Convergence of Edge Services &amp; Edge Infrastructure

Ranjan, Anurag; Guim, Francesc; Chincholkar, Mandar M.; Ramchandran, Prakash; Mishra, Ranjan; Ranganath, Sunku

doi:10.1109/nfv-sdn53031.2021.9665021

Cited by 8 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Every bar represents total cost of a path. Those costs are calculated using Equation (51). Lengths of segments are shown in Table 2, communication delays are shown in Equation (53) and the hosting cost is being calculated based on the segment hosting shown in Table 1.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…This approach can help to reduce the overall latency and bandwidth requirements of the communication. In addition, edge nodes can also perform other functions such as caching frequently accessed data, filtering or preprocessing data before they are transmitted to the cloud and providing additional security measures such as encryption and access control [ 50 , 51 ]. To demonstrate the idea, consider the topology shown in Figure 2 which shows a system

with three clouds and eight edges.…”

Section: Using Machine Learning For Partial Replication and Selecting...mentioning

confidence: 99%

“…The model built by machine learning helps choosing the optimal candidate for file segment replication based on history data. The cost that is calculated by regression is the presented by π in Equation (51). In this subsection, a comparison is conducted between the performance of the system when utilising machine learning and when selecting a server randomly.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

Mostafa

Kotb

Al-Arnaout

et al. 2023

Sensors

View full text Add to dashboard Cite

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

show abstract

Section: Simulation and Resultsmentioning

confidence: 99%

with three clouds and eight edges.…”

Section: Using Machine Learning For Partial Replication and Selecting...mentioning

confidence: 99%

See 1 more Smart Citation

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

Mostafa

Kotb

Al-Arnaout

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Our solution is related to the following. The convergence of edge services and an edge infrastructure is discussed in [22]. Their core focus is to improve network performance by reducing network degradation.…”

Section: Related Workmentioning

confidence: 99%

Snap IoT: A Decentralized Network Design Model

Reed,

Şaman Tosun,

Korkmaz

2024

IEEE Access

View full text Add to dashboard Cite

IoT edge computing is a network design model that captures and processes data at the network edge. The results are forwarded to a cloud service or, if additional processing is needed, a middle tier. By processing data at the edge and middle tier, edge networks achieve better load-balancing and improve performance; however, traditional edge network deployments represent a rigid participation model. Edge networks require physical access to an IoT device and often lock the device to a single edge network. These constraints make it difficult to construct the ideal network, as they reject IoT devices deployed at the network edge but not owned by the network administrator. Our goal is to remove these limitations by creating a network protocol that supports broader participation of IoT devices, cryptographically secures network data, and improves network performance by increasing captured data at the network edge. The protocol is named Snap to symbolize the ease of self assembly. Our experimental research focuses on temperature stability and the cycle efficiency of an HVAC system by utilizing a Snap network to combine two existing edge networks and increase the number of temperature measurement points. The additional measurement points improved the efficiency of the HVAC cycle strategy by increasing the square footage of measured building space. The additional temperature capture points supported an adjustment to the HVAC cycle strategy which resulted in reducing the disparity between the requested temperature and the resulting temperatures. Snap networks support a broader range of IoT sensors leading to increased measurement density, sample rate frequency, and coverage of the network edge.

show abstract

“…Such integration allows the creation of virtual network function chains and their deployment on edge servers. A Service Function Chaining (SFC) is composed of a set of VNFs chained together in a given order, representing a service (or application) provided to the end-users [45]. Such services take advantage of the lower latency provided by the edge and the flexibility offered by VNFs.…”

Section: Virtual Network Functions Service Function Chains and Edge C...mentioning

confidence: 99%

An Online Ensemble Method for Auto-scaling NFV-based Applications in the Edge

Silva,

Batista,

Delicato

et al. 2023

Preprint

View full text Add to dashboard Cite

The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizing Internet of Things (IoT) applications, particularly in scenarios characterized by high-speed, continuous data generation. Offline ML algorithms are unsuitable for data streams, as they require existing datasets to build prediction models. As an embodiment of ML, which embraces the fact that learning environments change over time, in Online Machine Learning (OML) the model is trained with each new observation in production time. Most OML algorithms are developed after the offline version and present different behaviors, considering bias and variance. Finding a suitable estimator to solve a ML problem is still a challenge. In this context, ensemble learning emerges as a promising approach for balancing the bias-variance tradeoff and improving prediction accuracy by aggregating outputs from multiple ML models. This paper introduces a novel ensemble method tailored for edge computing environments, designed to efficiently operate on resource-constrained devices while accommodating various online learning scenarios. The primary objective is to enhance predictive accuracy at the edge. We conducted extensive experimental evaluations to assess our proposed ensemble's predictive performance using synthetic and real datasets. Our ensemble outperformed state-of-the-art data stream algorithms and ensemble regressors across various regression metrics. Furthermore, we evaluated the ensemble's performance in the context of auto-scaling for Virtual Network Function (VNF)-based applications operating at the network's edge.

show abstract

Convergence of Edge Services & Edge Infrastructure

Cited by 8 publications

References 6 publications

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

Snap IoT: A Decentralized Network Design Model

An Online Ensemble Method for Auto-scaling NFV-based Applications in the Edge

Contact Info

Product

Resources

About