Meeting performance targets of co-located distributed applications in virtualized environments is a challenging goal. In this context, vertical and horizontal scaling are promising techniques; the former varies the resource sharing on each individual machine, whereas the latter deals with choosing the number of virtual machines employed. State-of-the-art approaches mainly apply vertical and horizontal scaling in an isolated fashion, in particular assuming a fixed and symmetric load balancing across replicas. Unfortunately this may result unsatisfactory when replicas execute in environments with different computational resources. To overcome this limitation, we propose a novel combined runtime technique to determine the resource sharing quota and the horizontal load balancing policy in order to fulfill performance goals such as response time and throughput of co-located applications. Starting from a performance model as a multi-class queuing network, we formulate a model-predictive control problem which can be efficiently solved by linear programming. A validation performed on a shared virtualized environment hosting two real applications shows that only a combined vertical and horizontal load balancing adaptation can efficiently achieve desired performance targets in the presence of heterogeneous computational resources.

Combined Vertical and Horizontal Autoscaling Through Model Predictive Control

Incerto E.;Tribastone M.;
2018

Abstract

Meeting performance targets of co-located distributed applications in virtualized environments is a challenging goal. In this context, vertical and horizontal scaling are promising techniques; the former varies the resource sharing on each individual machine, whereas the latter deals with choosing the number of virtual machines employed. State-of-the-art approaches mainly apply vertical and horizontal scaling in an isolated fashion, in particular assuming a fixed and symmetric load balancing across replicas. Unfortunately this may result unsatisfactory when replicas execute in environments with different computational resources. To overcome this limitation, we propose a novel combined runtime technique to determine the resource sharing quota and the horizontal load balancing policy in order to fulfill performance goals such as response time and throughput of co-located applications. Starting from a performance model as a multi-class queuing network, we formulate a model-predictive control problem which can be efficiently solved by linear programming. A validation performed on a shared virtualized environment hosting two real applications shows that only a combined vertical and horizontal load balancing adaptation can efficiently achieve desired performance targets in the presence of heterogeneous computational resources.
978-3-319-96982-4
978-3-319-96983-1
Control
Load balancing
Performance
Queuing networks
Resource sharing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/16499
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