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NETWORK AWARE SCHEDULING FOR VIRTUAL MACHINE WORKLOADS WITH INTERFERENCE MODELS

ABSTRACT

 Current virtualization solutions allow hardware to be partitioned into Virtual Machines with appropriate isolation levels.However resource intensive workloads can still lead to unexpected performance variance. Weidentify and reduce network-related interference effects using performance models based on the runtime characteristics of virtualized workloads.Using the extended set of training data, we predict performance degradation with existing modeling techniques. Finally, we validate the added value of the improved models by introducing new scheduling techniquesand comparing them to previous efforts and demonstrate how the inclusion of network-related parameters in performance models cansignificantly increase the performance of consolidated workloads.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- Hypervisors have a strong focus on fair scheduling with regards to computationally intensive workloads.   VM share same hardware while performing operations.	PROPOSED CONCEPT:- There by allowing more VMs to be collocated on the same hardware while providing more predictable performance. We extend the proposed approach to identify and reduce performance interference using network-oriented applications.
EXISTING TECHNIQUE:- SVM  technique Means (WM) Model	PROPOSED TECHNIQUE:- Slowest Sorted Cluster First (SSCF) Weighted Slowest Sorted Cluster First (WSSCF)
TECHNIQUE DEFINITION:- SVM to accurately identify applications  Which are insensitive to performance interference. Predictions are therefore only required for applications that are likely to exhibit slowdown. WM-more training data is needed to reduce the average prediction errors.	TECHNIQUE DEFINITION:- SSCF uses clustering information to runtime predictions. improves upon this design by making more extensive use of runtime predictions   WSSCF schedulers to correctly identify the potential impact and take corrective  Measures.
DRAWBACKS:- Performance Interference with resource intensive workloads. When multiple VMs share the same hardware, bottlenecks can occur both on the hardwareas well as the hypervisor level. Virtualizationstill causes significant throughput instability and abnormaldelay variations.	ADVANTAGES:- Increasing amount of CPU cores per server and relatively slower gain in I/O performance further highlight theneed to proactively address performance interference. Improvements in VM scheduling and the reduction ofvirtualization overhead can partially mitigate issues. Increased performance.