2 research outputs found
A Time-Efficient Solution to the General Resource Placement Problem in Cloud
Cloud based large-scale online services are faced with regionally distributed stochastic demands for various resources. With multiple regional cloud data centers, a crucial problem that needs to be settled is how to properly place resources to satisfy massive stochastic demands from many different regions. For the general stochastic demands oriented cross region resource placement problem, the time complexity of existing optimal algorithm is linear to total amount of resources and thus may be inefficient when dealing with a large number of resources. To end this, we propose an efficient algorithm, named discrete function based unbound resource placement (D-URP). Experiments show that in scenarios with general settings, D-URP can averagely achieve at least 97% revenue of optimal solution, with reducing time by three orders of magnitude. Moreover, due to the generality of problem setting, it can be extended to get efficient solution for a broad range of similar problems under various scenarios with different constraints. Therefore, D-URP can be used as an effective supplement to existing algorithm under time-tense scheduling scenarios with large number of resources
A Time-Efficient Solution to the General Resource Placement Problem in Cloud
Cloud based large-scale online services are faced with
regionally distributed stochastic demands for various resources.
With multiple regional cloud data centers, a crucial problem that
needs to be settled is how to properly place resources to satisfy
massive stochastic demands from many different regions. For the
general stochastic demands oriented cross region resource placement
problem, the time complexity of existing optimal algorithm
is linear to total amount of resources and thus may be inefficient
when dealing with a large number of resources. To end this, we
propose an efficient algorithm, named discrete function based
unbound resource placement (D-URP). Experiments show that
in scenarios with general settings, D-URP can averagely achieve
at least 97% revenue of optimal solution, with reducing time
by three orders of magnitude. Moreover, due to the generality
of problem setting, it can be extended to get efficient solution
for a broad range of similar problems under various scenarios
with different constraints. Therefore, D-URP can be used as
an effective supplement to existing algorithm under time-tense
scheduling scenarios with large number of resources