Adaptive Topologic Optimization for Large-Scale Stream Mining

Tekin

et al. 2016

IEEE Trans. Comput.

Self Cite

Abstract-Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single stream input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new systematic and efficient methodology and associated algorithms for online learning and energy-efficient scheduling of Big-Data streaming applications with multiple streams on many core systems with resource constraints. We formalize the problem of multi-stream scheduling as a staged decision problem in which the performance obtained for various resource allocations is unknown. The proposed scheduling methodology uses a novel class of online adaptive learning techniques which we refer to as staged multi-armed bandits (S-MAB). Our scheduler is able to learn online which processing method to assign to each stream and how to allocate its resources over time in order to maximize the performance on the fly, at run-time, without having access to any offline information. The proposed scheduler, applied on a face detection streaming application and without using any offline information, is able to achieve similar performance compared to an optimal semi-online solution that has full knowledge of the input stream where the differences in throughput, observed quality, resource usage and energy efficiency are less than 1, 0.3, 0.2 and 4 percent respectively.

Section: Related Workmentioning

confidence: 99%

“…We use s k to refer to the kth stream and there are N stream streams in total. Processing of these data streams is carried out in a chain of stages [8] [1].…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Big-Data Streaming Applications Scheduling Based on Staged Multi-armed Bandits

Tekin

et al. 2016

IEEE Trans. Comput.

Self Cite

“…In fact, the assumption of a periodic DAG limits the applicability of static approaches because highly optimized modern and emerging video coders do not always have periodic task-graphs (e.g., they may use adaptive group of pictures structures). Hence, applying techniques such as pipelining is not possible, especially for applications that do not adopt a fixed task-graph structure but instead adapt their task-graphs on the fly (e.g., stream mining applications [18]). Moreover, for H.264 video decoding, such pipelining techniques require buffering delays that are proportional to the Group of Pictures (GOP) size, which may be large.…”

Section: Related Workmentioning

confidence: 99%

A unified online directed acyclic graph flow manager for multicore schedulers

2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC)

Mastronarde³

et al. 2014

Self Cite

Abstract-Numerous Directed-Acyclic Graph (DAG) schedulers have been developed to improve the energy efficiency of various multi-core systems. However, the DAG monitoring modules proposed by these schedulers make a priori assumptions about the workload and relationship between the task dependencies. Thus, schedulers are limited to work on a limited subset of DAG models. To address this problem, we propose a unified online DAG monitoring solution independent from the connected scheduler and able to handle all possible DAG models. Our novel low-complexity solution processes online the DAG of the application and provides relevant information about each task that can be used by any scheduler connected to it. Using H.264/AVC video decoding as an illustrative application and multiple configurations of complex synthetic DAGs, we demonstrate that our solution connected to an external simple energy-efficient scheduler is able to achieve significant improvements in energy-efficiency and deadline miss rates compared to existing approaches.

“…Stream mining applications [20] are used to classify a high input of data stream and are in general modeled with a Directed-Acyclic-Graph (DAG) where each node denotes a task (e.g., classifier, features-extraction task), each edge from node j to node k indicates that task k execution depends on task j execution output. Fig.…”

Section: Introductionmentioning

confidence: 99%

Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

Ruggiero

2014 IEEE Computer Society Annual Symposium on VLSI

et al. 2014

Abstract-In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now widely used in several domains such as financial analysis, video annotation, surveillance, medical services, traffic prediction, etc. In order to cope with the Big-Data stream input and its high variability, modern stream mining applications implement systems with heterogeneous classifiers and adapt online to its input data stream characteristics variation. Moreover, unlike existing architectures for video processing and compression applications, where the processing units are reconfigurable in terms of parameters and possibly even functions as the input data is changing, in Big-Data stream mining applications the complete computing pipeline is changing, as entirely new classifiers and processing functions are invoked depending on the input stream. As a result, new approaches of reconfigurable hardware platform architectures are needed to handle Big-Data streams. However, hardware solutions that have been proposed so far for stream mining applications either target high performance computing without any power consideration (i.e., limiting their applicability in small-scale computing infrastructures or current embedded systems), or they are simply dedicated to a specific learning algorithm (i.e., limited to run with a single type of classifiers). Therefore, in this paper we propose a novel low-power manycore architecture for stream mining applications that is able to cope with the dynamic data-driven nature of stream mining applications while consuming limited power. Our exploration indicates that this new proposed architecture is able to adapt to different classifiers complexities thanks to its multiple scalable vector processing units and their re-configurability feature at runtime. Moreover, our platform architecture includes a memory hierarchy optimized for Big-Data streaming and implements modern fine-grained power management techniques over all the different types of cores allowing then minimum energy consumption for each type of executed classifier.