Joint Task Scheduling Research Articles

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.

Context switch cost aware joint task merging and scheduling for deep learning applications

Deep learning applications executed on mobile devices can provide artificial intelligence services with multi-access edge computing (MEC). Most of existing works on computation offloading neglect the context switch cost, which is one of the dominating factors that affect the finishing time of deep learning applications. This paper thus tries to investigate the context switch cost aware joint task merging and scheduling for deep learning applications. We formulate the problem as a non-linear integer programming, and prove that it is NP-Hard. The objective is to minimize the finishing time of deep learning applications with energy budget constraints. To solve the problem, an efficient joint merging and scheduling algorithm named fusion merging scheduling (FMS) is proposed to fully exploit the potential influence of context switch cost in distributed execution of deep learning tasks. A real-world platform is built to evaluate the performance of proposed algorithm. Experimental results show that FMS can reduce the average finishing time of deep learning applications by 79%, 63% and 75%, respectively, compared with a greedy scheduling algorithm and two existing benchmarks, while achieving similar energy costs.

Collaborative Computing and Resource Allocation for LEO Satellite‐Assisted Internet of Things

Satellite‐assisted internet of things (S‐IoT), especially the S‐IoT based on low earth orbit (LEO) satellite, plays an important role in future wireless systems. However, the limited on‐board communication and computing resource and high mobility of LEO satellites make it hard to provide satisfied service for IoT users. To maximize the task completion rate under latency constraints, collaborative computing and resource allocation among LEO networks are jointly investigated in this paper, and the joint task offloading, scheduling, and resource allocation is formulated as a dynamic mixed‐integer problem. To tack the complex problem, we decouple it into two subproblems with low complexity. First, the max‐min fairness is adopted to minimize the maximum latency via optimal resource allocation with fixed task assignment. Then, the joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long‐term benefits. Simulation results show that the proposed scheme has superior performance compared with other referred schemes.

Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing

Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.

A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

The virtual machine (VM) allocation problem is one of the main issues in cloud data centers. This article proposes a new metaheuristic method to optimize joint task scheduling and VM placement (JTSVMP) in cloud data center. The JTSVMP problem, though composed of two parts, namely task scheduling and VM placement, is treated as a joint problem to be resolved by using metaheuristic optimization algorithms (MOAs). The proposed co-optimization process aims to schedule task into the VM which has the least execution cost within deadline constraint and then to place the selected VM on most utilized physical host (PH) within capacity constraint. To evaluate the performance of our proposed co-optimization process, we compare the performances of two different scenarios, i.e., task scheduling algorithms and integrateion co-optimization of task scheduling and VM placement using MOAs, namely the basic glowworm swarm optimization (GSO), moth-flame glowworm swarm optimization (MFGSO) and genetic algorithm (GA). Simulation results show that optimizing joint task scheduling and VM placement leads to better overall results in terms of minimizing execution cost, makespan and degree of imbalance and maximizing PHs resource utilization.

Joint Task Scheduling and Energy Management for Heterogeneous Mobile Edge Computing With Hybrid Energy Supply

Mobile edge computing (MEC) has recently become a promising paradigm to meet the increasing computing requirement of mobile devices, and hybrid energy supply has been considered as an effective approach for saving the energy consumption of the MEC system and making it environmentally friendly. In particular, the joint task scheduling and energy management (TSEM) scheme plays a crucial role in reaping the benefits of MEC with hybrid energy supply. In this article, we focus on jointly optimizing the TSEM decisions to maximize the utility of the MEC system which accounts for both the computation throughput and the fairness among different cells, by formulating a stochastic optimization problem subject to the constraints of queue stability and energy budget. We transform the formulated problem into a deterministic problem and then decouple it into four independent subproblems, which can be solved in a distributed manner without future system statistical information. An online TSEM algorithm is developed to derive the optimal solutions to these subproblems. Mathematical analysis shows that TSEM can achieve a close-to-optimal system utility and realize the utility-queue tradeoff. The experimental results validate the advantages of TSEM in improving the system utility and stabilizing the queue length.

Profit-Based Algorithm of Joint Real-Time Task Scheduling and Resource Allocation in C-RANs

In cloud radio access networks (C-RANs), a local controller controls a set of lightweight remote radio heads (RRHs) for connecting users nearby, and creates virtual machines (VMs) for executing user tasks. In view of previous works, the strategies of minimizing system resource consumption or transmission delay are adopted in the issue of joint task scheduling and resource allocation. Different from them, this work puts forward the strategy of maximizing the profit of the joint issue. The problem is formulated as integer linear programming (ILP), and then an algorithm with bounded approximation ratio for solving the ILP is proposed. The simulation results show that the solution of the algorithm is very close to the optimal one of the ILP. In addition, another algorithm is proposed to control the tradeoff between performance and robustness of the solution under the assumption that the system resource can be expanded by a bounded factor.

Online Task Scheduling and Resource Allocation for Intelligent NOMA-Based Industrial Internet of Things

Fog computing (FC) has the potential to process computation-intensive tasks in Industrial Internet of Things (IIoT) systems. In parallel with the development of FC, non-orthogonal multiple access (NOMA) has been recognized as a promising technique to significantly improve the spectrum efficiency. In this paper, a NOMA-based FC framework for IIoT systems is considered, where multiple task nodes offload their tasks via NOMA to multiple nearby helper nodes for execution. We formulate a joint task scheduling and subcarrier allocation problem, with an objective to minimize the total cost in terms of the delay and energy consumption, while taking into account the practical communication and computation constraints. Note that the task scheduling includes task, computation resource, and power allocations. Since the task and subcarrier allocations involve binary variables, it is challenging to obtain an optimal solution for such a combinatorial problem. To this end, we solve the task scheduling and subcarrier allocation problem in an online learning fashion. During the online learning process, we propose an iterative algorithm to jointly optimize the subcarrier allocation and task scheduling in each time episode. Simulation results show that the proposed scheme can significantly reduce the sum cost compared to the baseline schemes.

Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

Internet of Things (IoT) computing offloading is a challenging issue, especially in remote areas where common edge/cloud infrastructure is unavailable. In this paper, we present a space-air-ground integrated network (SAGIN) edge/cloud computing architecture for offloading the computation-intensive applications considering remote energy and computation constraints, where flying unmanned aerial vehicles (UAVs) provide near-user edge computing and satellites provide access to the cloud computing. First, for UAV edge servers, we propose a joint resource allocation and task scheduling approach to efficiently allocate the computing resources to virtual machines (VMs) and schedule the offloaded tasks. Second, we investigate the computing offloading problem in SAGIN and propose a learning-based approach to learn the optimal offloading policy from the dynamic SAGIN environments. Specifically, we formulate the offloading decision making as a Markov decision process where the system state considers the network dynamics. To cope with the system dynamics and complexity, we propose a deep reinforcement learning-based computing offloading approach to learn the optimal offloading policy on-the-fly, where we adopt the policy gradient method to handle the large action space and actor-critic method to accelerate the learning process. Simulation results show that the proposed edge VM allocation and task scheduling approach can achieve near-optimal performance with very low complexity and the proposed learning-based computing offloading algorithm not only converges fast but also achieves a lower total cost compared with other offloading approaches.

Joint task scheduling and uplink/downlink radio resource allocation in PD-NOMA based mobile edge computing networks

Mobile edge computing (MEC) is a new paradigm that brings the computation capabilities closer to the edge of wireless networks. In doing so, the computation tasks of users can be offloaded to the network edge for remote executing. Since both the computing and transmission delays in uplink and downlink of each task offloading operation affect the total task execution delays, it is necessary to utilize the physical layer transmission opportunities in the task allocation algorithms. In this regard, we develop an efficient algorithm to minimize the total execution delay of users in a single-cell power-domain non-orthogonal multiple access (PD-NOMA) based MEC system with multiple users and single MEC server. Hence the computation task of each user can be partitioned into two separated parts, one for offloading to the network edge and another for locally computing. In the considered partial offloading scheme, we jointly obtain subcarrier and transmit power allocation policies for both the uplink and downlink transmissions with task scheduling and computation resource allocation at both the users and the MEC server. Numerical results demonstrate that when the communication and computation resources are jointly optimized at the task allocation algorithms, the network performance is improved nearly 30% compared to the existing joint computation and task allocation approach in which both the downlink and uplink data rates are assumed to be fixed.

Power Minimization-Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue, whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this paper provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.

Joint Scheduling of Tasks and Network Flows in Big Data Clusters

As an increasing number of big data processing platforms like Hadoop, Spark, and Storm appear and normally share the resources in the data center, it has been important and challenging to schedule various jobs from these platforms onto the underlying data center resources such that the overall job completion time is minimized. To solve the problem, the existing work either focus on the task-level scheduling techniques, such as Quincy and delay scheduling, or focus on the network flow scheduling techniques, such as D3 and preemptive distributed quick. These works deal with the scheduling of tasks and network flows separately and cannot achieve optimal performance. The reason is that the task scheduling without regard of the available network bandwidths may generate the task placement that causes serious network congestions and thus leads to long data transmission time. In this paper, we propose the joint scheduling technique by coordinating the task placement and the scheduling of network flows arising from these tasks. We develop a software-defined network (SDN)-based online scheduling framework which selects the task placement based on the available bandwidth on the SDN switches and at meanwhile optimally allocates the bandwidth to each data flow. Comprehensive trace-driven simulations show that the joint scheduling technique can take full use of the network bandwidth and thus reduce the job completion time by 55% on average compared with the benchmark methods.

Joint Optimal Pricing and Task Scheduling in Mobile Cloud Computing Systems

The evolving mobile cloud computing (MCC) paradigm enables mobile users to offload their computing tasks to cloud servers. In this paper, we study the following problems in MCC systems: 1) which tasks should be offloaded to cloud servers? 2) and what is the optimal price of cloud services? We jointly address these issues by formulating two levels of optimization problems. On the mobile users side, we formulate a utility maximization problem that takes the energy consumption, delay, and price of cloud services into account and obtain the optimal scheduling for both delay-sensitive and delay-tolerant applications. On the cloud service provider (CSP) side, we determine the optimal pricing strategy by formulating a profit maximization problem, which is non-convex in general. We further propose an algorithm using convexification and primal-dual methods to mitigate the non-convexity. Through numerical studies, we investigate the mobile users' behavior and the CSP's pricing strategy. Our results reveal that the proposed scheduler effectively balances the tradeoff between the energy consumption and delay in comparison with different schedulers proposed in the literature. Furthermore, we show that with the proposed pricing algorithm, the CSP can improve its profit by up to 25% compared with static and dynamic pricing strategies.

Clustered Spanning Tree for Integrated Energy Efficient Routing and Optimized Data Collection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) comprises of sensor nodes performing the function of event detection in a distributed network. With the sensor nodes being heavily energy-constrained, energy efficient routing is highly required. To facilitate energy consumption and reduce end-to-end delay in a sensor network, joint scheduling of tasks and messages has been widely used. However, dynamic routing was compromised. Research works conducted using multi-path dynamic routing reduced data loss and ensured data integrity, but efficient data collection at the sink node received less attention. In this paper, an integrated framework called, Cluster based Routing and Data Collection using Spanning Tree (CR-DCST) is designed to improve the routing efficiency and data collection at the sink node. We first formulate a Cluster based Routing, that uses a dynamic routing to select the cluster head (i.e., efficient route), aiming at reducing the energy consumption of the nodes. The Cluster based Routing uses a neighbor routing table to reduce the end-to-end delay. Then, a Spanning Tree framework based on dynamic routing for efficient data collection at the sink node is performed to improve the throughput of data being collected at the sink node. The Spanning Tree framework for efficient data collection is performed on the basis of constructing the Least Interference Tree (LIT). Finally, Energy Efficient Cluster based Spanning Tree (EECST) algorithm reduces the data loss in Wireless Sensor Networks. The effectiveness of the proposed framework using extensive experimental measurements demonstrated using NS2 confirms reducing the energy consumption and average end-to-end delay. Simulations are also conducted to validate the theoretical results in different test bed settings and show that the proposed framework can substantially improve the throughput and reduce the data loss at sink node during data collection in most scenarios.

Scheduling Grid Applications with Software Requirements

The aim of task scheduling is to minimize the makespan of applications, exploiting the best possible way to use shared resources. Applications have requirements which call for customized environments for their execution. One way to provide such environments is to use virtualization on demand. This paper presents two schedulers based on integer linear programming which schedule virtual machines (VMs) in grid resources and tasks on these VMs. The schedulers differ from previous work by the joint scheduling of tasks and VMs and by considering the impact of the available bandwidth on the quality of the schedule. Experiments show the efficacy of the schedulers in scenarios with different network configurations.