A weighted-fair-queuing (WFQ)-based dynamic request scheduling approach in a multi-core system

A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs

This paper presents a dynamic multi-application scheduling approach for running and scheduling parallel adaptive applications in Networks of Workstations (NOWs) and Clusters of Processors (COPs). Parallel adaptive applications have the property of varying their parallelism degree dynamically following availability of resources and changes in the underlying environment state. In our model, each parallel adaptive application is controlled by its own scheduler responsible for optimizing resources which it uses. Multi-application scheduling consists of sharing resources among applications fairly and uses a combined (time-sharing and space-sharing) scheduling approach. The scheduling approach is characterized especially by dynamic arrivals of applications, and remapping of allocation in order to handle dynamic arrivals and departures of applications and underlying environment state changes. Therefore, resources are fairly shared among applications. The multi-application scheduler interacts with the application schedulers in order to optimize the scheduling approach. The application schedulers are responsible for applying the multi-application scheduler decisions and internally optimizing the resources exploited by the application. Beyond the adaptive aspect which allows parallel applications to exploit idle cycles in NOWs with respect to the personal character of workstations, the proposed model provides a multi-application scheduling support which globally ensures better resource utilization and fair resource sharing.

In an age when enterprises are increasingly dependent on their suppliers and customers, individual companies should broaden their scope in order to include other organisations participating in the same value chain. Consequently, different parties of the chain ought to exchange information to make sound decisions in order to cut down global costs or improve customer service. This paper proposes a multi–agent approach for dynamic production and distribution scheduling in a simple supply chain. The approach is based on the continuous supervision of the active schedules and routes, in order to detect possible exceptions and apply corrective actions in a real–time and coordinated manner with other parties. Finally, the paper focuses on the design phase of the distribution stage, where the problem is mathematically formulated as a dynamic vehicle routing problem with time windows and backhauls (DVRP–TWB), and three combinatorial optimisation meta–heuristics are proposed as solving techniques.

Recent trends in grid computing development is moving towards a service-oriented architecture. With the momentum gaining for the service-oriented grid computing systems, the issue of deploying support for integrated scheduling and fault-tolerant approaches becomes paramount importance. To this end, we propose a scalable framework that loosely couples the dynamic job scheduling approach with the hybrid replications approach to schedule jobs efficiently while at the same time providing fault-tolerance. The novelty of the proposed framework is that it uses passive replication approach under high system load and active replication approach under low system loads. The switch between these two replication methods is also done dynamically and transparently.KeywordsGrid ComputingCluster TreeGrid ScheduleLocal SchedulerReport MessageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems. These problems have several challenging characteristics, the two most challenging of which include: 1) they span long- and short-term horizons, from strategic planning of flight schedules operated several months into the future, to real-time operations in which strategies must be developed and implemented immediately to recover scheduled operations from disruptions; and 2) they include multiple resources that must be coordinated, such as aircraft, crews, and passengers. While optimization approaches have been essential to the airline industry and effective in developing aircraft and crew schedules, historical models and approaches often fail to capture the complexity of airline operations. For example, approaches, often by necessity, involve a sequential, rather than an integrated process to develop schedules for aircraft and crews, and moreover, the process involves simplifying assumptions, including that future demands are known and deterministic, and that schedules are operated as planned. In more recent research on airline schedule optimization, advances have led to new schedule optimization models and approaches that more accurately reflect reality. As described in this presentation, the most notable contributions to these advances include: 1. Integrated Aircraft and Crew Schedule Optimization Approaches in which some of the aircraft and crew schedule decisions previously taken sequentially are integrated, moving closer to producing globally optimal schedules; 2. Dynamic Scheduling Approaches in which schedules are adjusted during the passenger booking period to reflect increased knowledge of booking patterns and to increase the schedule’s associated total revenue; and 3. Robust Optimization Approaches in which the stochastic nature of airline operations is modeled and realized schedule performance is optimized.

Current and future parallel programming models need to be portable and efficient when moving to heterogeneous multi-core systems. OmpSs is a task-based programming model with dependency tracking and dynamic scheduling. This paper describes the OmpSs approach on scheduling dependent tasks onto the asymmetric cores of a heterogeneous system. The proposed scheduling policy improves performance by prioritizing the newly-created tasks at runtime, detecting the longest path of the dynamic task dependency graph, and assigning critical tasks to fast cores. While previous works use profiling information and are static, this dynamic scheduling approach uses information that is discoverable at runtime which makes it implementable and functional without the need of an oracle or profiling. The evaluation results show that our proposal outperforms a dynamic implementation of Heterogeneous Earliest Finish Time by up to 1.15x, and the default breadth-first OmpSs scheduler by up to 1.3x in an 8-core heterogeneous platform and up to 2.7x in a simulated 128-core chip.

Dynamic job shop scheduling is an important but difficult problem in manufacturing systems which becomes complex particularly in uncertain environments with varying shop scenarios. Genetic programming based hyper-heuristics (GPHH) have been a successful approach for dynamic job shop scheduling (DJSS) problems by enabling the automated design of dispatching rules for DJSS problems. GPHH is a computationally intensive and time consuming approach. Furthermore, when complex shop scenarios are considered, it requires a large number of training instances. When faced with multiple shop scenarios and a large number of problem instances, identifying good training instances to evolve dispatching rules which perform well over diverse scenarios is of vital importance though challenging. Essentially this requires the tackling of exploration versus exploitation trade-off. To address this challenge, we propose a new framework for GPHH which incorporates active sampling of good training instances during evolutionary process. We propose a sampling algorithm based on the ε-greedy method to evolve a set of dispatching rules. Through our experiments, we demonstrate the ability of our framework to efficiently identify useful training instances toward evolving dispatching rules which outperform the existing training methods.

Instruction-level parallelism in a single stream of code for non-numerical applications has been the subject of many recent researches. This work extends the analysis to symbolic applications described with logic programming. In particular, the authors analyze the effects on performance of speculative execution, memory alias disambiguation, renaming and flow prediction. The obtained results indicate that one can reach a sustained parallelism of 4 (comparable with imperative languages), with the proper optimizations. The authors also show a comparison between static and dynamic scheduled approaches, outlining the conditions under which a dynamic solution can reach substantial improvements over a static one. In this way, they point out some important optimizations and parameters of a dynamic scheduling approach, indicating a guideline for future architectural implementations. >

Simulation studies in job shop scheduling—II: Performance of priority rules

The rapid expansion of real-time, latency-sensitive Internet-of-Things (IoT) applications has revealed the limits of centralized cloud infrastructures and driven computation toward a hierarchical IoT–fog–cloud continuum. Scheduling in this environment is challenging due to resource heterogeneity, dynamic arrivals and deadlines, and competing objectives such as latency, energy, and cost. This paper introduces Quantum-inspired Biased Dynamic Scheduler (QBDS), a novel scheduling framework that optimizes a configurable Composite Objective Function (COF) combining makespan, total energy consumption, total cost, load balance, resource utilization, and temporal metrics (waiting and response times). QBDS uses (1) a priority-aware task ranking that adaptively weights deadline slack, execution length, memory footprint and data size to form a globally informed execution order; (2) a sinusoidal, quantum-inspired biasing mechanism that perturbs normalized task and node metrics via randomized mixing weights and sine modulation to escape local optima and encourage exploration of underused resources; and (3) a penalty-aware multi-objective cost evaluator for assignment decisions. Extensive experiments, including ablation studies and comparisons with state-of-the-art metaheuristics and classical heuristics, demonstrate that QBDS consistently improves makespan, energy, cost, and resource utilization across diverse workloads and topologies, while scaling robustly under heavy load.

Dynamic scheduling should take into account machine scheduling in that flexible manufacturing system can provide alternative processing routes to produce the same product. Dispatching rules (DRs) are the most prevalent approach in the dynamic scheduling system. Existing rule-based scheduling approaches seldom address the machine selection. Considering both job selection and machine selection, we present contract net based scheduling approach which works by means of interactive biddings. Using dispatching rules, we construct five contract net protocol rules (CNP-rulcs) within the heterarchical control architecture to solve job-shop dynamic scheduling problems. The experimental results of multiple CNP-rules based scheduling approach are provided. Competitive results demonstrate that the CNP-rules not only yield evident improvements in the system performance, but also get better equipment utilization and load balancing level for the scheduling system.

Body Area Networks (BANs), targeting everywhere anytime health monitoring, operate in license-exempt frequency bands. They require a cross layer design that jointly considers the requirements of (1) access delay to the medium and (2) packet delivery ratio (PDR). In this paper, we experimentally evaluate the PDR of a BAN under static and dynamic scheduling schemes. Investigations show that static multi-hop routing enhances PDR with the help of packet relaying, but at the cost of using more transmission slots in the shared medium, which may compromise access delay. To reduce this cost, we propose a dynamic scheduling approach that adapts to BAN topology changes resulting from body movements. Our extensive experimental results indicate that for BANs colloccated with other wireless systems, our dynamic multi-hop scheduling is mandatory for satisfaction of the PDR constraint.

Improving reliability is one of the major concerns of scientific workflow scheduling in clouds. The ever-growing computational complexity and data size of workflows present challenges to fault-tolerant workflow scheduling. Therefore, it is essential to design a cost-effective fault-tolerant scheduling approach for large-scale workflows. In this paper, we propose a dynamic fault-tolerant workflow scheduling (DFTWS) approach with hybrid spatial and temporal re-execution schemes. First, DFTWS calculates the time attributes of tasks and identifies the critical path of workflow in advance. Then, DFTWS assigns appropriate virtual machine (VM) for each task according to the task urgency and budget quota in the phase of initial resource allocation. Finally, DFTWS performs online scheduling, which makes real-time fault-tolerant decisions based on failure type and task criticality throughout workflow execution. The proposed algorithm is evaluated on real-world workflows. Furthermore, the factors that affect the performance of DFTWS are analyzed. The experimental results demonstrate that DFTWS achieves a trade-off between high reliability and low cost objectives in cloud computing environments.

This paper proposes a dynamic scheduling scheme to provide quality of service (QoS) guarantees in wireless multiple access control (MAC). In order to achieve high bandwidth utilization and QoS guarantee ratio, token models of sources are constructed in the base station (BS) scheduler. A token model weighted fair queuing (WFQ) algorithm is developed to combine loss and delay requirements for uplink scheduling of real-time traffic. Dynamic priorities are implemented in a real-time traffic token model. In addition, an emergency token buffer is used to accommodate the bursty characteristics of rt-VBR (real-time variable bit rate). The backlogged token buffer compensates the backlogged flow suffering from channel error. Simulations are done to demonstrate that our DWFQ scheme can greatly improve both QoS performance and bandwidth allocation efficiency.

The distributed blocking flowshop scheduling problem (DBFSP) with new job insertions is studied. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. A deep reinforcement learning (DRL) algorithm is proposed to optimise the job selection model, and local modifications are made on the basis of the original scheduling plan when new jobs arrive. The objective is to minimise the total completion time deviation of all products so that all jobs can be finished on time to reduce the cost of storage. First, according to the definitions of the dynamic DBFSP problem, a DRL framework based on multi‐agent deep deterministic policy gradient (MADDPG) is proposed. In this framework, a full schedule is generated by the variable neighbourhood descent algorithm before a dynamic event occurs. Meanwhile, all newly added jobs are reordered before the agents make decisions to select the one that needs to be scheduled most urgently. This study defines the observations, actions and reward calculation methods and applies centralised training and distributed execution in MADDPG. Finally, a comprehensive computational experiment is carried out to compare the proposed method with the closely related and well‐performing methods. The results indicate that the proposed method can solve the dynamic DBFSP effectively and efficiently.