lab scheduling round robin with a timeslice of 4 ticks

Using CADS policy, we achieve 20% better cycles per instruction (CPI) in running memory intensive and compute intensive PARSEC parallel benchmarks simultaneously, and 16% better CPI with SPEC 2006 benchmarks.ĭeep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state spaces. CADS is also able to share the DRAM while guaranteeing fairness to all cores accessing memory.

Our scheduler utilizes locality among data requests from multiple cores and exploits parallelism in accessing multiple banks of DRAM. CADS uses Reinforcement Learning (RL) to alter its scheduling strategy dynamically at runtime. We introduce Core-Aware Dynamic Scheduler (CADS) for multicore memory controller. Hence, novel memory controllers that consider and adapt to the memory access characteristics, and share memory resources efficiently and fairly are necessary. Existing schedulers may disrupt locality and bank parallelism among data requests coming from different cores. They are unable to adapt to changing characteristics of memory workloads that run simultaneously on multiple cores. However, current multicore processors are using traditional memory controllers, which are designed for single-core processors. Since increased number of requests from multiple cores of processors becomes a source of bottleneck, scheduling the requests efficiently is necessary to utilize all the computing power these processors offer. Memory controller scheduling is crucial in multicore processors, where DRAM bandwidth is shared. Results for an 'adaptive timeslice parameter' for preemption show good saving on CPU cycles and efficient throughput time. We propose a machine-learning module to predict a better performing timeslice which is calculated based on static knowledge base and adaptive reinforcement learning based suggestive module. In this work we try to utilize the historical performances of a scheduler and predict the nature of current running process, thereby trying to reduce the number of preemptions. Each of these 'process preemption' leads to considerable overhead of CPU cycles which are valuable resource for runtime execution. A process which is allowed to utilize CPU resources for a fixed quantum of time (termed as timeslice for preemption) and is then preempted for another waiting process. It is in this regard that schedulers are provided with the ability to preempt a running process, by following any scheduling algorithm, and give us an illusion of simultaneous running of several processes. This problem gains more importance as the numbers of processes always outnumber the processors by large margins.

We present empirical evaluation of our approach in a maze-world navigation and their implications on 'adaptive timeslice parameter' show efficient throughput time.Īn operating system scheduler is expected to not allow processor stay idle if there is any process ready or waiting for its execution. We measure the importance of states, in option framework, by evaluating the impact of their absence and propose an algorithm to identify such checkpoint states. We propose a machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better performing timeslice. In this work we try to utilize the historical performances of a scheduler and try to reduce the number of redundant context switches. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and subsequent context switch leads to considerable overhead.

In this work we try to adapt options framework to model an operating system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting for its execution. The broader framework of 'Options' gives us a flexible way of representing such extended course of action in Markov decision processes. Temporally extended actions have been proved to enhance the performance of reinforcement learning agents.

Lab scheduling round robin with a timeslice of 4 ticks