Completion Time Constraint Research Articles

Minimization of Energy Consumption in TDMA-Based Wireless-Powered Multi-Access Edge Computing Networks

Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.

Energy Aware Task Allocation for Semi-Asynchronous Mobile Edge Learning

This paper extends the paradigm of "mobile edge learning (MEL)" by designing an energy-aware optimal task allocation scheme for training a machine learning (ML) model in a semi-asynchronous manner across multiple learners connected via the resource-constrained wireless edge network. The tasks are allocated such that the local dataset size selected at each learner ensures completion within a given global delay constraint and a local maximum energy consumption limit. Hence, the designed method is heterogeneity aware (HA) because it offers a trade-off between resource consumption and MEL performance by directly relating the time and energy consumption to the heterogeneous communication/computational capabilities of learners. Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed. The proposed HA semi-asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) synchronous/asynchronous (HU-Sync/Asyn) equal batch allocation schemes. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU-Sync/Asyn approaches and can even provide gains of up-to 25% compared to the HA-Sync scheme.

Energy Minimization for In-Band Full-Duplex Relaying Using an Amplify-and-Forward UAV Relay

This paper investigates an in-band full-duplex (IBFD) relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as an amplify-and-forward relay to forward data from a source to a destination. By considering ideal and practical IBFD cases, new received signal-to-noise ratio (SNR) expressions at the destination are derived. Armed with the newly derived SNR expressions and the existing one in literature, three different lower-bounds of the size of data received by the destination are calculated. Using the three lower-bounds of the size of data, an optimization problem corresponding to energy minimization of the IBFD relaying system subject to a total data-bit constraint in addition to a mission completion time (MCT) constraint is solved, leading to three sets of solutions. Computer simulation experiments are conducted and the accuracy of the derived SNR expressions and the three sets of solutions of the optimization problem are compared. The results demonstrated that the newly derived SNR expressions are more accurate than the existing one in literature and are more suitable to be utilized for energy minimization, and the proposed optimization method can complete the relaying task with the minimum energy and MCT. When the residual loop-interference channel power is equal to -100dB, the scheme of ideal IBFD only consumes up to 2.5% of the energy of the compared schemes regardless of the value of the total data-bit constraint.

UAV mission scheduling with completion time, flight distance, and resource consumption constraints

Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.

Joint Trajectory and Velocity-Time Optimization for Throughput Maximization in Energy-Constrained UAV

In this article, we aim to study an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) communication system where a rotary-wing UAV travels from the initial to the final location to communicate with multiple IoT ground devices. The limited onboard energy of the UAV poses a constraint to the overall system’s performance. UAV’s energy consumption is majorly based on its kinematics, i.e., UAV’s velocity and acceleration. Therefore, in this work, we maximize the sum user throughput by jointly optimizing the 3-D UAV trajectory, and velocity-time profile in the presence of onboard energy, velocity, acceleration, and completion time constraints. Noting the nonconvexity of the optimization problem, the original problem is decoupled into two subproblems. First, the trajectory is optimized considering the velocity constraint, while in the second subproblem, the velocity and time optimization in each time slot is carried out. Simulation results show insights on the UAV trajectory and velocity-time profile with the variation in the onboard energy availability. In addition, we demonstrate the superior performance of the proposed approach in comparison to the benchmark schemes.

Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs

Load balance in vehicular ad hoc networks (VANETs) is a challenge in vehicle-to-vehicle computation offloading, due to stochastic requests of users, heterogeneous service capabilities and high mobility of vehicles, etc. This paper aims to fill this gap by formulating a problem for load balance in a VANET, with the objective of minimizing the maximum load under transmit power, storage capacity, per task completion time and energy consumption constraints. The formulated problem is proved to be NP-hard, then it is investigated by decomposing it into two subproblems, i.e., how to offload tasks for the case of fixed transmit power and how to adjust transmit power for the given offloading decision. For the first subproblem, an approximation algorithm is proposed by offloading the tasks in the vehicle with the maximum load to the vehicle with minimum load. Meanwhile, a deep reinforcement learning algorithm is proposed, in order to focus on the network dynamics. A coalition based algorithm, a distributed coalition based algorithm, as well as an incentive algorithm based on deep reinforcement learning, are proposed to maximize the total payoff for the selfishness of vehicles. For the second subproblem, an adjustment strategy for transmit power is customized to further reduce the computing load. The algorithms are evaluated on an integrated simulation platform with open street map, SUMO, NS-3 and dataset of Google cluster-usage traces. Simulation results show that, the proposed algorithms outperform three state-of-the-art works for most cases, in terms of the maximum load. The proposed distributed algorithm can significantly accelerate the proposed centralized algorithm with acceptable increase in maximum load. Besides, the load can be further reduced by the proposed adjustment strategy.

Hybrid energy-efficient algorithm for efficient Internet of Things deployment

Mobile cloud computing (MCC) plays a significant role in obtaining virtualized data stored on cloud servers without additional waiting time. However, the limited battery power of mobile devices limits access to ample data. To address this problem, job scheduling algorithms based on completion time constraints are introduced to reduce energy consumption. The time complexity of scheduling algorithms increases for applications not based on completion time constraints. This paper proposes a hybrid energy-efficient (HEE) paradigm to support the Internet of Things (IoT). The proposed HEE algorithm leverages the features from known pheromone termite (PT) and cuckoo search (CS) algorithms, supporting IoT-enabled users and devices. The integration of PT and CS helps determine the optimal path that leads to improved throughput and reduced energy consumption. The performance of the HEE algorithm is considerably higher than that of the PT and CS algorithms when the number of tasks increases. The proposed paradigm involves several modules (e.g., physical machine (PM) module, controller module, blockchain-enabled authentication module, and data mining-enabled mobile cloud user (MCU) authentication). The PM module helps to attain a higher energy efficiency, regardless of whether the cloud’s level of utilization is at its maximum efficiency. The controller module schedules the PMs efficiently and thus effectively provides resources to MCUs. Securing the identity of MCUs is of paramount significance. Safeguarding the identity of the MCU, the blockchain-enabled authentication module is employed, which authenticates the MCU to control the malicious role of the adversary. Furthermore, the data-mining blockchain-enabled authentication module authenticates the insider and outsider MCUs based on the data contents. The legitimate MCUs are allowed, and illegitimate MCUs are restricted. The proposed HEE paradigm is tested on the CloudSim simulator. Finally, the performance of the HEE paradigm is compared with that of known algorithms in terms of energy, security, throughput, and latency.

An effective dynamic service composition reconfiguration approach when service exceptions occur in real-life cloud manufacturing

• A dynamic service composition reconfiguration model when service exceptions occur under practical constraints(DSCRWECPC) in real-life CMfg is established. • DSCRWECPC considers service exceptions, the strict completion time constraint of original CMSC, service occupancy time constraint, and the impact on service quality due to the reconfiguration. • An improved HHO(SCRIHHO) is developed through well-designed strategies aiming at the nature of the DSCRWECPC. • Results of numerical experiments and practical applications verify SCRIHHO is superior to PSO and GWO in tackling the real-world DSCRWECPC. Cloud Manufacturing Service Composition (CMSC), as one of the key issues of Cloud Manufacturing (CMfg), has already attracted much attention. Existing researches on CMSC mainly focus on the optimization efficiency in ideal conditions, while scarcely focus on how to efficiently reconfigure CMSC when service exceptions occur. Uncertain service exceptions often occur during CMSC's execution in real-life CMfg. Thus, it is an urgent issue to perform an adjustment for CMSC to continue to complete the processing task. Besides, some practical constraints are non-negligible in real-world CMfg. Thus, it is necessary to consider them when reconfiguring CMSC. To bridge these gaps, this paper proposes a dynamic service composition reconfiguration model when service exceptions occur under practical constraints (DSCRWECPC). This model redefines optimization objectives, including machining quality, service quality, and cost. Besides, DSCRWECPC considers service exceptions, the cloud manufacturing service occupancy time constraint, the strict time constraint of original CMSC, and dynamic service quality change as its practical constraints. To solve this model, this paper proposes a service composition reconfiguration algorithm (SCRIHHO) based on the strengthened Harris Hawks Optimizer (HHO). Finally, to certify SCRIHHO's performance, this paper conducts numerical experiments and the case application to perform comparisons between SCRIHHO and other algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO)). Results showed SCRIHHO in this paper is superior to PSO, GWO when tackling the practical DSCRWECPC in CMfg.

Dependency-Aware Task Scheduling in Vehicular Edge Computing

Vehicular edge computing (VEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this article, we study the problem of task scheduling in VEC, where multiple computation-intensive vehicular applications can be offloaded to roadside units (RSUs) and each application can be further divided into multiple tasks with task dependency. The tasks can be scheduled to different mobile-edge computing servers on RSUs for execution to minimize the average completion time of multiple applications. Considering the completion time constraint of each application and the processing dependency of multiple tasks belonging to the same application, we formulate the multiple tasks scheduling problem as an optimization problem that is NP-hard. To solve the optimization problem, we develop an efficient task scheduling algorithm. The basic idea is to prioritize multiple applications and prioritize multiple tasks so as to guarantee the completion time constraints of applications and the processing dependency requirements of tasks. The numerical results demonstrate that our proposed algorithm can significantly reduce the average completion time of multiple applications compared with benchmark algorithms.

Energy and time constrained scheduling for optimized quality of service

Minimizing the total completion time has practical application in providing the best quality of service. We consider energy and time constrained task scheduling for minimized total completion time and minimized total energy consumption. We show that there is a polynomial time algorithm to find a nonpreemptive schedule with the minimum total completion time for a given total energy consumption constraint. Similarly, there is a polynomial time algorithm to find a nonpreemptive schedule with the minimum total energy consumption for a given total completion time constraint.

Joint production and transportation scheduling in flexible manufacturing systems

This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.

Soft error-aware energy-efficient task scheduling for workflow applications in DVFS-enabled cloud

Abstract Dynamic Voltage and Frequency Scaling (DVFS) has been widely used as a promising power management method to reduce the energy consumption of cloud workflows. However, due to the increasing chip density, lowering CPU voltages improperly in cloud data centers may inevitably increase soft error rate during workflow execution. Consequently, failures of timely completion of workflow applications may often take place, which raises serious concerns during the operation and maintenance of cloud data centers. To address such a problem, this paper proposes a soft error-aware energy-efficient task scheduling approach for workflow applications in DVFS-enabled cloud data centers. Under reliability and completion time constraints requested by tenants, our approach can generate energy-efficient task schedules for workflows by allocating tasks to appropriate virtual machines with specific operating frequencies. Comprehensive experiments on various well-known scientific workflow benchmarks show the effectivenss of our approach. Compared with state-of-the-art methods, our approach can achieve more than 30% energy savings while satisfying both reliability and completion time requirements.

Parallel machine makespan minimization subject to machine availability and total completion time constraints

In this paper, we study the parallel machine scheduling subject to machine availability constraint. The jobs can be resumed after being preempted by another job or interrupted by the unavailable intervals. The goal is to minimize the makespan subject to the constraint that the total completion time is minimized. We study two different machine unavailability models. In the first model, each machine has a single unavailable interval which starts from time 0. In the second model, each machine can have multiple unavailable intervals, but at any time, there is at most one machine unavailable. For each model, we show that there is an optimal polynomial time algorithm.

A quick-response framework for multi-user computation offloading in mobile cloud computing

The execution of much sophisticated applications on the resource-constrained mobile device will lead to the fast exhaustion of the battery of mobile device. Therefore, mobile cloud computing (MCC) is regarded as an energy-effective approach by offloading tasks from mobile device to the resource-enough cloud, which cannot only save energy for mobile devices but also prolong the operation time of battery. However, it still remains a challenging issue to coordinate task offloading among mobile devices and get offloading results quickly at the same time. In this paper, we propose an agent-based MCC framework to enable the device to receive offloading results faster by making offloading decision on the agent. Moreover, to get an offloading strategy, we formulate the problem of maximizing energy savings among multiple users under the completion time and bandwidth constraints. To solve the optimization problem, we propose a Dynamic Programming After Filtering (DPAF) algorithm. In the algorithm, firstly, the original offloading problem is transformed to the classic 0–1 Knapsack problem by the filtering process on the agent. Furthermore, we adopt dynamic programming algorithm to find an optimal offloading strategy. Simulation results show that the framework can more quickly get response from agent than other schemes and the DPAF algorithm outperforms other solutions in energy saving.

Content-Aware Network Coding Over Device-to-Device Networks

Consider a scenario in which a source broadcasts a common content to a group of cooperating mobile devices that are within proximity of each other. Devices in this group may receive only partial content from the source due to packet losses over wireless broadcast links and these packet losses may differ for different devices. The remaining content missing at each device can then be recovered, thanks to cooperation among the devices by exploiting device-to-device (D2D) connections. In this context, the minimum amount of time that guarantees a complete acquisition of the common content at every device is referred to as the “completion time”. It has been shown that instantly decodable network coding (IDNC) reduces the completion time as compared with no network coding in this scenario. However, for applications such as video streaming, not all packets have the same importance and not all devices are interested in the same quality of content. This problem becomes more interesting and challenging when additional, but realistic constraints, such as strict deadline, bandwidth, or limited energy are added in the problem formulation. We assert that direct application of IDNC in such a scenario yields poor performance in terms of content quality and completion time. In this paper, we propose a novel Content- and Loss-Aware IDNC scheme that improves content quality and network coding opportunities jointly by taking into account the contribution of each packet to the desired quality of service (QoS) as well as the channel losses over D2D links. Our proposed Content- and Loss-Aware IDNC (i) maximizes the quality under the completion time constraint, and (ii) minimizes the completion time under the quality constraint. We demonstrate the benefits of Content- and Loss-Aware IDNC through simulations.

Profile-based application assignment for greener and more energy-efficient data centers

The cloud computing era has brought significant challenges in energy and operational costs of data centers. As a result, green initiatives with regard to energy-efficient management of data center infrastructure for cloud computing have become essential. Addressing a big class of widely deployed data centers with relatively consistent workload and applications, this paper presents a new profile-based application assignment approach for greener and more energy-efficient data centers. It builds realistic profiles from the raw data measured from data centers and then establishes a theoretical framework for profile-based application assignment. A penalty-based profile matching algorithm (PPMA) is further developed to obtain an assignment solution, which gives near-optimal allocations whilst satisfying energy-efficiency, resource utilization efficiency and application completion time constraints. Through experimental studies, the profiling approach is demonstrated to be feasible, scalable and energy-efficient when compared to the commonly used general and workload history based application management approaches.

Task Scheduling with Dynamic Voltage and Frequency Scaling for Energy Minimization in the Mobile Cloud Computing Environment

Mobile cloud computing (MCC) offers significant opportunities in performance enhancement and energy saving for mobile, battery-powered devices. Applications running on mobile devices may be represented by task graphs. This work investigates the problem of scheduling tasks (which belong to the same or possibly different applications) in the MCC environment. More precisely, the scheduling problem involves the following steps: (i) determining the tasks to be offloaded onto the cloud, (ii) mapping the remaining tasks onto (potentially heterogeneous) local cores in the mobile device, (iii) determining the frequencies for executing local tasks, and (iv) scheduling tasks on the cores (for in-house tasks) and the wireless communication channels (for offloaded tasks) such that the task-precedence requirements and the application completion time constraint are satisfied while the total energy dissipation in the mobile device is minimized. A novel algorithm is presented, which starts from a minimal-delay scheduling solution and subsequently performs energy reduction by migrating tasks among the local cores and the cloud and by applying the dynamic voltage and frequency scaling technique. A linear-time rescheduling algorithm is proposed for the task migration. Simulation results demonstrate significant energy reduction with the application completion time constraint satisfied.

An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm

Abstract In order to solve the problems of security threats on workflow scheduling in cloud computing environments, the security of tasks and virtual machine resources are quantified using a cloud model, and the users’ satisfaction degree with the security of tasks assigned to the virtual resources is measured through the similarity of the security cloud. On this basis, combined with security, completion time and cost constraints, an optimized cloud workflow scheduling algorithm is proposed using a discrete particle swarm. The particle in the particle swarm indicates a different cloud workflow scheduling scheme. The particle changes its velocity and position using the evolution equation of the standard particle swarm algorithm, which ensures that it is a feasible solution through the feasible solution adjustment strategies. The simulation experiment results show that the algorithm has better comprehensive performance with respect to the security utility, completion time, cost and load balance compared to other similar algorithms.

Research of Cloud Computing Task Scheduling Algorithm Based on Improved Genetic Algorithm

Use genetic algorithm for task allocation and scheduling has get more and more scholars' attention. How to reasonable use of computing resources make the total and average time of complete the task shorter and cost smaller is an important issue. The paper presents a genetic algorithm consider total task completion time, average task completion time and cost constraint. Compared with algorithm that only consider cost constraint (CGA) and adaptive algorithm that only consider total task completion time by the simulation experiment. Experimental results show that this algorithm is a more effective task scheduling algorithm in the cloud computing environment.

An optimisation model for the design of energy efficient discrete event controllers

In this work, we propose a mixed integer programming model that can be applied to design the rule base matrix of a discrete event controller (DEC) in order to optimise energy consumption of the resources while adhering to meet budget and completion time constraints. The model is solved using a hybrid linear programming and genetic algorithm (GA) approach and the obtained optimised schedule is transformed into matrices as inputs to the supervisory controller DEC. The proposed methodology is implemented on an assembly project and yields encouraging results in generating a good quality schedule within an acceptable time. It is also evident that the proposed method arrives at optimal solution with better completion time as compared to MIP method.