Migration-Aware Optimized Resource Allocation in B5G Edge Networks

Prastowo, Tadeus; Shah, Ayub; Palopoli, Luigi; Piro, Giuseppe

doi:10.1109/ccnc49033.2022.9700644

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Task migration is a significant approach to alleviating end-device resource restrictions. MEC-based systems require efficient migration schemes to achieve the required QoS [32]. The current task migration schemes make migration decisions based on the assumption that the migration service node has been established.…”

Section: Introductionmentioning

confidence: 99%

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Ateya¹,

Alhussan²,

Abdallah³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeterwave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator's infrastructure. The obtained results are introduced and discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Ateya¹,

Alhussan²,

Abdallah³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…In addition, when there are a huge number of sensors, edge AI scales much better, both in terms of communication overhead and of computation power required for centralized inference [ 6 ]. Finally, edge AI plays a central role when a fast closed loop is needed for real-time control purposes within industrial or harsh environments [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI

Pistellato

Bergamasco

Bigaglia

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable Gate Array) solutions for network inference is gaining massive attention from researchers and companies as well. In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). Such layers are designed to be effectively trained on classical GPUs (Graphics Processing Units) and then synthesized to FPGA hardware for real-time inference. The idea is to provide a trainable quantization layer, called Requantizer, acting both as a non-linear activation for neurons and a value rescaler to match the desired bit precision. This way, the training is not only quantization-aware, but also capable of estimating the optimal scaling coefficients to accommodate both the non-linear nature of the activations and the constraints imposed by the limited precision. In the experimental section, we test the performance of this kind of model while working both on classical PC hardware and a case-study implementation of a signal peak detection device running on a real FPGA. We employ TensorFlow Lite for training and comparison, and use Xilinx FPGAs and Vivado for synthesis and implementation. The results show an accuracy of the quantized networks close to the floating point version, without the need for representative data for calibration as in other approaches, and performance that is better than dedicated peak detection algorithms. The FPGA implementation is able to run in real time at a rate of four gigapixels per second with moderate hardware resources, while achieving a sustained efficiency of 0.5 TOPS/W (tera operations per second per watt), in line with custom integrated hardware accelerators.

show abstract

Architectural Exploration and Design for Ultra-Reliable Low-Latency Indoor Robotics Systems

Shah,

Passerone

2024

2024 IEEE 21st Consumer Communications &Amp; Networking Conference (CCNC)

View full text Add to dashboard Cite

Modern day communication systems are evolving to support computation-intensive and communication-sensitive applications that impose diverse quality of service requirements on the network in terms of latency, reliability, and bandwidth. Applications such as autonomous vehicles, industrial automation, and remote surgery will require ultra-reliable and low-latency communication, paving the way towards resourceful servers closer to user, i.e., multi-access edge computing. However, the stringent requirements on both communication and computation make effective network control difficult. The high dynamicity of the involved processing and interaction patterns requires planning and deployment of architectures with optimal design and cross-optimization of computation and communication resources. In this work, we address the design problem with QoS guarantees and architectural exploration and optimization, to provide computing and communication resources, accounting for dynamic mobility, traffic, and application patterns in the context of edge servers.

show abstract

Migration-Aware Optimized Resource Allocation in B5G Edge Networks

Cited by 4 publications

References 29 publications

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI

Architectural Exploration and Design for Ultra-Reliable Low-Latency Indoor Robotics Systems

Contact Info

Product

Resources

About