Scheduling Model Sample Clauses
Scheduling Model. The Employer may authorize a scheduling model provided that 66% of the Nurses in the affected unit agree, operational requirements permit, the provision of services are not affected, costs are not increased, and the schedule is completed within the timelines in the Collective Agreement for posting schedules. All schedules are subject to review by the Union and final approval and/or amendment by the Manager. The model adopted will be periodically reviewed and evaluated and the Employer reserves the right to discontinue it if it determines that the above preconditions are no longer being met. The Union and the Nurses affected will be given reasonable notice of discontinuance, no less than the length of any schedule posting period in effect. The Parties shall meet within twenty (20) days of the notice being given to discuss the reasons for the change. The Parties will attempt to develop an alternative rotation. If no alternative can be agreed upon, the provisions of Article 7.04 will apply.
Scheduling Model. J-1 The Union and the Hospital agree in principle to the concept of the 4 on 5 Off Scheduling Model. Should the Hospital or the nurses wish to implement the 4 on 5 off scheduling model on a particular unit, they shall do so according to the following criteria, initially on a test basis through the Scheduling Committee.
Scheduling Model. Where the employees’ wish to change the unit’s scheduling model they will approach the unit manager. If the manager agrees to implement the proposed scheduling model a vote will proceed. It is understood that such agreement by the Hospital shall not be withheld in an unreasonable or arbitrary manner. The voting procedure (with the exception of self scheduling) will be as follows:
Scheduling Model. Instance requests (to either start or stop an instance) are routed to a scheduler (as implemented by IaaS infrastructures such as Eucalyptus [6], Open Stack [9], and Cloud Stack [10]) which handles admission control and placement of instances on physical resources in a cluster of “nodes.” IaaS clouds typically define “instance types” that describe the resources that an instance will consume (CPU cores, memory, ephemeral disk storage, etc.). In the Eucalyptus systems (production and research) that we investigate in this work, we observe that the memory footprint associated with each instance type is such that the instance placement decision by the scheduler can be made strictly on core count. When an instance is admitted, the scheduler makes a place- ment decision by selecting a node on which the instance will run. In this study, we use simple first-fit placement in favor of more complex approaches to highlight the impact SLA-aware admission control. If an on-demand instance is requested, and the scheduler cannot find a node with available capacity, the scheduler selects one or more spot instances to terminate (evict) so that the on-demand instance can be scheduled. Further, our scheduler (like other Eucalyptus schedulers) assumes that the instance type definitions nest with respect to their core counts. For example, an empty 4-core node node is seen by the scheduler as having 1x 4-core slot, 2x 2-core slots, 4x 1-core slots, or 1x 2-core, 2x 1-core slots. The distinction between available cores and available slots is important when generating time-until-eviction estimates for different instance sizes and different cluster load levels.