A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

Multiaccess Edge Computing (MEC) brings additional computing power in proximity of mobile users, reducing latency, saving energy and alleviating the network's bandwidth. This proximity is beneficial, especially for mission-critical applications where each second matters, such as disaster management or military operations. Moreover, it enables MEC resources embedded on mobile units like drones or robots that are flexible to be deployed for mission-critical applications. However, the MEC servers are capacity-limited and thus need an acute management of their resources. The mobile resources also need a smart deployment scheme to deliver their services efficiently. In this survey, we review mission-critical applications, resource allocation and deployment of mobile resources techniques in the context of the MEC. First, we introduce the technical specifics and uses of MEC in mission-critical applications to highlight their needs and requirements. Then, we discuss the resource allocation schemes for MEC and assess their fit depending on the application needs. In the same fashion, we finally review the deployment of MEC mobile resources. We believe this work could serve as a helping hand to design efficient MEC resource management schemes that respond to challenging environments such as mission-critical applications.

Resource Allocation Schemes for 5G Network: A Systematic Review

Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.

Comparative Analysis of Scheduling Algorithms Performance in a Long Term Evolution Network

The advancement in cellular communications has enhanced the special attention given to the study of resource allocation schemes. This study is to enhance communications to attain efficiency and thereby offers fairness to all users in the face of congestion experienced anytime a new product is rolled out. The comparative analysis was done on the performance of Enhanced Proportional Fair, Qos-Aware Proportional Fair and Logarithmic rule scheduling algorithms in Long Term Evolution in this work. These algorithms were simulated using LTE system toolbox in MATLAB and their performances were compared using Throughput, Packet delay and Packet Loss Ratio. The results showed Qos-Aware Proportional Fair has a better performance in all the metrics used for the evaluation.

Resource Allocation Schemes For 5G Network: A Systematic Review

Fifth Generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising of device-to-device networks, macro-cells, and dissimilar categories of small-cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need and the lack of resources, the resource distribution problem desires to be focused capably to accomplish the traffic and to enhance network working. One of the utmost serious problems is to alleviate the jamming from the network in support of having a better QoS. However, there are limited review papers written on resource distribution, there is no particularize and organized review carry out in 5G resource allocation. Hence, this article covers and evaluates the argument using a classification of existing developing resource allocation schemes in 5G thoroughly by classifying the schemes to enhance the service quality. This survey comprises the discussion based on metrics used to evaluate the performance. It would also permit ahead beyond evidence on resource allocation methods in 5G and empowers the scholars to meet the present research areas to focus on.

A survey on computation resource allocation in IoT enabled vehicular edge computing

The number of vehicles is increasing at a very high rate throughout the globe. It reached 1 billion in 2010, in 2020 it was around 1.5 billion and experts say this could reach up to 2–2.5 billion by 2050. A large part of these vehicles will be electrically driven and connected to a vehicular network. Rapid advancements in vehicular technology and communications have led to the evolution of vehicular edge computing (VEC). Computation resource allocation is a vehicular network’s primary operations as vehicles have limited onboard computation. Different resource allocation schemes in VEC operate in different environments such as cloud computing, artificial intelligence, blockchain, software defined networks and require specific network performance characteristics for their operations to achieve maximum efficiency. At present, researchers have proposed numerous computation resource allocation schemes which optimize parameters such as power consumption, network stability, quality of service (QoS), etc. These schemes are based on widely used optimization and mathematical models such as the Markov process, Shannon’s law, etc. So, there is a need to present an organized overview of these schemes to help in the future research of the same. In this paper, we classify state-of-the-art computation resource allocation schemes based on three criteria: (1) Their optimization goal, (2) Mathematical models/algorithms used, and (3) Major technologies involved. We also identify and discuss current issues in computation resource allocation in VEC and mention the future research directions.

A Joint Modulation-Coding Scheme and Resource Allocation in LTE Uplink

In Long Term Evolution (LTE) Resource Allocation Algorithms (RAAs) are an area of work where researchers are seeking to optimize the efficient use of scarce radio resources. The selection of an optimal Modulation and Coding Scheme (MCS) that allows LTE to adapt to channel conditions is a second area of ongoing work. In the wireless part of LTE, these two factors, RAA and MCS selection, are the most critical in optimization. In this paper, the performance of three resource allocation schemes is compared, and a new allocation scheme, Average MCS (AMCS) allocation, is proposed. AMCS is seen to outperform both “Minimum MCS (MMCS)” and “Average Signal to Interference and Noise Ratio MCS (SINR AMCS)” in terms of improvements to LTE Uplink (UL) performance. The three algorithms were implemented in the Vienna LTE-A Uplink Simulator v1.5.