... We will now demonstrate how to use reinforcement learning to schedule UAV cluster tasks. Redistribution of tasks from heavily 8, we consider that a cluster … Sub-module description of QL scheduler and load balancer: Where Tw is the task wait time and Tx is the task execution time. The experimental results show that the scheduling strategy is better than the scheduling strategy based on the standard policy gradient algorithm, and accelerate the convergence speed. In FAC, iterates are scheduled in batches, where the size of a batch is a fixed ratio of the unscheduled iterates and the batch is divided into P chunks (Hummel et al., 1993). Q-Learning was selected due to the simplicity of its formulation, the ease with which parameters Jian covers data processing, building an unbiased simulator based on collected campaign data, and creating 10-fold training and testing datasets. and Fig. One of my favorite algorithms that I learned while taking a reinforcement learning course was q-learning. Q-learning is a very popular and widely used off-policy TD control algorithm. Present proposed technique also handles load distribution overhead which is the major cause of performance degradation in traditional dynamic schedulers. An initially intuitive idea of creating values upon which to base actions is to create a table which sums up the rewards of taking action a in state s over multiple game plays. Later Parent et al. from 12-32. Qt+1(s,a) denotes the state-action value of the next possible state at time t+1, r the immediate reinforcement and α is the learning rate of the agent. of processors for 5000 Episodes, Cost loaded processors to lightly loaded ones in dynamic load balancing needs There are some other challenges and Issues which Under more difficult conditions, its performance is significantly and disproportionately reduced. In this quick post I’ll discuss q-learning and provide the basic background to understanding the algorithm. Q-Table Generator generates Q-Table and Reward-Table and places reward Thus, a Q-learning algorithm for task scheduling based on Improved Support Vector Machine (ISVM) in WSNs, called ISVM-Q, is proposed to optimize the application performance and energy consumption of networks. be seen from these graphs that the proposed approach performs better than the The essential idea of our approach uses the popular deep Q -learning (DQL) method in task scheduling, where fundamental model learning is primarily inspired by DQL. As each agent would learn from the environments response, taking into consideration five vectors for reward calculation, the QL-Load Balancer can provide enhanced adaptive performance. The Performance Monitor monitors the resource and task information and signals for load imbalance and task completion to the Q-Learning Load Balancer in the form of RL (Reinforcement learning) Signal (described after sub-module description). The trial and error learning feature and the concept of reward makes the reinforcement learning distinct from other learning techniques. are considered by this research. The Task Manager Ò\$d«,:cb"èÙz-ÔT±ñú",A¥S}á A weighted Q-learning algorithm based on clustering and dynamic search was … Finally, the Log Generator generates log of successfully executed tasks. to the problem of scheduling and Load Balancing in the grid like environment Scheduling is all about keeping processors busy by efficiently distributing the workload. Computer systems can optimize their own performance by learning from experience without human assistance. Large degrees of heterogeneity add additional complexity to the scheduling problem. parameters using, Detailed Based on developments in WorkflowSim, experiments are conducted that comparatively consider the variance of makespan and load balance in task scheduling. The limited energy resources of WSN nodes have determined researchers to focus their attention at energy efficient algorithms which address issues of optimum communication, … Zomaya et al. Performance Monitor is responsible for backup of system failure and signals for load imbalance. However, Q-tables are difficult to solve for high-dimensional continuous state or action spaces. It is also responsible for backup in case of system failure. Majercik and Littman (1997) evaluated, how the load balancing problem can be formulated as a Markov Decision Process (MDP) and described some preliminary attempts to solve this MDP using guided on-line Q-learning and a linear value function approximator tested over small range of value runs. time and size of input task and forwards this information to State Action (2004) improved the application as a framework of multi-agent reinforcement learning for solving communication overhead. When the processing power varies from one site to another, a distributed system seems to be heterogeneous in nature (Karatza and Hilzer, 2002). Then, a task scheduling policy is established with … This threshold value indicates overloading and under utilization of resources. YC Fonseca-Reyna, Q-Learning Algorithm Performance For M-Machine, N-Jobs Flow Shop Scheduling Problems To Minimize Makespan Pair Selector. To solve these core issues like learning, planning and decision making Reinforcement Learning (RL) is the best approach and active area of AI. 2. given below: Repeat for each step of episode (Learning), Take action a, observe reward r, move next state s', QL History Generator stores state action pairs (s, Task Mapping Engine, Co-allocation is done by the Task Mapping Engine; It analyzes the submission based on actions taken and reward received (Kaelbling et al., 1996) (Sutton We can see from tables that execution time The closer γ is to 1 the greater the weight is given to future reinforcements. Co-Scheduling is done by the Task Mapping Engine on the basis of cumulative Q-value of agents. show the cost comparison for 500, 5000 and 10000 episodes respectively. Heterogeneous systems have been shown to produce higher performance for lower cost than a single large machine. Equation 9 defines, how many numbers of subtasks will be given to each resource. node heterogeneity and workload. The action of Q-learning with the highest expected Q value is selected in each state to update Q value, in which more accumulated … Allocating a large number of independent tasks to a heterogeneous computing is an estimation of how good is it to take the action at the state. increasing number of processors. Starting with the first category, Table 1-2 platform is still a hindrance. The optimality and scalability of QL-Scheduling was analyzed by testing it against adaptive and non-adaptive Scheduling for a varying number of tasks and processors. The load-based and throughput-based RBSs were not effective in performing dynamic scheduling. highlight the achievement of the goal of this research work, that of attaining 1. Average distribution of tasks for Resource R. Task Analyzer shows the distribution and run time performance of tasks Before scheduling the tasks, the QL Scheduler and Load balancer dynamically gets a list of available resources from the global directory entity. Multi-agent technique provides the benefit of scalability and robustness and learning leads the system to learn based on its past experience and generate better results over time using limited information. comparison of Q Scheduling vs. Other Scheduling with increasing number Distributed heterogeneous systems emerged as a viable alternative to dedicated parallel computing (Keane, 2004). Abstract: In this paper we describe a Markov Decision Process (MDP) based technique called Q-Learning which has been adapted for scheduling of tasks for wireless sensor networks (WSNs) with mobile nodes. First, the Q‐learning framework, including state set, action set, and rewards function is defined in a global view so as to forms the basis of the QFTS‐GV scheme. One expects to start with a high learning rate, which allows fast changes and lowers the learning rate as time progresses. (2005) proposed algorithm. It is adaptive version of Reinforcement Learning and does In addition to being readily scalable, DEEPCAS is completely model-free. The algorithm considers the packet priority in combination with the total number of hops and the initial deadline. Banicescu et al. algorithms. Instead, it redistributes the tasks from heavily loaded processors to lightly loaded ones based on the information collected at run-time. 1 A Double Deep Q-learning Model for Energy-efﬁcient Edge Scheduling Qingchen Zhang, Member, IEEE, Man Lin, Senior Member, IEEE, Laurence T. Yang, Senior Member, IEEE, Zhikui Chen, Samee U. Khan, Senior Member, IEEE, and Peng Li Abstract—Reducing energy consumption is a vital and challenging problem for the edge computing devices since they are always energy-limited. Complex nature of the application causes unrealistic assumptions about [2] pro-posed an intelligent agent-based scheduling system. Problem description: The aim of this research is to solve scheduling Load balancing attempts to ensure that the workload on each host is within a balance criterion of the workload present on every other host in the system. Both simulation and real-life experiments are conducted to verify the … Employs a Reinforcement Learning algorithm to find an optimal scheduling policy The second section consists of the reinforcement learning model, which outputs a scheduling policy for a given job set. The experiments presented here have used the Q-Learning algorithm first proposed by Watkins [38]. better optimal scheduling solutions when compared with other adaptive and non-adaptive A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things Abstract: Cognitive networks (CNs) are one of the key enablers for the Internet of Things (IoT), where CNs will play an important role in the future Internet in several application scenarios, such as healthcare, agriculture, environment monitoring, and smart metering. Verbeeck et al. and communication of resources. Thus, a Q‐learning based flexible task scheduling with global view (QFTS‐GV) scheme is proposed to improve task scheduling success rate, reduce delay, and extend lifetime for the IoT. We then extend our system model to a more intelligent microgrid system by adopting multi-agent learning structure where each customer can decide its energy consumption scheduling based on the observed retail price aiming at min- In this paper, we propose a task scheduling algorithm based on Q-Learning for WSNs called Q-Learning Scheduling on Time Division Multiple Access (QS-TDMA). Therefore, a dynamic scheduling system model based on multi-agent technology, including machine, buffer, state, and job agents, was built. that contribute to positive rewards by increasing the associated Q-values. To tackle … “Flow-shop Scheduling Based on Reinforcement Learning Algorithm.” Journal of Production Systems and Information Engineering, A Publication of the University of Miskolc 1: 83–90. By using Q-Learning, the multipath TCP node in the vehicular heterogeneous network can continuously learn interactively with the surrounding environment, and dynamically adjust the number of paths used for … Q-value 5-7 Guided Self Scheduling (GSS) (Polychronopoulos and Kuck, 1987) and factoring (FAC) (Hummel et al., 1993) are examples of non-adaptive scheduling algorithms. To repeatedly adjust in response to a dynamic environment, they will need the adaptability that only machine learning can offer. current input and gets its action set A, Reward Calculator calculates reward by considering five vectors as reward β is a constant for determining number of sub jobs calculated by averaging The essential idea of our approach uses the popular deep Q-learning (DQL) method in task scheduling, where fundamental model learning is primarily inspired by DQL. Resource Analyzer displays the load statistics. time for 10000 episodes vs. 6000 episodes with 30 input task and increasing Generally, in such systems no processor should remain idle while others are overloaded. The main contribution of this paper is to develop a deep reinforcement learning-based \emph{control-aware} scheduling (\textsc{DeepCAS}) algorithm to tackle these issues. This threshold value will be calculated from its historical performance on the basis of average load. To improve the performance of such grid like systems, the scheduling and load balancing must be designed in a way to keep processors busy by efficiently distributing the workload, usually in terms of response time, resource availability and maximum throughput of application. number of episodes and processors. comparison of QL Scheduling vs. Other Scheduling with increasing number Tasks that are submitted from Scheduling with Reinforcement Learning ... we adopt the Q-learning algorithm with proposing two im-provements: alternative state deﬁnition and virtual experience. of processors for 10000 Episodes, Cost Ultimately, the outcome indicates an appreciable and substantial improvement in performance on an application built using this approach. The experiments to verify and validate the proposed algorithm are divided into two categories. For second category of experiments Fig. performance improvements by increasing Learning. For this reason, scheduling is usually handled by heuristic methods which provide reasonable solutions for restricted instances of the problem (Yeckle and Rivera, 2003). Q-learning is one of the easiest Reinforcement Learning algorithms. For a given environment, everything is broken down into "states" and "actions." Again this graph shows the better performance of QL scheduler with other scheduling techniques. Action a must be chosen which maximizes, Q(s,a). to get maximum throughput. γ value is zero This could keep track of which moves are the most advantageous. This area of machine learning learns the behavior of dynamic environment through trial and error. This paper discusses how Reinforcement learning in general and Q-learning in particular can be applied to dynamic load balancing and scheduling in distributed heterogeneous system. The results showed considerable improvements upon a static load balancer. When in each state the best-rewarded action is chosen according to the stored Q-values, this is known as greedy-method. not need model of its environment. status information at the global scale. A further challenge to load balancing lies in the lack of accurate resource Most research on scheduling has dealt with the problem when the tasks, inter-processor communication costs and precedence relations are fully known. Cost is calculated by multiplying number of processors P with parallel execution time Tp. selected resources. Reinforcement learning signals: Modules description: The Resource Collector directly communicates to The Log Generator saves the collected information of each grid node and executed tasks information. d, e are constants determining the weight of each contribution from history The aspiration of this research was fundamentally a challenge to machine learning. The same algorithm can be used across a variety of environments. (2000) proposed Adaptive Weighted Factoring (AWF) algorithm which was applicable to time stepping applications, it uses equal processor weights in the initial computation and adapts the weight after every time step. In Q-Learning, the states and the possible actions in a given state are discrete and finite in number. and dynamically distribute the workload over all available resources in order quick information collection at run-time in order to use it for rectification Algorithm is Q-learning. on grid resources. To optimize the overall control performance, we propose the following sequential design of Aim: To optimize average job-slowdown or job completion time. Dynamic load balancing is NP complete. Execution For Q-learning, there is a significant drop The goal of this study is to apply Multi-Agent Reinforcement Learning technique non-adaptive techniques such as GSS and FAC and even against the advanced adaptive (2004) proposed, Minimalist decentralized algorithm for resource allocation in a simplified Grid-like environment. After each step, that comprised of 100 iterations, the best solution of each reinforcement learning method is selected and the job is run again, the learning agents switching from … Q-learning is a type of reinforcement learning that can establish a dynamic scheduling policy according to the state of each queue without any prior knowledge on the network status. They employed the Q-III algorithm to In this regard, the use of Reinforcement Learning is more precise and potentially computationally cheaper than other approaches. Action a must be chosen which maximizes, Q(s,a). Out put will be displayed after successful execution. After receiving RL signal Reward Calculator calculates reward and update Q-value in Q-Table. Distributed computing is a viable and cost-effective alternative to the traditional model of computing. list of available resources from Resource Collector. The In this scheme, a deep‐Q learning‐based heterogeneous earliest‐finish‐time (DQ‐HEFT) algorithm is developed, which closely integrates the deep learning mechanism with the task scheduling heuristic HEFT. of tasks for 500 Episodes and 8 processors. A distributed system is made up of a set of sites cooperating with each other for resource sharing. This algorithm was receiver initiated and works locally on the slaves. 8 highlight the achievement of attaining maximum throughput using Q-Learning while increasing number of tasks. On finding load imbalance, Performance Monitor signals QL Load Balancer to start its working and remapping the subtasks on under utilized resources. Results of Fig. https://scialert.net/abstract/?doi=jas.2007.1504.1510. In short we can say that, Load balancing and Scheduling are crucial factors for grid like distributed heterogeneous systems (Radulescu and van Gemund, 2000). An agent-based state is defined, based on which a distributed optimization algorithm can be applied. 1. The states are observations and samplings that we pull from the environment, and the actions are the … Given the dynamic and uncertain production environment of job shops, a scheduling strategy with adaptive features must be developed to fit variational production factors. over all submitted sub jobs from history. number of processors, Execution Q t+1 (s,a) denotes the state-action value of the next possible state at time t+1, r the immediate reinforcement and α is the learning rate of the agent. There was no information exchange between the agents in exploration phase. We propose a Q-learning algorithm to solve the problem of scheduling shared EVs to maximize the global daily income. In deep Q-learning, we use a neural network to approximate the Q-value function. Figure 8 shows the cost comparison with increasing number of tasks for 8 processors and 500 episodes. View of Q-Learning Scheduler and Load Balancer. Reinforcement learning: Reinforcement Learning (RL) is an active area of research in AI because of its widespread applicability in both accessible and inaccessible environments. I guess I introduced some very different terminologies here. of processors for 500 Episodes, Cost This technique neglected the need for co-allocation of different resources. comparison of QL Scheduling vs. Other Scheduling with increasing number These algorithms are touted as the future of Machine Learning as these eliminate the cost of collecting and cleaning the data. In RL, an agent learns by interacting with its environment and tries to maximize its long term return by performing actions and receiving rewards as shown in Fig. The Q-Value Calculator follows the Q-Learning algorithm to calculate Q-value In the past, Q‐learning based task scheduling scheme which only focuses on the node angle led to poor performance of the whole network. handles user requests for task execution and communication with the grid. However, Tp does not significantly change as processors are further increased The multidimensional computational matrices and povray is used as a benchmark to observe the optimized performance of our system. The results obtained from these comparisons outside the boundary will be buffered by the Task Collector. by handling co-allocation. time for 8000 episodes vs. 4000 episodes with 30 input task and increasing ment of a deep reinforcement learning-based control-aware scheduling algorithm, DEEPCAS. This allows the system Experimental results suggest that Q-learning improves the quality of load balancing in large scale heterogeneous systems. There was less emphasize on exploration phase and heterogeneity was not considered. The model of the reinforcement learning problem is based on the theory of Markov Decision Processes (MDP) (Stone and Veloso, 1997). The comparison between Q-learning & deep Q-learning is wonderfully illustrated below: (2004) work for each node and update these Q-Values in Q-Table. The state is given as the input and the Q-value of all possible actions is generated as the output. Peter, S. 2003. (Gyoung Hwan Kim, 1998) proposed genetic reinforcement learning (GRL) which regards scheduling problem as a RL problems to solve it. We consider a grid like environment consisting of multi-nodes. Q-learning: The Q-learning is a recent form of Reinforcement Learning. The results from Fig. 3. 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