Proceedings of IEEE CCIS2011
A LOAD-ADAPATIVE CLOUD RESOURCE SCHEDULING MODEL BASED ON ANT
COLONY
ALGORITHM Xin Lu, Zilong Gu
School of Information and Software Engineering, University of
Electronic Science and Technology of China, ChengDu, China
[email protected],[email protected]
Abstract Dynamic scheduling cloud resources according to the
change of the load are key to improve cloud computing on-demand
service capabilities. This paper proposes a load-adaptive cloud
resource scheduling model based on ant colony algorithm. By
real-time monitoring virtual machine of performance parameters,
once judging overload, it schedules fast cloud resources using ant
colony algorithm to bear some load on the load-free node. So that
it can meet changing load requirements. By analyzing an example
result , the model can meet the goals and requirements of
self-adaptive cloud resources scheduling and improve the efficiency
of the resource utilization. Keywords: cloud computing; ant colony
algorithm; adaptive load
1 Introduction Cloud computing is a calculation mode that can
provide dynamic flexibility, on-demand services, virtualized
resources[1]. For specific applications, the cloud service system
for the client applications still need pre-allocate the fixed cloud
resources. Since the applying for cloud services of client
applications is random and the load is also difficult to predict,
stationary parts in the cloud scheduling are difficult to solve the
problem that some nodes would overload during this progress. Thus,
according to the load changing of cloud applications, Allocating
the dynamic resource self-adaptively is meaningful. Dynamic
resource scheduling is one of the issues to be solved in a lot of
technical fields. For example, in the field of the database, Brown
et al [2] studied self-management of target-oriented disk cache ,
self-management of resource, memory management And in the
communications field, Xue Qiang et al [3] proposed a load-adaptive
time slot PQ (priority queue) algorithm to solve the conflict of
the real-time traffic that encountered the
high QoS parameters and the fairness of bandwidth in the IP
network. According to load parameters of real-time traffic , it
selects the appropriate cycle and forward real-time traffic. Hence
the delay will be control in the required range and the jitter will
be reduced. It also improves the non-real-time traffic forwarding
performance at the common time. Now, in the filed of cloud
computing literature[4][5] have presented the dynamic resource
scheduling idea, but did not give a solution that can be
implemented. This paper references literature[6] that proposed the
basic framework of load adaptive thinking in the database system,
and studies adaptive strategies of dynamic cloud resources
scheduling to satisfy the uncertain load application needs.
2 The problem of load-adaptive cloud resource scheduling
Load-adaptive cloud resource scheduling denotes in the cloud
computing environment, how to monitor the changing load and
adaptive dynamic schedule cloud resources to solve the uncertain
load capacity of the cloud platform. The problem of cloud resource
scheduling can be shown in Figure 1. The CCi node field can be
treated as an undirected graph G (V, E), where V is the CCi nodes
and E is a collection of connected nodes in the network. CCi is the
cluster controller node of the application server cluster. The
number of NCi physical machines is m in each CCi node .Each
physical machine can make K virtual machines that can be shown with
Vi (i= 1 .. K). In the cloud service platform, all the applications
would be scheduled to Vi node to run. When the load of applications
suddenly increases, some performance parameters of some of the Vi
node may exceed the specified threshold and cause the overloaded
problems. Scheduling mechanism of cloud services platform needs to
solve two key issues: 1) How to detect overloaded Vi nodes in a
real-time environment. 2) Which scheduling strategy should be use
to make a new Vi node
___________________________________ 978-1-61284-204-2/11/$26.00
2011 IEEE
bears some load and all nodes are load balancing on the
platform.
Figure 1 Topologyof cloud computing environment
3 Load-adaptive cloud resource scheduling model In order to
solve the problem of load adaptive resource scheduling. This paper
proposes the following adaptive cloud resource scheduling model. It
is shown as Figure 2:
Figure 2 Load-adaptive cloud resource scheduling model When a
user makes a request, it reaches the cloud services platform
controller at first. The controller then maps the request goes to a
specific virtual application server, which is Vi node on the map.
Each application server cluster has a cluster controller, which
monitors whether real-time CPU usage, memory and network bandwidth
of Vi node in virtual resource pool is exceeding the prescribed
threshold. If exceeding, it finds hot spots and immediately
schedule cloud resources in the cluster, then start the virtual
machine on which to accept some of the requests. Thus it would make
the performance parameters of hot spot return to normal levels.
This model needs to solve the problems of hot spot detection and
adaptive scheduling strategy.
3.1 Hot spot detection
To determine whether Vi is a hot spot, there are three criteria,
that are CPU usage, memory and network bandwidth, which are shown
as cpu_rou(vi), ram_rou(vi) and bandwidth(vi). Specific criteria is
shown as the following formula 1.
_ ( )
_ ( )
( )
i
i
i
cp u ro u v c l
ra m ro u v e l
b a n d w id th v b l
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