http://blog.gravitant.com/2011/09/26/an-analytic-approach-to-solving-load-balancing-problem-in-the-cloud/
IT management moves into a new dimension by the Cloud. In
traditional IT, most of the cost generation occurs in procurement of resources,
provisioning and maintenance. By nature, the cost generation is fairly static
in traditional IT. Fixed cost of hardware and data centers and stable variable
maintenance and provisioning costs contribute to this static cost structure.
Cloud’s dynamic nature affects cost management of enterprises in the Cloud,
too. Pricing strategies of cloud providers go along with principle of cloud as
a utility. Although many pricing options have a fixed portion for a reserved
capacity, the usage based cost is always a significant and varying part of
enterprise cloud costs. This dynamic cost structure increases the importance of
intelligent provisioning and management.
My previous article, “Cloud Sourcing
Optimization: A Conceptual Model Discussion”, in Gravitant’s blog,
introduces Gravitant’s efforts in optimization in Cloud analytics. The next of
the series is investigating analytic solution approaches to solving load
balancing problems.
The underlying problem is simply to determine when to turn off a
virtual machine (VM) due to low utilization without allowing utilization of any
VM to exceed a certain threshold level by turning on a new VM. The aim is to
keep VM utilization within a reasonable band to minimize provisioning cost
while satisfying workload demand. The question is what the “optimal” high-mark
and low-mark utilization values to turn on and off VMs are.
The obvious decision variables in a corresponding optimization
problem are high-mark utilization value, low-mark utilization value, whether an
existing VM is turned off due to low utilization, and whether a new VM is
created due to high utilization of any VM. Each turned of VM creates an extra load
of work on the rest of the VMs. Each new VM shares the load of a high-utilized
VM. Objective is to minimize total cost of provisioning. Set of
constraints can be summarized in three groups.
1- High-mark utilization: New utilization of the remaining VMs
after adding the used capacity of low-utilization VMs should be lower than
high-mark utilization value.
2- Low-mark utilization: Any VM should have a utilization more
than low-mark utilization value.
3- New VM creation: If a VM has a higher-than-high-mark
utilization, then a new VM is created.
Because there are both binary and continuous variables, the
optimization model tends to be a mixed integer programming model. However,
since the first set of constrains is quadratic, the exact definition of the
model is quadratically constrained mixed integer programming model. Some
straightforward enumeration over the set of VMs will help linearize the
constraint. Therefore, we will have a mixed integer linear programming model.
Although this static model may seem restrictive in a setting
with a varying amount of demand for virtual machines to meet under budget
limitations, it has ability to roll over time and transform into a dynamic
model which would fit very well to the span of provisioning and the nature of
the Cloud. The utilization band in which VMs are allowed to operate changes
dynamically and provides a flexible space for decision makers.
This article reveals the tip of the iceberg of the analytic
solutions which Gravitant offers as a cloud brokerage and management company
for the enterprises. Our set of analytic solutions that help enterprises move
into and operate in the Cloud will continue to grow and evolve.
