Scheduling Paradigm Research Articles

Optimization of Joint Scheduling for Automated Guided Vehicles and Unmanned Container Trucks at Automated Container Terminals Considering Conflicts

Port development is a critical component in constructing a resilient transportation infrastructure. The burgeoning integration of automated guided vehicles (AGVs) within container terminals, in conjunction with the orchestrated scheduling of unmanned container trucks (UCTs), is essential for the sustainable expansion of port operations in the future. This study examined the influence of AGVs in automated container terminals and the synergistic scheduling of UCTs on port operations. Comparative experiments were meticulously designed to evaluate the feasibility of integrated scheduling schemes. Through the development of optimization models that consider conflict-free paths for both AGVs and UCTs, as well as strategies for conflict resolution, a thorough analysis was performed. Advanced genetic algorithms were engineered to address task-dispatching models. In contrast, the A* optimization search algorithm was adapted to devise conflict-free and conflict-resolution paths for the two vehicle types. A range of scaled scenarios was utilized to assess the impact of AGVs and UCTs on the joint-scheduling process across various configuration ratios. The effectiveness of the strategies was appraised by comparing the resultant path outcomes. Additionally, comparative algorithmic experiments were executed to substantiate the adaptability, efficacy, and computational efficiency of the algorithms in relation to the models. The experimental results highlight the viability of tackling the joint-scheduling challenge presented by AGVs and UCTs in automated container terminals. When juxtaposed with alternative scheduling paradigms that operate independently, this integrated approach exhibits superior performance in optimizing the total operational costs. Consequently, it provides significant insights into enhancing port scheduling practices.

Information models of tactical and operational planning levels in energy construction

The strategic, tactical and operational levels of energy construction planning are considered. The purpose of the article is to present information models for solving multi-criteria tasks of forming schedules of various types. For the first information model based on vector representations of aggregations, a multilevel classification of criteria and ranking algorithms for multi-criteria tasks is proposed. A structural analysis of criteria and ranking algorithms for multi-criteria tasks is carried out. Schemes of hierarchies of multiple inclusion of criteria and ranking algorithms for information planning models are presented. Recursive definitions of criteria and ranking algorithms are obtained. For the second information model, a heuristic paradigm of centralized scheduling is described for various types of presentation of source data, namely: sets of independent applications, sets of application vectors and sets of hierarchical or network structures of applications. The scheduling paradigm is based on the use of two schemes for generating schedules and priority rules. Using the first constructive scheme, the initial schedule is cyclically formed.

Dependency-Aware Task Scheduling in TrustZone Empowered Edge Clouds for Makespan Minimization

Task offloading to edge servers has become a promising solution to tackle the computation resource poverty of the end devices. However, the zero-trust edge computing platform is highly challenged by the growing concern on security and privacy. Thus, Trust Execution Environment (TEE), like TrustZone, is advocated to empower edge clouds to enable secure task offloading. To explore TrustZone, the inevitable involvement of data encryption and decryption operations makes existing offloading strategies not applicable any more, especially when the task dependency is considered. In addition, TrustZone has distinguishable task scheduling paradigm as one CPU core does not allow multitask coexist at the same time. Taking the above issues into consideration, we investigate a dependency-aware task offloading problem for makespan minimization in TrustZone empowered edge clouds. By inventing an extended graph to describe the task execution process, we provide a formal statement to the problem and prove its NP-hardness. We then propose a Customized List Scheduling (CLS) based approximate algorithm and theoretically analyze its achievable performance. Extensive testbed based experiment results show that our approximation algorithm can effectively reduce the makespan and significantly outperforms existing state-of-the-art offloading approaches in TrustZone empowered edge clouds.

A Novel Heterogenous Dominant Sequence Clustering for Task Scheduling and Optimal Load Balancing Using JSO in Cloud

Cloud computing (CC) is rapidly increasing and being used more and more in information technology (IT) contexts. Among the hottest issues in the world of CC is task scheduling. In cloud datacentres, load balancing is accomplished using a variety of scheduling techniques, although lengthening the parallel time. By taking task duration and VM capacity into account, this study proposes a cluster-based task scheduling paradigm. The makespan and execution duration should be reduced, the suggested system engages in dynamic load balancing. In this study, we suggest a unique method called HDDSC-JSO, which employs the Jellyfish Swarm Optimization (JSO) algorithm for load balancing and heterogeneous Density based dominant sequence clustering (HDDSC) for job scheduling. Using the HDSC technique, It presents a graph of one or more groups representing user tasks, the tasks of users are first clustered. The Adapted Diverse Expeditious Termination Time (ADETT) method is used to score each work after task clustering. whenever the job with the greatest priority is scheduled first. Next, using a JSO algorithm, load balancing is carried out, distributing the load according to the weight and capacity of the server as well as the connection of the client to the server. Task allocation chooses a heavily weighted or unconnected server, lengthening the response time. Finally, measures including response time, makespan, resource consumption, and service dependability were used to assess the suggested architecture.

An Unsupervised Learning Paradigm for User Scheduling in Large Scale Multi-Antenna Systems

The tremendous growth of mobile networking and Internet of Things (IoT) demands efficient and reliable service for massive wireless systems. Multi-input-multi-output (MIMO) technologies successfully utilize spatial diversity to substantially improve spectral efficiency by scheduling multiple devices for simultaneous spectrum access. Efficient solutions to the NP-hard problem of scheduling large number of users are vital to interference mitigation and spectrum efficiency. Despite successes of machine learning in tackling large-scale optimization problems, direct adoption of supervised learning in MIMO user scheduling is difficult as there is no optimum solution to use as labeled training data, and unsupervised learning would identify similar user channel features instead of promoting channel diversity. In this work, we propose an effective and scalable user scheduling paradigm based on unsupervised learning to enhance spatial diversity in both uplink and downlink. Given users’ channel state information (CSI), we first cluster CSIs over the Grassmannian manifold to identify users with high CSI similarity, before scheduling them into MIMO access groups with low co-channel interference. Our paradigm is generalizable to a variety of different simple and scalable unsupervised learning tools and different diversity optimization criteria. Numerical tests demonstrate substantial gain in terms of spectrum efficiency and interference suppression at modest computation complexity.

SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed to map tasks precisely to virtual resources due to high dynamic nature in cloud environment which leads to increase of makespan and SLA violations will be increased. In this paper, authors proposed a task-scheduling mechanism, which considers task priorities and VMs. To model this scheduling paradigm we have chosen whale optimization through which our scheduler will take decisions for scheduling tasks precisely onto virtual resources in cloud environment. Entire simulation was carried out on CloudSim. Initially we have chosen random generated workload to run simulation and after that, we have considered a real-time workload named as BigDataBench and ran our simulation. Finally, we compared our proposed work with classical baseline mechanisms. From simulations we observed that proposed whale scheduler improved makespan for PSO, ACO, GA, and W-schedulers by 20.07%, 17.55%, 19.9%, and 6.35%, respectively, and 17.3%, 17.86%, 17.64%, and 5.93%, respectively, for BigDataBench workloads. SLA violations improved over PSO, ACO, GA, and W-Scheduler by 56.76%, 42.17%, 35.29%, and 24.53%, respectively, and 63.42%, 23.33%, 55.51%, and 40.1%, respectively, for BigDataBench workloads. From extensive simulation results, our proposed scheduler using whale optimization approach minimizes makespan and SLA violations to a great extent.

Effective comparison of meta-heuristics algorithm in cloud computing

Cloud Computing is an innovative model that is changing the industry procedure of inventing, developing, deploying, order, updating, maintaining, and budget for applications and the stock on which they run. It is a technique to increase the resource facility or proficiencies without financing in a new hardware or licensing new software. Due to its attractive features, organizations and individuals are getting involved in cloud technology paradigms. However, there are still a few obstacles in total deployment of cloud technology commercially, which is a challenging task. The complexness of the scheduling job, the resource parameter should be augmented by decreasing the execution time. Provisioning the resource and mapping the workload is a crucial role in improving the performance of the complete system. Mapping/Executing the cloudlet should be customer satisfaction and economical to the provider. Cloud technology is facing different challenging issues for allocating the job in an efficient manner to requested user in the peak time. This thesis is composed of most important works like Provisioning and scheduling the required job, RFD metaheuristics algorithm, Performance metrics calculation for response time, execution time, and cost. The current research investigation is the job Scheduling problem of cloud scheduling paradigm and to propose a new approach for provisioning and allocating the resources using RFD algorithm-based technique. The aim of implementing the RFD algorithm in the cloud is to efficiently allocate the resources and better utilization of cloud resources. The proposed effective approach of resource provisioning and scheduling algorithm is to solve the provision of resource and to execute workload efficiently in the cloud paradigm. The theme of research contains the proposed River formation dynamics algorithm which gives a better optimal solution for allocating the resources in the cloud in Comparing with “Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).” The implementation results show decreasing response time, cost, and execution time.

Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

The job scheduling paradigms include dispatching Internet of Things (IoT) critical services onto processing nodes. Here most energy is consumed in finding suitable virtual machines (VMs) that can execute IoT tasks without resource fragments. Therefore, a significant problem is minimizing energy consumption through efficient task placement that leads to load balance and minimizes resource leakage. To resolve this problem, we proposed a dual-phase metaheuristic algorithm called CSSA-DE. First, we conduct a clustering approach to group computing nodes into effective clusters. Each node is trained at different utilization levels, and the one that can yield the highest Performance-to-Power Ratio (PPR) is selected as the mega cluster head (MCH). Then, we integrated the sparrow search algorithm (SSA) with the differential evolution (DE) algorithm to expand the high search efficiency of finding an appropriate pair task-VM combination. Further, the integration phase can exploit the count of overloaded and underloaded VMs, reducing resource fragments. The performance of CSSA-DE is highly competitive and relatively better in multiple cases compared to state-of-the-art algorithms.

Energy management and operational planning of renewable energy resources-based microgrid with energy saving

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Photovoltaic and battery. • Presenting Hybrid Whale and Pattern Search Algorithm for optimizing Micro-grid. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. Energy resource scheduling is one of the major problems in power systems. This issue has become even more significant as renewable sources with intermittent power output are becoming more and more prevalent. Due to their low environmental impact and low operating costs, renewable energy sources (RESs) have attracted interest. The excess power generated by such generation methods may be a negative that affects power systems, hence issues relating to such systems should be properly handled. The optimal operation of a microgrid (MG) with several distributed generation (DG) units and uncertain behavior of RESs is suggested in this research using a stochastic optimization approach. So, for an MG fitted with a solar photovoltaic (PV) unit, this research proposes a day-ahead scheduling paradigm. In this regard, the effects of various climatic circumstances on the power production of the PV unit and the optimal scheduling of the MG have been examined in this research. In order to achieve this, statistics on solar irradiance were extracted from four distinct days from each of the four seasons. The single-objective optimization framework used to design the scheduling problem states that the objective function should be to minimize the total operating cost across the scheduling period. The aforementioned day-ahead scheduling problem can be solved by the "hybrid whale optimization algorithm and pattern search (HWOA-PS)” optimization algorithm, while both renewable and nonrenewable generating units, as well as an energy storage system are present. In order to confirm the higher performance of the recommended approach, a thorough comparison between the Hybrid WOA-PS algorithm and a few well-known optimization algorithms has also been conducted.

Reducing Wait Times for Radiology Exams Around Holiday Periods: A Monte Carlo Simulation.

Reducing patient wait times is a key operational goal and impacts patient outcomes. The purpose of this study is to explore the effects of different radiology scheduling strategies on exam wait times before and after holiday periods at an outpatient imaging facility using computer simulation.An idealized Monte Carlo simulation of exam scheduling at an outpatient imaging facility was developed based on the actual distribution of scheduled exams at outpatient radiology sites at a tertiary care medical center. Using this simulation, we examined three scheduling strategies: (1) no scheduling modifications, (2) increase imaging capacity before or after the holiday (i.e. increase facility hours), and (3) use a novel rolling release scheduling paradigm. In the third scenario, a fraction of exam slots are blocked to long-term follow-up exams and made available only closer to the exam date, thereby preventing long-term follow-up exams from filling the schedule and ensuring slots are available for non-follow-up exams. We examined the effect of these three scenarios on utilization and wait times, which we defined as the time from order placement to exam completion, during and after the holiday period.The baseline mean wait time for non-follow-up exams was 5.4days in our simulation. When no scheduling modifications were made, there was a significant increase in wait times in the week preceding the holiday when compared to baseline (10.0days vs 5.4days, p < 0.01). Wait times remained elevated for 4weeks following the holiday. Increasing imaging capacity during the holiday and post-holiday period by 20% reduced wait times by only 6.2% (9.38days vs 10.0days, p < 0.01). Increasing capacity by 50% resulted in a 7.1% reduction in wait times (9.28days, p < 0.01), and increasing capacity by 100% resulted in a 13% reduction in wait times (8.75days, p < 0.01). In comparison, using a rolling release model produced a reduction in peak wait times equivalent to doubling capacity (8.76days, p < 0.01) when 45% of slots were reserved. Improvements in wait times persisted even when rolling release was limited to the 3weeks preceding or 1week following the holiday period. Releasing slots on a rolling basis did not significantly decrease utilization or increase wait times for long-term follow-up exams except in extreme scenarios where 80% or more of slots were reserved for non-follow-up exams.A rolling release scheduling paradigm can significantly reduce wait time fluctuations around holiday periods without requiring additional capacity or impacting utilization.

Pediatric Endoscopy Blocks: Enhanced Efficiency and Endoscopist Satisfaction.

Endoscopic procedures are increasingly performed for children and adolescents and these interventions represent a central element of both pediatric gastroenterology (GI) care and revenues. It remains unclear, however, which scheduling paradigm maximizes efficiency of resource utilization and delivery of care in this arena. In this study, we evaluate the impact of shifting from shared endoscopy blocks to individual, provider-specific endoscopy blocks for scheduling endoscopic procedures in our tertiary care academic center. The implemented endoscopy block system involves a single endoscopist performing procedures in an allocated room for the entire half-day period of time, with most providers having 1-3.5 day blocks per month. We analyzed block fragmentation (multiple providers in a single block), block utilization, and procedure volume, comparing the 8-month period prior to implementation of the block system (pre-implementation period) with the 8-month period following implementation of the block system (post-implementation period). Provider satisfaction and perceptions were assessed by survey pre- and post-implementation. Standard descriptive statistics were utilized for analysis. In the pre- and post-implementation periods, 22 half-day blocks were allocated to pediatric GI. In the pre-implementation period, mean utilization of these blocks was 65.9% (range: 47%-77%). In the post-implementation period, mean % utilization was 79.8% (range: 64%-89%). Overall endoscopy procedure volume increased from 279 in the pre-implementation period to 419 in the post-implementation period. Provider perception of endoscopy efficiency was enhanced by the block system, with 68.4% of pediatric GI respondents rating endoscopy as "efficient" post-implementation (vs 19% pre-implementation) and 63.2% indicating that the block system directly enhanced efficiency. A total of 63.2% reported increased personal procedure volume and 84.2% reported perceived increase in overall procedure volume with block system implementation. Strategic approaches for scheduling endoscopic procedures carry the potential to enhance efficiency and experience for both providers and patients. Implementation of the endoscopy block system was associated with increased procedure volumes and block utilization as well as a more positive experience for the majority of providers. Schedule adjustments with analysis of associated efficiency and satisfaction metrics can optimize pediatric endoscopy practice.

Cache Interference-aware Task Partitioning for Non-preemptive Real-time Multi-core Systems

Shared caches in multi-core processors introduce serious difficulties in providing guarantees on the real-time properties of embedded software due to the interaction and the resulting contention in the shared caches. Prior work has studied the schedulability analysis of global scheduling for real-time multi-core systems with shared caches. This article considers another common scheduling paradigm: partitioned scheduling in the presence of shared cache interference. To achieve this, we propose CITTA, a cache interference-aware task partitioning algorithm. We first analyze the shared cache interference between two programs for set-associative instruction and data caches. Then, an integer programming formulation is constructed to calculate the upper bound on cache interference exhibited by a task, which is required by CITTA. We conduct schedulability analysis of CITTA and formally prove its correctness. A set of experiments is performed to evaluate the schedulability performance of CITTA against global EDF scheduling and other greedy partition approaches such as First-fit and Worst-fit over randomly generated tasksets and realistic workloads in embedded systems. Our empirical evaluations show that CITTA outperforms global EDF scheduling and greedy partition approaches in terms of task sets deemed schedulable.

Holistic Resource Allocation Under Federated Scheduling for Parallel Real-time Tasks

With the technology trend of hardware and workload consolidation for embedded systems and the rapid development of edge computing, there has been increasing interest in supporting parallel real-time tasks to better utilize the multi-core platforms while meeting the stringent real-time constraints. For parallel real-time tasks, the federated scheduling paradigm, which assigns each parallel task a set of dedicated cores, achieves good theoretical bounds by ensuring exclusive use of processing resources to reduce interferences. However, because cores share the last-level cache and memory bandwidth resources, in practice tasks may still interfere with each other despite executing on dedicated cores. Such resource interferences due to concurrent accesses can be even more severe for embedded platforms or edge servers, where the computing power and cache/memory space are limited. To tackle this issue, in this work, we present a holistic resource allocation framework for parallel real-time tasks under federated scheduling. Under our proposed framework, in addition to dedicated cores, each parallel task is also assigned with dedicated cache and memory bandwidth resources. Further, we propose a holistic resource allocation algorithm that well balances the allocation between different resources to achieve good schedulability. Additionally, we provide a full implementation of our framework by extending the federated scheduling system with Intel’s Cache Allocation Technology and MemGuard. Finally, we demonstrate the practicality of our proposed framework via extensive numerical evaluations and empirical experiments using real benchmark programs.

A Novel Distributed Paradigm for Energy Scheduling of Islanded Multiagent Microgrids

Restructuring in power systems has resulted in the development of microgrids (MGs) as entities that could be operated in grid-connected or islanded modes while managing the operation of their systems. On the other hand, privatization and integration of independently operated distributed resources in energy systems have caused the introduction of multi-agent structures. In this regard, new operational management methodologies should be employed by the MG operator (MGO) to efficiently operate the system while addressing the distributed nature of multi-agent structures. Accordingly, this paper aims to provide a new algorithm to operate an islanded multi-agent MG utilizing the peer-to-peer (P2P) management concept; which copes with the distributed nature of the system. Consequently, each agent would independently schedule its respective local resources, while participating in the hourly P2P market scheme. Moreover, MGO manages the power transactions among the agents. Furthermore, different types of power generation resources are modeled in the proposed optimization scheme while scenario-based stochastic optimization, as well as the condition-value-at-risk index, are deployed to address the uncertainty and the operational risk associated with the operational optimization of renewable energies. Finally, the developed framework is implemented on a 10-bus-MG test system to investigate its effectiveness in the management of the system and also on a 33-bus-MG test system to study its scalability.

Toward smart manufacturing scheduling from an ontological approach of job-shop uncertainty sources

An integral application of the enabling technologies of Industry 4.0 in the job-shop scheduling problem (JSSP) must contemplate the automation and autonomy of the involved decision-making processes as a goal, which is the main purpose of the smart manufacturing scheduling (SMS) paradigm. In a real production context, uncertainty acts as a barrier that hinders this goal being met and, therefore, any SMS model should integrate uncertainty generators in one way or another. This paper proposes an ontological framework that identifies and structures the entities shaping the joint domain formed by the job-shop scheduling process in its itinerary toward the SMS paradigm, the sources of uncertainty that it faces, and the interrelationship type that link these entities. This ontological framework will serve in future research as a conceptual basis to design new quantitative models that, from a holistic perspective, will address the stochasticity of manufacturing environments and incorporate the management of disturbances into the realtime resolution of automatic and autonomous job-shop scheduling.

Controlling the Complexity of Hierarchical Scheduling Frameworks

Hierarchical scheduling frameworks are a new scheduling paradigm where multiple system schedules are integrated (one-within-another). HSFs presents a multi-layered complexity problem that system engineers are struggling to contain. A promising trend in the aerospace and defense industry is to employ Digital Engineering’s Model-Based Systems Engineering (MBSE) to deal with the complexity of HSFs. MBSE permits the abstraction of application-specific details that can radically speed up system design exploration. Thus, this paper investigates how the output from an HSF algorithm can be converted into an MBSE modeling language that enables architectural exploration for resource allocation. The Unified Modeling Language (UML) Modeling and Analysis of Real-Time and Embedded Systems (MARTE) Profile is the chosen unified modeling language of MBSE. The modeling language is used with an HSF application for demonstration purposes. The approach in this paper seeks to limit tool use by combining an inline verification method (Genetic Algorithm) with a new MBSE workflow.

CORBA-Based Class Scheduling Framework Using Decorator and Facade Design Patterns

Class scheduling problem has been regarded as one of the most difficult yet important challenges for developers developing timetabling applications. Different algorithms or schemes were devised to address such problems. Yet, considering the growing complexity of the input factors, it contributes to difficulties in maintaining the said system. In order to aid in the development and maintenance of such a system, this paper presents an object-oriented approach to designing a class scheduling paradigm using decorator and facade design patterns with CORBA architecture. This technique was based on the concepts of several existing object-oriented components of Windows GUI platforms. The paradigm designs are expressed using Unified Modeling Language (UML) and illustrated in a Java programming environment. The result of the study is a simplified method for designing an efficient and reusable component for the class scheduling model.

Conflict-Resilient Incremental Offloading of Deep Neural Networks to the Edge of Smart Environment

Novel smart environments, such as smart home, smart city, and intelligent transportation, are driving increasing interest in deploying deep neural networks (DNN) in edge devices. Unfortunately, deploying DNN at resource-constrained edge devices poses a huge challenge. These workloads are computationally intensive. Moreover, the edge server-based approach may be affected by incidental factors, such as network jitters and conflicts, when multiple tasks are offloaded to the same device. A rational workload scheduling for smart environments is highly desired. In this work, we propose a Conflict-resilient Incremental Offloading of Deep Neural Networks at Edge (CIODE) for improving the efficiency of DNN inference in the edge smart environment. CIODE divides the DNN model into several partitions by layer and incrementally uploads them to local edge nodes. We design a waiting lock-based scheduling paradigm to choose edge devices for DNN layers to be offloaded. In detail, an advanced lock mechanism is proposed to handle concurrency conflicts. Real-world testbed-based experiments demonstrate that, compared with other state-of-the-art baselines, CIODE outperforms the DNN inference performance of these popular baselines by 20%to 70%and significantly improves the robustness under the insight of neighboring collaboration.

Computational intelligence paradigm for job shop scheduling and routing in an uncertain environment

ABSTRACT Computational Intelligence (CI) is a more efficient paradigm for solving real-world problems in uncertain conditions. The traditional CI approaches are not capable to provide the complete and sufficient solutions for problems. Therefore, new techniques are necessary to efficiently solve these issues seriously. New techniques, such as Emergent Intelligence (EI), Multi-Agent System (MAS), etc., provide robust, generic, flexible, and self-organised to solve complex real-world problems. In this paper, we discuss Emergent Intelligence (EI) and its uniqueness in solving problems in an uncertain environment. We also discuss EI, Swarm Intelligence (SI) and MultiAgent System (MAS)-based problem-solving in an uncertain environment and compared their performance. We have considered two different problems: job shop scheduling using EI and MAS and route establishment for routing using MAS, SI and EI in an uncertain environment. Each problem is categorically analysed and solved step by step using MAS, SI and EI in a dynamic environment. We measure the performance of these three methods by varying the number of agents, tasks and time. Performance measures are compared and shown to demonstrate the importance of EI over MAS and SI for solving problems in an uncertain environment.

Power Routing: A New Paradigm for Maintenance Scheduling

Currently, the necessity of efficient and reliable power systems is also increasing because of the strict requirements that standards and regulations impose, but still costs have to remain low. The monitoring and control of the components' lifetime can lead to reduce maintenance costs.However, overcoming the related challenges is not a straightforward task, as it involves knowledge of power device physics, smart management of electrical quantities, and optimal maintenance planning and scheduling. It represents a multidisciplinary issue being faced in the last decade.