces significant latency, making it less suitable for real-time tasks. These decentralized strategies are increasingly critical as edge networks grow more heterogeneous and resource-constrained [4].

Mattkwan at 14:28, 24 April 2025

2025-04-24T14:28:59Z

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	*Methodology: strategies to best tackle the actions mentioned above, categorized as centralized and decentralized		*Methodology: strategies to best tackle the actions mentioned above, categorized as centralized and decentralized

	[[File:Edge arch.png\|600px\|thumb\|center\| Architecture and examples of cloud vs edge vs thing/user]]		Aspects of these factors will be discussed below.

			[[File:Edge arch.png\|600px\|thumb\|center\| Architecture and examples of cloud vs edge vs thing/user [1]]]

	===7.1.2 Core Challenges in Scheduling===		===7.1.2 Core Challenges in Scheduling===
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	For security and privacy, the main challenges are categorized as such: system-level, service-level, and data-level. System-level discusses the overall reliability of the edge system, whether by intrusion or malfunction. Service-level discusses user authentication/authorization and validation of offload nodes. Lastly, data-level discusses the trustworthiness and protection of the data as it passes between IoT devices and edge nodes, as the data can contain sensitive information.		For security and privacy, the main challenges are categorized as such: system-level, service-level, and data-level. System-level discusses the overall reliability of the edge system, whether by intrusion or malfunction. Service-level discusses user authentication/authorization and validation of offload nodes. Lastly, data-level discusses the trustworthiness and protection of the data as it passes between IoT devices and edge nodes, as the data can contain sensitive information.

	A large portion of research on this topic is done on resource allocation and computation offloading. Resource allocation focuses on efficiently distributing computing, communication, and storage resources to support offloaded tasks. Some studies consider single-resource allocation (e.g., just bandwidth or CPU cycles), while many optimize joint allocation of multiple resources (e.g., computing and communication). More comprehensive approaches also include caching strategies to reduce latency. Allocation decisions aim to balance energy use, service quality, and operational cost, often using advanced techniques like optimization algorithms or machine learning to dynamically adapt to changing workloads.		A large portion of research on this topic is also done on resource allocation and computation offloading. Resource allocation focuses on efficiently distributing computing, communication, and storage resources to support offloaded tasks. Some studies consider single-resource allocation (e.g., just bandwidth or CPU cycles), while many optimize joint allocation of multiple resources (e.g., computing and communication). More comprehensive approaches also include caching strategies to reduce latency. Allocation decisions aim to balance energy use, service quality, and operational cost, often using advanced techniques like optimization algorithms or machine learning to dynamically adapt to changing workloads [2].

	Computation offloading in edge computing determines whether and how much of a task should be processed locally or offloaded to another node (edge or cloud). Offloading can occur as both vertical- or horizontal-offloading – that is, device-to-edge, edge-to-cloud, cloud-to-edge, edge-to-edge, or even device-to-device – and helps optimize factors like latency, energy consumption, and system cost. Offloading can be binary (all or nothing) or partial (a portion of the task is offloaded), and decisions depend on resource availability, task size, and QoS requirements.		Computation offloading in edge computing determines whether and how much of a task should be processed locally or offloaded to another node (edge or cloud). Offloading can occur as both vertical- or horizontal-offloading – that is, device-to-edge, edge-to-cloud, cloud-to-edge, edge-to-edge, or even device-to-device – and helps optimize factors like latency, energy consumption, and system cost. Offloading can be binary (all or nothing) or partial (a portion of the task is offloaded), and decisions depend on resource availability, task size, and QoS requirements [3].

	===7.1.3 Scheduling Strategies and Algorithms===		===7.1.3 Scheduling Strategies and Algorithms===

	At a high level, the collaboration manner used in an edge computing architecture will determine the system's strengths and weaknesses. The various collaboration models are among devices (things), edge servers, and cloud infrastructure. Things-edge collaboration allows smart devices (things) to offload computation to nearby edge servers, reducing latency and conserving device energy [3]. This model is commonly used in scenarios where rapid responses are crucial, such as in autonomous vehicles or mobile applications. Things-edge-cloud collaboration extends this by leveraging both edge and cloud resources—tasks are dynamically split or redirected based on resource availability and performance goals, enabling scalability for complex, data-intensive applications like AI and 3D sensing for industrial IoT.		At a high level, the collaboration manner used in an edge computing architecture will determine the system's strengths and weaknesses. The various collaboration models are among devices (things), edge servers, and cloud infrastructure. Things-edge collaboration allows smart devices (things) to offload computation to nearby edge servers, reducing latency and conserving device energy [3]. This model is commonly used in scenarios where rapid responses are crucial, such as in autonomous vehicles or mobile applications. Things-edge-cloud collaboration extends this by leveraging both edge and cloud resources—tasks are dynamically split or redirected based on resource availability and performance goals, enabling scalability for complex, data-intensive applications like AI and 3D sensing for industrial IoT.
	Edge-edge and edge-cloud collaboration further enhance system flexibility and efficiency. In edge-edge collaboration, overloaded edge servers can offload tasks to other edge nodes, promoting better load balancing for QoS requirements. Meanwhile, edge-cloud collaboration enables cloud services to offload computation closer to the user. For example, video transcoding taking place on a home WiFi point instead of in the cloud layer.

			Edge-edge and edge-cloud collaboration further enhance system flexibility and efficiency. In edge-edge collaboration, overloaded edge servers can offload tasks to other edge nodes, promoting better load balancing for QoS requirements. Meanwhile, edge-cloud collaboration enables cloud services to offload computation closer to the user. For example, video transcoding taking place on a home WiFi point instead of in the cloud layer [3].

	The task/resource scheduling can be split between centralized methods vs. distributed methods. Examples of centralized methods include convex optimization, approximate algorithm, heuristic algorithm, and machine learning. Examples of distributed methods include game theory, matching theory, auction, federated learning, and blockchain. The effectivity of each method can be measured by the following performance indicators: latency, energy consumption, cost, utility, profit, and resource utilization [1].		The task/resource scheduling can be split between centralized methods vs. distributed methods. Examples of centralized methods include convex optimization, approximate algorithm, heuristic algorithm, and machine learning. Examples of distributed methods include game theory, matching theory, auction, federated learning, and blockchain. The effectivity of each method can be measured by the following performance indicators: latency, energy consumption, cost, utility, profit, and resource utilization [1].

	[[File:Research techniques.png\|600px\|thumb\|center\| Diagram of research techniques in task/resource scheduling]]		[[File:Research techniques.png\|600px\|thumb\|center\| Diagram of research techniques in task/resource scheduling [1]]]

	Here, we will discuss these techniques in more detail. Convex optimization is widely used and well-studied for its mathematical rigor and ability to support sub-optimal yet efficient solutions, such as through Lyapunov optimization for online decision-making. However, its practicality in real-world deployments is limited by the complexity of solving certain problems in parallel. Simpler methods like approximate algorithms—including Markov Decision Processes and k-means clustering—offer more flexibility and ease of implementation but often suffer from local optima and unreliable performance. Similarly, heuristic algorithms, often based on greedy strategies, provide quick and practical solutions but may also fail to reach the global optimum. Machine learning techniques, especially deep learning, scale effectively with hardware and can model complex non-linear patterns, yet they introduce challenges in explainability and require intensive training.		Here, we will discuss these techniques in more detail. Convex optimization is widely used and well-studied for its mathematical rigor and ability to support sub-optimal yet efficient solutions, such as through Lyapunov optimization for online decision-making. However, its practicality in real-world deployments is limited by the complexity of solving certain problems in parallel. Simpler methods like approximate algorithms—including Markov Decision Processes and k-means clustering—offer more flexibility and ease of implementation but often suffer from local optima and unreliable performance. Similarly, heuristic algorithms, often based on greedy strategies, provide quick and practical solutions but may also fail to reach the global optimum. Machine learning techniques, especially deep learning, scale effectively with hardware and can model complex non-linear patterns, yet they introduce challenges in explainability and require intensive training [4].

	There are several techniques in the decentralized and distributed methods side as well. Game theory models users as rational agents seeking equilibrium, providing practical yet possibly sub-optimal solutions. Matching theory is useful for binary offloading scenarios but struggles with more nuanced partial offloading. Auction-based models mirror economic systems, aligning task requests with resource availability. Meanwhile, federated learning addresses privacy and scalability by training models collaboratively across devices without sharing raw data, though it requires careful orchestration. Blockchain, another decentralized approach, ensures data integrity and trust in scheduling decisions but introduces significant latency, making it less suitable for real-time tasks. These decentralized strategies are increasingly critical as edge networks grow more heterogeneous and resource-constrained.		There are several techniques in the decentralized and distributed methods side as well. Game theory models users as rational agents seeking equilibrium, providing practical yet possibly sub-optimal solutions. Matching theory is useful for binary offloading scenarios but struggles with more nuanced partial offloading. Auction-based models mirror economic systems, aligning task requests with resource availability. Meanwhile, federated learning addresses privacy and scalability by training models collaboratively across devices without sharing raw data, though it requires careful orchestration. Blockchain, another decentralized approach, ensures data integrity and trust in scheduling decisions but introduces significant latency, making it less suitable for real-time tasks. These decentralized strategies are increasingly critical as edge networks grow more heterogeneous and resource-constrained [4].

	===7.1.5 Future Trends and Research Directions===		===7.1.5 Future Trends and Research Directions===
	Much research is being done in this emerging field, but a significant push is being done within this topic in the fields of computation migration and task partitioning. That is, finding cooperation between edge devices and not only sharing tasks, but sharing parts within each task. Dividing the task, determining the nature of the subtasks, and finding optimal offloading ratios between edge nodes is a key area of study.		Much research is being done in this emerging field, but a significant push is being done within this topic in the fields of computation migration and task partitioning. That is, finding cooperation between edge devices and not only sharing tasks, but sharing parts within each task [5]. Dividing the task, determining the nature of the subtasks, and finding optimal offloading ratios between edge nodes is a key area of study.

	Another ongoing research direction is in green computing. That is, using renewable resources for electricity to power edge devices. This is an important research direction as computing usage grows in smart cities and more technically advanced societies, but it adds an additional consideration in the field of task and resource scheduling, since energy is consumed in collaboration models for both processing and transmission. Not only that, but the energy provided from renewables can be unstable, requiring dynamic setups to maintain uptime [6].		Another ongoing research direction is in green computing. That is, using renewable resources for electricity to power edge devices. This is an important research direction as computing usage grows in smart cities and more technically advanced societies, but it adds an additional consideration in the field of task and resource scheduling, since energy is consumed in collaboration models for both processing and transmission. Not only that, but the energy provided from renewables can be unstable, requiring dynamic setups to maintain uptime [6].


	[[File:Research issues.png\|600px\|thumb\|center\| Diagram of categorized research issues]]		[[File:Research issues.png\|600px\|thumb\|center\| Diagram of categorized research issues [1]]]

	===7.1.6 Conclusion===		===7.1.6 Conclusion===
	*Key takeaways and ~~impact~~ of scheduling on edge ~~ecosystems~~		Here we have discussed task and resource as an exciting path forward to unlocking the full potential of edge computing. As edge systems continue to evolve and scale, the ability to intelligently manage computation offloading, resource allocation, and collaboration across heterogeneous and distributed nodes becomes increasingly important. These scheduling mechanisms can not only reduce latency and energy consumption but also enable scalability, resilience, and responsiveness in real-world applications, such as autonomous vehicles and industrial IoT networks. This emerging work will allow edge computing systems to operate more efficiently, securely, and adaptively in dynamic environments.

	===References===		===References===
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	[2] Lin, Li, et al. "Computation offloading toward edge computing." Proceedings of the IEEE 107.8 (2019): 1584-1607.		[2] Lin, Li, et al. "Computation offloading toward edge computing." Proceedings of the IEEE 107.8 (2019): 1584-1607.

	[3] C. Wang, C. Dong, J. Qin, X. Yang, and W. Wen, “Energy-efficient offloading policy for resource allocation in distributed mobile edge computing,” in Proc. IEEE Symp. Comput. Commun. (ISCC), 2018, pp. 366–372.		[3] Islam, Akhirul, et al. "A survey on task offloading in multi-access edge computing." Journal of Systems Architecture 118 (2021): 102225.

	~~[3] Jiang~~, ~~Congfeng~~, et al. "~~Energy aware~~ edge computing~~: A survey~~." ~~Computer Communications 151~~ (~~2020~~): ~~556-580~~.

	[4] Khan, Wazir Zada, et al. "Edge computing: A survey." Future Generation Computer Systems 97 (2019): 219-235.		[4] Khan, Wazir Zada, et al. "Edge computing: A survey." Future Generation Computer Systems 97 (2019): 219-235.

	[5] ~~Islam~~, ~~Akhirul~~, ~~et al~~. ~~"A survey~~ on ~~task offloading~~ in ~~multi-access~~ edge computing.~~" Journal of Systems Architecture 118~~ (~~2021~~)~~: 102225~~.		[5] K. Xiao, Z. Gao, C. Yao, Q. Wang, Z. Mo, and Y. Yang, “Task offloading and resources allocation based on fairness in edge computing,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), 2019, pp. 1–6.

	[6] ~~J. Feng~~, ~~Q. Pei~~, F. ~~R. Yu, X. Chu, and B. Shang, “Computation offloading and resource allocation for wireless powered mobile~~ edge computing ~~with latency constraint,” IEEE Wireless Commun. Lett., vol. 8, no. 5, pp. 1320–1323, Oct~~. ~~2019~~.		[6] Jiang, Congfeng, et al. "Energy aware edge computing: A survey." Computer Communications 151 (2020): 556-580.

	== 7.2 Edge for AR/VR ==		== 7.2 Edge for AR/VR ==

Mattkwan: /* 7.1.5 Future Trends and Research Directions */

2025-04-24T14:11:30Z

7.1.5 Future Trends and Research Directions

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	===7.1.5 Future Trends and Research Directions===		===7.1.5 Future Trends and Research Directions===
	*Federated learning for decentralized scheduling		Much research is being done in this emerging field, but a significant push is being done within this topic in the fields of computation migration and task partitioning. That is, finding cooperation between edge devices and not only sharing tasks, but sharing parts within each task. Dividing the task, determining the nature of the subtasks, and finding optimal offloading ratios between edge nodes is a key area of study.

	Another ongoing research direction is in green computing. That is, using renewable resources for electricity to power edge devices. This is an important research direction as computing usage grows in smart cities and more technically advanced societies, but it adds an additional consideration in the field of task and resource scheduling, since energy is consumed in collaboration models for both processing and transmission. Not only that, but the energy provided from renewables can be unstable, requiring dynamic setups to maintain uptime [6].		Another ongoing research direction is in green computing. That is, using renewable resources for electricity to power edge devices. This is an important research direction as computing usage grows in smart cities and more technically advanced societies, but it adds an additional consideration in the field of task and resource scheduling, since energy is consumed in collaboration models for both processing and transmission. Not only that, but the energy provided from renewables can be unstable, requiring dynamic setups to maintain uptime [6].

	*Open challenges in adaptive and autonomous scheduling

	[[File:Research issues.png\|600px\|thumb\|center\| Diagram of categorized research issues]]		[[File:Research issues.png\|600px\|thumb\|center\| Diagram of categorized research issues]]


	===7.1.6 Conclusion===		===7.1.6 Conclusion===
	*Key takeaways and impact of scheduling on edge ecosystems		*Key takeaways and impact of scheduling on edge ecosystems

Mattkwan at 13:59, 24 April 2025

2025-04-24T13:59:34Z

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	For security and privacy, the main challenges are categorized as such: system-level, service-level, and data-level. System-level discusses the overall reliability of the edge system, whether by intrusion or malfunction. Service-level discusses user authentication/authorization and validation of offload nodes. Lastly, data-level discusses the trustworthiness and protection of the data as it passes between IoT devices and edge nodes, as the data can contain sensitive information.		For security and privacy, the main challenges are categorized as such: system-level, service-level, and data-level. System-level discusses the overall reliability of the edge system, whether by intrusion or malfunction. Service-level discusses user authentication/authorization and validation of offload nodes. Lastly, data-level discusses the trustworthiness and protection of the data as it passes between IoT devices and edge nodes, as the data can contain sensitive information.

			A large portion of research on this topic is done on resource allocation and computation offloading. Resource allocation focuses on efficiently distributing computing, communication, and storage resources to support offloaded tasks. Some studies consider single-resource allocation (e.g., just bandwidth or CPU cycles), while many optimize joint allocation of multiple resources (e.g., computing and communication). More comprehensive approaches also include caching strategies to reduce latency. Allocation decisions aim to balance energy use, service quality, and operational cost, often using advanced techniques like optimization algorithms or machine learning to dynamically adapt to changing workloads.

			Computation offloading in edge computing determines whether and how much of a task should be processed locally or offloaded to another node (edge or cloud). Offloading can occur as both vertical- or horizontal-offloading – that is, device-to-edge, edge-to-cloud, cloud-to-edge, edge-to-edge, or even device-to-device – and helps optimize factors like latency, energy consumption, and system cost. Offloading can be binary (all or nothing) or partial (a portion of the task is offloaded), and decisions depend on resource availability, task size, and QoS requirements.

	===7.1.3 Scheduling Strategies and Algorithms===		===7.1.3 Scheduling Strategies and Algorithms===

			At a high level, the collaboration manner used in an edge computing architecture will determine the system's strengths and weaknesses. The various collaboration models are among devices (things), edge servers, and cloud infrastructure. Things-edge collaboration allows smart devices (things) to offload computation to nearby edge servers, reducing latency and conserving device energy [3]. This model is commonly used in scenarios where rapid responses are crucial, such as in autonomous vehicles or mobile applications. Things-edge-cloud collaboration extends this by leveraging both edge and cloud resources—tasks are dynamically split or redirected based on resource availability and performance goals, enabling scalability for complex, data-intensive applications like AI and 3D sensing for industrial IoT.
			Edge-edge and edge-cloud collaboration further enhance system flexibility and efficiency. In edge-edge collaboration, overloaded edge servers can offload tasks to other edge nodes, promoting better load balancing for QoS requirements. Meanwhile, edge-cloud collaboration enables cloud services to offload computation closer to the user. For example, video transcoding taking place on a home WiFi point instead of in the cloud layer.


	The task/resource scheduling can be split between centralized methods vs. distributed methods. Examples of centralized methods include convex optimization, approximate algorithm, heuristic algorithm, and machine learning. Examples of distributed methods include game theory, matching theory, auction, federated learning, and blockchain. The effectivity of each method can be measured by the following performance indicators: latency, energy consumption, cost, utility, profit, and resource utilization [1].		The task/resource scheduling can be split between centralized methods vs. distributed methods. Examples of centralized methods include convex optimization, approximate algorithm, heuristic algorithm, and machine learning. Examples of distributed methods include game theory, matching theory, auction, federated learning, and blockchain. The effectivity of each method can be measured by the following performance indicators: latency, energy consumption, cost, utility, profit, and resource utilization [1].

	[[File:Research techniques.png\|600px\|thumb\|center\| Diagram of research techniques in task/resource scheduling]]		[[File:Research techniques.png\|600px\|thumb\|center\| Diagram of research techniques in task/resource scheduling]]

	~~===7~~.~~1.4 Edge~~-~~Cloud Collaboration===~~		Here, we will discuss these techniques in more detail. Convex optimization is widely used and well-studied for its mathematical rigor and ability to support sub-optimal yet efficient solutions, such as through Lyapunov optimization for online decision-making. However, its practicality in real-world deployments is limited by the complexity of solving certain problems in parallel. Simpler methods like approximate algorithms—including Markov Decision Processes and k-means clustering—offer more flexibility and ease of implementation but often suffer from local optima and unreliable performance. Similarly, heuristic algorithms, often based on greedy strategies, provide quick and practical solutions but may also fail to reach the global optimum. Machine learning techniques, especially deep learning, scale effectively with hardware and can model complex non-linear patterns, yet they introduce challenges in explainability and require intensive training.
	*Task offloading strategies and decision ~~models~~
	**Computational perspective		There are several techniques in the decentralized and distributed methods side as well. Game theory models users as rational agents seeking equilibrium, providing practical yet possibly sub-optimal solutions. Matching theory is useful for binary offloading scenarios but struggles with more nuanced partial offloading. Auction-based models mirror economic systems, aligning task requests with resource availability. Meanwhile, federated learning addresses privacy and scalability by training models collaboratively across devices without sharing raw data, though it requires careful orchestration. Blockchain, another decentralized approach, ensures data integrity and trust in scheduling decisions but introduces significant latency, making it less suitable for real-time tasks. These decentralized strategies are increasingly critical as edge networks grow more heterogeneous and resource-constrained.
	***Centralized, ~~decentralized, distributed~~
	**Decision-~~making perspective~~
	***Cloud-assisted, ~~ue driven~~, ~~sdn~~ based, ~~collaborative~~
	**Algorithmic paradigm
	***Greedy heuristics, ~~integer programming~~, ~~machine~~ learning, ~~branch and bound~~, ~~dynamic programming~~, ~~convex optimization~~
	*Hybrid scheduling ~~frameworks~~ for ~~improved performance~~

	===7.1.5 Future Trends and Research Directions===		===7.1.5 Future Trends and Research Directions===
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	[2] Lin, Li, et al. "Computation offloading toward edge computing." Proceedings of the IEEE 107.8 (2019): 1584-1607.		[2] Lin, Li, et al. "Computation offloading toward edge computing." Proceedings of the IEEE 107.8 (2019): 1584-1607.

			[3] C. Wang, C. Dong, J. Qin, X. Yang, and W. Wen, “Energy-efficient offloading policy for resource allocation in distributed mobile edge computing,” in Proc. IEEE Symp. Comput. Commun. (ISCC), 2018, pp. 366–372.

	[3] Jiang, Congfeng, et al. "Energy aware edge computing: A survey." Computer Communications 151 (2020): 556-580.		[3] Jiang, Congfeng, et al. "Energy aware edge computing: A survey." Computer Communications 151 (2020): 556-580.

Mattkwan at 13:49, 24 April 2025

2025-04-24T13:49:41Z

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	===7.1.5 Future Trends and Research Directions===		===7.1.5 Future Trends and Research Directions===
	*Federated learning for decentralized scheduling		*Federated learning for decentralized scheduling
	*Green computing and ~~sustainability considerations~~
	**“Although marvelous solutions are proposed in ~~existing works~~, ~~most of them consider the extra~~ energy ~~can be harvested continuously [72], [186]~~. ~~However, in practice~~, the energy ~~harvesting process may~~ be unstable, ~~which poses a significant challenge in designing an efficient resource scheduling strategy~~.”		Another ongoing research direction is in green computing. That is, using renewable resources for electricity to power edge devices. This is an important research direction as computing usage grows in smart cities and more technically advanced societies, but it adds an additional consideration in the field of task and resource scheduling, since energy is consumed in collaboration models for both processing and transmission. Not only that, but the energy provided from renewables can be unstable, requiring dynamic setups to maintain uptime [6].

	*Open challenges in adaptive and autonomous scheduling		*Open challenges in adaptive and autonomous scheduling

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	[5] Islam, Akhirul, et al. "A survey on task offloading in multi-access edge computing." Journal of Systems Architecture 118 (2021): 102225.		[5] Islam, Akhirul, et al. "A survey on task offloading in multi-access edge computing." Journal of Systems Architecture 118 (2021): 102225.

			[6] J. Feng, Q. Pei, F. R. Yu, X. Chu, and B. Shang, “Computation offloading and resource allocation for wireless powered mobile edge computing with latency constraint,” IEEE Wireless Commun. Lett., vol. 8, no. 5, pp. 1320–1323, Oct. 2019.

	== 7.2 Edge for AR/VR ==		== 7.2 Edge for AR/VR ==

Airikuh: /* 7.2 Edge for AR/VR */

2025-04-23T20:55:30Z

7.2 Edge for AR/VR

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	\| A Conceptual Framework for Integrating Building Information Modelling & Augmented Reality (3D Architectural Visualizations)		\| A Conceptual Framework for Integrating Building Information Modelling & Augmented Reality (3D Architectural Visualizations)
	\|}		\|}



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	=== VR, What Is It? ===		=== VR, What Is It? ===
	Virtual Reality (VR) is a technology that fully immerses users in a virtual world.		Virtual Reality (VR) is a technology that fully immerses users in a virtual world through a three dimensional artificial environment.


	=== Features of VR ===		=== Features of VR ===
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	'''Interaction:'''		'''Interaction:'''
	* The degree to which a user can alter the virtual world is referred to as interaction. Full interaction is made possible through VR Systems, such as head mounted goggles, special gloves, and even wired clothing. In this manner, users can fully interact with the virtual world, viewing it from contrasting angles and even reshaping the environment. On the other end of the spectrum, little to no interaction is available when users see a 3D movie. Here only one view is possible, with the story unable to be interacted with by users [2].		* The degree to which a user can alter the virtual world is referred to as interaction. Full interaction is made possible through VR Systems, such as head mounted goggles, special gloves, and even wired clothing. In this manner, users can fully interact with the virtual world, viewing it from contrasting angles and even reshaping the environment. On the other end of the spectrum, little to no interaction is available when users see a 3D movie. Here only one view is possible, with the story unable to be interacted with by users [2].




	=== Types of VR ===		=== Types of VR ===
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	'''Fully Immersive VR'''		'''Fully Immersive VR'''
	* The most immersive VR, Fully Immersive VR, allows the user to be completely engulfed in the virtual reality experience. This is accomplished by Head-Mounted Displays (HMDs) and data gloves.		* The most immersive VR, Fully Immersive VR, allows the user to be completely engulfed in the virtual reality experience. This is accomplished by Head-Mounted Displays (HMDs) and data gloves.



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	\| Unreleased Intel Alloy		\| Unreleased Intel Alloy
	\|}		\|}



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	* Limited Ability to Track Eye Movements and Facial Expressions		* Limited Ability to Track Eye Movements and Facial Expressions
	* Rendering Issues		* Rendering Issues



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	Due to the need for high computational power, advanced graphics capabilities, and accuracy of real-time depth sensing and perception, many applications currently face rendering issues. Such errors and delays in the rendering process adversely impact the user’s experience, as these issues lead to insufficient and inaccurate data [5].		Due to the need for high computational power, advanced graphics capabilities, and accuracy of real-time depth sensing and perception, many applications currently face rendering issues. Such errors and delays in the rendering process adversely impact the user’s experience, as these issues lead to insufficient and inaccurate data [5].