A Tuning: ArchitectureApplication AutoTuning for InMemory DataProcessing Frameworks Processing big data eventually leads to an upsurge in datacentersu0027 power consumption, which is one of the pivotal concerns to be addressed. Many of the existing works focus on optimizing either power or performance, which is not the best parameter to consider for achieving high energy efficiency with low operational costs. Furthermore, the existing works require profiling of big data applications exhaustively and only consider tuning of either architectural or software parameters, often leading to suboptimal settings. To cope up with the abovementioned drawbacks of the existing works, we propose a system, A Tuning (Architecture  Application Autotuning) which enables us to determine a close to optimal settings by simultaneously optimizing for Energy Delay Product(EDP), representing energy efficiency. The proposed A Tuning involves a) profile the incoming unknown applications to different types (computebound, memorybound and etc.) based on known applications classification result; b) colocate the applications, and c) employs a machine learningbased model to determine the optimal settings and tune from both architectural and application settings for the colocated applications. By applying the proposed ATuning system, datacenters achieve up to 4xc3x97EDP improvement compared to fairshare methodology and 2.5xc3x97compared with recent works such as BestConfig.

Machine Learningbased Prediction for Dynamic Architectural Optimizations Embedded system complexity is rapidly evolving, becoming more desktopsystemlike, requiring more complex optimization methods to adhere to more stringent design constraints (e.g., area, thermal pressures, performance, battery lifetime, etc.) given restricted resources, widely varying applications, and environmental stimuli. Configurable architectural parameters (e.g., voltage, frequency, pipeline depth, cache specifics, etc.) can be specialized to meet these applicationspecific constraints and requirements (e.g., available parallelism, cache locality and access patterns, etc.) based on the available resources (e.g., heterogeneous cores with different architectural parameters). Unfortunately, due to this environmentaland applicationdependent information, embedded system designers are challenged with meeting constraints and requirements with no a priori knowledge of this dynamic runtime information, which results in a very large dynamic design space. In this work, we propose a machinelearningbased method to meet these runtime optimization challenges using a fast and reactive methodology. Our methodology uses offline feature selection and neural network training to create a model that can be used online to predict the best configurable parameter values based on unknown runtime applications, environmental stimuli, resource availability, user expectations, etc. Without loss of generality, we propose a cache line size onlinedynamic prediction module. Results show that our prediction module achieves 86% and 91% classification accuracies, comparable to prior work, for predicting the instruction and data cache line sizes, respectively, and achieves average energy consumption optimizations within 1% of the applicationsxe2x80x99 optimal energy consumptions determined using exhaustive simulation.