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As mentioned previously, an exhaustive search over all possible resource allocation plans may not be feasible.
The naive way is to evaluate all possible resource allocation plans at each decision stage.
RAP exploits this phenomenon to discover burstiness aware resource allocation plans.
These settings allow the exhaustive enumeration of all possible resource allocation plans for these applications.
RAP iteratively generates various resource allocation plans and uses WAM to evaluate the global SLO violation metric of these plans.
In particular, we describe an intelligent search technique that identifies the optimal solution without resorting to an exhaustive search of all possible resource allocation plans.
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At decision stage 0, an initial resource allocation plan is generated by allocating one web resource instance to each application over each resource allocation interval.
As discussed in "Overview" section, the output of the optimization is a resource allocation plan represented by the number of resources allocated to each tier n per application a, in each resource allocation interval t.
We define a resource allocation plan as the number and type of resources allocated to a set of applications over a given number of resource allocation intervals within a defined planning horizon.
In addition, we establish a new Gini coefficient to reflect the fairness of resource allocation plan, by regarding the DEA efficiency and resource portion allocated to each DMU as service unit and equity unit, respectively.
For each subsequent decision stage i the black dots represent the mean SLO violation percentages obtained for the various possibilities of allocating i additional web server instances to the initial resource allocation plan generated at decision stage 0. Fig. 5 Optimality of RAP variants.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com