Joint Task Scheduling Research Articles

Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing

Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network’s edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime.

Joint Service Deployment and Task Scheduling for Satellite Edge Computing: A Two-Timescale Hierarchical Approach

Joint Service Deployment and Task Scheduling for Satellite Edge Computing: A Two-Timescale Hierarchical Approach

Wireless Powered Metaverse: Joint Task Scheduling and Trajectory Design for Multi-Devices and Multi-UAVs

Wireless Powered Metaverse: Joint Task Scheduling and Trajectory Design for Multi-Devices and Multi-UAVs

DRL-Based Joint Task Scheduling and Trajectory Planning Method for UAV-Assisted MEC Scenarios

DRL-Based Joint Task Scheduling and Trajectory Planning Method for UAV-Assisted MEC Scenarios

Multi-UAV Cooperative Task Offloading and Resource Allocation in 5G Advanced and Beyond

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Joint Server Deployment and Task Scheduling for the Maximal Profit in Mobile-Edge Computing

Joint Server Deployment and Task Scheduling for the Maximal Profit in Mobile-Edge Computing

Joint scheduling and offloading of computational tasks with time dependency under edge computing networks

Mobile edge computing is an emerging technology paradigm that improves mobile devices’ computational power and efficiency by offloading computational tasks to edge servers. However, there are still some problems: (1) Ignoring the complex time dependencies of compute-intensive or latency-sensitive tasks, leading to the omission of offload scheduling opportunities for fine-grained sub-tasks within a task, thus reducing the computational power of the mobile device and the utilization of edge resources; (2) Overly biased towards optimizing task execution latency and ignoring the balance between data transmission latency and energy loss, reducing the scheme versatility in natural computing environments. Aiming at the above problems, this paper models the complex dependencies, transmission time, and execution time between computing tasks, constructs a mathematical model of the scheduling and offloading process of computing tasks, and a joint task scheduling and offloading method (N-JSU) based on Nash equilibrium is designed. This offloading strategy is divided into two steps: first, the average latency of each task is analyzed, and the scheduling order of the computational tasks is determined based on the highest response ratio of the tasks; secondly, the optimal solution is determined by iterative search to offload the most suitable IoT tasks or computing tasks to the edge server. The experimental results show that this offloading strategy can effectively reduce the latency of computationally intensive edge network tasks and improve the resource utilization of the edge server.

Joint task scheduling and multi-UAV deployment for aerial computing in emergency communication networks

Joint task scheduling and multi-UAV deployment for aerial computing in emergency communication networks

New Bridge to Cloud: An Ultra-Dense LEO Assisted Green Computation Offloading Approach

Mobile edge computing and cloud computing have emerged as effective technologies to alleviate the increasing computational workload of mobile devices. As a promising enabling 6G technology, the ultra-dense (UD) low earth orbit (LEO) satellite network with low communication latency and high throughput is considered a new bridge for cloud computation offloading. In this paper, we investigate energy-efficient cloud and edge computing in UD-LEO-assisted terrestrial-satellite networks. An optimization problem aiming at minimizing the energy consumption of the computation tasks is formulated. The optimization problem is a mixed-integer non-linear programming problem. To solve this problem, we decompose it into two subproblems, i.e., a joint user association and task scheduling subproblem, and an adaptive computation resource allocation subproblem. For the first subproblem, we model the input of a forward neural network (NN) as the large-scale information (i.e., channel gain and task arrival rates) and obtain the optimal solution by transforming the direct output of the NN. For the second subproblem, we introduce a successive convex approximation method to optimize it iteratively. The simulation results show that our proposed user association and task scheduling strategy outperforms two benchmark algorithms in terms of energy consumption under a strict delay bound and high user density.

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms.

Energy-Constrained Online Scheduling for Satellite-Terrestrial Integrated Networks

In satellite-terrestrial integrated networks, it is a common practice to schedule real-time tasks from low Earth orbit (LEO) satellites to ground stations (GSs) for data processing. However, the joint task scheduling and resource allocation under unknown environment dynamics (e.g., transmission latency) remains to be a challenging problem. First, the tradeoff between task latencies and energy consumption should be carefully considered when making decisions to minimize task latencies under time-averaged energy consumption constraints. Second, to learn the environment uncertainties and minimize the system performance loss (i.e., regret) in terms of task latencies, both online feedback and offline history should be leveraged efficiently, and the accompanying exploration-exploitation tradeoff should be dealt with in a proper way. In this article, we formulate the joint task scheduling and resource allocation problem as a constrained combinatorial multi-armed bandit (CMAB) problem. To solve the problem, by integrating online learning, online control, and offline historical information, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task scheduling and Resource allocation scheme with Data-driven Bandit Learning</i> called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRDBL</i> . Our theoretical and numerical results show that TRDBL achieves a sublinear time-averaged regret while satisfying the time-averaged energy consumption constraints.

Joint Offloading Scheduling and Resource Allocation in Vehicular Edge Computing: A Two Layer Solution

Vehicular Edge Computing (VEC) is a promising paradigm for autonomous driving. It can reduce delay and energy consumption of tasks. The problem of joint task offloading scheduling and resource allocation in VEC is a challenge issue. In this paper, we investigate the problem of joint task offloading, task scheduling, and resource allocation in VEC, and the fast changing channel between a vehicle and an edge server. A target problem of joint considering task offloading scheduling, resource allocation and time-varying channel in VEC is formulated. The goal is to minimize the delay and energy consumption of tasks to guarantee the Quality of Service (QoS) of VEC. Constraints on the completion time, the energy consumption, and the computing capability are considered for each task. The resulting mixed integer optimization problem is decomposed into a two-layer optimization problem. In the upper layer, we use a Deep Q-Network (DQN) to solve the task offloading scheduling problem. In the lower level, the CPU frequency allocation is determined using the Gradient Descent (GD) method. Numerical results illustrate that the proposed algorithm can minimize the delay and energy consumption of VEC for different network parameter settings.

Blockchain-based Collaborative Edge Intelligence for Trustworthy and Real-Time Video Surveillance

Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally and reduces the latency caused by massive data transmission. Generally, a single edge device can hardly afford the computation-intensive video analytics tasks. Most existing solutions incorporate cloud servers to handle the overloaded tasks. However, such an edge-cloud collaboration approach still suffers from unpredictable latency and privacy concerns because the remote cloud is centralized and distant from the cameras. In this work, we designed a blockchain-based collaborative edge intelligence (BCEI) approach for trustworthy and real-time video surveillance. In BCEI, geo-distributed edge devices form a peer-to-peer network to maintain a permissioned blockchain and share data and computation resources to perform computation-intensive video analytics tasks. The video analytics results are written on the blockchain in an immutable manner to guarantee trustworthiness. To reduce task execution time, we formulate and solve a joint stream mapping and task scheduling problem to schedule video streams and machine learning models among edge devices. A pedestrian reidentification prototype is implemented and deployed based on BCEI with the extensive performance evaluation, indicating the superiority of BCEI in latency reduction and system throughput improvement by leveraging collaboration among edge devices.

Heterogeneous Markov Decision Process Model for Joint Resource Allocation and Task Scheduling in Network Slicing Enabled Internet of Vehicles

In the Internet of Vehicles (IoV) with network slicing functions, both the inter-slice resource allocation and the intra-slice task scheduling have received extensive attention. However, the joint optimization of these two problems has not been fully studied. This letter focuses on the complexity issue of the joint resource allocation and task scheduling problem in the IoV with both ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) slices. A heterogeneous Markov decision process (HMDP) which models the resource allocation process and the task scheduling process in two different layers is proposed. Benefiting from the two-layer structure of the HMDP, the size of action space is significantly reduced, and the asynchronous decisions of different sub-MDPs are enabled. The corresponding layered deep reinforcement learning (DRL) architecture is also designed to solve the HMDP-based optimization problem. Simulation results show that the layered DRL-based algorithm outperforms other methods.

Multiagent RL Aided Task Offloading and Resource Management in Wi-Fi 6 and 5G Coexisting Industrial Wireless Environment

With the emergence of industrial Internet of Things (IIoT), intensive computation workload will be imposed to industrial end units (IEUs). By leveraging mobile edge computing (MEC), the local computational tasks can be offloaded to servers deployed in mobile edge networks with low latency. This article proposes the intelligent cost-and-energy-effective task offloading in the 5G and Wi-Fi 6 coexisting heterogeneous IIoT networks. The novel joint task scheduling and resource allocation approach comprises the following two parts: a Lyapunov optimization-based component to decide local task scheduling and computing power and an online multiagent reinforcement learning component together with a game theory-based algorithm to select offloading link and decide transmit power, respectively. Simulation results demonstrate the proposed approach holds obvious advantage over the compared “intuition” and “cost optimal” approaches in the efficiency of making comprehensive decision that improves energy efficiency and cost while controlling task delay in the multi-IEU and multiaccess-node MEC systems.

Joint task scheduling, resource allocation, and UAV trajectory under clustering for FANETs

This paper establishes a new layered flying ad hoc networks (FANETs) system of mobile edge computing (MEC) supported by multiple UAVs, where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members (CMs) within a cluster to its cluster head (CH). Then, we need to determine whether each CH' tasks are executed locally or offloaded to one of the MEC UAVs for remote execution (i.e., task scheduling), and how much resources should be allocated to each CH (i.e., resource allocation), as well as the trajectories of all MEC UAVs. We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them, respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.

Service Provisioning for UAV-Enabled Mobile Edge Computing

Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.

5G-Enabled UAV-to-Community Offloading: Joint Trajectory Design and Task Scheduling

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

Joint service-function deployment and task scheduling in UAVFog-assisted data-driven disaster response architecture

Joint service-function deployment and task scheduling in UAVFog-assisted data-driven disaster response architecture

Joint Task Scheduling and Containerizing for Efficient Edge Computing

Container-based operation system (OS) level virtualization has been adopted by many edge-computing platforms. However, for an edge server, inter-container communications, and container management consume significant CPU resources. Given an application composed of interdependent tasks, the number of such operations is closely related to the dependency between the scheduled tasks. Thus, to improve the execution efficiency of an application in an edge server, task scheduling and task containerizing need to be considered together. To this end, a joint task scheduling and containerizing (JTSC) scheme is developed in this article. Experiments are first carried out to quantify the resource utilization of container operations. System models are then built to capture the features of task execution in containers in an edge server with multiple processors. With these models, joint task scheduling and containerizing is conducted as follows. First, tasks are scheduled without considering containerization, which results in initial schedules. Second, based on system models and guidelines gained from the initial schedules, several containerization algorithms are designed to map tasks to containers. Third, task execution durations are updated by adding the time for inter-container communications, and then the task schedules are updated accordingly. The JTSC scheme is evaluated through extensive simulations. The results show that it reduces inefficient container operations and enhances the execution efficiency of applications by 60 percent.