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Similar to the previous experiment, we consider a planning horizon of 8 h and a resource allocation interval of 1 h.
In the first experiment four different applications are subjected to a non-bursty workload, i.e., exponential session arrivals, over a planning horizon of 4 h with a resource allocation interval of 1 h.
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For example, cloud SPs can study the effect of dynamically changing resource allocations to applications at resource allocation intervals of 30 min within a planning horizon of 24 h.
Time base is divided into equal length beacon periods (superframes), and each superframe is further divided into equal length allocation slots, which is numbered from 0, 1, …, x, where x≤255, as shown in Fig. 2. Allocation interval for specific node may span more than one allocation slot and can be expressed by the number of slots at the start and end.
This is because in each of these decision stages the bottleneck tier is not the same in all resource allocation intervals for some of the workloads considered in the experiment.
The difference is that there may be several allocation assignment frames following the length field in broadcast manner, each of which dedicates the start and the end of allocation intervals for the specific node.
In each resource allocation interval, the number of database instance cores is kept constant at 1 for each application.
The total number of resource instances allocated to tier n of application a in resource allocation interval t is denoted by C a,n,t.
The maximum number of available web server instances per resource allocation interval is set to 7.
The maximum number of available web server instances per resource allocation interval is limited to six in order to enumerate all possible resource allocation plans.
The main power of RAP comes from exploring a resource allocation interval smaller than the planning horizon and exploiting the burstiness across these intervals.
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