6 research outputs found
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Value-based allocation of computing system resources
Allocation of all resources to maximize the total value of the completion times for all jobs in a multiprogrammed computing system is investigated in this study. In traditional multiprogrammed operating systems, scheduling use of the central processor and main memory has been treated separately from allocation of other system resources. This study investigates the benefits of allocating all resources in a single framework using explicitly specified payoff functions.A model of resource allocation and scheduling forms the basis of the investigation. To aid understanding and designing resource allocation strategies, the model provides for uniform treatment of all resources. Each process is modeled as a series of resource requests and releases. The process requests resources. The operating system must either grant the requests or suspend the process. The performance of the scheduler is represented by the set of response times produced when scheduling a job mix.A new resource allocation strategy which overcomes deficiencies of existing schedulers is presented. Explicit specification of the value of jobs as a function of the time taken to complete them allows the use of utility theory evaluations in making resource allocation decisions and provides the system manager better control over the operation of the system.Dynamic determination of the opportunity costs of resource assignments are used advantageously in making resource allocation decisions. Simulation experiments showed that value-based allocation is feasible. Because value-based scheduling gives the system manager more flexibility in specifying system goals, it is more adaptable to specific requirements than traditional schedulers. When its parameters were set to approximate the value function of a modern multilevel queue scheduler, the value-based scheduler performed as well as the multi level queue scheduler
Learning Dynamic Priority Scheduling Policies with Graph Attention Networks
The aim of this thesis is to develop novel graph attention network-based models to automatically learn scheduling policies for effectively solving resource optimization problems, covering both deterministic and stochastic environments. The policy learning methods utilize both imitation learning, when expert demonstrations are accessible at low cost, and reinforcement learning, when otherwise reward engineering is feasible. By parameterizing the learner with graph attention networks, the framework is computationally efficient and results in scalable resource optimization schedulers that adapt to various problem structures. This thesis addresses the problem of multi-robot task allocation (MRTA) under temporospatial constraints. Initially, robots with deterministic and homogeneous task performance are considered with the development of the RoboGNN scheduler. Then, I develop ScheduleNet, a novel heterogeneous graph attention network model, to efficiently reason about coordinating teams of heterogeneous robots. Next, I address problems under the more challenging stochastic setting in two parts. Part 1) Scheduling with stochastic and dynamic task completion times. The MRTA problem is extended by introducing human coworkers with dynamic learning curves and stochastic task execution. HybridNet, a hybrid network structure, has been developed that utilizes a heterogeneous graph-based encoder and a recurrent schedule propagator, to carry out fast schedule generation in multi-round settings. Part 2) Scheduling with stochastic and dynamic task arrival and completion times. With an application in failure-predictive plane maintenance, I develop a heterogeneous graph-based policy optimization (HetGPO) approach to enable learning robust scheduling policies in highly stochastic environments. Through extensive experiments, the proposed framework has been shown to outperform prior state-of-the-art algorithms in different applications. My research contributes several key innovations regarding designing graph-based learning algorithms in operations research.Ph.D
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NBS monograph
From Introduction: "This is the third in a planned series of reports involving selective literature reviews of research and development requirements and areas of continuing R & D concern in the computer and information sciences and technologies.