A Comprehensive Survey on Orbital Edge Computing: Systems, Applications, and Algorithms

The number of satellites, especially those operating in low-earth orbit (LEO), is exploding in recent years. Additionally, the use of COTS hardware into those satellites enables a new paradigm of computing: orbital edge computing (OEC). OEC entails more technically advanced steps compared to single-satellite computing. This feature allows for vast design spaces with multiple parameters, rendering several novel approaches feasible. The mobility of LEO satellites in the network and limited resources of communication, computation, and storage make it challenging to design an appropriate scheduling algorithm for specific tasks in comparison to traditional ground-based edge computing. This article comprehensively surveys the significant areas of focus in orbital edge computing, which include protocol optimization, mobility management, and resource allocation. This article provides the first comprehensive survey of OEC. Previous survey papers have only concentrated on ground-based edge computing or the integration of space and ground technologies. This article presents a review of recent research from 2000 to 2023 on orbital edge computing that covers network design, computation offloading, resource allocation, performance analysis, and optimization. Moreover, having discussed several related works, both technological challenges and future directions are highlighted in the field.Comment: 18 pages, 9 figures and 5 table

A Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

The In-Network Computing (COIN) paradigm is a promising solution that leverages unused network resources to perform some tasks to meet up with computation-demanding applications, such as metaverse. In this vein, we consider the metaverse partial computation offloading problem for multiple subtasks in a COIN environment to minimise energy consumption and delay while dynamically adjusting the offloading policy based on the changing computation resources status. We prove that the problem is NP and thus transformed it into two subproblems: task splitting problem (TSP) on the user side and task offloading problem (TOP) on the COIN side. We modelled the TSP as an ordinal potential game (OPG) and proposed a decentralised algorithm to obtain its Nash Equilibrium (NE). Then, we model the TOP as Markov Decision Process (MDP) proposed double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, our COIN agent explores the NE of the TSP and the deep neural network. Finally, simulation results show that our proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.Comment: 14 pages, 9 figure

A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions

Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time

Resource Management in Multi-Access Edge Computing (MEC)

This PhD thesis investigates the effective ways of managing the resources of a Multi-Access Edge Computing Platform (MEC) in 5th Generation Mobile Communication (5G) networks. The main characteristics of MEC include distributed nature, proximity to users, and high availability. Based on these key features, solutions have been proposed for effective resource management. In this research, two aspects of resource management in MEC have been addressed. They are the computational resource and the caching resource which corresponds to the services provided by the MEC. MEC is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to end-user Internet of Things (IoT) and mobile devices. MEC would support latency-critical user applications such as driverless cars and e-health. These applications will depend on resources and services provided by the MEC. However, MEC has limited computational and storage resources compared to the cloud. Therefore, it is important to ensure a reliable MEC network communication during resource provisioning by eradicating the chances of deadlock. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning resources on MEC to achieve a highly reliable and readily available system to support latency-critical applications. In this research, a deadlock avoidance resource provisioning algorithm has been proposed for industrial IoT devices using MEC platforms to ensure higher reliability of network interactions. The proposed scheme incorporates Banker’s resource-request algorithm using Software Defined Networking (SDN) to reduce communication overhead. Simulation and experimental results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms. Additionally, this research explores the use of MEC as a caching platform as it is proclaimed as a key technology for reducing service processing delays in 5G networks. Caching on MEC decreases service latency and improve data content access by allowing direct content delivery through the edge without fetching data from the remote server. Caching on MEC is also deemed as an effective approach that guarantees more reachability due to proximity to endusers. In this regard, a novel hybrid content caching algorithm has been proposed for MEC platforms to increase their caching efficiency. The proposed algorithm is a unification of a modified Belady’s algorithm and a distributed cooperative caching algorithm to improve data access while reducing latency. A polynomial fit algorithm with Lagrange interpolation is employed to predict future request references for Belady’s algorithm. Experimental results show that the proposed algorithm obtains 4% more cache hits due to its selective caching approach when compared with case study algorithms. Results also show that the use of a cooperative algorithm can improve the total cache hits up to 80%. Furthermore, this thesis has also explored another predictive caching scheme to further improve caching efficiency. The motivation was to investigate another predictive caching approach as an improvement to the formal. A Predictive Collaborative Replacement (PCR) caching framework has been proposed as a result which consists of three schemes. Each of the schemes addresses a particular problem. The proactive predictive scheme has been proposed to address the problem of continuous change in cache popularity trends. The collaborative scheme addresses the problem of cache redundancy in the collaborative space. Finally, the replacement scheme is a solution to evict cold cache blocks and increase hit ratio. Simulation experiment has shown that the replacement scheme achieves 3% more cache hits than existing replacement algorithms such as Least Recently Used, Multi Queue and Frequency-based replacement. PCR algorithm has been tested using a real dataset (MovieLens20M dataset) and compared with an existing contemporary predictive algorithm. Results show that PCR performs better with a 25% increase in hit ratio and a 10% CPU utilization overhead