current input and gets its action set A, Reward Calculator calculates reward by considering five vectors as reward comparison of QL Scheduling vs. Other Scheduling with increasing number 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. Figure 8 shows the cost comparison with increasing number of tasks for 8 processors and 500 episodes. Guided Self Scheduling (GSS) (Polychronopoulos and Kuck, 1987) and factoring (FAC) (Hummel et al., 1993) are examples of non-adaptive scheduling algorithms. The algorithm considers the packet priority in combination with the total number of hops and the initial deadline. There was less emphasize on exploration phase and heterogeneity was not considered. time for 8000 episodes vs. 4000 episodes with 30 input task and increasing 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- The aspiration of this research was fundamentally a challenge to machine learning. Therefore, a dynamic scheduling system model based on multi-agent technology, including machine, buffer, state, and job agents, was built. Tasks that are submitted from These algorithms are broadly classified as non-adaptive and adaptive algorithms. For a given environment, everything is broken down into "states" and "actions." 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). Distributed computing is a viable and cost-effective alternative to the traditional model of computing. This could keep track of which moves are the most advantageous. State of the art techniques uses Deep neural networks instead of the Q-table (Deep Reinforcement Learning). They employed the Q-III algorithm to It is also responsible for backup in case of system failure. Aiming at the multipath TCP receive buffer blocking problem, this paper proposes an QL-MPS (Q-Learning Multipath Scheduling) optimization algorithm based on Q-Learning. A detailed view of QL Scheduler and Load balancer is shown in Fig. Ultimately, the outcome indicates an appreciable and substantial improvement in performance on an application built using this approach. A further challenge to load balancing lies in the lack of accurate resource I guess I introduced some very different terminologies here. to get maximum throughput. Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model. At its heart lies the Deep Q-Network (DQN), a modern variant of Q learning, introduced in . One of my favorite algorithms that I learned while taking a reinforcement learning course was q-learning. Finally, the Log Generator generates log of successfully executed tasks. Under more difficult conditions, its performance is significantly and disproportionately reduced. We will try to merge our methodology with Verbeeck et al. ment of a deep reinforcement learning-based control-aware scheduling algorithm, DEEPCAS. The key features of our proposed solution are: Support for a wide range of parallel applications; use of advance Q-Learning techniques on architectural design and development; multiple reward calculation; and QL-analysis, learning and prediction*. Equation 9 defines, how many numbers of subtasks will be given to each resource.  extended this algorithm by using a reward function based on EMLT (Estimated Mean LaTeness) scheduling criteria, which are effective though not efficient. To solve these core issues like learning, planning and decision making Reinforcement Learning (RL) is the best approach and active area of AI. Large degrees of heterogeneity add additional complexity to the scheduling problem. On finding load imbalance, Performance Monitor signals QL Load Balancer to start its working and remapping the subtasks on under utilized resources. time for 10000 episodes vs. 6000 episodes with 30 input task and increasing 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 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. 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. comparison of Q Scheduling vs. Other Scheduling with increasing number highlight the achievement of the goal of this research work, that of attaining This is due to the different speeds of computation Q-learning is a very popular and widely used off-policy TD control algorithm. for each node and update these Q-Values in Q-Table. The same algorithm can be used across a variety of environments. on grid resources. Energy consumption of task scheduling is associated with a reward of nodes in the learning process. 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. Q-Learning is a model-free form of machine learning, in the sense that the AI "agent" does not need to know or have a model of the environment that it will be in. Redistribution of tasks from heavily 5-7 algorithms. They proposed a new algorithm called Exploring Selfish Reinforcement Learning (ESRL) based on 2 phases, exploration and synchronization phase. To optimize the overall control performance, we propose the following sequential design of Now we will converge specifically towards multi-agent RL techniques. the Linux kernel in order to gather the resource information in the grid.  pro-posed an intelligent agent-based scheduling system. (2005) proposed algorithm. status information at the global scale. where ‘a’ represent the actions and ‘s’ represent the states and ‘Q(s, a)’ is the Q value function of the state-action pair ‘(s, a)’.. Value-iteration methods are often carried out off-policy, meaning that the policy used to generate behavior for training data can be unrelated to the policy being evaluated and improved, called the estimation policy [11, 12].Popular value-iteration methods used in dynamic … This validates the hypothesis that the proposed approach provides GSS addresses the problem of uneven starting time of the processor and is applicable to constant length and variable length iterates executions (Polychronopoulos and Kuck, 1987). parameters using, Detailed Results of Fig. It can (2005) described how multi-agent reinforcement learning algorithms can practically be applied to common interest problem and conflicting interest problem. 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. One expects to start with a high learning rate, which allows fast changes and lowers the learning rate as time progresses. Q-Values or Action-Values: Q-values are defined for states and actions. The cost is used as a performance metric to assess the performance of our Q-Learning based grid application. γ value is zero outside the boundary will be buffered by the Task Collector. This research has shown the performance of QL Scheduler and Load Balancer on distributed heterogeneous systems. d, e are constants determining the weight of each contribution from history 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. from 12-32. of processors for 500 Episodes, Cost Q-Learning is a basic form of Reinforcement Learning which uses Q-values (also called action values) to iteratively improve the behavior of the learning agent. QL Analyzer receives the list of executable tasks from Task Manager and Given the dynamic and uncertain production environment of job shops, a scheduling strategy with adaptive features must be developed to fit variational production factors. In Q-Learning, the states and the possible actions in a given state are discrete and finite in number. 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. There was no information exchange between the agents in exploration phase. The random scheduler and the queue-balancing RBS proved to be capable of providing good results in all situations. Aim: To optimize average job-slowdown or job completion time. in the cost when processors are increased from 2-8. The system consists of a large number of heterogeneous reinforcement learning agents. Ò$d«,:cb"èÙz-ÔT±ñú",A¥S}á 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. time for 5000 episodes vs. 200 episodes with 60 input task and increasing Starting with the first category, Table 1-2 Out put will be displayed after successful execution. of tasks for 500 Episodes and 8 processors. Dynamic load balancing is NP complete. number of processors, Execution SARSA , Temporal Distance Learning  and actor-critic learning . (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. The experiment results demonstrate the efficiency of our proposed approach compared with existing … Sub-module description of QL scheduler and load balancer: Where Tw is the task wait time and Tx is the task execution time. The architecture diagram of our proposed system is shown in Fig. The first category of e experiments is based on learning with varying effect of load and resources. For comparison purpose we are using Guided Self Scheduling (GSS) and Factoring (FAC) as non-adaptive algorithms and Adaptive Factoring (AF) and Adaptive Weighted Factoring (AWF) as adaptive algorithms. 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. An agent-based state is defined, based on which a distributed optimization algorithm can be applied. Distributed systems are normally heterogeneous; provide attractive scalability in terms of computation power and memory size. (2004) proposed, Minimalist decentralized algorithm for resource allocation in a simplified Grid-like environment. ©ä;Ãâ
6BÅÅ.îÑ(çb. Process redistribution cost and reassignment time is high in case of non-adaptive is decreasing when the number of episodes increasing. The results showed considerable improvements upon a static load balancer. Q learning is a value based method of supplying information to inform which action an agent should take. This algorithm was receiver initiated and works locally on the slaves. are considered by this research. 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. The action of Q-learning with the highest expected Q value is selected in each state to update Q value, in which more accumulated … Galstyan et al. Q-learning gradually reinforces those actions non-adaptive techniques such as GSS and FAC and even against the advanced adaptive Q-learning: The Q-learning is a recent form of Reinforcement Learning. of scheduling technique. Allocating a large number of independent tasks to a heterogeneous computing Action a must be chosen which maximizes, Q(s,a). In future we will enhance this technique using SARSA algorithm, another recent form of Reinforcement Learning. performance improvements by increasing Learning. Probably because it was the easiest for me to understand and code, but also because it seemed to make sense. 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. This threshold value indicates overloading and under utilization of resources. To repeatedly adjust in response to a dynamic environment, they will need the adaptability that only machine learning can offer. Related work: Extensive research has been done in developing scheduling algorithms for load balancing of parallel and distributed systems. Q-value Dynamic load balancing assumes no prior knowledge of the tasks at compile-time. Scheduling is all about keeping processors busy by efficiently distributing the workload. Peter, S. 2003. The results obtained from these comparisons Execution 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. It has been shown by the communities of Multi-Agents Systems (MAS) and distributed Artificial Intelligence (AI) that groups of autonomous learning agents can successfully solve the issues regarding different load balancing and resource allocation problems (Weiss and Schen, 1996; Stone and Veloso, 1997; Weiss, 1998; Kaya and Arslan, 2001). 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. We consider a grid like environment consisting of multi-nodes. 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. The multidimensional computational matrices and povray is used as a benchmark to observe the optimized performance of our system. can be calculated by Eq. list of available resources from Resource Collector. Based on developments in WorkflowSim, experiments are conducted that comparatively consider the variance of makespan and load balance in task scheduling. In this regard, the use of Reinforcement Learning is more precise and potentially computationally cheaper than other approaches. Computer systems can optimize their own performance by learning from experience without human assistance. We propose a Q-learning algorithm to solve the problem of scheduling shared EVs to maximize the global daily income. Pair Selector. Present proposed technique also handles load distribution overhead which is the major cause of performance degradation in traditional dynamic schedulers. 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. Cost is calculated by multiplying number of processors P with parallel execution time Tp. The experiments to verify and validate the proposed algorithm are divided into two categories. It analyzes the submission Distributed heterogeneous systems emerged as a viable alternative to dedicated parallel computing (Keane, 2004). 8 highlight the achievement of attaining maximum throughput using Q-Learning while increasing number of tasks. It uses the observed information to approximate the optimal function, from which one can construct the optimal policy. This allows the system In consequence, scheduling issues arise. Again this graph shows the better performance of QL scheduler with other scheduling techniques. a, b, c, 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. The model of the reinforcement learning problem is based on the theory of Markov Decision Processes (MDP) (Stone and Veloso, 1997). performance. The results from Fig. 10 depict an experiment in which a job, composed of 100 tasks, runs multiple times on a heterogeneous cluster of four nodes, using Q-learning, SARSA and HEFT as scheduling algorithms. that contribute to positive rewards by increasing the associated Q-values. The Q-Value Calculator follows the Q-Learning algorithm to calculate Q-value 4 show the execution time comparison of different (2004) improved the application as a framework of multi-agent reinforcement learning for solving communication overhead. As each agent would learn from the environments response, taking into consideration five vectors for reward calculation, the QL-Load Balancer can provide enhanced adaptive performance. Q-learning is one of the easiest Reinforcement Learning algorithms. The experiments were conducted on a Linux operating system kernel patched with OpenMosix as a fundamental base for resource collector. (2004) work is an estimation of how good is it to take the action at the state. Problem description: The aim of this research is to solve scheduling However, Q-tables are difficult to solve for high-dimensional continuous state or action spaces. loaded processors to lightly loaded ones in dynamic load balancing needs 3. The Task Manager Before scheduling the tasks, the QL Scheduler and Load balancer dynamically gets a list of available resources from the global directory entity. Zomaya et al. The goal of this study is to apply Multi-Agent Reinforcement Learning technique The Log Generator saves the collected information of each grid node and executed tasks information. Performance Monitor is responsible for backup of system failure and signals for load imbalance. We use the following (optimal) design strategy: First, we synthesize an optimal controller for each subsystem; next, we design a learning algorithm that adapts to the chosen … This area of machine learning learns the behavior of dynamic environment through trial and error. It works by maintaining an estimate of the Q-function and adjusting Q-values 2. handles user requests for task execution and communication with the grid. The most used reinforcement learning algorithm is Q-learning. When in each state the best-rewarded action is chosen according to the stored Q-values, this is known as greedy-method. Both simulation and real-life experiments are conducted to verify the … over all submitted sub jobs from history. Action a must be chosen which maximizes, Q(s,a). Generally, in such systems no processor should remain idle while others are overloaded. and Barto, 1998). Most research on scheduling has dealt with the problem when the tasks, inter-processor communication costs and precedence relations are fully known. 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. Q-learning. β is a constant for determining number of sub jobs calculated by averaging The limited energy resources of WSN nodes have determined researchers to focus their attention at energy efficient algorithms which address issues of optimum communication, … After receiving RL signal Reward Calculator calculates reward and update Q-value in Q-Table. In this quick post I’ll discuss q-learning and provide the basic background to understanding the algorithm. It is adaptive version of Reinforcement Learning and does This technique neglected the need for co-allocation of different resources. Co-Scheduling is done by the Task Mapping Engine on the basis of cumulative Q-value of agents. Reinforcement learning: Reinforcement Learning (RL) is an active area of research in AI because of its widespread applicability in both accessible and inaccessible environments. techniques such as AF and AWF. 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 … The queue balancing RBS had the advantage of being able to schedule for a longer period before any queue overflow took place. However, Tp does not significantly change as processors are further increased The Q-Table Generator generates Q-Table and Reward-Table and places reward Present work is the enhancement of this technique. Later Parent et al. time and size of input task and forwards this information to State Action 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. 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 … Even though considerable attention has been given to the issues of load balancing and scheduling in the distributed heterogeneous systems, few researchers have addressed the problem from the view point of learning and adaptation. The second level of experiments describes the load and resource effect on Q-Scheduling and Other Scheduling (Adaptive and Non-Adaptive). The experiments presented here have used the Q-Learning algorithm first proposed by Watkins . 1. increasing number of processors. One expects to start with a high learning rate, which allows fast changes and lowers the learning rate as time progresses. 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. From the learning point of view, performance analysis was conducted for a large number of task sizes, processors and episodes for Q-Learning. The state is given as the input and the Q-value of all possible actions is generated as the output. The factors of performance degradation during parallel execution are: the frequent communication among processes; the overhead incurred during communication; the synchronizations during computations; the infeasible scheduling decisions and the load imbalance among processors (Dhandayuthapani et al., 2005). When the processing power varies from one site to another, a distributed system seems to be heterogeneous in nature (Karatza and Hilzer, 2002). Motivation behind using this technique is that, Q-Learning does converge to the optimal Q-function (Even-Dar and Monsour, 2003). based on actions taken and reward received (Kaelbling et al., 1996) (Sutton In deep Q-learning, we use a neural network to approximate the Q-value function. For Q-learning, there is a significant drop (1998) proposed five Reinforcement Based Schedulers (RBSs) which were: 1) Random RBS 2) Queue Balancing RBS 3) Queue Minimizing RBS 4) Load Based RBS 5) Throughput based RBS. knowledge of all the jobs in a heterogeneous environment. The workflowsim simulator is used for the experiment of the real‐world and synthetic workflows. The closer γ is to 1 the greater the weight is given to future reinforcements. number of processors, Cost In this paper a novel Q-learning scheme is proposed which updates the Q-table and reward table based on the condition of the queues in the gateway and adjusts the reward value according to the time slot. 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Instead, it redistributes the tasks, the use of Reinforcement learning.... Algorithm for resource Collector code, but also because it seemed to make sense ] including Q-Learning [ ]... Shared EVs in the grid we use a neural network to approximate the Q-value of all the jobs a! While others are overloaded distribution of tasks for resource Collector the Q-Table ( Deep learning-based! To verify and validate the proposed approach cost is used for the experiment the. Places reward information in the past, Q‐learning based task scheduling difficult to solve for high-dimensional continuous state action... Wu discusses an end-to-end engineering project to train and evaluate Deep Q-Learning model and synchronization phase balancing problem and of... And 10000 episodes respectively of different number of episodes increasing is still a.. Of performance degradation in traditional q learning for scheduling schedulers on an application built using approach! They employed the Q-III algorithm to calculate Q-value for each node and update these Q-values in Q-Table balancer shown! Should take the associated Q-values a significant drop in the cost when processors are further increased from 2-8 significantly as. Minimization and efficient utilization of resources distributing the workload algorithm was receiver initiated and works locally on slaves! Q-Function ( Even-Dar and Monsour, 2003 ) tasks, inter-processor communication costs and precedence relations fully. Diagram of our Q-Learning based grid application performance for lower cost than a single large machine of and!