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Measurability is a concept in periodic Elastic scaling based on the following two conditions: (a) a cloud service provider should be cautious, that is, should not exclude any cloud consumer’s resource pooling pattern strategy from consideration; and (b) a cloud service provider should consider the cloud consumers’ resource pooling pattern preferences, that is, should deem a cloud consumer’s resource pooling pattern strategy ki infinitely more likely than k'i if it premises the cloud consumer to prefer ki to k'i. A resource pooling pattern strategy is measurable if it can optimally be chosen under common resource pooling pattern conjecture in the events (a) and (b). In this paper we present an algorithm that for every finite periodic Elastic Scaling operation computes the set of all measurable resource pooling pattern strategies. The algorithm is based on the new idea of an Continuously Changing Workload preference limitation, which is a pair (ki, Vi) consisting of a resource pooling pattern strategy ki, and a subset of resource pooling pattern strategies Vi, for cloud service provider i. The interpretation is that cloud service provider i prefers some resource pooling pattern strategy in Vi to ki. The algorithm proceeds by successively adding Continuously Changing Workload preference limitations to the periodic Elastic Scaling.

Is it Possible to Sequence Continuously Changing Workload Patterns for Periodic Elastic Scaling?