Task Assignment Policy Research Articles

Optimal tasks assignment policy in multi-task multi-attempt missions

Motivated by real-world applications like unmanned reconnaissance aerial vehicles, this paper considers a multi-task multi-attempt mission system, where each task may be attempted multiple times and each attempt may be performed simultaneously by multiple components to enhance the task completion probability. Such an active redundancy, on the other hand, incurs high cost and high risk associated with the failures of the components. To balance the reward and the risk, this paper formulates and solves a new optimization problem, which determines the number of components performing each uncompleted task in each attempt, referred to as the task assignment policy (TAP), minimizing the expected mission cost (EMC). A recursive algorithm is proposed to evaluate the EMC (aggregating the expected operational cost, cost of lost components, and cost associated with uncompleted tasks) for the considered multi-task multi-attempt system under a given TAP. Based on the suggested EMC evaluation algorithm, the genetic algorithm is implemented to solve the optimal TAP problem. A detailed case study of an unmanned reconnaissance aerial vehicle system performing five independent surveillance tasks is conducted to examine the impacts of several model parameters on the EMC, task successful completion probabilities, and the optimal TAP solutions.

Dynamic operations and maintenance of an unmanned aerial vehicle swarm for continuous emergency communication

Unmanned aerial vehicle (UAV)-aided continuous emergency communications have recently emerged as a key solution to provide data transmission for disaster areas, thanks to their flexible deployment and high mobility. In practice, due to the limited onboard energy and state deterioration, UAVs need energy supplement and maintenance. However, existing researches mainly focus on UAV deployment and rarely study policies related to their operations and maintenance. To ensure the continuous and reliable execution of communication tasks, a dynamic operations and maintenance policy is proposed to assign tasks and determine maintenance activities for UAVs. First, a dynamic operations and maintenance policy composed of a task assignment policy and a maintenance policy is proposed. Next, the dynamic operations and maintenance joint optimization problem is formulated as a Markov decision process (MDP) to optimize the performance of the UAV swarm, including coverage, fairness, operations and maintenance cost. Then, a deep reinforcement learning approach is tailored to optimize the proposed MDP, where the repeated states are eliminated by state preprocessing, and an action mask method is utilized to satisfy operational constraints. Finally, the proposed approach is tested by its application in the operations and maintenance of a UAV swarm for continuous emergency communication.

Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice

The power distribution network business gradually extends from the grid domain to the social service domain, and the new business keeps expanding. The edge device uses microservice architecture and container technology to realize the processing of different services by one physical device. Although the power distribution network IoT with cloud-edge architecture has good scalability, scenarios with insufficient resources for edge devices may occur. In order to support the scheduling and collaborative processing of tasks under the resource-constrained scenario from the edge device, this paper proposes a cloud-edge collaborative online scheduling method for the distribution of station area tasks under the microservice architecture. The article models the characteristics of power tasks and their constraints in the cloud-edge containerized scenario, designs the priority policy and task assignment policy based on the cloud-edge scheduling mechanism of containerized power tasks, and schedules the tasks in real time by an improved online algorithm. Simulation results show that the algorithm proposed in this paper has high task execution efficiency, can improve the completion rate of important tasks with limited resources of edge devices, and improves system security through resource replacement.

Task Assignment for UAV Swarm Saturation Attack: A Deep Reinforcement Learning Approach

Task assignment is a challenging problem in multiple unmanned aerial vehicle (UAV) missions. In this paper, we focus on the task assignment problem for a UAV swarm saturation attack, in which a deep reinforcement learning (DRL) framework is developed. Specifically, we first construct a mathematical model to formulate the task assignment problem for a UAV swarm saturation attack and consider it as a Markov Decision Process (MDP). We then design a policy neural network using the attention mechanism. We also propose a training algorithm based on the policy gradient method so that our agent can learn an effective task assignment policy. The experimental results have shown that our DRL method can generate high-quality solutions for different problem scales, which meets the requirements of real-time and flexibility in the actual situation.

Novel task assignment policies using enhanced hyper-heuristic approach in cloud

Cloud computing plays a vital role in all fields of today's business. The processor sharing server farm is one of the most used server farms in the cloud environment. The key challenge for the mentioned server farms is to provide an optimal scheduling policy to process the computational jobs in the cloud. Many scheduling policies were introduced and deployed by the existing approaches to build an optimal cloud environment. The existing approaches of the heuristic algorithms such as meta-heuristic and hyper-heuristic approaches were the most frequently used scheduling algorithms for the past years. These approaches work well only in the limited types of tasks and resources in a processor sharing server farms in the cloud environment. In the proposed system, novel task assignment policies have been proposed by enhancing the hyper-heuristic approach for the type low task and high resource in the cloud environment. The results of the proposed approach are compared with the existing approaches and the performance evaluation of the proposed approach is also done. As a result, the proposed enhanced hyper-heuristic approach performs well for processor sharing server farms in the cloud environment.

Novel task assignment policies using enhanced hyper-heuristic approach in cloud

Novel task assignment policies using enhanced hyper-heuristic approach in cloud

An SMDP-based approach to thermal-aware task scheduling in NoC-based MPSoC platforms

In this paper, we consider the operation of a thermal-aware task scheduler, dispatching tasks from an arrival queue as well as setting the voltage and frequency of the processing cores to optimize the mean temperature margin of the entire chip (i.e., cores as well as the NoC routers). We model the decision process of the task scheduler as a semi-Markov decision problem (SMDP) to account for the most common uncertainties prevalent in MPSoC systems (including: the stochastic nature of the workload inter-arrival times, time-varying workload characteristics, the uncertain chip thermal profile as well as random inter-task communications). SMDP is among the fairly general variants of continuous-time optimization frameworks from the stochastic control theory and is a much more efficient choice compared to discrete-time formalisms (which would lead to an increase in task waiting times and degraded system performance). To solve the formulated SMDP, we propose two reinforcement learning (RL) algorithms that are capable of computing the optimal task assignment policy without requiring the statistical knowledge of the stochastic dynamics underlying the system states. The proposed algorithms also rely on function approximation techniques to handle the infinite length of the task queue as well as the continuous nature of temperature readings. Compared to previous work, the simulations demonstrate nearly 6 Kelvin reduction in system average peak temperature and 66 milliseconds decrease in mean task service time.

SERVER COORDINATION IN QUEUEING SYSTEMS: WHEN AND HOW?

Standard server assignment policies for multi-server queueing stations include the noncollaborative policy, where the servers work in parallel on different jobs; and the fully collaborative policy, where the servers work together on the same job. However, if each job can be decomposed into subtasks with no precedence relationships, then we consider a form of server coordination named task assignment where the servers work in parallel on different subtasks of the same job. We identify the task assignment policy that maximizes the long-run average throughput of a queueing station with finite internal buffers when blocked servers can be idled or reassigned to either replace or collaborate with other servers on unblocked subtasks. We then compare the server coordination policies and show that the task assignment is best when the servers are highly specialized; otherwise, the fully collaborative or noncollaborative policies are preferable depending on whether the synergy level among the servers is high or not. We also provide numerical results that quantify our previous comparison. Finally, we address buffer allocation in longer lines where there are precedence relationships between some of the tasks, and present numerical results that suggest our comparisons for one queueing station generalize to longer lines.

Adaptive task scheduling in IoT using reinforcement learning

PurposeThe intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.Design/methodology/approachTo realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.FindingsExperimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.Originality/valueThe proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

A Socially-Aware Hybrid Computation Offloading Framework for Multi-Access Edge Computing

Computation offloading manages resource-intensive and mobile collaborative applications (MCA) on mobile devices where much processing is replicated with multiple users in the same environment. In this article, we propose a novel hybrid multicast-based task execution framework for multi-access edge computing (MEC), where a crowd of mobile devices at the network edge leverage network-assisted device-to-device (D2D) collaboration for wireless distributed computing (MDC) and outcome sharing. The framework is socially aware in order to build effective D2D links. A key objective of this framework is to achieve an energy-efficient task assignment policy for mobile users. Specifically, we first introduce the socially aware hybrid computation offloading (SAHCO) system model, which combines of MEC offloading and D2D offloading in detail. Then, we formulate the energy-efficient task assignment problem by taking into account the necessary constraints. We next propose a Monte Carlo Tree Search based algorithm, named, TA-MCTS for the task assignment problem. Simulation results show that compared to four alternative benchmark solutions in literature, our proposal can reduce energy consumption up to 45.37 percent.

Performance balancing size-interval routing policies

We study a parallel-queue system with Poisson arrivals, in which a dispatcher sends the incoming traffic to K queues using Size Interval Task Assignment (SITA) policy that aims to equalize the performance of all queues. We study existence and uniqueness of the allocation thresholds for a large set of performance functions of the queues. We also provide a family of performance functions of the queues such that the performance of the system is characterized. For a particular case of the latter family of functions, we show that the performance of the SITA policy we study coincides with the performance of the SITA policy in which the load is balanced. We investigate the optimality of the SITA policy under study and, according to our numerical experiments, for FCFS queues and Bounded Pareto distributed job sizes, the SITA policy we study is almost optimal, when we consider the mean queue length and as well as when we consider the mean slowdown.

Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud

An Infrastructure-as-a-service (IaaS) cloud is a new paradigm which offers computing and storage services in form of virtual machines to the cloud users. Due to increasing demand and diversity of the applications, most of the servers are become overloaded or imbalanced and affects the performance of the cloud system. Most of the existing strategies have developed a series of algorithms to pick out the optimal target server to achieve the immediate load balancing. However, it is proved in the literature that immediate load balancing does not meet the high execution efficiency and resource utilization of the servers. In this paper, we propose a new load balancing mechanism for a long-term process referred as LB-RC (load balancing resource clustering). The meta-heuristic Bat-algorithm is applied to obtain optimal resource clustering and their cluster centers for faster convergence. We also propose a new dynamic task assignment policy to achieve the minimum makespan and execution cost within the given constraints. The proposed algorithm is tested and compared with the existing algorithms over various synthetic datasets and performance matrices to prove its superiority.

An online approach for joint task assignment and worker evaluation in crowd-sourcing

The paper tackles the problem of finding the correct solution to a set of multiple choice questions or labeling tasks, by adaptively assigning them to workers in a crowdsourcing system. When we do not initially know anything (besides common a priori statistics) about the workers and the questions involved, such problem becomes quite challenging and requires to jointly learn workers’ abilities and questions’ difficulties, while adaptively assigning questions to the most appropriate workers so as to maximize our chances to find which are the correct answers. To address such problem, we first cast it into a suitably constructed Bayesian framework which permits us to obtain an analytically tractable (closed form) single-question inference step, and then we address the more general framework via the Expectation Propagation algorithm, an approximated message-passing iterative technique. We then exploit the (time-varying) information gathered by the inference framework as adaptive weights for a maximum weight matching task assignment policy, proposing a computationally efficient algorithm which maximizes the entropy reduction for the questions assigned at each step. Experimental results both on synthetic and real-world data shows that the proposed algorithm can significantly improve accuracy in predicting the correct solution to multiple choice questions.

Semi-online task assignment policies for workload consolidation in cloud computing systems

Satisfying on-demand access to cloud computing infrastructures under quality-of-service constraints while minimising the wastage of resources is an important challenge in data centre resource management. In this paper we tackle this challenge in a semi-online workload management system allocating tasks with uncertain duration to physical servers. Our semi-online framework, based on a bin packing approach, allows us to gather information on incoming tasks during a short time window before deciding on their assignments. Our contributions are as follows: (i) we propose a formal framework capturing the semi-online consolidation problem; (ii) we propose a new dynamic and real-time allocation algorithm based on the incremental merging of bins; and (iii) an adaptation of standard bin packing heuristics with a local search algorithm for the semi-online context considered here. We provide a systematic study of the impact of varying time-period size and varying the degrees of uncertainty on the duration of incoming tasks. The policies are compared in terms of solution quality and solving time on a data-set extracted from a real-world cluster trace.Our results show that, around periods of high demand, our best policy saves up to 40% of the resources compared to the other polices, and is robust to uncertainty in the task durations. Finally, we show that small increases in the allowable time window allows a significant improvement, but that larger time windows do not necessarily improve resource usage for real world datasets.

Performance Degradation in Parallel-Server Systems

We consider a parallel-server system with homogeneous servers where incoming tasks, arriving at rate $\lambda $ , are dispatched by $n$ dispatchers, each of them balancing a fraction $1/n$ of the load to $K/n$ servers. Servers are first-come-first-served (FCFS) queues and dispatchers implement size interval task assignment policy with equal load (SITA-E), a size-based policy such that the servers are equally loaded. We compare the performance of a system with $n>1$ dispatchers and a single dispatcher. We show that the performance of a system with $n$ dispatchers, $K$ servers, and arrival rate $\lambda $ coincides with that of a system with one dispatcher, $K/n$ servers, and arrival rate $\lambda /n$ . We define the degradation factor as the ratio between the performance of a system with $K$ servers and arrival rate $\lambda $ and the performance of a system with $K/n$ servers and arrival rate $\lambda /n$ . We establish a partial monotonicity on $n$ for the degradation factor and, therefore, the degradation factor is lower bounded by one. We then investigate the upper bound of the degradation factor for particular distributions. We consider two continuous service time distributions: uniform and bounded Pareto and a discrete distribution with two values, which is the distribution that maximizes the variance for a given mean. We show that the performance degradation is small for uniformly distributed job sizes but that for Bounded Pareto and two points distributions it can be unbounded. We have investigated the degradation using the distribution obtained from real traces.

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

In this article we propose a novel D2D Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage network-assisted D2D collaboration for computation and communication resource sharing. A key objective of this framework is to achieve energy-efficient collaborative task executions at the network edge for mobile users. Specifically, we first introduce the D2D Crowd system model in detail, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph-matching-based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows superior performance of more than 50 percent energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into account a variety of application factors.

A workload-dependent task assignment policy for crowdsourcing

Crowdsourcing marketplaces have emerged as an effective tool for high-speed, low-cost labeling of massive data sets. Since the labeling accuracy can greatly vary from worker to worker, we are faced with the problem of assigning labeling tasks to workers so as to maximize the accuracy associated with their answers. In this work, we study the problem of assigning workers to tasks under the assumption that workers’ reliability could change depending on their workload, as a result of, e.g., fatigue and learning. We offer empirical evidence of the existence of a workload-dependent accuracy variation among workers, and propose solution procedures for our Crowdsourced Labeling Task Assignment Problem, which we validate on both synthetic and real data sets.

Clustering-Based Task Scheduling in a Large Number of Heterogeneous Processors

Parallelization paradigms for effective execution in a Directed Acyclic Graph (DAG) application have been widely studied in the area of task scheduling. Schedule length can be varied depending on task assignment policies, scheduling policies, and heterogeneity in terms of each processor and each communication bandwidth in a heterogeneous system. One disadvantage of existing task scheduling algorithms is that the schedule length cannot be reduced for a data intensive application. In this paper, we propose a clustering-based task scheduling algorithm called Clustering for Minimizing the Worst Schedule Length (CMWSL) to minimize the schedule length in a large number of heterogeneous processors. First, the proposed method derives the lower bound of the total execution time for each processor by taking both the system and application characteristics into account. As a result, the number of processors used for actual execution is regulated to minimize the Worst Schedule Length (WSL). Then, the actual task assignment and task clustering are performed to minimize the schedule length until the total execution time in a task cluster exceeds the lower bound. Experimental results indicate that CMWSL outperforms both existing list-based and clustering-based task scheduling algorithms in terms of the schedule length and efficiency, especially in data-intensive applications.

A Better Model for Task Assignment in Server Farms

An age-old problem in the design of server farms is the choice of the task assignment policy. This is the algorithm that determines how to assign incoming jobs to servers. Popular policies include Round-Robin assignment, Join-the-Shortest-Queue, Join-Queue-with-Least-Work, and so on. While much research has studied assignment policies, little has taken into account server-side variability -- the fact that the server we choose might be temporarily and unpredictably slow. We show that when server-side variability dominates runtime, replication of jobs can be very beneficial. We introduce the Replication-d algorithm that replicates each arrival to d servers chosen at random, where the job is considered "done" as soon as the first replica completes. We provide an exact closed-form analysis of Replication-d. We next introduce a much more general model, one which takes both the inherent job size distribution and the server-side variability into account. This is a departure from traditional queueing models which only allow for one "size" distribution. We propose and analyze a new task assignment policy, Replicate-Idle-Queue (RIQ), which is designed to perform well given these dual sources of variability.

Performance evaluation of RUNT algorithm

This paper evaluates the performance of Reduction to Uniprocessor Transformation (RUNT) with Voltage and Frequency Scaling, called Static RUNT (S-RUNT) and Dynamic RUNT (D-RUNT), respectively. Simulation results show that how to assign tasks to servers in RUNT influences energy consumption and the worst-fit heuristic is the best in many cases. In addition, the idle task assignment policy saves more energy consumption in D-RUNT and D-RUNT outperforms S-RUNT if the actual case execution time of each task is shorter than its worst case execution time.