Heuristic Scheduling Algorithm Research Articles

Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum Circuits

Qubit mapping is a critical aspect of implementing quantum circuits on real hardware devices. Currently, the existing algorithms for qubit mapping encounter difficulties when dealing with larger circuit sizes involving hundreds of qubits. In this article, we introduce an innovative qubit mapping algorithm, Duostra, tailored to address the challenge of implementing large-scale quantum circuits on real hardware devices with limited connectivity. Duostra operates by efficiently determining optimal paths for double-qubit gates and inserting SWAP gates accordingly to implement the double-qubit operations on real devices. Together with two heuristic scheduling algorithms, Limitedly Exhaustive Search and Shortest-Path Estimation, it yields results of good quality within a reasonable runtime, thereby striving toward achieving quantum advantage. Experimental results showcase our algorithm’s superiority, especially for large circuits beyond the NISQ era. For example, on large circuits with more than 50 qubits, we can reduce the mapping cost on an average 21.75% over the virtual best results among QMAP, t \(|ket\rangle\) , Qiskit, and SABRE. Besides, for mid-size circuits such as the SABRE-large benchmark, we improve the mapping costs by 4.5%, 5.2%, 16.3%, 20.7%, and 25.7%, when compared to QMAP, TOQM, t \(|ket\rangle\) , Qiskit, and SABRE, respectively.

Heuristic algorithm for lot sizing and scheduling on identical parallel machines

In this paper a newly developed heuristic algorithm for lotsizing and scheduling on identical parallel machines is presented. A basic concept and an aim of lotsizing and scheduling are described and clarified. Furthermore, models for considered problems for fixed and variable demand as well as for one and many identical parallel machines are presented. A concept and a formal description of newly developed heuristic algorithm is described. The paper includes in details the obtained results and solutions of conducted experiments and the description of used data sets.

Branch-and-Bound and Heuristic Algorithms for Group Scheduling with Due-Date Assignment and Resource Allocation

Green scheduling that aims to enhance efficiency by optimizing resource allocation and job sequencing concurrently has gained growing academic attention. To tackle such problems with the consideration of scheduling and resource allocation, this paper considers a single-machine group scheduling problem with common/slack due-date assignment and a controllable processing time. The objective is to decide the optimized schedule of the group/job sequence, resource allocation, and due-date assignment. To solve the generalized case, this paper proves several optimal properties and presents a branch-and-bound algorithm and heuristic algorithms. Numerical experiments show that the branch-and-bound algorithm is efficient and the heuristic algorithm developed based on the analytical properties outruns the tabu search.

Time-aware deterministic bandwidth allocation scheme in TDM-PON for time-sensitive industrial flows

A programmable logic controller deployed in the cloud or edge cloud (cloud PLC) contributes to flexible and scalable industrial manufacturing processes. For the industrial Internet, a time-division multiplexing passive optical network is a promising access technology for connecting cloud PLC and industrial devices. Time-sensitive (TS) industrial applications such as motion control and mobile robotics impose strict delay and jitter requirements, pursuing deterministic transmission capability, that is, guaranteeing that the upper bounds on delay and jitter of the TS industrial flow meet its requirements. However, traditional bandwidth allocation schemes aim to leverage statistical multiplexing to improve bandwidth resource utilization efficiency, so it guarantees only the “average delay/jitter” of the service and cannot meet the deterministic requirements of the TS industrial flow. This paper presents a time-aware deterministic bandwidth allocation (TA-DetBA) scheme to satisfy the deterministic transmission of TS industrial flows. To this purpose, first, we constrain the position of the transmission window (i.e., bandwidth) allocated by optical line termination to the optical network unit according to the arrival characteristics (i.e., flow arrival time and flow cycle) and requirements (i.e., delay and jitter) of the flow. Subsequently, we propose an integer linear programming scheduling model and a greedy-based time slot equalization heuristic scheduling algorithm to improve schedulability and reduce the average delay of best-effort flows. Simulation results show that our proposed TA-DetBA scheme guarantees the deterministic delay and jitter of TS industrial flow. Compared with the fixed bandwidth allocation (FBA) scheme, the average resource utilization efficiency of the TA-DetBA scheme is improved by up to 20.6% higher than FBA.

Design and Analysis of Heuristic Algorithms for Energy-Constrained Task Scheduling With Device-Edge-Cloud Fusion

Mobile edge computing with device-edge-cloud fusion provides a new type of heterogeneous computing environment. We consider task scheduling with device-edge-cloud fusion (without energy concern) and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems. The main contributions of the paper are summarized as follows. We design three heuristic algorithms for task scheduling with device-edge-cloud fusion and prove an asymptotic performance bound. We design one heuristic algorithm for energy-constrained task scheduling with device-edge-cloud fusion, which solves the two subproblems of task scheduling and power allocation in an interleaved way. We derive lower bounds for the optimal solutions for both task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion, so that the performance of our heuristic algorithms can be compared with that of an optimal algorithm. We experimentally evaluate the performance of our heuristic algorithms and find that the performance of our heuristic algorithms are very close to that of optimal algorithms. To the best of our knowledge, this is the first paper which studies task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems and conducts analytical performance evaluation.

A heuristic algorithm for equipment scheduling at an automated container terminal with multi-size containers

With the increasing volume of shipping containers, container multimodal transport and port scheduling have attracted much attention. The allocation and dispatching of handling equipment to minimize completion time and energy consumption have always been a focus of research. This paper considers a scheduling problem at an auto-mated landmaritime multimodal container terminal with multi-size containers, in which operating facilities and equipment such as quay cranes, vehicles, yard cranes, and external container trucks are involved. Moreover, the diversity of container sizes and the location of handshake areas in yards are concerned. A mixed integer program-ming model is established to schedule all operating facilities and equipment. To solve the mathematical model is a NP-hard problem, which is difficult to be solved by conventional methods. Then we propose a heuristic algorithm which merges multiple targets into one and designs an improved genetic algorithm based on the heuristic combi-nation strategy in which 20-ft containers are paired-up to the same yard before allocation. After that, some exper-iments are designed to prove the effectiveness of the model and the algorithm. The effect of configurations on efficiency and energy consumption under different conditions is discussed, and the influences of different parame-ters and the proportion of 20-ft containers are also compared. Furthermore, the influence of locations of hand-shake area with different yard quantities are compared. To conclude, there is an optimal number of equipment to be allocated. If few equipment is used, the operation time will be prolonged; if too many, the energy consumption will be increased. When the yard operation is the bottleneck, the handover location should be in the centre, other-wise other locations might be feasible. When the proportion of 20-ft containers that can be combined is large, the method proposed in this paper has advantages over traditional methods. The proposed algorithm has made a breakthrough in improving efficiency and reducing energy consumption.

Novel Heuristic Algorithm for Flexible Job Shop Scheduling based on the Longest Processing Time Rules to Minimize Makespan

This research examined the scheduling of jobs with multiple stages unto identical parallel machines to minimize the makespan. The work is motivated by a Flexible Manufacturing System case that produces various parts and has multiple machining centers. Early research towards this system proposed a stage-by-stage independent scheduling, resulting in a non-optimal solution. This study aimed to create a better solution for the system by developing a novel heuristic algorithm based on the classical longest processing time algorithm and simultaneously considering processing time for all stages when deciding the job sequencing and job-machine allocation. The algorithm is defined as Modified LPT for Multiple Identical Machine with Multi-process Capability (M-LPT MIMMPC). We performed a numerical experiment to assess the algorithm's performance by incorporating various cases. We concluded that the resulting makespans are always better than LPT's theoretical bound for parallel machine scheduling. In some cases, it successfully gave an optimal value. Although the experiment scope was still limited, the algorithm showed promising performance results.

A New Algorithm for Real-Time Scheduling and Resource Mapping for Robot Operating Systems (ROS)

A new version of a robot operating system (ROS-2) has been developed to address the real-time and fault constraints of distributed robotics applications. However, current implementations lack strong real-time scheduling and the optimization of response time for various tasks and applications. This may lead to inconsistent system behavior and may affect system performance. This article presents a new and efficient heuristic scheduling algorithm for the ROS-2 framework to improve resource mapping and system scheduling for robotics applications. The proposed scheduling design includes grouping callbacks into executors, assigning executor priority, and sequencing callbacks inside executors. The timing constraints and functional properties are expressed in the formal design model, and real-time scheduling is performed with respect to the timing constraints. The benefits of the proposed solution are demonstrated by a set of experimental results. Using this algorithm, it is shown that the number of executors needed to schedule a set of callbacks is minimized and the system schedulability is maximized for loaded and overloaded cases.

Rules for selecting orders from the precedence in production systems

Analytical rules for selecting orders from the precedence by using heuristic scheduling algorithms in MES and APS class systems are considered. The proposed sampling rules are presented for single-stage and multistage planning systems, subjected to the intensity of orders. Keywords heuristic scheduling algorithm, order intensity coefficient, one-stage systems, multi-stage systems, slack time, directive due dates

Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

EE-MPTCP: An Energy-Efficient Multipath TCP Scheduler for IoT-Based Power Grid Monitoring Systems

The Internet-of-Things (IoT) based monitoring system has significantly promoted the intelligence and automation of power grids. The inspection robots and wireless sensors used in the monitoring system usually have multiple network interfaces to achieve high throughput and reliability transmission. The concurrent usage of these available interfaces with Multipath TCP (MPTCP) can enhance the quality of service of the communications. However, traditional MPTCP scheduling algorithms may bring about data disorder and even buffer blocking, which severely affects the transmission performance of MPTCP. And the common MPTCP improvement mechanisms for IoT lack sufficient attention to energy consumption, which is important for the battery-limited wireless sensors. With the aim to promote conservative energy without loss of throughput, this paper develops an integrated multipath scheduler for energy consumption optimization named energy-efficient MPTCP (EE-MPTCP). EE-MPTCP first constructs a target optimization function which considers both network throughput and energy consumption. Then, based on the proposed MPTCP transmission model and existing energy efficiency model, the network throughput and energy consumption of each path can be estimated. Finally, a heuristic scheduling algorithm is proposed to find a suitable set of paths for each application. As confirmed by experiments based on Linux testbed as well as the NS3 simulation platform, the proposed scheduler can shorten the average completion time and reduce the energy consumption by up to 79.9% and 79.2%, respectively.

Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment

Fog cloud computing promotes the combination of fog and cloud nodes to satisfy the requests of data accessing from IoT (Internet of Things) devices. To reduce the data delay and enhancing the QoS (Quality of Service), effective task scheduling is one of the major requirements in fog cloud environment. Numerous approaches have been deployed by the researchers for maintaining the QoS requirements. However, the emergence of bursty traffic affects the process of task scheduling due to high service latency. Effective QoS is promoted by minimizing the costs of computation, communication and deadline violation cost. To achieve the main objective of cost minimization, the proposed work is implemented by adopting a novel model called HFSGA (Hybrid Flamingo Search with a Genetic Algorithm) for better task scheduling. Seven basic benchmark optimization test functions are used to compare the performance of HFSGA with other well-known algorithms. Also, Friedman Rank Test is performed to demonstrate the significance of the results. The implemented model presents better outcomes with respect to PDST (Percentage of deadline satisfied task), makespan and cost. When compared to the existing algorithms that include Ant colony optimization (ACO), Particle swarm optimization (PSO), Genetic Algorithm (GA), Min-CCV, Min-V and Round Robin (RR) approach, the proposed work shows better output in satisfying the task scheduling process.

MPEFT: a makespan minimizing heuristic scheduling algorithm for workflows in heterogeneous computing systems

MPEFT: a makespan minimizing heuristic scheduling algorithm for workflows in heterogeneous computing systems

Limited Duplication-Based List Scheduling Algorithm for Heterogeneous Computing System.

Efficient scheduling algorithms have been a leading research topic for heterogeneous computing systems. Although duplication-based scheduling algorithms can significantly reduce the total completion time, they are generally accompanied by an exorbitant time complexity. In this paper, we propose a new task duplication-based heuristic scheduling algorithm, LDLS, that can reduce the total completion time and maintains a low time complexity. The scheduling procedure of LDLS is composed of three main phases: In the beginning phase, the maximum number of duplications per level and per task is calculated to prevent excessive duplications from blocking regular tasks. In the next phase, the optimistic cost table (OCT) and ranking of tasks are calculated with reference to PEFT. In the final phase, scheduling is conducted based on the ranking, and the duplication of each task is dynamically determined, enabling the duplicated tasks to effectively reduce the start execution time of its successor tasks. Experiments of algorithms on randomly generated graphs and real-world applications indicate that both the scheduling length and the number of better case occurrences of LDLS are better than others.

A Mathematical Formulation and a Heuristic for the Spatial Scheduling of Mega-Blocks in Shipbuilding Industry

Abstract In shipbuilding industry, to reduce the staying time of the ship in a dock, floating crane and floating dock are used to handle the mega-blocks, thus it increased the turnover of the dock. Therefore, spatial scheduling for mega-block in an assembly yard becomes important in shipbuilding industry. However, spatial scheduling of mega-blocks has some unique characteristics compared with the well-known classic bin-packing problem, and not many studies can be found on this issue. This research proposes an enhanced mathematical formulation and a heuristic algorithm for spatial scheduling of mega-block arrangement problem. The formulation provides tight bound compared with the existing formulation, and the proposed heuristic can obtain good quality of the solution in a reasonable amount of time even when the size of the instance became large. Introduction Shipbuilding industry involves constructing ships and other floating vessels. Block is a base element of the shipbuilding process, and the blocks are assembled and welded in the block assembly shop, and finally erected to a ship in a dry dock. A size of a block is around 15~30 meters with a weight of around 30~300 tons (Zheng et al. 2011), and a large ship consists of around 150 blocks (Koh et al. 2011b). Some blocks are outsourced and timely delivery and quality of blocks have an absolute impact on the ship’s quality. Also, it is possible to shorten the dock duration by receiving blocks from several companies at the same time.

Retraction Note to: Load balancing based hyper heuristic algorithm for cloud task scheduling

Retraction Note to: Load balancing based hyper heuristic algorithm for cloud task scheduling

Optimizing time delay in cloud using enhanced multi‐hold inherited maximization algorithm to reduce cost and improve bandwidth

AbstractOptimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM.

Comparative analysis of task level heuristic scheduling algorithms in cloud computing

Comparative analysis of task level heuristic scheduling algorithms in cloud computing

Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments

Cloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In this paper, we mainly focus on the efficient scheduling of distributed simulation tasks in collaborative cloud and edge environments. Since simulation tasks are usually tightly coupled with each other by sending many messages and the status of tasks and hosts may also change frequently, it is essentially a dynamic bin-packing problem. Unfortunately, popular methods, such as meta-heuristics, and accurate algorithms are time-consuming and cannot deal with the dynamic changes of tasks and hosts efficiently. In this paper, we present Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task. After formulating such a scheduling problem as a min-cost max-flow (MCMF) problem, incremental MCMF algorithms are adopted to accelerate the procedure of calculating an optimal flow and heuristic scheduling algorithm, with the awareness of task migration cost, designed to assign tasks. Simulation experiments on Alibaba cluster trace show that Pool can schedule all of the tasks efficiently and is almost 5.8 times faster than the baseline method when few tasks and hosts change in the small problem scale.

Tightly-Packed Repetitive Schedules: A Tetris Challenge

A large portion of new and rehabilitation projects for the civil infrastructure involves repetitive tasks that mandate high degree of synchronization among the resources to meet project constraints. Despite their large need for support, few tools exist commercially. This is due, in part, to the computational and visualization challenges of existing repetitive scheduling techniques, which complicate the process and make it less understood, particularly when the repetitive units are not identical and when resources are limited. This presentation introduces enhancements by dealing with repetitive scheduling as a game of Tetris, where tasks can change geometry to fit tightly together to save project duration. This is demonstrated through novel mathematical formulations, as well as a new heuristic scheduling algorithm (First-Come-First-Serve). It will be shown that the proposed geometrical adjustments are more advantageous in improving repetitive schedules than using expensive accelerations, and the new visuals make the schedule more legible. This research is a step towards making repetitive scheduling a mainstream tool, to benefit the construction industry.