Dynamic application instance placement in data center environments
Techniques are disclosed for determining placements of application instances on computing resources in a computing system such that the application instances can be executed thereon. By way of example, a method for determining an application instance placement in a set of machines under one or more resource constraints includes the following steps. An estimate is computed of a value of the first metric that can be achieved by a current application instance placement and a current application load distribution. A new application instance placement and a new application load distribution are determined, wherein the new application instance placement and the new load distribution optimize the first metric.
1 . A method for determining an application instance placement in a set of machines under one or more resource constraints, the method comprising the steps of:
computing an estimate of a value of a first metric that can be achieved by a current application instance placement and a current application load distribution; and
determining a new application instance placement and a new application load distribution that optimizes the first metric.
2 . The method of claim 1 , wherein the determining step further comprises the new application instance placement improving upon the first metric and the new load distribution improving upon a second metric.
3 . The method of claim 1 , wherein the determining step further comprises shifting an application load.
4 . The method of claim 3 , wherein the determining step further comprises changing the application instance placement without pinning to determine a first candidate placement.
5 . The method of claim 4 , wherein the determining step further comprises changing the application instance placement with pinning to determine a second candidate placement.
6 . The method of claim 5 , wherein the determining step further comprises selecting a best placement from the first candidate placement and the second candidate placement as the new application instance placement.
7 . The method of claim 1 , wherein the determining step is performed multiple times.
8 . The method of claim 1 , further comprising the step of balancing an application load across the set of machines.
9 . The method of claim 1 , wherein the first metric comprises a total number of satisfied demands.
10 . The method of claim 1 , wherein the first metric comprises a total number of placement changes.
11 . The method of claim 1 , wherein the first metric comprises an extent to which an application load is balanced across the set of machines.
12 . The method of claim 1 , wherein one of the one or more resource constraints comprises a processing capacity.
13 . The method of claim 1 , wherein one of the one or more resource constraints comprises a memory capacity.
14 . The method of claim 1 , wherein the second metric comprises a degree of correlation between residual resources on each machine of the set of machines.
15 . The method of claim 1 , wherein the second metric comprises a number of underutilized application instances.
16 . Apparatus for determining an application instance placement in a set of machines under one or more resource constraints, the apparatus comprising:
a memory; and
at least one processor coupled to the memory and operative to: (i) compute an estimate of a value of a first metric that can be achieved by a current application instance placement and a current application load distribution; and (ii) determine a new application instance placement and a new application load distribution that optimizes the first metric.
17 . An article of manufacture for determining an application instance placement in a set of machines under one or more resource constraints, comprising a machine readable medium containing one or more programs which when executed implement the steps of:
computing an estimate of a value of a first metric that can be achieved by a current application instance placement and a current application load distribution; and
determining a new application instance placement and a new application load distribution that optimizes the first metric.