Task assignment in a multiprocessor system

A shift-based model to solve the integrated staff rostering and task assignment problem with real-world requirements

High-performance computing systems typically employ heterogeneous multicore design to improve both execution performance and efficiency. Task assignment is critical in exploiting the diversity of computation capability, energy consumption, as well as communication cost on heterogeneous multicore processors. In this paper, we explore the opportunity of task assignment on heterogeneous multicore processors to minimize execution and communication costs considering time constraint. The general heterogeneous task assignment problem is NP-Complete. However, we find that optimal task assignment can be achieved for widely used, tree-shaped task graphs using dynamic programming. We first propose a dynamic programming algorithm, the Optimal Tree Assign (OTA) algorithm, to generate optimal assignments for trees. Then, we develop the Integer Linear Programming model of the general task assignment problem for Directed Acyclic Graphs. A polynomial-time heuristic, the Extended Tree Assignment algorithm, is also proposed to produce near-optimal solutions for the general heterogeneous task assignment problem efficiently. The experimental results show that the proposed algorithms outperform both homogeneous task assignment method and greedy strategy for all the benchmarks. The OTA algorithm reduces the total system time by 42.5 percent and 23.5 percent on average compared with the homogeneous task assignment method and greedy algorithm, respectively.

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

This paper addresses task assignment problem fortree-structure task model on heterogeneous multicore embedded systems with time constraint considering both execution time and communication load. The goal is to minimize the total system cost for a given task graph representing a set of tasks and data dependencies in a heterogeneous multicore systemwhile the time constraint is satisfied. Instead of assigning all the tasks to processors of the same type in a homogeneous environment, heterogeneous task assignment usually can reduce the system cost by exploring various capacities and costs in a heterogeneous multicore system. The general heterogeneous assignment problem is NP-complete. In this paper, we show that optimal task assignment can be found for some widely-used, special task graphs, such as tree-structure graph, using dynamic programming. A dynamic programming algorithm, the Tree Assign (TA) algorithm, is proposed in this paper to solve the heterogeneous task assignment problem for tree-structure task graphs. The experimental results show that our algorithm reduces the total system cost by 31.8% compared with assignment results on homogeneous multicore systems. Also, our algorithm achieves an average cost reduction by 21.9% compared with greedy algorithm.

The power consumption of modern high-performance processors is becoming a major concern because it leads to increased heat dissipation and decreased reliability. While many techniques have been proposed to reduce power consumption for uni-processors, there has been considerably less work on multi-processor systems. In this paper we focus on power-aware scheduling for multi-processor real-time systems. Based on the idea of slack sharing among processors, we propose two novel scheduling algorithms for task sets with and without precedence constraints. These scheduling techniques reclaim the time unused by a task to reduce the execution speed of future tasks, and thus reduce the total energy consumption of the system. Simulation results indicate that our algorithms achieve up to 60% energy savings on multi-processor systems with variable voltage processors.

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms.

As a type of distributed computing, volunteer computing (VC) has provided unlimited computing capacity at a low cost in recent decades. The architecture of most volunteer computing platforms (VCPs) is a master–worker model, which defines a master–slave relationship. Therefore, VCPs can be considered asymmetric multiprocessing systems (AMSs). As AMSs, VCPs are very promising for providing computing services for users. Users can submit tasks with deadline constraints to the VCPs. If the tasks are completed within their deadlines, VCPs will obtain the benefits. For this application scenario, this paper proposes a new task assignment problem with the maximum benefits in VCPs for the first time. To address the problem, we first proposed a list-based task assignment (LTA) strategy, and we proved that the LTA strategy could complete the task with a deadline constraint as soon as possible. Then, based on the LTA strategy, we proposed a maximum benefit scheduling (MBS) algorithm, which aimed at maximizing the benefits of VCPs. The MBS algorithm determined the acceptable tasks using a pruning strategy. Finally, the experiment results show that our proposed algorithm is more effective than current algorithms in the aspects of benefits, task acceptance rate and task completion rate.

We have developed real-time methods to synthesize cooperative strategies for the multi-vehicle task assignment problem in an adversarial environment. By introducing a set of tasks to be completed by the team of vehicles and a trajectory generation primitive for each vehicle, we formulate the multi-vehicle control problem as a task assignment problem. The continuous component of the problem is captured by the trajectory primitive, and the combinatorial component is captured by task assignment. We have developed an efficient branch and bound solver for the task assignment component of the problem. In this paper, we analyze the computational complexity of our solver with variations in parameters of the problem. We found a phase transition in the ratio of the maximum velocity of opposing vehicles, and we found a phase transition in the ratio of the number of vehicles per team. The results show that the task assignment problem is difficult to solve when the capabilities of the two teams are comparable and easy to solve when one team is more capable than the other.

For the task assignment problem in a smart logistic center with many autonomous mobile robots, a task assignment method is proposed based on a market auction idea. Due to the existence of asymmetrical shelf-shift tasks within the center and diverse self-finished cost of each task besides the correlation between tasks, the problem of multi-robot task assignment in a logistic center is different from multi-robot routing problem. By introducing the correlation function and self-finished cost function, the model of task assignment is presented. Then, the bidding strategy is given by analyzing the performance difference. On these bases, an auction approach is developed for the static and dynamic task assignment. Finally, the simulation results show the applicability of the algorithm.

Using multiple unmanned aerial vehicles (UAVs) to perform some tasks cooperatively has received growing attention in recent years. Task assignment is a difficult problem in mission planning. Multiple tasks assignment problem for cooperating homogeneous UAVs is considered as a traditional combinatorial optimization problem. This paper addresses the problem of assigning multiple tasks to cooperative homogeneous UAVs, minimizing the total cost and balancing the cost of each UAV. We propose a centralized task assignment scheme which is based on minimum spanning tree. This scheme involves two phases. In the first phase, we use the Kruskal algorithm and the breadth first search algorithm to assign all tasks to UAVs and get a proper initial task assignment solution. The second phase involves the Pareto optimization improvement in the solution generated from the first phase. For a single UAV, we use the dynamic programming algorithm to calculate the total cost of completing all assigned tasks. The performance of the proposed scheme is compared to that of heuristic simulated annealing algorithm. The simulation results show that the proposed scheme can solve the homogeneous multi-UAV cooperative task assignment problem effectively.

Vehicular mobile crowdsensing (VMCS) has emerged as powerful and promising paradigm for mobile crowdsensing (MCS) by deploying sensing devices on connected vehicles to collect perception data. Task assignment plays a crucial role in determining the efficiency and performance of in VMCS systems. However, the large number of connected vehicles and sensing tasks involved, coupled with the implication of task information and vehicle status over time, which poses new challenges to system stability. Therefore, this paper puts forth an edge computing-enabled task assignment for VMCS, seeking to enhance the efficiency of task assignment by scheduling a reasonable match between sensing tasks and connected vehicles, and to reduce decision-making delays. Specifically, the task assignment problem is acknowledged as an optimization process with the objective of reducing the tasks waiting duration by considering the task completion sequence rationality under the task time constraint and the energy limitation of the vehicles, and is demonstrated to be an NP-hard problem. Firstly, a valid task sequence is mainly generated for vehicles prior to task assignment to reduce the collision rate between different lists of vehicular tasks. Secondly, this issue is resolved by employing a cooperative game theory-based coalition formation game. On the basis of the introduction of the coalition formation game, the switching rule and historical selection set are used to prevent cyclical coalition switching, and a convergence-guaranteed algorithm with low complexity is proposed to achieve a Nash stable solution. Finally, a thorough set of experiments are performed under various dynamic conditions to show that our suggested scheme is superior to other comparison schemes in terms of task completion percentage, average waiting time, and running time.

The task assignment problem is one of assigning tasks of a parallel program among the processors of a distributed computing system in order to reduce the job turnaround time and to increase the throughput of the system. Since the task assignment problem is known to be NP‐complete except in a few special situations, satisfactory suboptimal solutions obtainable in a reasonable amount of computation time are generally sought. In the paper we introduce a technique based on the problem‐space genetic algorithm (PSGA) for the static task assignment problem in both homogeneous and heterogeneous distributed computing systems to reduce the task turnaround time and to increase the throughput of the system by properly balancing the load and reducing the interprocessor communication time among processors. The PSGA based approach combines the power of genetic algorithms, a global search method, with a simple and fast problem‐specific heuristic to search a large solution space efficiently and effectively to find the best possible solution in an acceptable CPU time. Experimental results on test examples from the literature show considerable improvements in both the assignment cost and the CPU times over the previous work. The proposed scheme is also applied to a digital signal processing (DSP) system consisting of 119 tasks to illustrate its balancing properties and computational advantage on a large system. The proposed scheme offers 12–30% improvement in the assignment cost as compared to the previous best known results for the DSP example.

Energy efficient design for hardware and software implementations plays an important role in pivoting the system towards reliability without any considerable limitations. Energy consumption is a critical design issue in real time system. The main challenge in battery operated system is maintaining high performance. Dynamic voltage scaling and dynamic frequency scaling is used to adjust supply voltage and processor frequency to adapt the workload demand for better energy management. From the literature survey, the problem of local optima, resources constraints, task assignment and scheduling are identified for energy management. To solve these problems, local with restarts algorithm (LSR) and voltage assignment algorithms (PVAP) are proposed to address the problem of Processor and Voltage Assignment with Probability by adopting a probabilistic approach of tasks in real-time embedded systems. A task DAG (Directed Acyclic Graph) is used to model the PVAP problem. In order to facilitate dynamic voltage scaling, voltage assignment algorithm is worked out with execution time and energy units as considerations and it encounters a common node problem. As an effective aid local search with restarts algorithm is used this proves effective against local optima too. The experiment has been done on different benchmark sets.

This paper considers the problem of task assignment in heterogeneous distributed computing systems with the goal of minimizing the total execution and communication costs. An iterated local search algorithm is proposed for finding the suboptimal task assignment in a reasonable amount of computation time. We study the performance of the proposed algorithm over a wide range of parameters such as the problem scales, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness of the algorithm is manifested by comparing it with other competing algorithms in the relevant literature.

This paper presents the first systematic investigation of the potential performance gains for crowdsourcing systems, deriving from available information at the requester about individual worker earnestness (reputation). In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori“ decision rule. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the LRA decision rule introduced in the literature.