A Stochastic Model to Investigate Data Center
Performance and QoS in IaaS Cloud Computing Systems
ABSTRACT:
Cloud data
center management is a key problem due to the numerous and heterogeneous
strategies that can be applied, ranging from the VM placement to the federation
with other clouds. Performance evaluation of cloud computing infrastructures is
required to predict and quantify the cost-benefit of a strategy portfolio and
the corresponding quality of service (QoS) experienced byusers. Such analyses
are not feasible by simulation or on-the-field experimentation, due to the
great number of parameters that have to be investigated. In this paper, we
present an analytical model, based on stochastic reward nets (SRNs), that is
both scalable to model systems composed of thousands of resources and flexible
to represent different policies and cloud-specific strategies. Several
performance metrics are defined and evaluated to analyze the behavior of a
cloud data center: utilization, availability, waiting time, and responsiveness.
A resiliency analysis is also provided to take into account load bursts.
Finally, a general approach is presented that,starting from the concept of
system capacity, can help system managers to opportunely set the data center
parameters under different working conditions.
EXISTING SYSTEM:
Ø In order to integrate business requirements and
application level needs, in terms of Quality of Service (QoS), cloud service
provisioning is regulated by Service Level Agreements (SLAs): contracts between
clients and providers that express the price for a service, the QoS levels
required during the service provisioning, and the penalties associated with the
SLA violations. In such a context, performance evaluation plays a key role
allowing system managers to evaluate the effects of different resource
management strategies on the data center functioning and to predict the
corresponding costs/benefits.
Ø Cloud systems differ from traditional distributed
systems. First of all, they are characterized by a very large number of
resources that can span different administrative domains. Moreover, the high
level of resource abstraction allows to implement particular resource
management techniques such as VM multiplexing or VM live migration that, even
if transparent to final users, have to be considered in the design of
performance models in order to accurately understand the system behavior.
Finally, different clouds, belonging to the same or to different organizations,
can dynamically join each other to achieve a common goal, usually represented
by the optimization of resources utilization. This mechanism, referred to as
cloud federation, allows to provide and release resources on demand thus
providing elastic capabilities to the whole infrastructure.
DISADVANTAGES
OF EXISTING SYSTEM:
Ø On-the-field experiments are mainly focused on the
offered QoS, they are based on a black box approach that makes difficult to correlate
obtained data to the internal resource management strategies implemented by the
system provider.
Ø Simulation does not allow to conduct comprehensive
analyses of the system performance due to the great number of parameters that
have to be investigated.
PROPOSED SYSTEM:
Ø In this paper, we present a stochastic model, based on
Stochastic Reward Nets (SRNs), that exhibits the above mentioned features
allowing to capture the key concepts of an IaaS cloud system.
Ø The proposed model is scalable enough to represent
systems composed of thousands of resources and it makes possible to represent
both physical and virtual resources exploiting cloud specific concepts such as
the infrastructure elasticity. With respect to the existing literature, the
innovative aspect of the present work is that a generic and comprehensive view
of a cloud system is presented.
Ø Low level details, such as VM multiplexing, are easily
integrated with cloud based actions such as federation, allowing to investigate
different mixed strategies. An exhaustive set of performance metrics are
defined regarding both the system provider (e.g., utilization) and the final
users (e.g., responsiveness).
ADVANTAGES
OF PROPOSED SYSTEM:
Ø To provide a fair comparison among different resource
management strategies, also taking into account the system elasticity, a
performance evaluation approach is described.
Ø Such an approach, based on the concept of system
capacity, presents a holistic view of a cloud system and it allows system
managers to study the better solution with respect to an established goal and
to opportunely set the system parameters.
SYSTEM
ARCHITECTURE:
MODULES:
1.
System Queuing
2.
Scheduling
Module
3.
VM Placement
Module
4.
Federation
Module
5.
Arrival Process
MODULES
DESCRIPTION:
1.
System Queuing:
Job requests (in terms
of VM instantiation requests) are en-queued in the system queue. Such a queue
has a finite size Q, once its limit is reached further requests are rejected.
The system queue is managed according to a FIFO scheduling policy.
2.
Scheduling Module:
When a resource is
available a job is accepted and the corresponding VM is instantiated. We assume
that the instantiation time is negligible and that the service time (i.e., the
time needed to execute a job) is exponentially distributed with mean1/μ.
3.
VM Placement:
According to the VM
multiplexing technique the cloud system can provide a number M of logical
resources greater than N. In this case, multiple VMs can be allocated in the
same physical machine (PM), e.g., a core in a multicore architecture. Multiple
VMs sharing the same PM can incur in a reduction of the performance mainly due
to I/O interference between VMs.
4.
Federation Module:
Cloud federation allows
the system to use, in particular situations, the resources offered by other
public cloud systems through a sharing and paying model. In this way, elastic
capabilities can be exploited in order to respond to particular load
conditions. Job requests can be redirected to other clouds by transferring the
corresponding VM disk images through the network.
5.
Arrival Process:
Finally, we respect to
the arrival process we will investigate three different scenarios. In the first
one (Constant arrival process) we assume the arrival process be a homogeneous
Poisson process with rate λ. However, large scale distributed systems with
thousands of users, such as cloud systems, could exhibit
self-similarity/long-range dependence with respect to the arrival process. The
last scenario (Bursty arrival process) takes into account the presence of a
burst whit fixed and short duration and it will be used in order to investigate
the system resiliency
SYSTEM
REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø
System : Pentium IV 2.4 GHz.
Ø
Hard Disk :
40 GB.
Ø
Floppy Drive : 1.44
Mb.
Ø
Monitor : 15
VGA Colour.
Ø
Mouse : Logitech.
Ø Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
Ø Operating system : Windows
XP/7.
Ø Coding Language : JAVA/J2EE
Ø IDE : Netbeans 7.4
Ø Database : MYSQL
REFERENCE:
Dario Bruneo ,“A
Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud
Computing Systems”,VOL. 25, NO. 3, MARCH 2014.
