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

2021 ◽  
Vol 2 (2) ◽  
pp. 61-80
Author(s):  
Nina Santi ◽  
Nathalie Mitton
Keyword(s):  
Resource Allocation ◽  
Resource Management ◽  
Edge Computing ◽  
Acute Management ◽  
Military Operations ◽  
Computing Power ◽  
Management Schemes ◽  
Mission Critical ◽  
Time Critical ◽  
Resource Allocation Schemes

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.

scholarly journals Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2191
Author(s):  
Dimitrios Dechouniotis ◽  
Nikolaos Athanasopoulos ◽  
Aris Leivadeas ◽  
Nathalie Mitton ◽  
Raphael Jungers ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Dynamic Networks ◽  
Service Differentiation ◽  
Edge Computing ◽  
Decision Strategy ◽  
Network Computing ◽  
Time Critical ◽  
Net Framework ◽  
And Storage

The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

scholarly journals Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

AbstractThe energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

scholarly journals Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

2020 ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

Abstract The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

scholarly journals Computational Resource Allocation Strategy in a Public Blockchain Supported by Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sujie Shao ◽  
Weichao Gong ◽  
Shaoyong Guo ◽  
Xuesong Qiu
Keyword(s):  
Resource Allocation ◽  
Resource Constraints ◽  
Edge Computing ◽  
Market Model ◽  
Distributed Data ◽  
Computational Resource ◽  
Computing Power ◽  
Network Applications ◽  
Computing Services ◽  
Computationally Intensive

Blockchain, as an emerging distributed data management technology, has attracted extensive attention in recent years. In particular, a public blockchain network can ensure data security by addressing computationally intensive cryptographic tasks. Therefore, for node devices, sufficient computing power is required. However, mobile devices with limited computing power do not meet the conditions required by public blockchain network applications (OZEX, CoininAsia, BitRewards, etc.). To copy with the mentioned problems, nodes can offload computing tasks to edge computing services with low latency. This paper mainly focuses on the trade between edge computing providers (ECP) and nodes. We build a computational resource market model based on auction. Meanwhile, we propose two strategies to deal with two methods of offloading to achieve higher system profit. We also prove that the proposed strategy has individual rationality, authenticity under resource constraints. The simulation results have significance for administrators of a public blockchain network to improve the efficiency of computing resource allocation.

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

2021 ◽  
Author(s):  
Naren ◽  
Abhishek Kumar Gaurav ◽  
Nishad Sahu ◽  
Abhinash Prasad Dash ◽  
G. S. S. Chalapathi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Mathematical Models ◽  
Network Performance ◽  
Edge Computing ◽  
High Rate ◽  
Maximum Efficiency ◽  
Future Research ◽  
The Future ◽  
Future Research Directions ◽  
Resource Allocation Schemes

AbstractThe 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.

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