Off-line Scheduling Research Articles

An Optimization Model for Offline Scheduling Policy of Low-Density Parity-Check Codes

An Optimization Model for Offline Scheduling Policy of Low-Density Parity-Check Codes

Performance Comparison of Offline Scheduling Algorithms for the Time-Aware Shaper (TAS)

Performance Comparison of Offline Scheduling Algorithms for the Time-Aware Shaper (TAS)

A two-phase heuristic algorithm for power-aware offline scheduling in IaaS clouds

The paper aims at mitigating hot-spots during Offline Scheduling in IaaS (Infrastructure-as-a-Service) cloud systems. Unlike previous studies, the research focuses on identifying and resolving hot-spots not at servers, but at server racks. A two-phase algorithm for performing power-aware offline scheduling is proposed. The first phase aims at identifying and mitigating hot-spots at racks, while the second phase performs VM consolidation, i.e. minimization of the number of occupied servers while maintaining a feasible VM mapping and low migration costs. The proposed algorithm takes into account the dynamic nature of VM's resource consumption: it does not only resolve detected hot-spots, but also tries to avoid hot-spots in a reasonable future time period. The algorithm was tested with the data from a real IaaS cloud with different sets of algorithm's parameters. Experimental evaluation showed that the statistical estimates of the future VM's resource consumption provide the most reliable mapping, which is a result of minimization of the number of new hot-spot occurrences.

Event-Triggered Hybrid Energy-aware Scheduling in Manufacturing Systems

Incorporating renewable energy sources (RESs) into manufacturing systems has been an active research area in order to address many challenges originating from the unpredictable nature of RESs such as photovoltaics. In the energy-aware scheduling for manufacturing systems, the traditional off-line scheduling techniques cannot always work well due to their lack of robustness with respect to uncertainties coming from imprecise models or unexpected situations. On the other hand, on-line scheduling or rescheduling, which can improve the robustness by using the model and the latest measurements simultaneously, suffer from a high computational cost. This work proposes a hybrid scheduling framework, which combines the advantages of both off-line scheduling and on-line scheduling, to provide a balanced solution between robustness and computational cost. A novel concept of partially-dispatchable state is introduced. It can be treated as a constant in scheduling when the model works well. When the model does not work well, it is triggered as the variable to tune to improve the performance. Such an event-triggered structure can reduce the number of rescheduling and computational costs while achieving a reasonable performance and enhancing system robustness. Moreover, the choice of partially-dispatchable state also provides an extra design freedom in achieving green manufacturing. Simulation examples on a manufacturing system, which consists of a 100-kW solar photovoltaic system, a 10-machine flow shop production line, a 50-kWh energy storage system, a 100-kW gas turbine, and the grid for power supply, demonstrate the validity and applicability of this event-triggered hybrid scheduling (ETHS) framework.

A Compressor Off-Line Washing Schedule Optimization Method With a LSTM Deep Learning Model Predicting the Fouling Trend

Abstract Compressor fouling is one of the most prevalent fault modes that contribute to the performance degradation of a gas turbine power plant. Off-line washing is a standard maintenance procedure to recover the fouling degradation, but with washing cost. In this paper, an off-line washing schedule optimization method with a long short-term memory (LSTM) prediction model is proposed to maximize the plant net profit. First, a mechanism model-based gas path analysis method is developed to identify the fouling indications of compressor flow rate degradation (DGC) and compressor efficiency degradation (DEC). Second, a sliding window prediction method based on LSTM is proposed to accurately predict the nonlinear fouling trends. The prediction models are trained and tested by the true trends of the DGC and DEC that are identified from the field data of a real gas turbine power plant. The comparison results prove that the LSTM algorithm outperforms other machine learning algorithms. The mean relative square error of the DGC LSTM model is 9.72 × 10−4, and DEC is 4.08 × 10−4. Finally, a detailed economic model is developed by coupling the fouling prediction model with the gas turbine performance model. On this basis, an optimization method of the washing schedule is developed to maximize the net profit. Two case studies, under full load and field data, are carried out to verify the proposed optimization method. The results show that the washing schedules of the two case studies are much similar, in which three washing tasks with gradually reduced intervals are provided. Furthermore, the comparison results of different schedules show that the proposed optimal schedule has a huge potential in saving the net profit. It can save 3.26 million Yuan compared with the practical schedule adopted by the real power plant.

Integrated On-line and Off-line Scheduling of Real-Time Tasks using Co-processor Scheduling Unit : Toward Dual-Mode kernels

Integrated On-line and Off-line Scheduling of Real-Time Tasks using Co-processor Scheduling Unit : Toward Dual-Mode kernels

Energy-efficient capture of stochastic events based on on-line scheduling scheme

We improve the off-line scheduling scheme of existing wireless sensor network. Firstly, we introduce Bayesian statistical method in synchronous wireless sensor network. Then, we let duration and interval, the reflection of characteristics of stochastic events, obey exponential distribution. Next, we make Bayes posterior estimation on parameter. Based on Bayesian estimate, we obtain the analytical solution of the capture probability of stochastic events and sensor energy efficiency of capture events. Finally, we propose an on-line scheduling scheme for synchronous wireless sensor networks. This paper compares and analyzes the simulation experiments in on-line scheduling scheme and off-line scheduling scheme, and the results show that compared with off-line scheduling scheme with constant distribution parameter values, on-line scheduling scheme can effectively reduce the probability of missing stochastic events and increase the probability of capturing events, further save energy consumption of wireless sensor network, and extend network lifetime.

Scheduling Optimization of Time-Triggered Cyber-Physical Systems Based on Fuzzy-Controlled QPSO and SMT Solver

The time-triggered communication paradigm is a cost-efficient way to meet the real-time requirements of cyber-physical systems. It is a non-deterministic polynomial NP-complete problem for multi-hop networks and non-strictly periodic traffic. A two-level scheduling approach is proposed to simplify the complexity during optimization. In the first level, a fuzzy-controlled quantum-behaved particle swarm optimization (FQPSO) algorithm is proposed to optimize the scheduling performance by assigning time-triggered frame instances to the basic periods of each link. In order to prevent population from high aggregation, a random mutation mechanism is used to disturb particles at the aggregation point and enhance the diversity at later stages. Fuzzy logic is introduced and well designed to realize a dynamic adaptive adjustment of the contraction–expansion coefficient and mutation rate in FQPSO. In the second level, we use an improved Satisfiability Modulo Theories (SMT) scheduling algorithm to solve the collision-free and temporal constraints. A schedulability ranking method is proposed to accelerate the computation of the SMT-based incremental scheduler. Our approach can co-optimize the jitter and load balance of communication for an off-line schedule. The experiments show that the proposed approach can improve the performance of the scheduling table, reduce the optimization time, and reserve space for incremental messages.

Hybrid Robust H2/H∞ Fault-Tolerant Control with Random Delay NCS

In this paper, a hybrid fault-tolerant control method with off-line design and online scheduling is proposed for NCS with actuator faults, random delay, and external finite energy disturbance. The problem of less conservatism of robust generalized H2/H∞ hybrid fault-tolerant control is studied. Firstly, a closed-loop fault model of the system with random delay parameters was established according to the Bernoulli 0-1 distribution; all possible prior faults are divided into a few intervals according to certain rules, and then an interval fault-tolerant controller is designed off-line according to the prior faults of each interval. Secondly, when the fault is estimated online, the corresponding interval fault-tolerant controller is called through the scheduling mechanism to achieve rapid fault tolerance of prior faults within the interval and mitigate the impact of other faults within the interval, which provides a guarantee for subsequent safe reconstruction control. Finally, the effectiveness of the proposed method is verified by Matlab simulation.

Estimation Oriented Co-design of Sensor Scheduling over Stochastic Delayed Channel under Power Constraints

This paper investigates the scheduling problem over a delayed channel. Different from most existing documents, a novel hybrid model is proposed which combines both delay and packet-loss to minimize the error covariance update at the estimator side. With the help of this setup, a co-design problem between power consumption and estimation performance is considered. We first derive out a globally optimal off-line schedule. Moreover, an on-line schedule based on a designed threshold is proposed to further enhance the performance, which is aided by feedback information. Comparisons between on-line and off-line strategies are illustrated by numerical simulations, which has shown the superiority of on-line one to the off-line one.

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.

Fault-tolerant real-time tasks scheduling with dynamic fault handling

Abstract Predictable performance when coping with transient failures is of paramount importance in safety-critical real-time systems. Various software fault-tolerant techniques are employed towards this goal among which check-pointing is a relatively cost-effective scheme. In this paper, we propose an efficient fault-tolerant scheduling framework with run-time fault handling protocol, where criticality levels can be adaptively inserted for fault handling according to run-time fault workload. In contrast to prior works which apply with task re-execution strategy, the proposed framework adaptively determines on-demand re-executions only on the faulty checkpoint segments, rather than on the whole job. Towards this, a unified overrun handling protocol is developed to handle fault recovery adaptively to avoid over-provisioning of resources. In addition, we develop an off-line schedulability analysis technique for the proposed scheduling algorithm. The simulation results show that our fault-tolerant scheduling framework can bring up to 81% improvement in supporting low-criticality service without sacrifice in the MC-schedulability compared with the existing techniques.

Robust Scheduling of Time-Constrained Dual-Arm Cluster Tools With Wafer Revisiting and Activity Time Disturbance

Wafer revisiting and residency time constraints complicate the scheduling problem of cluster tools in semiconductor manufacturing. Random disturbance to the activity time in operating a tool further complicates such a scheduling problem. To solve this challenging problem, this paper proposes a robust real-time schedule which consists of a real-time controller (RTC) and an off-line schedule. The former is developed to offset the activity time disturbance such that the wafer sojourn time fluctuation in a process module is minimized. With the RTC, to find the off-line schedule, necessary and sufficient schedulability conditions under which a feasible schedule exists are derived and these conditions can be easily checked. Then, the off-line schedule can be efficiently found by the proposed algorithms based on nondisturbed activity time if a feasible schedule exists. With the obtained real-time schedule, it is shown that the productivity of the system is maximized. Finally, examples are used to illustrate the proposed approach.

Adaptive Scheduling for Multicluster Time-Triggered Train Communication Networks

The execution time of conventional incremental off-line schedule approaches for time-triggered networks increases rapidly when networks become larger. When the traffic in a network changes, they need to reschedule all influenced flows once again. Traffic changes at the cluster level involve many data flows. An incremental scheduler cannot react quickly to such changes. We propose an algorithm based on mixed integer linear programming and counterexample guided methodology. Our algorithm can generate adaptive schedule for cluster-level changes of the system. The adaptive schedule can react quickly to the changes during runtime. Our algorithm enhances the incremental schedulers. It allows schedulers to react to changing at both the flow level and the cluster level. Experiments show that our approach is effective. In the scenarios of coupling train consists, our algorithm can generate the schedule table of the train network within a few seconds.

Controllability and Observability Gramians as Information Metrics for Optimal Design of Networked Control Systems

This paper addresses the problem of optimal control and scheduling of Networked Control Systems over limited bandwidth deterministic networks using some insight on the interplay between the control and information theory. The motivation is related to the necessity of choice a communication sequence which maximize control signal impact on the plant behavior. The solution is obtained by decomposing the overall problem in a twofold one. The first level problem aims obtaining the periodic off-line or static scheduling function of control signals based on system properties, communication constrains, periodicity of scheduling sequence, performance criteria and maximization of the degree of reachability/observability of the periodic system. A Mixed Integer Quadratic Programming (MIQP) problem is formulated and solved obtaining a periodic and stable NCS. The solution of the second level problem is based on the structure of the static scheduling function obtained from the first level solution and the dependance of the degree of reachability/observability on the current state of the system.

Dynamic scheduling for wireless multicast in massive MIMO HetNet

Consider a massive multiple input and multiple output (MIMO) heterogeneous network (HetNet) scenario where the macro base station is coupled with low-power nodes (LPNs) to provide wireless multicast transmission to user equipments (UEs) within a single macro cell. The Markov decision process (MDP) model is adopted to model the dynamic scheduling problem introduced by the mobility of UEs. With asymptotic conclusion for the massive MIMO multicast transmission, we propose a MDP-based off-line scheduling problem with determined system parameters and derive a linear programming (LP) model to solve the problem. As for the system with undetermined parameters, we adopt Q-learning algorithm to solve the optimal policy. To derive the low-complexity algorithm, the state aggregation method is utilized to build an series of sub-problems to estimate the original complex MDP problem. Finally we use the MDP Toolbox in MATLAB to run simulations to test the proposed algorithm. The performance of the proposed algorithm is shown and analyzed in details.

Using Simulation for Scheduling and Rescheduling of Batch Processes

The problem of scheduling multiproduct and multipurpose batch processes has been studied for more than 30 years using math programming and heuristics. In most formulations, the manufacturing recipes are represented by simplified models using state task network (STN) or resource task network (RTN), transfers of materials are assumed to be instantaneous, constraints due to shared utilities are often ignored, and scheduling horizons are kept small due to the limits on the problem size that can be handled by the solvers. These limitations often result in schedules that are not actionable. A simulation model, on the other hand, can represent a manufacturing recipe to the smallest level of detail. In addition, a simulator can provide a variety of built-in capabilities that model the assignment decisions, coordination logic and plant operation rules. The simulation based schedules are more realistic, verifiable, easy to adapt for changing plant conditions and can be generated in a short period of time. An easy-to-use simulator based framework can be developed to support scheduling decisions made by operations personnel. In this paper, first the complexities of batch recipes and operations are discussed, followed by examples of using the BATCHES simulator for off-line scheduling studies and for day-to-day scheduling.

Cloud Avoidance Scheduling Algorithm for Agile Optical Satellites

The existence of cloud seriously influences the imaging quality and efficiency of traditional optical satellites, which can be overcome thanks to the enhancement of the mobility of the new generation of agile satellites. The problem of agile optical satellite scheduling considering real-time cloud information is therefore investigated. A two-phased scheduling framework, containing off-line scheduling on the ground and on-line rescheduling onboard, is proposed. An algorithm based on the ant colony algorithm is designed to solve this problem. An on-line re-scheduling algorithm based on confliction sliding strategy is proposed. A series of experiments are carried out to testify the effectiveness of the algorithm. The results show that almost 50 % of the satellite observation capacity is saved with consideration of real-time cloud information.

Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications

In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of $\mathcal{O}(N^2)$ in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.

Off-Line and Dynamic Production Scheduling – A Comparative Case Study

Abstract A comprehensive case study of manufacturing scheduling solutions development is given. It includes highly generalized scheduling problem as well as a few scheduling modes, methods and problem models. The considered problem combines flexible job shop structure, lot streaming with variable sublots, transport times, setup times, and machine calendars. Tabu search metaheuristic and constraint programming methods have been used for the off-line scheduling. Two dynamic scheduling methods have also been implemented, i.e., dispatching rules for the completely reactive scheduling and a multi-agent system for the predictivereactive scheduling. In these implementations three distinct models of the problem have been used, based on: graph representation, optimal constraint satisfaction, and Petri net formalism. Each of these solutions has been verified in computational experiments. The results are compared and some findings about advantages, disadvantages, and suggestions on using the solutions are formulated.