This study confirmed an increase in incidence of CD in children younger than 15 years in the Czech Republic.

This work describes the Grid and cluster scheduling simulator Alea 2 designed for study, testing and evaluation of various job scheduling techniques. This event-based simulator is able to deal with common problems related to the job scheduling like the heterogeneity of jobs, resources, and the dynamic runtime changes such as the arrivals of new jobs or the resource failures and restarts. The Alea 2 is based on the popular GridSim toolkit [31] and represents a major extension of the Alea simulator, developed in 2007 [16]. The extension covers both improved design, extended functionality as well as the improved scalability and the higher simulation speed. Finally, new visualization interface was introduced into the simulator. The main part of the simulator is a complex scheduler which incorporates several common scheduling algorithms working either on the queue or the schedule (plan) based principle. Additional data structures are used to maintain information about the resource status, the objective functions and for collection and visualization of the simulation results. Many typical objectives such as the machine usage, the average slowdown or the average response time are included. The paper concludes with an example of the Alea 2 execution using a real-life workload, discussing also the scalability of the simulator.

This paper proposes a novel schedule-based approach for scheduling a continuous stream of batch jobs on the machines of a computational Grid. Our new solutions represented by dispatching rule Earliest GapEarliest Deadline First (EG-EDF) and Tabu search are based on the idea of filling gaps in the existing schedule. EG-EDF rule is able to build the schedule for all jobs incrementally by applying technique which fills earliest existing gaps in the schedule with newly arriving jobs. If no gap for a coming job is available EG-EDF rule uses Earliest Deadline First (EDF) strategy for including new job into the existing schedule. Such schedule is then optimized using the Tabu search algorithm moving jobs into earliest gaps again. Scheduling choices are taken to meet the Quality of Service (QoS) requested by the submitted jobs, and to optimize the usage of hardware resources. We compared the proposed solution with some of the most common queue-based scheduling algorithms like FCFS, EASY backfilling, and Flexible backfilling. Experiments shows that EG-EDF rule is able to compute good assignments, often with shorter algorithm runtime w.r.t. the other queue-based algorithms. Further Tabu search optimization results in higher QoS and machine usage while keeping the algorithm runtime reasonable.

Abstract.In this work we analyze the performance of scheduling algorithms with respect to fairness. Existing works frequently consider fairness as a job related issue. In our work we analyze fairness with respect to different users of the system as this is a very important real-life problem. First, we discuss how fair are selected popular scheduling algorithms with respect to different users of the system. Next, we present an extension to the well known Conservative backfilling algorithm. Instead of "ad hoc" decisions, the schedule is now created subject to evaluation and optimization. Notably, the fairness is considered as an important metric, which accompanies standard performance related metrics such as slowdown or wait time. To achieve that, an inclusion of fairness as an optimization criterion is proposed. The new extension improves the performance and fairness of Conservative backfilling with respect to other classical techniques such as FCFS, EASY backfilling or aggressive backfilling without reservations.