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GLOBALSOFT TECHNOLOGIES 
IEEE PROJECTS & SOFTWARE DEVELOPMENTS 
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE 
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS 
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com 
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
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)
 Hard Disk - 20 GB 
SOFTWARE CONFIGURATION:- 
 Operating System : Windows XP 
 Programming Language : JAVA 
 Java Version : JDK 1.6 & above.

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2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT Multicast capacity-in-manet-wit...2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT Multicast capacity-in-manet-wit...
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT Multicast capacity-in-manet-wit...
 
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT Efficient data-query-in-intermi...
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT Efficient data-query-in-intermi...2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT Efficient data-query-in-intermi...
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT Efficient data-query-in-intermi...
 
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A system-for-denial-of-service-...
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A system-for-denial-of-service-...2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A system-for-denial-of-service-...
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A system-for-denial-of-service-...
 
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A probabilistic-misbehavior-det...
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A probabilistic-misbehavior-det...2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A probabilistic-misbehavior-det...
2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A probabilistic-misbehavior-det...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Trajectory improves data delivery...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Trajectory improves data delivery...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Trajectory improves data delivery...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Trajectory improves data delivery...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The design-and-evaluation-of-an-i...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The design-and-evaluation-of-an-i...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The design-and-evaluation-of-an-i...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The design-and-evaluation-of-an-i...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The client assignment problem for...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The client assignment problem for...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The client assignment problem for...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT The client assignment problem for...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Streaming applications on bus bas...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Streaming applications on bus bas...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Streaming applications on bus bas...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Streaming applications on bus bas...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Securing brokerless publish subsc...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Securing brokerless publish subsc...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Securing brokerless publish subsc...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Securing brokerless publish subsc...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Secure outsourced-attribute-based...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Secure outsourced-attribute-based...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Secure outsourced-attribute-based...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Secure outsourced-attribute-based...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Rre a-game-theoretic-intrusion-re...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Rre a-game-theoretic-intrusion-re...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Rre a-game-theoretic-intrusion-re...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Rre a-game-theoretic-intrusion-re...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Peer assisted vod systems an effi...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Peer assisted vod systems an effi...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Peer assisted vod systems an effi...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Peer assisted vod systems an effi...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On false-data-injection-attacks-a...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On false-data-injection-attacks-a...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On false-data-injection-attacks-a...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On false-data-injection-attacks-a...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On social delay tolerant networki...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On social delay tolerant networki...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On social delay tolerant networki...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT On social delay tolerant networki...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Constructing load balanced data a...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Constructing load balanced data a...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Constructing load balanced data a...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Constructing load balanced data a...
 
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT An ontology based hybrid approach...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT An ontology based hybrid approach...2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT An ontology based hybrid approach...
2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT An ontology based hybrid approach...
 

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2014 IEEE JAVA PARALLEL DISTRIBUTED PROJECT Tracon interference aware scheduling for data intensive applications in virtualized environments

  • 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com 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.