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TRACON: Interference-Aware Scheduling for Data Intensive
Applications in Virtualized Environments
Abstract
Large-scale data centers leverage virtualization technology to achieve excellent
resource utilization, scalability, and high availability. Ideally, the performance of an
application running inside a virtual machine (VM) shall be independent of co-located
applications and VMs that share the physical machine. However, adverse
interference effects exist and are especially severe for data-intensive applications in
such virtualized environments. In this work, we present TRACON, a novel Task and
Resource Allocation CONtrol framework that mitigates the interference effects from
concurrent data-intensive applications and greatly improves the overall system
performance. TRACON utilizes modeling and control techniques from statistical
machine learning and consists of three major components: the interference
prediction model that infers application performance from resource consumption
observed from different VMs, the interference-aware scheduler that is designed to
utilize the model for effective resource management, and the task and resource
monitor that collects application characteristics at the runtime for model adaption.
We simulate TRACON with a wide variety of data-intensive applications including
bioinformatics, data mining, video processing, email and web servers, etc. The
evaluation results show that TRACON can achieve up to 50% improvement on
application runtime, and up to 80% on I/O throughput for data-intensive applications
in virtualized data centers.
Existing system
Large-scale data centers leverage virtualization technology to achieve excellent
resource utilization, scalability, and high availability. Ideally, the performance of an

2. application running inside a virtual machine (VM) shall be independent of co-located
applications and VMs that share the physical machine. However, adverse
interference effects exist and are especially severe for data-intensive applications in
such virtualized environments.
Proposed system
we present TRACON, a novel Task and Resource Allocation CONtrol framework
that mitigates the interference effects from concurrent data-intensive applications
and greatly improves the overall system performance. TRACON utilizes modeling
and control techniques from statistical machine learning and consists of three major
components: the interference prediction model that infers application performance
from resource consumption observed from different VMs, the interference-aware
scheduler that is designed to utilize the model for effective resource management,
and the task and resource monitor that collects application characteristics at the
runtime for model adaption. We simulate TRACON with a wide variety of data-intensive
applications including bioinformatics, data mining, video processing, email
and web servers, etc. The evaluation results show that TRACON can achieve up to
50% improvement on application runtime, and up to 80% on I/O throughput for data-intensive
applications in virtualized data centers.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)

3.  Hard Disk - 20 GB
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.