In this paper we propose a flexible and scalable distributed storage framework called flex Store that can adapt to variations in available or consumable power and demonstrate its performance in the context of reduplicated virtual machine disks. We propose and investigate smart control techniques in order to cope with the power constraints either introduced as a result of increasing node density in the storage arrays (consumable power constraints) or introduced when a mix of renewable (green) and conventional (brown) energy sources are used to power the data enter. The key component in the proposed storage framework is the policy engine which is a software layer that provides interfaces to define performance requirements of the applications (and also energy related policies). The policy engine enforces those policies in the storage system by adjusting the allocation of storage resources. The experimental results demonstrate the ability of the framework to dynamically adapt to the changes in workload and power constraints and minimize performance impacts. Our evaluation of the prototype shows that the adaptive replication mechanisms can reduce the IO latencies by around 65% during energy plenty situations and the impact of adaptation actions on IO latencies during energy constrained situations is reduced by more than 40% compared to the case without the adaptive replication and optimized adaptation mechanisms.
['Muthukumar Murugan', 'Krishna Kant', 'Ajaykrishna Raghavan', 'David Hung-Chang Du']
FlexStore: A Software Defined, Energy Adaptive Distributed Storage Framework