New cost metrics for iterative task assignment algorithms in heterogeneous computing systems

Task assignment and scheduling algorithms are powerful tools for autonomously coordinating large teams of robotic or AI agents. However, the decisions these system make often rely on components designed by domain experts, which can be difficult for non-technical end-users to understand or modify to their own ends. In this paper we propose a preliminary design for a flexible natural language interface for a task assignment system. The goal of our approach is both to grant users more control over a task assignment system's decision process, as well as render these decisions more transparent. Users can direct the task assignment system via natural language commands, which are applied as constraints to a mixed-integer linear program (MILP) using a large language model (LLM). Additionally, our proposed system can alert users to potential issues with their commands, and engage them in a corrective dialogue in order to find a viable solution. We conclude with a description of our planned user-evaluation in the simulated environment Overcooked and describe next steps towards developing a flexible and transparent task allocation system.

This works develops a novel hierarchical algorithm for task assignment (TA), coordination and communication of multiple UAVs engaging multiple targets and conceives an ad-hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for target list routing. The TA algorithm is based on a graph-theoretic approach, in which a node locates all the detectable targets, identifies them and computes its distance to each target. The node then produces an attack plan that minimizes the sum of distances of the UAVs in the subtree of a given node to the targets. Simulation experiments show that the combination of flocking and TA algorithms gives the best performance. Clear-cut advantages of the TA algorithm are shown to exist in cases where the hit probability tends to one. An improvement of efficiency, representing the ratio between killed targets and the number of missile launches, is obtained for larger numbers of UAVs only if the engagement times are long enough, utilizing the improved coverage achieved by more UAVs.

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms.

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

With the advent of semi-autonomous robotic assistance systems, their integration into human teams is starting to gain steam as part of the vision of human+machine hybrid activities. Unlike their fully autonomous counterparts, semi-autonomous robotic systems mainly rely on human assistance from time to time via teleoperation when human expertise is needed to accomplish a given task. As these robots will need to request human assistance via teleoperation, mapping these requests to human teleoperators stands as a difficult optimization problem. In this paper, after shedding some light on our envisioned FiWi enhanced network infrastructure and its role in realizing the emerging Tactile Internet, we formulate the problem of joint prioritized scheduling and assignment of delay-constrained teleoperation tasks to human operators with the objective to minimize the average weighted task completion time, maximum tardiness, and average operational expenditure (OPEX) per task. We then propose our context-aware prioritized scheduling and task assignment (CAPSTA) algorithm to achieve suitable trade-offs between the contradicting objectives of the problem. Further, to estimate the end-to-end packet delay of local and non-local teleoperation over FiWi enhanced networks, we develop our analytical framework, which flexibly allows for the coexistence of conventional human-to-human (H2H) and haptic human-to-machine (H2M) traffic.

This work develops distributed hierarchical task assignments and communication algorithms for flocks of unmanned aerial vehicles dispersed in an unknown theater while engaging multiple moving targets. The dynamical changes require distributed algorithms, without relying on a central agent, which may constitute a single point-of-failure and is exposed to communication breaks. Our methodology overcomes the typical deficiencies of a centralized solution by organizing the agents in spanning trees. Whenever possible, the spanning trees merge into a single tree that clusters the maximum number of agents. Using relaxation methods and inputs from its parent and children, every agent attempts to optimize its own solution for task assignment while communicating using an ad-hoc protocol. Simulation experiments show clear-cut advantages of using the proposed task assignment and communication algorithms.

The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the type of heterogeneous targets present in the actual scene. Subsequently, an adaptive crowding distance calculation (ACD-NSGA-II) is proposed based on the relative position of solutions in space, taking into account the spatial distribution of parent solutions and constraints imposed by uncertain targets and terrain. Finally, comparative experiments using digital simulation are conducted under two different target probability scenarios. Moreover, the improved algorithm is further evaluated across 100 cases, and a comparison of the Pareto solution set with other algorithms is conducted to demonstrate the algorithm’s overall adaptability.

Pipelined execution of streaming applications enable processing of high-throughput data under performance constraint. We present an integrated approach to synthesizing pipelined software for dual-core architectures. We target streaming applications modeled as task graphs that are amenable to static analysis. We develop a versatile task assignment algorithm that considers the combined effect of workload imbalance between processors and inter-processor communication. Our technique, which runs in pseudo-linear time, provably maximizes application throughput. Furthermore, we develop an approximation algorithm for task assignment whose complexity is strictly polynomial. It provides the designer with an adjustable knob to controllably trade solution quality with algorithm runtime and memory requirement. Empirical throughput measurements using an FPGA-based dual-core system validate our theoretical results. Our exact algorithm consistently outperforms a recent competitor. Compared to exact task assignment, the approximate method runs about 3 times faster, requires about 20 times less memory, and results in only 1% to 5% throughput loss.

The periodic task set assignment problem in the context of multiple processors has been studied for decades. Different heuristic approaches have been proposed, such as the Best-Fit (BF), the First-Fit (FF), and the Worst-Fit (WF) task assignment algorithms. However, when processors are not dedicated but only periodically available to the task set, whether existing approaches still provide good performance or if there is a better task assignment approach in the new context are research problems which, to our best knowledge, have not been studied by the real-time research community. In this paper, we present the Best-Harmonically-Fit (BHF) task assignment algorithm to assign periodic tasks on multiple periodic resources. By periodic resource we mean that for every fixed time interval, i.e., the period, the resource always provides the same amount of processing capacity to a given task set. Our formal analysis indicates that if a harmonic task set is also harmonic with a resource's period, the resource capacity can be fully utilized by the task set. Based on this analysis, we present the Best-Harmonically-Fit task assignment algorithm. The experimental results show that, on average, the BHF algorithm results in $53.26$ , $42.54$ , and $27.79$ percent higher resource utilization rate than the Best-Fit Decreasing (BFD), the First-Fit Decreasing (FFD), and the Worst-Fit Decreasing (WFD) task assignment algorithms, respectively; but comparing to the optimal resource utilization rate found by exhaustive search, it is about $11.63$ percent lower.

We consider the scenario of wireless sensor networks where a given application has to be deployed and each application task has to be assigned to each node in the best possible way. Approaches where decisions on task execution are taken by a single central node can avoid the exchange of data packets between task execution nodes but cannot adapt to dynamic network conditions, and suffer from computational complexity. To address this issue, in this paper, we propose an adaptive and decentralized task allocation negotiation algorithm (TAN) for cluster network topologies. It is based on noncooperative game theory, where neighboring nodes engage in negotiations to maximize their own utility functions to agree on which of them should execute single application tasks. Performance is evaluated in a city scenario, where the urban streets are equipped with different sensors and the application target is the detection of the fastest way to reach a destination, and in random WSN scenarios. Comparisons are made with three other algorithms: 1) baseline setting with no task assignment to multiple nodes; 2) centralized task assignment lifetime optimization; and 3) a dynamic distributed algorithm, DLMA. The result is that TAN outperforms these algorithms in terms of application completion time and average energy consumption.

SRAMs are aimed at multiprocessing

A framework for task assignment in heterogeneous computing systems is presented in this work. The framework is based on a learning automata model. The proposed model can be used for dynamic task assignment and scheduling and can adapt itself to changes in the hardware or network environment. The important feature of the scheme is that it can work on multiple cost criteria, optimizing each criterion individually. The cost criterion could be a general metric like minimizing the total execution time, or an application specific metric defined by the user. The application task is modeled as a task flow graph (TFG), and the network of machines as a processor graph (PG). The automata model is constructed by associating every task in the TFG with a variable structure learning automaton. The actions of each automaton correspond to the nodes in the PG. The reinforcement scheme of the automaton considered is a linear scheme. Different heuristic techniques that guide the automata model to the optimal solution are presented. These heuristics are evaluated with respect to different cost metrics.

Iterative techniques for restoring colour images are presented. These iterative algorithms have been developed for the restoration of monochromatic images. The colour images are modeled as three spatially related monochromatic images or channels. The interchannel correlation is incorporated into the iterative restoration algorithms in an indirect and in a direct way. Experimental results with simulated and real photographically blurred images are presented. Based on experiments the authors conclude that the incorporation of the interchannel correlation into the algorithms does not necessarily improve the quality of the restored images over the algorithms that ignore the interchannel correlation, but they result in considerable computation savings.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper proposed an unmanned air vehicle (UAV) swarm task and resource dynamic assignment algorithm based on task sequence mechanism. By establishing a task sequence, each UAV strictly separates the necessary task time and synchronization waiting time. For the found new targets, each UAV quickly determines its available time period. According to the available time and task resources, an auction algorithm and consensus algorithm are used to decompose the task assignment into initial distributed assignment phase and swarm consensus phase to develop real-time conflict-free task solutions for UAV swarm. The simulation experiment verified that the algorithm could assign different tasks to UAVs in a real-time distributed manner with the limited resources of each UAV.

We present distributed algorithms for multirobot task assignment where the tasks have to be completed within given deadlines. Each robot has a limited battery life and thus there is an upper limit on the amount of time that it has to perform tasks. Performing each task requires certain amount of time (called the task duration) and each robot can have different payoffs for the tasks. Our problem is to assign the tasks to the robots such that the total payoff is maximized while respecting the task deadline constraints and the robot's battery life constraints. Our problem is NP-hard since a special case of our problem is the classical generalized assignment problem (which is NP-hard). There are no known algorithms (distributed or centralized) for this problem with provably good guarantees of performance. We present a distributed algorithm for solving this problem and prove that our algorithm has an approximation ratio of 2. For the special case of constant task duration we present a distributed algorithm that is provably almost optimal. Our distributed algorithms are polynomial in the number of robots and the number of tasks. We also present simulation results to depict the performance of our algorithms. Note to Practitioners-In this paper, we present provably good multirobot task assignment algorithms, while considering practical constraints like task deadlines and limited battery life of robots. Such constraints are relevant in many applications including parts movement by robots in manufacturing, delivery of goods by unmanned vehicles, and search and rescue operations. Our solution is applicable to a group of heterogeneous robots with different suitability (i.e., payoffs) for different tasks. Our distributed approach is independent of the underlying robot communication network topology, and thus can be applied to a wide range of robot network deployments. Finally, our approach is easy to implement, has low communication requirements, and it is scalable, since its running time is linear in the number of robots and tasks.