Wednesday, 23 July 2014
TRACON: Interference-Aware Scheduling for Data-Intensive Applications in Virtualized Environments
TRACON: INTERFERENCE-AWARE SCHEDULING FOR DATA-INTENSIVE APPLICATIONS IN VIRTUALIZED ENVIRONMENTS
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.
Cloud computing has achieved tremendous success in offering Infrastructure/Platform/Software as a Service, in an on-demand fashion, to a large number of clients. This is evident in the popularity of cloud software services, e.g., Gmail and Facebook, and the rapid development of cloud platforms, e.g., Amazon EC2. The key enabling factor for cloud computing is the virtualization technology, e.g., Xen, that provides an abstraction layer on top of the underlying physical resources and allows multiple operating systems and applications to simultaneously run on the same hardware. As virtual machine monitors (VMM) encapsulate different applications into each separate guest virtual machine (VM), a cloud provider can leverage VM consolidation and migration to achieve excellent resource utilization and high availability in large data centers.
DISADVANTAGES OF EXISTING SYSTEM:
v Applications are optimized for the hard drive based storage
systems by issuing large sequential reads and writes.
v It very likely leads to high I/O interference and low performance.
In this work, we study the performance effects of co-located data-intensive applications, and develop TRACON1, a novel Task and Resource Allocation Control framework that mitigates the interference from concurrent applications. TRACON leverages modeling and control techniques from statistical machine learning and acts as the core management scheme for a virtualized environment. The evaluation shows that TRACON can achieve up to 50% improvement on application runtime and up to 80% on I/O throughput for data intensive applications.
ADVANTAGES OF PROPOSED SYSTEM:
v It models can adapt in the runtime when it is detected that they no longer accurately model the application's performance.
v The system can make optimized scheduling decisions that lead to significant improvements in both application performance and resource utilization.
Speed - 1.1 Ghz
RAM - 512 MB(min)
Hard Disk - 40 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - LCD/LED
Operating system : Windows XP.
Coding Language : JAVA
Data Base : MySQL
Tool : Netbeans
Ron C. Chiang H. Howie Huang,“ TRACON: Interference-Aware Scheduling for Data-Intensive Applications in Virtualized Environments” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2011 INTERNATIONAL CONFERENCE.