Static Task Allocation Research Articles

Static Task Allocation Method for Multi-Machines in Cooperative Operations Combining OGFR-GA and MLW-Prim

Aiming at the operational efficiency of small farm machinery groups in cooperative operations in hilly areas, this study proposes a static task allocation model. This method combines the optimal gene fragment retention method based on a genetic algorithm (OGFR-GA) and the method for generating multi-loop weighted connected graphs based on Prim’s algorithm (MLW-Prim). The collaborative objective function for the harvester group was established by considering factors such as operation time, fuel consumption, and distance. The OGFR-GA was designed and applied multiple times to obtain several optimal gene fragments corresponding to the number of farm machines. These fragments were used as critical paths in the weighted connected graph generated based on farm machinery performance parameters and task parameters. The MLW-Prim method was proposed to construct this weighted connected graph and realize the static task allocation model for multi-machine cooperative operations. Simulation experiments showed that the model combining OGFR-GA and MLW-Prim achieved optimal values with fewer iterations, and reduced both group cost and cost variance compared to traditional algorithms. This method meets the static task allocation needs for multi-machine cooperative operations in agricultural production and can be integrated with intelligent operations in mountainous and hilly regions, laying a theoretical foundation for improving efficiency.

The methods of task pre‐allocation and reallocation for multi‐UAV cooperative reconnaissance mission

AbstractNowadays, multi unmanned aerial vehicle (multi‐UAV) systems have been widely used in battlefield. The rationality of mission plan can directly affect the effectiveness of multi‐UAV system. The existing multi‐UAV task allocation model lack a comprehensive modelling of task pre‐allocation and task reallocation issues. However, in actual task execution, task pre‐allocation and task reallocation are a holistic problem. Therefore, based on the background of multi‐UAV cooperative reconnaissance, the authors establish a multi‐UAV cooperative reconnaissance task pre‐allocation and reallocation model (MCRTPR). There are two kinds of task allocation in MCRTPR model. One is task pre‐allocation, which is a static task allocation before the mission begin. Another is task reallocation, that is a dynamic task allocation during the mission. For task pre‐allocation, a particle swarm optimisation algorithm based on experience pool (EPPSO) is proposed. And for task reallocation, the authors design a partial task reallocation algorithm based on contract network protocol (CNP‐PTR). The experimental results show that, compared with some state‐of‐the‐art algorithms, EPPSO can get the lowest fitness value under various experimental conditions, and CNP‐PTR is able to handle task reallocation problem caused by multiple kinds of dynamic events.

Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets

This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm.

Implementation and Evaluation of Dynamic Task Allocation for Human–Robot Collaboration in Assembly

Human–robot collaboration is becoming increasingly important in industrial assembly. In view of high cost pressure, resulting productivity requirements, and the trend towards human-centered automation in the context of Industry 5.0, a reasonable allocation of individual assembly tasks to humans or robots is of central importance. Therefore, this article presents a new approach for dynamic task allocation, its integration into an intuitive block-based process planning framework, and its evaluation in comparison to both manual assembly and static task allocation. For evaluation, a systematic methodology for comprehensive assessment of task allocation approaches is developed, followed by a corresponding user study. The results of the study show for the dynamic task allocation on the one hand a higher fluency in the human–robot collaboration with good adaptation to process delays, and on the other hand a reduction in the cycle time for assembly processes with sufficiently high degrees of parallelism. Based on the study results, we draw conclusions regarding assembly scenarios in which manual assembly or collaborative assembly with static or dynamic task allocation is most appropriate. Finally, we discuss the implications for process planning when using the proposed task allocation framework.

Human-robot activity allocation algorithm for the redesign of manual assembly systems into human-robot collaborative assembly

ABSTRACT As a key and enabling technology of Industry 4.0, there is no doubt that manufacturing enterprises will benefit from the introduction of collaborative robots (cobots) in their production processes. A large part of collaborative applications will arise from the re-design of existing manual assembly workstations. For this reason, companies need methods and guidelines for the proper adoption of these systems. Thus, this work presents a systematic methodology for the static allocation of assembly tasks between the human and the robot by considering a human-centric approach and the requirements in terms of technical feasibility, safety and ergonomics, quality, and economics. The proposed approach will support the manufacturers in overcoming technical barriers related to the proper design of collaborative solutions and in the implementation of more sustainable, high-quality, and efficient manufacturing processes starting from manual assemblies. The methodology has been proven and validated in an industrial case study. Results show how the proposed algorithm can support companies in the parallel improvement of the operator’s working conditions as well as the company performance at the same time.

A Personalized Task Allocation Strategy in Mobile Crowdsensing for Minimizing Total Cost.

Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works usually assume a static task allocation by sorting the cost of users to complete the tasks, where the cost is measured by the expense of time or distance. In this paper, we argue that the task allocation process is actually a dynamic combinational optimization problem because the previous allocated task will influence the initial state of the user to finish the next task, and the user’s preference will also influence the actual cost. To this end, we propose a personalized task allocation strategy for minimizing total cost, where the cost for a user to finish a task is measured by both the moving distance and the user’s preference for the task, then instead of statically allocating the tasks, the allocation problem is formulated as a heterogeneous, asymmetric, multiple traveling salesman problem (TSP). Furthermore, we transform the multiple-TSP to the single-TSP by proving the equivalency, and two solutions are presented to solve the single-TSP. One is a greedy algorithm, which is proved to have a bound to the optimal solution. The other is a genetic algorithm, which spends more calculation time while achieving a lower total cost. Finally, we have conducted a number of simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The simulation results could match the results of theoretical analysis.

Efficient task scheduling on virtual machine in cloud computing environment

PurposeCloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization.Design/methodology/approachCloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing.FindingsTo evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time.Originality/valueThe simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

A Heterogeneous Parallel LU Factorization Algorithm Based on a Basic Column Block Uniform Allocation Strategy

Most supercomputers are shipped with both a CPU and a GPU. With the powerful parallel computing capability of GPUs, heterogeneous computing architecture produces new challenges for system software development and application design. Because of the significantly different architectures and programming models of CPUs and GPUs, conventional optimization techniques for CPUs may not work well in a heterogeneous multi-CPU and multi-GPU system. We present a heterogeneous parallel LU factorization algorithm for heterogeneous architectures. According to the different performances of the processors in the system, any given matrix is partitioned into different sizes of basic column blocks. Then, a static task allocation strategy is used to distribute the basic column blocks to corresponding processors uniformly. The idle time is minimized by optimized sizes and the number of basic column blocks. Right-looking ahead technology is also used in systems configured with one CPU core to one GPU to decrease the wait time. Experiments are conducted to test the performance of synchronization and load balancing, communication cost, and scalability of the heterogeneous parallel LU factorization in different systems and compare it with the related matrix algebra algorithm on a heterogeneous system configured with multiple GPUs and CPUs.

Dynamic and Static Task Allocation for Hard Real-Time Video Stream Decoding on NoCs

Hard real-time (HRT) video systems require admission control decisions that rely on two factors. Firstly, schedulability analysis of the data-dependent, communicating tasks within the application need to be carried out in order to guarantee timing and predictability. Secondly, the allocation of the tasks to multi-core processing elements would generate different results in the schedulability analysis. Due to the conservative nature of the state-of-the-art schedulability analysis of tasks and message flows, and the unpredictability in the application, the system resources are often under-utilised. In this paper we propose two blocking-aware dynamic task allocation techniques that exploit application and platform characteristics, in order to increase the number of simultaneous, fully schedulable, video streams handled by the system. A novel, worst-case response time aware, search-based, static hard real-time task mapper is introduced to act as an upper-baseline to the proposed techniques. Further evaluations are carried out against existing heuristic-based dynamic mappers. Improvements to the admission rates and the system utilisation under a range of different workloads and platform sizes are explored.

Energy-aware task allocation for energy harvesting sensor networks

Ambient energy harvesting is a solution to mitigate the typical finite energy supply of sensor nodes in wireless sensor networks (WSNs). On the one hand, the uncertainty of energy availability in energy harvesting systems makes network protocol design challenging. On the other hand, the fact that energy is continuously replenished opens up avenues for protocol design based on prediction of future energy arrivals. One of the key application scenarios for sensor networks is task allocation, in which a central entity allocates tasks to a set of geographically distributed sensor nodes to accomplish an overall objective. In this work, we consider a scenario in which the sensor nodes are equipped with devices capable of harvesting ambient energy, e.g., solar panels to harvest the Sun’s energy, and focus on energy-aware strategies for adaptive task allocation. We decompose the static task allocation problem into two phases: scheduling of the task graph and task mapping to the appropriate sensor nodes. The solution objectives are to minimize the makespan and maximize the fairness in energy-driven task mapping (i.e., energy-balancing), while satisfying the task precedence constraints and energy harvesting causality constraints. We employ a novel energy prediction model which incorporates seasonal changes in solar energy harvesting as well as sudden weather changes. In case of an error in available energy prediction, a dynamic adaptation phase runs to avoid violation of the task allocation objectives. The performance of our proposed algorithms, in terms of energy-balancing and scheduling length, is evaluated through simulation and compared with other approaches, including a genetic algorithm as a baseline. We achieve more balanced residual energy levels across the network while attaining a near optimum scheduling length. By utilizing the dynamic adaptation phase, for certain runs of simulation, the missing ratio, which is the percentage of times in which the task allocation fails due to a temporal shortage of energy availability, is dramatically decreased.

Adaptive workload-aware task scheduling for single-ISA asymmetric multicore architectures

Single-ISA Asymmetric Multicore (AMC) architectures have shown high performance as well as power efficiency. However, current parallel programming environments do not perform well on AMC because they are designed for symmetric multicore architectures in which all cores provide equal performance. Their random task scheduling policies can result in unbalanced workloads in AMC and severely degrade the performance of parallel applications. To balance the workloads of parallel applications in AMC, this article proposes an adaptive Workload-Aware Task Scheduler (WATS) that consists of a history-based task allocator and a preference-based task scheduler. The history-based task allocator is based on a near-optimal, static task allocation using the historical statistics collected during the execution of a parallel application. The preference-based task scheduler, which schedules tasks based on a preference list, can dynamically adjust the workloads in AMC if the task allocation is less optimal due to approximation in the history-based task allocator. Experimental results show that WATS can improve both the performance and energy efficiency of task-based applications, with the performance gain up to 66.1% compared with traditional task schedulers.

Performance modeling and analysis of correlated parallel computations

A performance analysis methodology for correlated parallel computations based on statistical theory is proposed. Divide-and-conquer strategy is widely used in solving problems in parallel by partitioning and allocating a number of given tasks to available computing resources. When the tasks exhibit run-time-dependent behaviors during execution and share a universal distribution function in their execution times, analysis of parallel execution time can be performed with the assistance of probabilistic and statistical models. Correlation (dependence) in execution times among tasks has posed a significant factor in influencing the analysis accuracy which is unmanageable by any known analysis methodologies. We establish a relation between a task’s or a processor’s execution time and the parallel execution time, in terms of expected value as well as variance when each task’s execution time can be closely modeled by a normal distribution, for either uncorrelated or correlated tasks. This relation is then applied to the modeling and analysis of various parallel computation paradigms in which different communication and synchronization patterns along the processing are present. The method proposed has a wider application scope and gives more accurate prediction results than previously known approaches. We also show that, as an extended application of the analysis method to a large scope of problems, load balance among processors can be vastly improved with some novel static task allocation technique in manipulating the correlation among tasks. Experimental results in analyzing a parallel tree search algorithm and two parallel sorting algorithms show very accurate analysis and prediction with the proposed method.

Static Task Allocation in Real-time Distributed Computer Control Systems

The task allocation problem consists in defining a space and time sharing of system resources that can meet all logical and operational constraints of the tasks being considered. For real-time tasks with predictable behavior (periodic), static allocation provides a reliable way to meet temporal scheduling requirements, together with other operational constraints, such as task replication for fault-tolerance. Unfortunately, finding an optimal feasible allocation is known to be NP-hard, and thus heuristic approaches have been proposed. In this paper, the use of a new heuristic to generate static allocations for critical periodic tasks in real-time distributed control systems is described.

LOAD BALANCING IN HOME-BASED SOFTWARE DSMS

Load balancing is a critical issue for achieving good performance in parallel and distributed systems. However, this issue is neglected in the research area of software DSMs in the past decade. Based on the observation that scientific applications can be classified into two categories: iterative and non-iterative, we propose two dynamic scheduling schemes for these two cases respectively in this paper. For iterative scientific applications, a dynamic task migration technique is proposed which characterizes itself with integrating computation migration and data migration together. An affinity-based self scheduling (ABS) is proposed for non-iterative scientific applications, which take both the static and dynamic processor affinity into consideration when scheduling. The target experiment platform is a state-of-the-art home-based DSM system named JIAJIA. Performance evaluation results show that the novel task migration scheme improves the performance ranging from 36% to 50% compared with a static task allocation scheme in a metacomputing environment, and performs better than traditional task (computation-only) migration approach about 12.5% for MAT, and 37.5% for SOR and EM3D. Higher resource utilization is achieved via the new task migration scheme too. In comparison with other loop scheduling schemes, the ABS achieves the best performance among all scheduling schemes in a metacomputing environment because of the reduction of synchronization overhead and the great improvement of waiting time resulting from load imbalance.

Task allocation in distributed computing systems

In this paper, we investigate the static task allocation problem for heterogeneous distributed computing systems with the goal of minimizing total execution and communication time (i.e. task completion time). An objective function is formulated to evaluate the optimality of mapping modules of a task to a set of heterogeneous processors connected with some communication links of varying transmission rates. We present three algorithms: one to obtain optimal task allocation and other two to obtain suboptimal allocation. The accuracy and efficiency of the proposed algorithms are demonstrated through a simulation study and results are reported.

An improved cost function for static partitioning of parallel circuit simulations using a conservative synchronization protocol

Distributing computation among multiple processors is one approach to reducing simulation time for large VLSI circuit designs. However, parallel simulation introduces the problem of how to partition the logic gates and system behaviors of the circuit among the available processors in order to obtain maximum speedup. A complicating factor that is often ignored is the effect of the time-synchronization protocol (conservative [1] or optimistic [2]). Inherent in the partitioning problem is the question of how to effectively measure the relative quality of a partition. This paper describes an objective cost function for measuring the relative quality of a task partition that includes a synchronization factor for a conservative NULL-message protocol. A graph-based partitioning tool based on this cost function is used to perform the static task allocation for parallel simulation of a structural VHDL circuit. Results for two 1000 – 4000 gate circuits demonstrate that the additional consideration of the synchronization protocol in the cost function generates partitions that exhibit improved speedup.

Optimal algorithms for synthesis of reliable application-specific heterogeneous multiprocessors

Fast and optimally-reliable application-specific multiprocessor-synthesis is critical in system-level design, especially in medical, automotive, space, and military applications. Previous work in multiprocessor-synthesis and task-allocation for performance and reliability requires exponential time, and therefore, is useful only for small examples. We present the first deterministic and provably-optimal algorithm (RELSYN-OPT) to synthesize real-time, reliable multiprocessors using a heterogeneous library of N processors and L link types. We prove that for a series-parallel graph with M subtasks and nested-depth d, the worst-case computational complexity of RELSYN-OPT Is O(M/spl middot/(L+N)/spl middot/N/sup d/). For tree-structured task graphs, RELSYN-OMT runs in O(M/spl middot/(L+N)), and is asymptotically optimum, RELSYN-OPT, because of its speed, applies to static and dynamic task allocation for an ultra-reliable distributed processing environment for which, until now, research has produced only suboptimal heuristic solutions.

Static task allocation of concurrent programs for distributed computing systems with processor and resource heterogeneity

In this paper, we investigate the problem of static task allocation of concurrent programs for distributed computing systems with processor and resource heterogeneity, i.e. given a set of concurrent communicating modules and a two processor distributed computing system, in which processors may have differing speeds and each processor may have certain resources not available with the other processor (i.e. unique resources), to which processor should each module be assigned so as to minimize the completion time? This task allocation problems is NP-hard and hence, we provide an efficient heuristic algorithm for obtaining satisfactory suboptimal solution to it in a reasonable amount of computation time and demonstrate the effectiveness of our algorithm with simulation studies. Our algorithm works by constructing an augmented task graph and taking a load balanced mincut on it and has a time complexity of O(( m + u) 2) where m is the number of concurrent communicating modules and u is the total number of unique resources in the two processor system. Finally, we extend our heuristic algorithm to solve the problem of static task allocation of concurrent programs to an n processor distributed computing system with processor and resource heterogeneity.

Bin-packing by simulated annealing

The process of gradually settling a combinatorial system into configurations of globally minimum energy has variously been called simulated annealing, statistical cooling, and so on. In the past, very large combinatorial optimization problems have been solved using this technique. It has also been shown that this method is effective in obtaining close-to-optimal solutions for problems known to be NP complete. Further, this technique is applicable to a whole class of problems that satisfy a few requirements. The purpose of this paper is to illustrate an efficient version of the simulated annealing method as applied to a variant of the bin-packing problem. The computational complexity of the method is linear in input size similar to various well-known heuristic methods for the problem. The solutions obtained, however, are much better than any of the heuristic methods. The particular variant of the bin-packing problem we consider has several practical applications such as static task allocation in process scheduling and batch processing. At the time of this writing, we have not yet seen a stochastic solution to the bin-packing problem in the literature. One of the distinguishing features of our research is the high quality of solutions obtained by our method. Extensive simulation experiments we have carried out show that the solutions obtained by the stochastic method show a significant improvement over those obtained by any of the well-known heuristic methods.

Optimal granularity of test generation in a distributed system

The problem of test generation for logic circuits is known to be NP-hard, and hence it is very hard to speed up the test generation process due to its backtracking mechanism. The authors present an approach to parallel processing of test generation for logic circuits in a loosely coupled distributed network of general-purpose computers. They analyze the effects of the allocation of target faults to processors, the optimal granularity (grain size of target faults), and the speedup ratio of the multiple-processor system to a single-processor system. To analyze the case in which a test pattern generated for one fault can also be a test pattern for other faults if fault simulation is performed, they introduce a ratio of newly processed faults to target faults and derive the expressions of optimal granularity in cases of both static and dynamic task allocation. They also derive an expression of the speedup of a multiple-processor system in the homogeneous case. The analysis indicates that the speedup approaches N, the number of servers, if the data transfer time per fault and the waiting time per communication are much smaller than the processing time per fault and if the decrease ratio of newly processed faults due to overlapped processing is much smaller than the ratio of newly processed faults. >