Task Execution Delay Research Articles

Agent-enabled task offloading in UAV-aided mobile edge computing

With the appearance of various mobile applications, such as automatic driving and augmented reality, it is difficult for the power and computing ability of mobile terminals to satisfy user demands. Therefore, an increasing number of terminal devices are requesting computing resources on the edge cloud. Because an unmanned aerial vehicle (UAV) is quite flexible and closer to the user side, an UAV can be adopted to assist mobile edge computing (MEC) while executing task offloading, which may reduce the pressure on edge clouds. However, it is unreasonable for users to make blind requests for resources due to the information asymmetry between a user and a service provider, and thus the quality of experience of user may be reduced. In this paper, an agent is introduced into the offloading of computing tasks, and a novel framework of agent-enabled task offloading in UAV-aided MEC(UMEC) is put forth to help the user, UAV, and edge cloud execute the offloading of computing tasks. With the intelligence and perceptibility of an agent, a system model is formulated in this paper to guide the agent in obtaining the optimum computing offloading plan, with minimum task execution delay and energy consumption. Simulation results showed that the introduction of an agent may significantly reduce delay and energy consumption, and the effectiveness of agent has been illustrated.

Partial Offloading Scheduling and Power Allocation for Mobile Edge Computing Systems

Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.

Programmable Hierarchical C-RAN: From Task Scheduling to Resource Allocation

Traffic delay is a key metric to measure the quality-of-service of next-generation wireless communication networks. In this paper, we consider a cloud radio access network architecture with a hierarchical structure of virtual controllers and multiple clusters of remote radio heads (RRHs). A high-level controller coordinates control plane decisions among local controllers and each local controller is in charge of a cluster of RRHs. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute the users’ baseband processing tasks. Then, under the considered architecture, we aim to minimize the average delay consisting of task execution delay and signal transmission delay under total power constraint, by joint optimization of task scheduling and resource allocation, including VM allocation and RRH assignment. Due to the non-deterministic polynomial-time hardness (NP-hardness) of the joint optimization problem, we translate it into a matroid constrained submodular maximization problem and propose heuristic algorithms to find solutions with 0.5-approximation. Besides, both centralized and distributed control schemes are considered. In the centralized control scheme, all decisions about task scheduling, VM allocation, and RRH assignment are made in the high-level controller. But in the distributed control scheme, the high-level controller is only in charge of task scheduling based on graph theory and the local controllers are responsible for their respective VM allocation and RRH assignment. The simulation results show that the proposed algorithms can achieve better performance than the separate optimization of VM allocation and RRH assignment.

User Preference Aware Task Coordination and Proactive Bandwidth Allocation in a FiWi-Based Human–Agent–Robot Teamwork Ecosystem

Cooperative human–agent–robot teamwork (HART) provides enormous opportunities for present-day human users to orchestrate their real-time tasks in a coordinated fashion. However, given human users’ different preferences for real-time HART task execution, e.g., lower delay and monetary cost, the selection of proper task coordination services has emerged as an important research problem by taking dynamically changing cloud agent/robot resources, network bandwidth utilization, as well as delay-sensitive and delay-tolerant HART task properties into account. To cope with these challenges, in this paper, we explore the synergy between caching, computation, and communications for achieving cost-effective HART task execution. To exploit the locality of different HART-centric tasks and local/non-local cloud agent/robot resources for different HART-centric task execution, we consider integrated fiber-wireless (FiWi) enhanced networks with computation task offloading as well as fiber backhaul sharing and WiFi offloading capabilities. More precisely, to minimize task execution delay and monetary cost, we propose a user preference aware HART task coordination framework that selects the appropriate dedicated or non-dedicated robot and cloud agent for given caching and computing HART task execution requirements. Further, to cope with varying bandwidth resources, we propose a proactive bandwidth allocation policy for the execution of both delay-sensitive and delay-tolerant HART tasks execution across FiWi enhanced network infrastructures. We evaluate the performance of our proposed preference aware task offloading scheme and compare it to various baseline schemes in terms of different key performance indicators, including the task execution time and monetary cost saving ratio, communication to computation ratio, and offloading gain overhead ratio. Our findings indicate that the proposed delay cost saving policy exhibits a 27% higher task execution time saving ratio and a 48% lower monetary cost saving ratio than the proposed monetary cost saving policy in a typical scenario.

User mobility aware task assignment for Mobile Edge Computing

Mobile Edge Computing (MEC) has emerged as a prospective computing paradigm to provide pervasive computing and storage services for mobile and big data applications. In MEC, many small cell base stations (sBSs) are deployed to establish a mobile edge network (MEN). These sBSs can be usually accessed directly by mobile users. The computational tasks are first offloaded from mobile users to the MEN and then executed in one or several specific sBSs in the MEN. While the offloading decision has been well studied, the task execution delay on the MEN side is overlooked. This paper aims at reducing the task execution delay by task scheduling in MENs. Specifically, we jointly consider the task properties, the user mobility and network constraints. The problem is formalized as a constraint satisfaction problem and a lightweight heuristic solution is proposed for fast scheduling. We conduct simulation experiments to study the performance of the proposed work. The results show that our work is able to significantly reduce the task execution delay in MENs and thus reduces the end-to-end delay for MEC tasks.

Solving Task Scheduling Problem in Cloud Computing Environment Using Orthogonal Taguchi-Cat Algorithm

In cloud computing datacenter, task execution delay is no longer accidental. In recent times, a number of artificial intelligence scheduling techniques are proposed and applied to reduce task execution delay. In this study, we proposed an algorithm called Orthogonal Taguchi Based-Cat Swarm Optimization (OTB-CSO) to minimize total task execution time. In our proposed algorithm Taguchi Orthogonal approach was incorporated at CSO tracing mode for best task mapping on VMs with minimum execution time. The proposed algorithm was implemented on CloudSim tool and evaluated based on makespan metric. Experimental results showed for 20VMs used, proposed OTB-CSO was able to minimize makespan of total tasks scheduled across VMs with 42.86%, 34.57% and 2.58% improvement over Minimum and Maximum Job First (Min-Max<em>)</em>, Particle Swarm Optimization with Linear Descending Inertia Weight (PSO-LDIW) and Hybrid Particle Swarm Optimization with Simulated Annealing (HPSO-SA) algorithms. Results obtained showed OTB-CSO is effective to optimize task scheduling and improve overall cloud computing performance with better system utilization.

Orthogonal Taguchi-based cat algorithm for solving task scheduling problem in cloud computing

In cloud computing datacenter, task execution delay is a common phenomenal cause by task imbalance across virtual machines (VMs). In recent times, a number of artificial intelligence scheduling techniques are applied to reduced task execution delay. These techniques have contributed toward the need for an ideal solution. The objective of this study is to optimize task scheduling based on proposed orthogonal Taguchi-based cat swarm optimization (OTB-CSO) in order to reduce total task execution delay. In our proposed algorithm, Taguchi orthogonal approach was incorporated into tracing mode of CSO to scheduled tasks on VMs with minimum execution time. CloudSim tool was used to implement the proposed algorithm where the impact of the algorithm was checked with 5, 10 and 20 VMs besides input tasks and evaluated based on makespan and degree of imbalance metrics. Experimental results showed that for 20 VMs used, our proposed OTB-CSO was able to minimize makespan of the total tasks scheduled across VMs with 42.86, 34.57 and 2.58% improvement over minimum and maximum job first (Min–Max), particle swarm optimization with linear descending inertia weight (PSO-LDIW) and hybrid PSO with simulated annealing (HPSO-SA) and likewise returned degree of imbalance with 70.03, 62.83 and 35.68% improvement over existing algorithms. Results obtained showed that OTB-CSO is effective to optimize task scheduling and improve overall cloud computing performance through minimizing task execution delay while ensuring better system utilization.

A Novel Execution Mode Selection Scheme for Wireless Computing

Computation offloading is an effective way to alleviate the resource limited problem of mobile devices. However, the offloading is not an always advantageous strategy for under some circumstances the overhead in time and energy may turn out to be greater than the offloading savings. Therefore, an offloading decision scheme is in demand for mobile devices to decide whether to offload a computation task to the server or to execute it in a local processor. In this paper, the offloading decision problem is translated into a dynamic execution mode selection problem, the objective of which is to minimize the task execution delay and reduce the energy consumption of mobile devices. A novel execution mode adjustment mechanism is introduced to make the execution process more flexible for real-time environment variation. Numerical results indicate that the proposed scheme can significantly reduce the task execution delay in an energy-efficient way.