We consider the problem of scheduling a group of heterogeneous, distributed processes, with different relative priorities and service preferences , to a group of heterogeneous virtual machines. Assuming linearly elastic IT resource needs, we extend prior results on proportional fair and  max-min fair scheduling to a constrained multiresource case for a fam- ily of fairness criteria(including our recently proposed Per- Server Dominant-Share Fairness) . Performance comparison is made by illustrative numerical example. We conclude with a discussion of scheduling problems for a public cloud with heterogeneous instances and servers .
We first consider the static problem of allocating resources to ( i.e. , scheduling) multiple distributed application framework s, possibly with different priorities and server preferences , in a private cloud with heterogeneous servers. Several fai r scheduling mechanisms have been proposed for this purpose. We extend pr ior results on max-min and proportional fair scheduling to t his constrained multiresource and multiserver case for generi c fair scheduling criteria. The task efficiencies (a metric r elated to proportional fairness) of max-min fair allocations found b y progressive filling are compared by illustrative examples . They show that "server specific" fairness criteria and those that are b ased on residual (unreserved) resources are more efficient .
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consider the problem of scheduling a group of heterogeneous, distributed processes, with different relative priorities and service preferences , to a group of heterogeneous virtual machines. Assuming linearly elastic IT resource needs, we extend prior results on proportional fair
first consider the static problem of allocating resources to ( i.e. , scheduling) multiple distributed application framework s, possibly with different priorities
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server preferences , in a private cloud with heterogeneous servers. Several fai r scheduling mechanisms have been proposed for this purpose. We extend pr ior results on
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fair scheduling to a constrained multiresource case for a fam- ily of fairness criteria(including our recently proposed Per- Server Dominant-Share Fairness) . Performance comparison is made by illustrative numerical example. We conclude with a discussion of scheduling problems for a public cloud with heterogeneous instances and servers
and proportional fair scheduling to t his constrained multiresource and multiserver case for generi c fair scheduling criteria. The task efficiencies (a metric r elated to proportional fairness) of max-min fair allocations found b y progressive filling are compared by illustrative examples . They show that "server specific" fairness criteria and those that are b ased on residual (unreserved) resources are more efficient