An Iterated Local Search Algorithm for Task Assignment in Distributed Computing Systems

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.

Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique based on the honeybee mating optimization (HBMO) algorithm for solving the problem with the objective of minimizing the total execution and communication costs. We discuss the adaptation and implementation of the HBMO search strategy to the task assignment problem. Through simulations over a wide range of parameters, we demonstrate the performance of our method by comparing it with three existing task assignment techniques from the literature.

An improved partial solution to the task assignment and multiway cut problems

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.

Task Assignment is one of fundamental problems in multiple UAV systems. Decision of one UAV has effect on the others in the same team. Because the performance no longer measures from an individual vehicle, the tasks have to be distributed to maximize the team performance. An underlying problem is computational burden that grows considerably as the system gets larger. Many approaches cannot extend to larger systems because of this problem. The objective of this paper is to develop a quasi-decentralized methodology to solve the task assignment problem using less computational time. The assignment is decomposed into sub-problems that can be solved quickly. Then the results are combined in order to determine the best solution for the team performance. This approach is also more adaptable for dynamic environment. The coordination of multiple UAVs is utilized as a platform to demonstrate the methodology. The performance is compared to a centralized approach. For a system with five UAVs and ten goals, the cost is 5% higher than the optimal cost, while the run time is cut down by 55% on average.

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.

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.

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.

With the popularity of mobile devices and Online To Offline (O2O) marketing model, various spatial crowdsourcing platforms, such as Gigwalk, WeGoLook, TaskRabbit and gMission, are getting popular. An important task of spatial crowdsourcing platforms is to allocate spatial tasks to suitable workers. Existing approaches only simply focus on maximizing the number of completed spatial tasks but neglect the influence of supplies and demands from real crowdsourcing market, which leads to different optimal objectives for crowdsourcing task assignments. In this paper, to address the shortcomings of the existing approaches, we first propose a more general spatial crowdsourcing task assignment problem, called Market-driven Optimal Task Assignment (MOTA) problem, consisting of two real scenarios, Excess Demand of Crowd Workers and Insufficient Supply of Spatial Tasks, in daily life. Unfortunately, we prove that the two variants of this problem are NP-Hard. Thus, we design two approximation algorithm to solve this problem. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on synthetic datasets.

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.

The author studies the complexity of the problem of allocating modules to processes in a distributed system to minimize total communication and execution costs. He shows that unless P=NP, there can be no polynomial-time epsilon -approximate algorithm for the problem, nor can there exist a local search algorithm that requires polynomial time per iteration and yields an optimum assignment. Both results hold even if the communication graph is planar and bipartite. On the positive side, it is shown that if the communication graph is a partial k-tree or an almost-tree with parameter k, the module allocation problem can be solved in polynomial time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

Mobile crowdsensing is a new paradigm, which refers to mobile users exploiting smart devices to conduct complex tasks. Opportunistic crowdsensing derives from mobile crowd sensing In this paper, we focus on task assignment problem in opportunistic crowdsensing, in which the mobility model of users is predictable. In order to minimize the average makespan of all tasks, we propose a Multi-stage multi-task Online Task Assignment (MOTA) algorithm. The algorithm adopts greedy strategy and multi-stage assignment strategy for multi-task assignment problem. Simulation experiments show that the average makespan of all tasks can be reduced by 10% to 15% on average.