Heterogeneous Task Allocation Research Articles

Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

Makespan-Minimizing Heterogeneous Task Allocation under Temporal Constraints

Task allocation is an essential element for determining the capability of multi-UAV systems to perform various tasks. This paper presents a procedure called a “rebalancing algorithm” for generating task-performing routes in heterogeneous multi-UAV systems. The algorithm adopts a greedy-based heuristic approach to find solutions efficiently in dynamically changing environments. A novel variable named “loitering” is introduced to satisfy temporal constraints, resulting in improved performance compared to heuristic algorithms: a sequential greedy algorithm, a genetic algorithm, and simulated annealing. The rebalancing algorithm is divided into two phases to minimize the makespan, i.e., the initial allocation and reallocation phases. Simulation results demonstrate the proposed algorithm’s effectiveness in highly constrained conditions and its suitability for heterogeneous systems. Additionally, the results show a reduction in calculation time and improved performance compared to the heuristic algorithms.

Online Organizing Large-Scale Heterogeneous Tasks and Multi-Skilled Participants in Mobile Crowdsensing

Online gathering large-scale heterogeneous tasks and multi-skilled participant can make the tasks and participants to be shared in real time. However, their online gathering will bring many intractable objective requirements, which makes task-participant matching become extremely complex. To cope well with the gathering, we design a hierarchy tree and time-series queue to organize tasks and participants. The data structures we designed can effectively meet the all requirements that are brought due to tasks and participants gathering online. In addition, based on the designed data structures, we study online large-scale heterogeneous task allocation problem from three aspects: the computing pattern, the tree creation method, and the extension of matching strategy. Our best method (TsPY) is based on parallel computing in the computing pattern, adopts time first and then space in the tree creation method, and increases the short-distance first strategy in the matching strategy. Finally, we conducted detailed experiments under the conditions of different participant geographical distributions (i.e. uniform distribution, Gaussian distribution, and check-in empirical distribution), different sensing methods (i.e. participatory sensing and opportunistic sensing), and different recommendation methods (i.e. point recommendation and trajectory recommendation).

An efficient algorithm for task allocation with the budget constraint

This paper studies a heterogeneous task allocation problem with the budget constraint. Existing works on task allocation mainly tackle this well-known NP-hard problem from an optimisation perspective. They have not been able to cater to the extra needs of scalability and robustness in large-scale systems. Furthermore, some general allocation mechanisms do not consider system budget and agent cost. Thus, they cannot guarantee to obtain valid solutions when the budget is constrained. This paper models the task allocation problem as a game whose players are the agents to be assigned to the teams working on the tasks, and align the task allocation objective (i.e., system optimality) with the game-theoretic solution concept of Nash equilibrium. Based on this formulation, a novel algorithm, called CF, is proposed in this paper. CF searches for a valid Nash equilibrium solution using a greedy strategy that aims to improve system utility while takes into consideration of the overall system budget constraint. CF is a scalable, anytime, and monotonic algorithm, which in turn, makes it robust for the deployment in large-scale systems. CF can also be used as a local search algorithm for improving the quality of any existing valid allocation solution. Comprehensive empirical studies have been carried out in this paper to demonstrate that CF is effective in all budget states and achieves a solution quality better than the state-of-the-art algorithms.

Group task allocation approach for heterogeneous software crowdsourcing tasks

It is more common for multiple users to collaborate to develop a software application in a P2P collaborative working environment. In collaborative software development, the rational allocation of software development tasks is of great significance. However, heterogeneous of software development tasks, such as the value of the task, the skill required, the effort required and difficulty, increase the complexity of task allocation. This paper proposes an allocation approach of crowd intelligence software development task in which multiple individuals collaborate to complete software development tasks. The heterogeneous task allocation problem in the crowd intelligence software development system is formulated as an optimization problem. Then, the process of task allocation is modelled using the hidden Markov model. In our study, due to the insufficiency of data characteristics, we propose to construct a generator using Generative Adversarial Networks(GANs) to solve this problem. Then, the Baum-Welch algorithm is used for detailed analysis and calculation of model parameters. And on this basis, effective task allocation strategies for maximizing the total value of tasks obtained by the workers are explored through the Viterbi algorithm. Based on the Agile Manager (AM) dataset, which contains a large scale real human task allocation strategy, the model learns from human decision-making strategies that have achieved good outcomes. Based on the Agile Manager dataset, this approach is evaluated experimentally. The results show that it outperforms the artificial intelligence (AI) player in the AM game platform.

A Cost-Efficient Container Orchestration Strategy in Kubernetes-Based Cloud Computing Infrastructures with Heterogeneous Resources

Containers, as a lightweight application virtualization technology, have recently gained immense popularity in mainstream cluster management systems like Google Borg and Kubernetes. Prevalently adopted by these systems for task deployments of diverse workloads such as big data, web services, and IoT, they support agile application deployment, environmental consistency, OS distribution portability, application-centric management, and resource isolation. Although most of these systems are mature with advanced features, their optimization strategies are still tailored to the assumption of a static cluster. Elastic compute resources would enable heterogeneous resource management strategies in response to the dynamic business volume for various types of workloads. Hence, we propose a heterogeneous task allocation strategy for cost-efficient container orchestration through resource utilization optimization and elastic instance pricing with three main features. The first one is to support heterogeneous job configurations to optimize the initial placement of containers into existing resources by task packing. The second one is cluster size adjustment to meet the changing workload through autoscaling algorithms. The third one is a rescheduling mechanism to shut down underutilized VM instances for cost saving and reallocate the relevant jobs without losing task progress. We evaluate our approach in terms of cost and performance on the Australian National Cloud Infrastructure (Nectar). Our experiments demonstrate that the proposed strategy could reduce the overall cost by 23% to 32% for different types of cloud workload patterns when compared to the default Kubernetes framework.

Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

This paper focuses on decentralized task allocation and sequencing for multiple heterogeneous robots. Each task is defined as visiting a point in a subset of the robot configuration space — this definition captures a variety of tasks including inspection and servicing. The robots are heterogeneous in that they may be subject to different differential motion constraints. Our approach is to transform the problem into a multi-vehicle generalized traveling salesman problem (GTSP). To solve the GTSP, we propose a novel decentralized implementation of large-neighborhood search (LNS). Our solution approach leverages the GTSP insertion methods proposed in Fischetti et al. [A branch-and-cut algorithm for the symmetric generalized traveling salesman problem, Oper. Res. 45(3) (1997) 378–394]. to repeatedly remove and reinsert tasks from each robot path. Decentralization is achieved using combinatorial-auctions between the robots on tasks removed from robot’s path. We provide bounds on the length of the dynamically feasible robot paths produced by the insertion methods. We also show that the number of bids in each combinatorial auction, a crucial factor in the runtime, scales linearly with the number of tasks. Finally, we present extensive benchmarking results to characterize both solution quality and runtime, which show improvements over existing decentralized task allocation methods.