With the rapid development of Internet-of-Things (IoT) devices and mobile communication technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm to extend cloud computing and storage capabilities to the edge of cellular networks, near to IoT devices. MEC enables IoT devices with limited battery capacity and computation/storage capabilities to execute their computation-intensive and latency-sensitive applications at the edge of the networks. However, to efficiently execute these applications in MEC systems, each task must be properly offloaded and scheduled onto the MEC servers. Additionally, the MEC servers may intelligently balance and share their computing resources to satisfy the application QoS and QoE. Therefore, effective resource allocation (RA) mechanisms in MEC are vital for ensuring its foreseen advantages. Recently, Machine Learning (ML) and Deep Learning (DL) have emerged as key methods for many challenging aspects of MEC. Particularly, ML and DL play a crucial role in addressing the challenges of RA in MEC. This paper presents a comprehensive survey of ML/DL-based RA mechanisms in MEC. We first present tutorials that demonstrate the advantages of applying ML and DL in MEC. Then, we present enabling technologies for quickly running ML/DL training and inference in MEC. Afterward, we provide an in-depth survey of recent works that used ML/DL methods for RA in MEC from three aspects: (1) ML/DL-based methods for task offloading; (2) ML/DL-based methods for task scheduling; and (3) ML/DL-based methods for joint resource allocation. Finally, we discuss key challenges and future research directions of applying ML/DL for resource allocation in MEC networks.

Efficient scheduling algorithms are key for attaining high performance in heterogeneous computing systems. In this paper, we propose a new list scheduling algorithm for assigning task graphs to fully connected heterogeneous processors with an aim to minimize the scheduling length. The proposed algorithm, called Improved Predict Priority Task Scheduling (IPPTS) algorithm has two phases: task prioritization phase, which gives priority to tasks, and processor selection phase, which selects a processor for a task. The IPPTS algorithm has a quadratic time complexity as the related algorithms for the same goal, that is O(t 2 x p), for t tasks and p processors. Our algorithm reduces the scheduling length significantly by looking ahead in both task prioritization phase and processor selection phase. In this way, the algorithm is looking ahead to schedule a task and its heaviest successor task to the optimistic processor, i.e., the processor that minimizes their computation and communication costs. The experiments based on both randomly generated graphs and graphs of real-world applications show that the IPPTS algorithm significantly outperforms previous list scheduling algorithms in terms of makespan, speedup, makespan standard deviation, efficiency, and frequency of best results.

With the recent advancements in the information and communication technologies, large number of devices are connecting to the Internet, hence large volumes of data in different formats and from different sources are generating. Consequently, on one hand dynamic and heterogeneous data sharing and management, in the ecosystem of Internet of Things (IoT), where every smart object is connected to Internet, presents new research challenges. On the other hand, citizen privacy preserving is another challenge, because he/she has to send his/her information to a service provider, to obtain the required information. This information is sensitive since it can reveal information about an individual. An attacker or a malicious service provider can utilize this sensitive information for their own business or something else. This paper presents a Secure Framework for Future Smart City (SEFSCITY), for better city living and governance, based on Cloud Computing IoT and Distributed Computing. We first present the architecture of SEFSCITY, which is based on Multi-Cloud and Cloud Federation approach; then we propose a security protocol for our framework. In our security model, we use Zero-Knowledge Protocol based on Elliptic Curve Discrete Logarithm Problem. Finally, we validate our architecture by conducting several scenarios that we have implemented using Cloud Analyst tool. The results show that in all scenarios, the cost infrastructure remains the same for the cloud customer, and our approach is benefic for the cloud provider in term of revenues and data processing time.

Relation extraction (RE) is critical in information extraction (IE) and knowledge graph construction. RE aims to identify the semantic relations between entities from natural language texts. Traditional RE models often rely on many manually annotated training samples, which are limited when data is scarce. Therefore, exploring how to perform RE under few-shot conditions has become a research focus. Recently, prompt learning has attracted attention from researchers due to its ability to fully activate the potential of Pre-trained Language Models (PLMs), especially making significant progress in Few-Shot RE (FSRE). This article comprehensively reviews FSRE based on prompt learning. We first introduce the fundamental concepts of FSRE and prompt learning. Then, we systematically review recent research advances in FSRE with prompt learning, focusing on two perspectives: template construction and model fine-tuning strategies. Next, we summarize the benchmark datasets, evaluation metrics, and experimental results of representative works in FSRE. Afterward, we present practical applications of prompt-based FSRE in specialized domains. Finally, we discuss the critical challenges and future research directions of FSRE tasks based on prompt learning.