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A time interval can be presented as a duration (e.g., 10 days) or a calendar date (e.g., May 10). This study examines the influence of time descriptions on task scheduling. Results of 10 studies (N = 2,129) present evidence that people tend to schedule their tasks earlier when the time interval is described as a duration than a date. This time description effect is accounted for by different mindsets. People tend to have an implemental mindset when time is described as a duration (vs. date) because of stronger feelings of being inside the time frame. This mindset affects decisions on scheduling when to initiate future tasks. [ABSTRACT FROM AUTHOR]

People rely on task management applications and digital assistants to capture and track their tasks, and help with executing them. The burden of organizing and scheduling time for tasks continues to reside with users of these systems, despite the high cognitive load associated with these activities. Users stand to benefit greatly from a task management system capable of prioritizing their pending tasks, thus saving them time and effort. In this article, we make three main contributions. First, we propose the problem of task prioritization, formulating it as a ranking over a user’s pending tasks given a history of previous interactions with a task management system. Second, we perform an extensive analysis on the large-scale anonymized, de-identified logs of a popular task management application, deriving a dataset of grounded, real-world tasks from which to learn and evaluate our proposed system. We also identify patterns in how people record tasks as complete, which vary consistently with the nature of the task. Third, we propose a novel contextual deep learning solution capable of performing personalized task prioritization. In a battery of tests, we show that this approach outperforms several operational baselines and other sequential ranking models from previous work. Our findings have implications for understanding the ways people prioritize and manage tasks with digital tools, and in the design of support for users of task management applications. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) environment delivers the services requested by the computing devices over the internet. In recent years with the internet epoch, the cloud computing environment is progressed as a significant distributed platform. However, the prime concern associated with the CC environment as task scheduling. Several algorithms were proposed with the objective of optimizing the scheduling process in the CC environment. To fill this gap in this work an integrated Fisher linear discriminant and discrete global swarm-based task scheduling (FLD-DGSTS) method is proposed. Fisher linear discriminant independent task prioritizer algorithm is employed to accurately enhance the quality of solution with minimum makespan and memory. Next, the discrete glowworm swarm optimization process is applied for scheduling cloud user tasks. The fitness function (i.e., CPU cycles, bandwidth, memory, and energy) is measured for addressing optimization and avoiding local convergence. The experimental evaluation of the proposed FLD-DGSTS method is compared to existing task scheduling algorithms with the CloudSim toolkit. The results demonstrate that the FLD-DGSTS method gives better performance with reduces the makespan and memory and ensures high scheduling efficiency than the state-of-the-art task scheduling methods. [ABSTRACT FROM AUTHOR]

Recently, wide-area distributed computing environments have become popular owing to their huge resource capability. In a wide-area distributed computing environment, joint scheduling of tasks and data is the main strategy to improve system performance. However, the geographically distributed diverse resources exhibit high variations, making it challenging to design efficient joint scheduling of tasks and data. To accurately adapt to the dynamic variations of geographically distributed diverse resources and achieve a high system performance, this study proposes a hypergraph-partitioning-based online joint scheduling method. The proposed method constructs a hypergraph of geographically distributed tasks, data, and diverse resources to clearly describe the correlation among the three elements and quantitatively reflect the time cost of different process in the environment. The hypergraph is dynamically updated according to the generated scheduling scheme and the collected information to reflect the dynamic variations of resource states. Then, a hypergraph partition optimization mechanism is proposed to generate efficient joint scheduling schemes, thus reducing the overall completion time in the system. The experimental results indicate that compared with the state-of-the-art joint scheduling methods, the proposed method reduces the overall completion time by up to 25.67% and significantly reduces the task waiting time, although it makes a concession in the data migration time. [ABSTRACT FROM AUTHOR]

Reliability and energy efficiency are two hostile objectives considered in designing task scheduling in most real-time multiprocessor systems on chip (MPSoC). Addressing and improving one of them may affect and degrade the efficiency of the other one and vice versa. In this paper, we intend to examine these challenges and ultimately achieve an optimal energy consumption and reliability state. This paper presents a novel scheduling technique that can adapt to the limitations of real-time systems and have optimal energy consumption and reliability. This is done by minimizing the overlap of tasks, adjusting the speed of processors and the number of backups of each task. The proposed scheme reduces energy consumption on average by 11% to 42% compared to the previous state-of-the-art techniques and keeps the reliability at a high level. [ABSTRACT FROM AUTHOR]

Suspension-based locks are widely used in real-time systems to coordinate simultaneous accesses to exclusive shared resources. Although suspension-based locks have been well studied for sequential real-time tasks, little work has been done on this topic for parallel real-time tasks. This article for the first time studies the problem of how to extend existing sequential-task locking protocols and their analysis techniques to the parallel task model. More specifically, we extend two locking protocols OMLP and OMIP, which were designed for clustered scheduling of sequential real-time tasks, to federated scheduling of parallel real-time tasks. We present corresponding blocking analysis techniques, and develop path-oriented techniques to analyze and count blocking time. Schedulability tests with different efficiency and accuracy are further developed. Experiments are conducted to evaluate the performance of our proposed approaches against the state-of-the-art. [ABSTRACT FROM AUTHOR]

A number of scheduling algorithms have been proposed for real-time parallel tasks modeled as Directed Acyclic Graphs (DAGs). Many of them focus on scheduling DAG tasks with implicit deadlines. Fewer studies have considered DAG tasks with constrained deadlines or arbitrary deadlines. In this study, we propose a scheduling strategy based on fluid scheduling theory and we target DAG tasks with constrained or arbitrary deadlines. We prove that the proposed algorithm has a capacity augmentation bound of $\frac{1}{2}(1+\beta +\sqrt{(1+\beta)^2-\frac{4}{m}})$ 1 2 (1 + β + (1 + β) 2 - 4 m ) when scheduling multiple DAG tasks with constrained deadlines, in which $m$ m is the number of processors and $\beta$ β is the maximum ratio of task period to deadline. This value is lower than the current best result $\beta +2\sqrt{(\beta +1-\frac{1}{m})(1-\frac{1}{m})}$ β + 2 (β + 1 - 1 m) (1 - 1 m) . We also prove that a capacity augmentation bound of $\frac{1}{2}(1+\sqrt{2}+\sqrt{(1+\sqrt{2})^2-\frac{4\sqrt{2}}{m}})$ 1 2 (1 + 2 + (1 + 2) 2 - 4 2 m ) is guaranteed by our algorithm in the case of scheduling multiple DAG tasks with deadlines greater than periods. To the best of our knowledge, this is the first capacity augmentation bound that has been proven for scheduling multiple DAG tasks with deadlines greater than periods. Our experiments show that our algorithm outperforms the state of the art scheduling algorithms in the percentage of schedulable task sets. [ABSTRACT FROM AUTHOR]

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is the provision of on‐demand computing resources over the internet and on a pay‐as‐you‐go basis, ranging from software to computation power. Task scheduling and its execution is a fundamental requirement of cloud environment. However, dynamic scheduling of tasks on basis of priority is a challenging area such that the tasks finish before their deadline. Earliest Deadline First (EDF) has been considered in literature for task scheduling to meet the deadlines. However, basic EDF (i.e., which schedules tasks on basis of deadline only)is not suitable for cloud environment. Therefore, this work proposes modified Preemptive EDF (p‐EDF) and Non‐Preemptive EDF (np‐EDF) algorithms considering task priority and cloud provider cost. As both algorithms have their own merits and de‐merits, a hybrid EDF is further proposed which makes decision dynamically whether to cause preemption or not, using a Determiner function. The objective of the work is to avoid unnecessary wastage of CPU power and time due to unnecessary preemptions, along with avoiding unnecessary deadline misses such that the high priority task does not wait for the low priority task to end. Simulation results show that the proposed algorithm outperforms other considered benchmark scheme for different performance parameters such as Deadline Miss Count, Preemption Count and average waiting time. [ABSTRACT FROM AUTHOR]

Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot. [ABSTRACT FROM AUTHOR]

With the rapid development of parallel programming techniques and the widespread use of multiprocessors, scheduling and analysis techniques supporting parallel real-time tasks become a critical topic for multiprocessor real-time systems. Global scheduling that allows the vertices of a parallel task to execute on any processor is a promising scheduling approach with guaranteed theoretical bounds and wide use in practice. However, the complex internal structure of parallel tasks leads to extensive inner- and inter-task interference, which leads to significant pessimism in the worst-case timing analysis. In this paper, a global fixed-priority (G-FP) scheduling with constrained parallelism for parallel real-time tasks based on the sporadic directed acyclic graph (DAG) model is proposed. Each DAG task is assigned a parallel threshold, such that the number of processors occupied by the task is limited to the parallel threshold of the task at a time. We propose a heuristic algorithm to set the parallel threshold and present a response-time analysis (RTA) to exploit the feature of the constrained parallel scheduling. Experiments with randomly generated tasks show that the proposed approach improves the schedulability upon G-FP and federated scheduling in terms of acceptance ratio. [ABSTRACT FROM AUTHOR]

Several scheduling algorithms that have been proposed for Real-Time Operating System (RTOS) are supposed to be optimal. However, optimal scheduling is only theoretical due to the possibility of system overload where it cannot meet the deadlines of tasks. Besides, these algorithms are implemented in the RTOS, which generates additional overheads that can lead to the "nonscheduling" of certain independent tasks. In this paper, we propose an original solution for nonschedulable independent tasks in embedded systems. This solution, named Hybrid Fuzzy Earliest Deadline First Scheduling algorithm (HFEDFS), is based on the Earliest Deadline First algorithm (EDF) and Fuzzy Logic. It is characterized by a rejection policy and a rescheduling mechanism. The experimental results show that our proposed algorithm improves the system's performance. To reduce extra overheads of RTOS, this algorithm is implemented on a Field-Programmable Gate Array (FPGA) circuit (Xilinx Virtex-5 LX50T-1156 board from DIGILENT). [ABSTRACT FROM AUTHOR]

As the power density of modern CPUs is gradually increasing, thermal management has become one of the primary concerns for multicore systems, where task scheduling and dynamic voltage/frequency scaling (DVFS) play a pivotal role in effectively managing the system temperature. In this article, we propose CARTAD, a new reinforcement learning (RL)-based task scheduling and DVFS method for temperature minimization and latency guarantee on multicore systems. The novelty of CARTAD framework is that we exploit the machine learning technique to analyze the applications’ intermediate representations (IRs) generated by a compiler and identify an important feature which is critical for predicting the application’s performance. With the newly explored feature, we construct an RL-based scheduler with the more effective state representation and reward function such that the system temperature can be minimized while guaranteeing applications’ latency. We implement and evaluate CARTAD on real platforms in comparison with the state-of-the-art approaches. Experimental results show CARTAD can reduce the maximum temperature by up to 16 °C and the average temperature by up to 10 °C. [ABSTRACT FROM AUTHOR]

These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications. [ABSTRACT FROM AUTHOR]

When managing a constellation of nanosatellites, one may leverage this structure to improve the mission's quality-of-service (QoS) by optimally distributing the tasks during an orbit. In this sense, this research proposes an offline energy-aware task scheduling problem formulation regarding the specifics of constellations, by considering whether the tasks are individual, collective, or stimulated to be redundant. By providing such an optimization framework, the idea of estimating an offline task schedule can serve as a baseline for the constellation design phase. For example, given a particular orbit, from the simulation of an irradiance model, the engineer can estimate how the mission value is affected by the inclusion or exclusion of individuals objects. The proposed model, given in the form of a multi-objective mixed-integer linear programming model, is illustrated in this work for several illustrative scenarios considering different sets of tasks and constellations. We also perform an analysis of the Pareto-optimal frontier of the problem, identifying the feasible trade-off points between constellation and individual tasks. This information can be useful to the decision-maker (mission operator) when planning the behavior in orbit. [ABSTRACT FROM AUTHOR]

• Frequency constraints lower scheduling difficulty significantly. • Problem formulation is key when aiming for optimal solutions. • Core partitioning is more effective than partitioning along the time axis. • Fully heuristic scheduling approach is viable for larger task sets. Static scheduling of independent, moldable tasks on parallel machines with frequency scaling comprises decisions on core allocation, assignment, frequency scaling and ordering, to meet a deadline and minimize energy consumption. Constraining some of these decisions reduces the solution space, i.e. may increase energy consumption, but may also open the path to new, near-optimal approaches. We investigate how constraints of different steps influence energy consumption, starting with an unrestricted scheduler for moldable tasks. The constraints are partly derived from existing schedulers, but also generalized in a systematic way. We present integer linear programs for all scheduling variants. We compare energy consumption of schedules for a benchmark suite of synthetic task sets of different sizes and for task sets derived from real applications. In addition, we check how close the results are to the optimum results when the ILP solver meets a timeout. Our results indicate that constraints on task execution order, which avoid explicit representation of task order in ILPs, are mostly responsible for near-optimal energy consumption among large task sets. Furthermore, we find that for all steps except allocation, non-optimal fast heuristics can be used without sacrificing too much energy for the resulting schedule. Finally, we can show that an ILP for a new scheduler, for which also a heuristic version exists, is comparable in quality to more complicated schedulers. [ABSTRACT FROM AUTHOR]

Cloud computing is very popular because of its unique features such as scalability, elasticity, on-demand service, and security. A large number of tasks are performed simultaneously in a cloud system, and an effective task scheduler is needed to achieve better efficiency of the cloud system. Task scheduling algorithm should determine a sequence of execution of tasks to meet the requirements of the user in terms of Quality of Service (QoS) factors (e.g., execution time and cost). The key issue in recent task scheduling is energy efficiency since it reduces cost and satisfies the standard parameter in green computing. The most important aim of this paper is a comparative analysis of 67 scheduling methods in the cloud system to minimize energy consumption during task scheduling. This work allows the reader to choose the right scheduling algorithm that optimizes energy properly, given the existing problems and limitations. In addition, we have divided the algorithms into three categories: heuristic-based task scheduling, meta-heuristic-based task scheduling, and other task scheduling algorithms. The advantages and disadvantages of the proposed algorithms are also described, and finally, future research areas and further developments in this field are presented. [ABSTRACT FROM AUTHOR]

The task-schedulingalgorithm is a key module to satisfy various complex user requirements, and improve the usage flexibility and efficiency of data relay satellites networks (DRSN). In this context, we first propose a novel application mode for DRSN, in which users are allowed to submit multiple optional service time windows and specify a preferred antenna as well as an expected execution duration for each task. Meanwhile, the start time of a service time window can be adjusted within a specified range. A mathematical programming model that maximizes the completion ratio of tasks and the expectation satisfaction of users is established. Moreover, a conflict resolution-assisted iterative task-scheduling algorithm (CRITS) is designed, composing of five closely dependent operators: resource matching, service durations generation, conflict evaluation, conflict resolution, and solution update. To verify the effectiveness of the proposed CRITS, extensive experiments are carried out. The experimental results demonstrate the competitive performance of CRITS in addressing the DRSN scheduling problem. In comparison with two heuristic algorithms (heuristic algorithm based on time-freedom degree and a heuristic algorithm based on task priority) and a meta-heuristic algorithm (adaptive variable neighborhood descent combined with a tabu list), the proposed CRITS increases the overall completion ratio of tasks by 6.65, 10.26, and 10.96%, respectively. [ABSTRACT FROM AUTHOR]

The cloud computing paradigm is evolving rapidly to address the challenges of new emerging paradigms, such as the Internet of Things (IoT) and fog computing. As a result, cloud services usage is increasing dramatically with the recent growth of IoT-based applications. To successfully fulfill application requirements while efficiently harnessing cloud computing power, intelligent scheduling approaches are required to optimize the scheduling of IoT application tasks on computing resources. In this paper, the chimp optimization algorithm (ChOA) is incorporated with the marine predators algorithm (MPA) and disruption operator to determine the optimal solution to IoT applications' task scheduling. The developed algorithm, called CHMPAD, aims to avoid entrapment in the local optima and improve the exploitation capability of the basic ChOA as its main drawbacks. Experiments are conducted using synthetic and real workloads collected from the Parallel Workload Archive to demonstrate the applicability and efficiency of the presented CHMPAD method. The simulation findings reveal that CHMPAD can achieve average makespan time improvements of 1.12–43.20% (for synthetic workloads), 1.00–43.43% (for NASA iPSC workloads), and 2.75–42.53% (for HPC2N workloads) over peer scheduling algorithms. Further, our evaluation results suggest that our proposal can improve the throughput performance of fog computing. [ABSTRACT FROM AUTHOR]

Information and technology have witnessed significant improvement with the introduction of Internet of things (IoT) applications, and most of the IoT applications are dependent on the cloud. Cloud computing is assisting IoT applications by providing storage, analysis, and processing services on the cloud. However, Fog computing is the new paradigm that supports the cloud by providing scheduling, resources optimization, and energy optimization services. Scheduling tasks based on MIPs size and prioritizing the tasks with smaller MIPs size first make critical tasks with larger MIPs wait, which ultimately increases the delay and may result in some serious problems. This paper proposes a methodology for critical tasks having large MIPs size by scheduling and prioritizing the tasks based on the nature of the task. The proposed methodology for latency-critical applications reduces latency, energy consumption, and network utilization. This paper proposed a scheduler "Critical task First Scheduler" (CTFS), which schedules tasks depending on the nature of the requests, which are classified as either critical or noncritical. The proposed methodology is implemented in a healthcare scenario, and the simulations are performed in iFogSim simulator. Critical requests, such as emergency notifications, are prioritized and designated as critical, requiring immediate processing. The environment was kept the same for all the approaches that are implemented to demonstrate the effectiveness of the proposed approach. The results of the proposed approach were compared with First Come First Served (FCFS), Shortest Job First (SJF), and cloud-only approaches to demonstrate the effectiveness of the proposed approach in terms of latency, energy consumption, and network utilization. Simulation results show that the proposed CTFS approach outperformed the compared techniques for all three comparison parameters. [ABSTRACT FROM AUTHOR]

Dynamically adaptive streaming over HTTP requires a large-scale server to transcode various bitrate versions in which different preset parameters can be used to provide different video qualities at each resolution. When transcoding servers contain a heterogeneous mix of CPUs and GPUs, the task scheduler must choose a processor and preset parameter for each transcoding task to meet the transcoding deadlines while achieving the best possible video quality. We apply regression analysis to sample variable-bit-rate videos to provide accurate (mean absolute percentage error values from 1.3% to 13.9%) model for predicting bitrate, transcoding time and video quality at each resolution on different processors. We build this into a greedy allocation and scheduling algorithm which first satisfies deadlines with low video quality, and then redistributes the workload to improve that quality while continuing to meet the deadlines. This scheme was both simulated and implemented on a testbed server. It satisfies all deadlines while outperforming standard algorithms by between 3.12% and 15.59% in terms of popularity-weighted video quality divided by bitrate. [ABSTRACT FROM AUTHOR]

Models for energy-efficient static scheduling of parallelizable tasks with deadlines on frequency-scalable parallel machines comprise moldable vs. malleable tasks and continuous vs. discrete frequency levels, plus preemptive vs. non-preemptive task execution with or without task migration. We investigate the tradeoff between scheduling time and energy efficiency when going from continuous to discrete core allocation and frequency levels on a multicore processor, and from preemptive to non-preemptive task execution. To this end, we present a tool to convert a schedule computed for malleable tasks on machines with continuous frequency scaling [Sanders and Speck, Euro-Par (2012)] into one for moldable tasks on a machine with discrete frequency levels. We compare the energy efficiency of the converted schedule to the energy consumed by a schedule produced by the integrated crown scheduler [Melot et al., ACM TACO (2015)] for moldable tasks and a machine with discrete frequency levels. Our experiments with synthetic and application-based task sets indicate that the converted Sanders Speck schedules, while computed faster, consume more energy on average than crown schedules. Surprisingly, it is not the step from malleable to moldable tasks that is responsible but the step from continuous to discrete frequency levels. One-time frequency scaling during a task's execution can compensate for most of the energy overhead caused by frequency discretization. [ABSTRACT FROM AUTHOR]

Tasks have high requirements for response delay and security in intelligent manufacturing. Industrial data have the characteristics of high privacy. However, cloud services are difficult to implement for low latency-sensitive applications and privacy data tasks. Therefore, the offloading technology in edge computing can offload the computing tasks of terminal devices to the edge of the network, which can effectively reduce the delay and match the needs of intelligent manufacturing. Unreasonable task scheduling cannot meet the needs of real-time scheduling between edge servers and cloud servers. In this paper, we establish a joint low-delay optimization model of task scheduling and dynamic replacement-release caching (DRRC) mechanism, which couples a privacy selection strategy for tasks to protect privacy. Tasks are scheduled to different location by the privacy of sensitive data, which can improve the security of data and meet the calculation request of different tasks. DRRC mechanism caches tasks according to the size of the task and replaces it with the weight of the task data, and adds automatic release mechanism. To solve the task scheduling strategy, we design the improved genetic-differential evolution algorithm. Extensive simulations reveal that the proposed algorithm has a better performance in minimizing latency compared with other scheduling algorithms. At the same time, the caching mechanism has a better hit rate. [ABSTRACT FROM AUTHOR]

Effective deployment of multi-robot teams requires solving several interdependent problems at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must answer four important questions: what (task planning), how (motion planning), who (task allocation), and when (scheduling). Although there are rich bodies of work dedicated to various combinations of these questions, a fully integrated treatment of all four questions lies beyond the scope of the current literature, which lacks even a formal description of the complete problem. In this article, we address this absence, first by formalizing this class of multi-robot problems under the banner Simultaneous Task Allocation and Planning with Spatiotemporal Constraints (STAP-STC), and then by proposing a solution that we call Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (GRSTAPS). GRSTAPS interleaves task planning, task allocation, scheduling, and motion planning, performing a multi-layer search while effectively sharing information among system modules. In addition to providing a unified solution to STAP-STC problems, GRSTAPS includes individual innovations both in task planning and task allocation. At the task planning level, our interleaved approach allows the planner to abstract away which agents will perform a task using an approach that we refer to as agent-agnostic planning. At the task allocation level, we contribute a search-based algorithm that can simultaneously satisfy planning constraints and task requirements while optimizing the associated schedule. We demonstrate the efficacy of GRSTAPS using detailed ablative and comparative experiments in a simulated emergency-response domain. Results of these experiments conclusively demonstrate that GRSTAPS outperforms both ablative baselines and state-of-the-art temporal planners in terms of computation time, solution quality, and problem coverage. [ABSTRACT FROM AUTHOR]

Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU. [ABSTRACT FROM AUTHOR]

The central cloud facilities based on virtual machines offer many benefits to reduce the scheduling costs and improve service availability and accessibility. The approach of cloud computing is practical due to the combination of security features and online services. In the tasks transfer, the source and target domains have differing feature spaces. This challenge becomes more complicated in network traffic, which leads to data transfer delay, and some critical tasks could not deliver at the right time. This paper proposes an efficient optimization method for task scheduling based on a hybrid multi-verse optimizer with a genetic algorithm called MVO-GA. The proposed MVO-GA is proposed to enhance the performance of tasks transfer via the cloud network based on cloud resources' workload. It is necessary to provide adequate transfer decisions to reschedule the transfer tasks based on the gathered tasks' efficiency weight in the cloud. The proposed method (MVO-GA) works on multiple properties of cloud resources: speed, capacity, task size, number of tasks, number of virtual machines, and throughput. The proposed method successfully optimizes the task scheduling of a large number of tasks (i.e., 1000–2000). The proposed MVO-GA got promising results in optimizing the large cloud tasks' transfer time, which reflects its effectiveness. The proposed method is evaluated based on using the simulation environment of the cloud using MATLAB distrusted system. [ABSTRACT FROM AUTHOR]

The cloud runtime environment is dynamic; therefore, allocating tasks to computing resources might include various scenarios. Metaheuristic algorithms are usually used to choose appropriate scheduling scenarios; however, they suffer from premature convergence, trapping in local optima, and imbalance between the exploration and exploitation of search space. The multi-verse optimizer (MVO) algorithm also suffers from similar problems. In this research, both Gabor filter and opposition-based learning methods are applied in the MVO algorithm to present the new algorithm GOMVO. The multi-tracker optimization (MTO) is applied in the GOMVO to present the new MTO-GOMVO hybrid algorithm. Then the scheduling framework MTOA-GOMVO@DSF is presented that applies the MTO-GOMVO metaheuristic algorithms in cloud computing scheduling. In the sequel, at first, the GOMVO algorithm is benchmarked applying CEC2017 benchmark functions and compared with several baseline algorithms in terms of mean error. Second, MTOA-GOMVO is also evaluated against related baseline algorithms in terms of mean error. Finally, MTOA-GOMVO is also applied in cloud computing to schedule independent tasks to virtual machines to improve average execution time, response time, throughput, and SLA violations. Simulation results applying NASA-iPSC real dataset showed that MTOA-GOMVO outweighs the baseline metaheuristic algorithms and performs well in scheduling cloud computing tasks. [ABSTRACT FROM AUTHOR]

It is critical but challenging to provide efficient information services to support disaster-response operations in disaster-hit areas. A UAVFog-assisted data-driven disaster-response architecture, which combines unmanned aerial vehicles (UAVs) and fog computing paradigm, showed many advantages in response latency and on-the-fly deployment. This paper aims to jointly optimize the deployment of service functions (SFs) and the task scheduling at UAVFog nodes to minimize the task response latency. After introducing the collaboration structure between UAVFog nodes, joint SF deployment and task scheduling is formulated as an optimization problem. Then, three algorithms are put forward to tackle the problem: 1) Dependency and topology-aware SF deployment (DeToSFD) algorithm is developed to determine the initial deployment location of each SF; 2) Context-aware greedy task scheduling (CoGTS) algorithm is put forward to schedule an arrived task; 3) Congestion-aware SF reallocation (CoSFR) algorithm is developed to reallocate SFs in case of congestion at an instance of an SF. Finally, a series of experiments are conducted to evaluate the performance of the proposed algorithms. Experimental results show that DeToSFD, CoGTS, and CoSFR could greatly reduce the task response latency of the UAVFog system in diverse parameter settings. [ABSTRACT FROM AUTHOR]

Scheduling is a critical process in cyber-physical systems to ensure the computation will be over within the physical system's deadline. Under the cyber-physical system, the processor is distributive and hydrogenous. Less latency task scheduling under this distributive cyberphysical system with a hydrogenous processor and resource is challenging. This article presents a decision tree based less complex mechanism of task scheduling in a heterogeneous processor environment this proposed mechanism model the tasks and the processor resource, current load level, their individual computational capability, memory availability, communication delay in the distributive system to move the task from one point to another point is taken into account for the scheduling purpose the numerical results prove that the proposed mechanism able to schedule the task quickly and with more task deadline meet the ratio. [ABSTRACT FROM AUTHOR]

Cloud computing will continue to play a critical role due to its ability to deliver various types of on-demand information technology (IT) services according to customer needs. Unfortunately, cloud computing has faced many challenges. The task scheduling problem is considered one of the main challenges because the user has to pay for a resource. Therefore, a good mapping of users' tasks on the available resources is required to reduce the total execution time of the users' tasks (i.e., reduce makespan), execution cost, enhance load balancing, increase resource utilization, etc. Many heuristic algorithms have been existed to solve task scheduling problems. Although, the Heterogeneous Earliest Finish Time (HEFT) heuristic algorithm is considered one of the best task scheduling algorithms in a heterogeneous environment, it does not concern load balancing. In this paper, a modification has been introduced to the HEFT algorithm to satisfy load balancing. The modified algorithm is called Load Balancing HEFT (LB-HEFT). A comparative study has been done among the proposed LB-HEFT, the Enhancement Heterogeneous Earliest Finish Time (E-HEFT), and HEFT algorithms to evaluate the performance of the proposed LB-HEFT algorithm. According to the comparative results, it is found that the proposed LB-HEFT algorithm outperforms the existing E-HEFT and HEFT algorithms by improving load balancing by 43.49% and 72.59%, respectively in average, enhancing resource utilization by 2.28% and 5.61%, respectively in average, and reducing makespan by 7.55%, and 3.75% respectively in average. [ABSTRACT FROM AUTHOR]

The importance of cloud computing grows staggeringly by the day as newer technology is introduced resulting in greater connectivity across systems worldwide inculcating higher demand for resources supplied by cloud providers. Every datacentre responsible for providing these resources consumes a hefty amount of power and cloud service providers have to bear the monetary costs that come with it. Recent studies show that energy consumption of resources at utilizations over 70% grows non-linearly and hence it is best to avoid resources reaching high levels of utilization. In this paper, we introduce EPTS (Energy-saving Pre-emptive Task Scheduling) that utilizes the 0-70% interval of utilization of resources and evenly distributes the energy consumption of tasks by pre-empting tasks consuming high amounts of power by less power-demanding tasks. These pre-empted tasks are rescheduled into the virtual machines where they may result in lower utilization rates, hence increasing energy conservation. We compare EPTS with MaxUtil and FCFS (First Come First Serve) via thorough experimentation on different synthetic datasets and show that EPTS outperforms MaxUtil and FCFS by approximately 50% in terms of energy consumption. [ABSTRACT FROM AUTHOR]

Recent advancements of virtualization technologies for parallel processing involve scheduling containerized tasks in a workflow. Since a container can include multiple tasks, it can be reused or shared among applications. If every task in a workflow uses its dedicated container without sharing among any tasks, each container image must be downloaded for each task. As a result, many computational resources are required to process and the communication latency related to container image downloading can become a bottleneck for the makespan. In task scheduling algorithms for workflows, this characteristic produces a new challenging issue that how effectively shares containers among tasks to avoid redundant container image download processes and redundant task allocations. One of the fundamental problems is that no policy has been established for simultaneously satisfying effective container sharing, maintaining the degree of task parallelism, and effective computational resource utilization. In this paper, we propose a clustering-based containerized task scheduling algorithm for clouds, namely, shareable functional task clustering for utilizing virtualized resources (SF-CUV). The objective of SF-CUV is to minimize the makespan with less computational resources and containers than other algorithms by clustering tasks and sharing each container among tasks. SF-CUV consists of two phases: (i)task clustering and pre-virtual CPU (vCPU) allocation phase to derive an accurate scheduling priority, and (ii)task ordering and actual task reallocation phase. Experimental results obtained via simulation and in a real environment show that SF-CUV can utilize both vCPUs and containers with a shorter makespan compared with other approaches. [ABSTRACT FROM AUTHOR]

• Addressing the worst-case performance of labeling methods like the Coffman-Graham algorithm. • Focus on delayed precedence constraints in the single- and multi-processor case. • New lower bounds on worst-case approximation guarantees are derived. • The results extend to more general level-based or critical-path scheduling techniques. • An open problem by Bernstein, Rodeh, and Gertner from 1989 is resolved. There is some evidence that labeling schemes as employed for instance in the famous Coffman-Graham algorithm may provide superior worst-case approximation guarantees than purely path- or level-based list schedules in the presence of (delayed) precedence constraints. In 1989, Bernstein, Rodeh, and Gertner proved that this also holds true for their labeling scheme targeting unit execution time tasks on a single processor provided that all delays imposed by a single task on all of its successors are uniformly either zero or some fixed positive integer. They further conjectured that this superiority is preserved when allowing the delays imposed by a task to differ successor-wise. It is shown in this note however that their labeling scheme as well as more general ones may perform as bad as any list schedule in this case. Moreover, a new lower bound on the worst-case performance of labeling methods in the multiprocessor setting is derived. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has recently emerged as an effective paradigm to enhance the computing capability of capability-limited mobile devices (MDs). In this article, we consider an MEC system consisting of a number of MEC servers and one MD which generates a series of tasks characterized by their dependency relationships. We study computation scheduling and offloading problem of the tasks. To improve the task processing performance, we first propose a parallel transmission and execution (PTE) scheme, based on which we design a computation scheduling and offloading algorithm. Considering the fairness among tasks in terms of task transmission and execution time, we formulate the computation scheduling and offloading problem as a constrained worst-case latency optimization problem which minimizes the maximum completion time of all the tasks. As the original optimization problem cannot be solved conveniently, we first categorize the tasks into high priority tasks (HPTs), medium priority tasks (MPTs) and low priority tasks (LPTs) based on their task execution status and causal relationship. Then, a dynamic priority-based computation scheduling and offloading algorithm is proposed, which designs computation scheduling and offloading strategy for dynamically-changed HPTs and MPTs, respectively. In particular, for HPTs, a multiple knapsack-based heuristic algorithm is proposed, and a task weight and data size-based computation scheduling and offloading algorithm is further proposed for MPTs. Numerical results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

As for the special architecture of the stack processor, to improve the real-time performance, by adopting a multi-task stacking technology and the Forth self-generator technology, this paper proposed a stack-processor-based preemptive and round-robin scheduling method, which made the multi-tasks run on the Forth stack processor in a real-time manner and made up for the deficiencies of the processor in the real-time multi-task operating system. The experiments show that, compared with the current register processor-based embedded Forth real-time systems, the proposed method improves the real-time performance obviously on the three vital metrics of real-time task performance:maximum interrupt time, task context switching time, and task response time. As a result, it ensures the efficiency and security of Forth system applications, and meets the application demand for the Forth stack processor real-time multitasking operating system. [ABSTRACT FROM AUTHOR]

Task scheduling is important in cloud manufacturing because of customers' increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms. [ABSTRACT FROM AUTHOR]

Industrial manipulators should be able to execute difficult tasks in the minimum cycle time in order to increase performance in a robotic work cell. This paper is focused on determining the near optimum route of a manipulator's end-effector which is requested to reach a predefined set of demand points in a robotic work cell. Two subproblems are related with this goal: the motion planning problem and the task scheduling problem. A new approach is presented in this paper for simultaneously planning collision-free motion and scheduling time near optimum route along the demand points. A combination of a geometrical approach and an adaptive neuro-fuzzy system is employed to consider the multiple manipulator's configurations, while a special genetic algorithm is designed to solve the derived optimization problem. The experiments show that the proposed method has the capacity to determine both the near optimum manipulator configurations and the near optimum sequence of demand points. [ABSTRACT FROM AUTHOR]

According to the challenge point framework, task difficulty has to be appropriate to learner skill level. The pure blocked or random practice controls the task difficulty during practice monotonically. Therefore, the purpose of this study was to investigate the effect of algorithm-based practice schedule and task similarity on motor learning in older adults. For this purpose, 60 older adults were randomly assigned into six groups of blocked-similar, algorithm-similar, random-similar, blocked-dissimilar, algorithm-dissimilar, and random-dissimilar. Sequential motor tasks were used for learning. Participants practiced absolute timing goals in similar (1350, 1500, 1650 ms) or dissimilar (1050, 1500, 1950 ms) conditions according to their practice schedule. Twenty-four hours after the acquisition phase, retention, and transfer tests were performed. Algorithm-practice was a hybrid practice schedule (blocked, serial, and random practice in forward/backward switching) that switching the schedules was according to error trial number (n ≤ 33%) in each block based on error range of absolute timing goals (± 5%). The results showed that the blocked-practice outperforms the other groups during the acquisition phase, whereas the algorithm-practice outperforms the other groups in retention and transfer in both similar and dissimilar conditions. These findings were discussed according to the challenge point framework. [ABSTRACT FROM AUTHOR]

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan. [ABSTRACT FROM AUTHOR]

Objective: This study evaluated task-scheduling decisions in the context of emergency departments by comparing patterns of emergency physicians' task-scheduling models across levels of experience.Background: Task attributes (priority, difficulty, salience, and engagement) influence task-scheduling decisions. However, it is unclear how attributes interact to affect decisions, especially in complex contexts. An existing model of task scheduling, strategic task overload management-no priority (STOM-NP), found that an equal weighting of attributes can predict task-scheduling behavior. Alternatively, mathematical modeling estimated that priority alone could make similar predictions as STOM-NP in a parsimonious manner. Experience level may also influence scheduling decisions.Method: An experimental design methodology shortened a judgment analysis approach to compare a priori task-scheduling decision strategies. Emergency physicians with two levels of experience rank-ordered 10 sets of 3 tasks varying on 4 task attributes in this complex environment.Results: Bayesian statistics were used to identify best-fit decision strategies. STOM-NP and priority-only provided the best model fits. STOM-NP fit the lower-experienced physicians best, whereas priority-only-using only one cue-fit the higher-experienced physicians best.Conclusion: Models of decision strategies for task-scheduling decisions were extended to complex environments. Experts' level of experience influenced task-scheduling decisions, where the scheduling decisions of more-experienced experts was consistent with a more frugal decision process. Findings have implications for training and evaluation.Application: We assessed models of cues that influence task-scheduling decisions, including a parsimonious model for task priority only. We provided a sample approach for shortening methods for understanding decisions. [ABSTRACT FROM AUTHOR]

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system. The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system. [ABSTRACT FROM AUTHOR]

We study a single machine scheduling problem with coupled tasks under limited resource availability. Each job comprises of two tasks which are separated by an exact amount of delay. We assume that the processing time of the initial task and the duration of the delay period are equal and the same for all jobs. The processing time of the completion task, however, is job-dependent and modelled as a convex function of the amount of resource the job is allocated. The scheduling objective consists of minimizing the makespan, subject to an upper-bound on the resource availability. We provide several properties of an optimal solution and develop an O (n 2) time algorithm for the problem. [ABSTRACT FROM AUTHOR]

In highly advanced real-time critical applications, the demand of multi-core processing is increasing as the tasks are assigned to cores in the most balanced manner. The balancing of tasks on the cores requires an efficient load balancing algorithm. To balance the loads amongst the cores, all harmonic tasks can be grouped together and shall be assigned on a single core. The concept of harmonic task set is effective as it tends to reach a utilization of one, and thus, the system achieves a higher utilization. The proposed paper is focused on harmonic-aware load balancing algorithm for a set of periodic real-time tasks having constrained deadlines, where the deadline of the tasks is less than the period of the tasks. The extensive analysis was performed to establish the superiority of harmonic task sets in real-time applications. The results of this analysis are reported in this paper. It was observed that by considering the tasks' harmonic relationship, the proposed load balancing approach greatly improves the feasibility of real-time tasks on a multi-core processor to meet their deadlines. [ABSTRACT FROM AUTHOR]

This study compared effects of student choice of task sequence to two variations in teacher-manipulated task sequences on on-task behavior of elementary-aged students with disabilities. Researchers modified Call et al.'s (J Appl Behav Anal 42: 723–728, 2009) demand assessment to determine high-, moderate-, and low-probability tasks. Next, researchers applied the results from the demand assessment to inform teacher-manipulated variations in task sequences: a high- to low-probability task sequence and low- to high-probability task sequence. These sequences were then embedded in a visual activity schedule (VAS). Results of task sequence manipulation embedded in a VAS indicated slightly higher median percentages of on-task behavior for the high- to low-probability task sequence. Future directions for research based on these preliminary data are discussed. [ABSTRACT FROM AUTHOR]

In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient. [ABSTRACT FROM AUTHOR]

Many applications of physics modeling use regular meshes on which computations of highly variable cost over time can occur. Distributing the underlying cells over manycore architectures is a critical load balancing step that should be performed the less frequently possible. Graph partitioning tools are known to be very effective for such problems, but they exhibit scalability problems as the number of cores and the number of cells increase. We introduce a dynamic task scheduling and mesh partitioning approach inspired by physical particle interactions. Our method virtually moves cores over a 2D/3D mesh of tasks and uses a Voronoi domain decomposition to balance workload. Displacements of cores are the result of force computations using a carefully chosen pair potential. We evaluate our method against graph partitioning tools and existing task schedulers with a representative physical application, and demonstrate the relevance of our approach. [ABSTRACT FROM AUTHOR]

Parallel test is an efficient approach for improving test efficiency in the aerospace field. To meet the challenges of implementing multiunit parallel test in practical projects, this paper presented a mixed-integer linear programming (MILP) model for solving the task scheduling problem. A novel sequence-based iterative (SBI) method is proposed to solve the model in reasonable time. The SBI method is composed of an implied sequence finding procedure (ISF) and a sequence-based iterative optimization (SBIO) procedure. The first procedure can reduce the search space by fixing free sequence variables according to the original test flowcharts, and the second procedure can solve the model iteratively in a reasonable amount of time. In addition, two indexes, namely, speed rate and average resource utilization rate, are introduced to evaluate the proposed methods comprehensively. Computational results indicate that the proposed method performs well in real-world test examples, especially for larger examples that cannot be solved by the full-space method. Furthermore, it is proved that the essence of the parallel test is trading space for time. [ABSTRACT FROM AUTHOR]

Although several studies have explored the effects of task sequencing on second language (L2) production, there is no established set of criteria to sequence tasks for learners in L2 writing classrooms. This study examined the effect of simple-complex task sequencing manipulated along both resource-directing (± number of elements) and resource-dispersing (± planning time) factors on L2 writing compared to individual task performance using Robinson's (2010) SSARC model of task sequencing. Upper-intermediate L2 learners (N = 90) were randomly divided into two groups: (1) participants who performed three writing tasks in a simple-complex sequence, and (2) participants who performed either the simple, less complex, or complex task. Findings revealed that simple-complex task sequencing led to increases in syntactic complexity, accuracy, lexical complexity, and fluency, as compared to individual task performance. Results are discussed in light of the SSARC model, and theoretical and pedagogical implications are provided. [ABSTRACT FROM AUTHOR]