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Over these last years, the number of cores witnessed a spectacular increase in digital signal and general use processors. Concurrently, significant researches are done to get benefit from the high degree of parallelism. Indeed, these researches are focused to provide an efficient scheduling from hardware/software systems to multicores architecture. The scheduling process consists on statically choose...
In our previous works (Liu & Su, 2016a [1], 2016b [2]), we had studied the scheduling of medical resource order and distribution based on an influenza diffusion model. In paper [1], the order size in distribution centers (DCs) was set to be a constant number, and it was improved to be a decision variable in paper [2]. A core decision variable, the number of beds assigned for epidemic patients...
Shared computing environments such as Cloud, HPC and Grid Computing present a challenge for scheduling systems as they seek to balance incoming requests with available resources, maintain high utilization, be fair among users, and cope with environmental dynamicity. In this paper, we will introduce the FUD theorem. The FUD theorem is based on the premise that a scheduler's desire to optimize the three...
In recent years many Emergency Departments (ED) across the country and abroad are reporting an increase in the flow of admitted patients. Although the problem of overcrowding of ED has been in the spotlight recently and research has been conducted in the direction of patient flow optimization, we are still waiting for concrete and feasible results. In this paper we propose a new way of looking at...
Cloud computing provides a cost-effective computing platform for big data workflows where moldable parallel computing models such as MapReduce are widely applied to meet stringent performance requirements. The granularity of task partitioning in each moldable job has a significant impact on workflow completion time and financial cost. We investigate the properties of moldable jobs and design a big-data...
Safe and optimal deployment of data-streaming applications on many-core platforms requires the realistic estimation of task Worst-Case Execution Time (WCET). On the other hand, task WCET depends on the deployment solution, due to the varying number of interferences on shared resources, thus introducing a cyclic dependency. Moreover, WCET is still an over-approximation of the Actual Execution Time...
Nonlinear Model Predictive Control often suffers from excessive computational complexity, which becomes critical when fast plants are to be controlled. This papers presents an approach to NMPC that exploits the quasi-LPV framework. For quasi-LPV systems, the scheduling variables are determined by the state variables and/or inputs. By calculating an estimate of the state variables during prediction,...
The analysis of scientific simulation data enables scientists to derive insights from their simulations. This analysis of the simulation output can be performed at the same execution site as the simulation using the same resources or can be done at a different site. The optimal output frequency is challenging to decide and is often chosen empirically. We propose a mathematical formulation for choosing...
The concept of workflow is used for modeling many of the data-intensive scientific applications executed on data grids. A Workflow is a series of interdependent tasks during which data is processed by different tasks. Scheduling the workflows in the grids is the process of assigning tasks to appropriate resources with the aim of achieving goals such as reducing workflow completion time while considering...
We make use of integer programming to model the scheduling of a fleet of vessels based on a predetermined operational cycle. The model consists of an objective function to be minimized and a large number of constraints that replicates the number of vessels, the operational cycle and the ambition levels. The objective function is zero when the selected ambition level is met. We use the software Mathematica...
A hybrid approach for mapping applications represented as Directed Acyclic Graphs (DAGs) is introduced in this work. It combines the Benders decomposition principle, which integrates Integer Linear and Constraint Programming (ILP and CP) methods, with a pure ILP model to find optimal solutions. The cuts that are generated during the iterative Benders solution process are later exploited by the ILP...
A real-time charging scheme with vehicle-to-grid (V2G) capability is developed in this paper to manage the aggregated charging loads of electric vehicles (EVs) in a parking station. To ensure the charging fairness of all connected EVs is satisfied, a fuzzy inference system is proposed to determine the charging or discharging priority level of each EV based on the associated remaining charging time,...
Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study...
Chemotherapy outpatient planning and scheduling is a complex problem. Many factors feed this complexity such as the variability inherited in all the stages of the oncology and infusion process, and uncertainty in some of its parameters. This paper explains the problem and identifies the different solution methods. Related literature is combined and summarized with focusing on their contributions....
In data-intensive cluster computing platforms such as Hadoop YARN, performance and fairness are two important factors for system design and optimizations. Many previous studies are either for performance or for fairness solely, without considering the tradeoff between performance and fairness. Recent studies observe that there is a tradeoff between performance and fairness because of resource contention...
Efficient mapping of Virtual Machines~(VMs) onto physical servers is a key problem for cloud infrastructure providers as hardware utilization directly impacts profit. Today, this mapping is commonly only performed when new VMs are created, but as VM workloads fluctuate and server availability varies, any initial mapping is bound to become suboptimal over time. We introduce a set of heuristic methods...
Scheduling is one key issue in Data Center Networks (DCN). Earlier research work usually focuses on flow-level scheduling, while more and more people are aware of the benefits of task-level scheduling in recent years. Most existing task-level scheduling methods schedule flows of one task together in order to reduce average completion time. However, few works discuss the efficient task-level scheduling...
Emergency Departments (ED) is the center of the hospital management's efforts. It constitutes a complex system with limited resources and random demands. The goal of this paper is to optimize the number of the human and material resources. We focus particularly on medical staff (physicians and nurses) and beds in emergency department. We propose a mixed integer linear programming (MILP) that minimizes...
Elasticity is a key feature of current cloud computing platforms. Dependent on their demand tenants can dynamically scale up and down their applications. To increase their revenue, cloud providers are used to over-provision their clusters, but they still have to reserve capacity to avoid that services get unresponsive and cause SLO violation during bursts. In this paper, we propose CLOUDFARM, a PaaS...
Stochastic Resource Constrained Project Scheduling (SRCPS) is among the hardest combinatorial problems. Exact calculations of interesting measures, such as expected project duration and the probability of satisfying the deadline, using known probabilities are in #P even for relaxed instances of the problem where resource constraints are ignored. The most common approach is to use substantial simulation...
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