Aging-Aware Energy-Efficient Workload Allocation for Mobile Multimedia Platforms

Multicore platforms are characterized by increasing variability and aging effects which imply heterogeneity in core performance, energy consumption and reliability. In particular, wear-out effects such as Negative-Bias-Temperature-Instability (NBTI) require run-time adaptation of system resource utilization to time-varying and uneven platform degradation, so as to prevent premature chip failure. In this context, task allocation techniques can be used to deal with heterogeneous cores and extend chip lifetime while minimizing energy and preserving Quality of Service (QoS). We propose a new formulation of the task allocation problem for variability affected platforms, which manages per-core utilization to achieve a target lifetime while minimizing energy consumption during the execution of rate-constrained multimedia applications. We devise an adaptive solution that can be applied on-line and approximates the result of an optimal, off-line version. Our allocator has been implemented and tested on real-life functional workloads running on a timing accurate simulator of a next-generation industrial multicore platform. We extensively assess the effectiveness of the on-line strategy both against the optimal solution and also compared to alternative state-of-the-art policies. The proposed policy outperforms state-of-the-art strategies in terms of lifetime preservation, while saving up to 20% of energy consumption without impacting timing constraints.