A Taxonomy of Evolutionary Inspired Solutions for Energy Management in Green Computing: Problems and Resolution Methods

Kołodziej, Joanna; Khan, Samee U.; Zomaya, Albert Y.

doi:10.1007/978-3-642-30154-4_10

Cited by 9 publications

(15 citation statements)

References 29 publications

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“…Different types of supervised learning ANNs include: (a) back propagation and (b) modified back propagation neural networks (Luger, 2008). Major application areas of supervised learning are pattern recognition and text classification (Jo, 2010;Kolodziej et al, 2012). In unsupervised learning (clustering), the neural network tends to perform clustering by adjusting the weights based on similar inputs and distributing the task among interconnected processing elements (Luger, 2008).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A survey on text mining in social networks

Irfan¹,

King²,

Grages³

et al. 2015

The Knowledge Engineering Review

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In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

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Section: Artificial Neural Networkmentioning

confidence: 99%

A survey on text mining in social networks

Irfan¹,

King²,

Grages³

et al. 2015

The Knowledge Engineering Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the taxonomy defined for cloud computing in [15], the power and energy management methodologies in distributed computing environments can be classified into two main categories, namely static energy management (SEM) methods and dynamic energy management (DEM) techniques, as it is presented in Fig. 1 (see also [28]). …”

Section: Related Workmentioning

confidence: 99%

“…A lot of interesting examples of recently developed static and dynamic power and energy management techniques in the distributed computing environments are presented in the following surveys [5,40,43,44]. [28] Although a significant volume of the research has been provided in energy effective scheduling and resource allocation in large-scale computing systems, still not so large family of energy-aware genetic-based grid and cloud schedulers have been developed. Most of those approaches need an implementation of specially designed genetic operators, such as partially matching or cycle crossover and swap or rebalancing mutation mechanisms primarily designed for solving the complex combinatorial optimization problems [25].…”

Section: Related Workmentioning

confidence: 99%

“…Various types of information and data processed in the large-scale dynamic grid environment may be incomplete, imprecise, fragmentary and overloading, which complicates the assignment scores, availability of resources, and the may increase the amount of energy used in the system [43]. Heuristic approaches have shown great potential to solve many demanding, real-world decision and optimization problems in uncertain large-scale environments and seem to be the effective means for designing energy-aware grid schedulers in CGs [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical genetic-based grid scheduling with energy optimization

et al. 2012

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An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds. In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.

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“…Genetic algorithm (GA) has proposed to solve task scheduling problems [2]. Moreover, GA is one of evolutionary inspired algorithms that are used in green computing [3]. The PATS problem with N tasks (each task requires a VM) and M physical machines can generate M N possible placements.…”

Section: Introductionmentioning

confidence: 99%

A GPU-Based Enhanced Genetic Algorithm for Power-Aware Task Scheduling Problem in HPC Cloud

Quang-Hung¹,

Tan²,

Phat³

et al. 2014

Information and Communication Technology

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Abstract. In this paper, we consider power-aware task scheduling (PATS) in HPC clouds. Users request virtual machines (VMs) to execute their tasks. Each task is executed on one single VM, and requires a fixed number of cores (i.e., processors), computing power (million instructions per second -MIPS) of each core, a fixed start time and non-preemption in a duration. Each physical machine has maximum capacity resources on processors (cores); each core has limited computing power. The energy consumption of each placement is measured for cost calculating purposes. The power consumption of a physical machine is in a linear relationship with its CPU utilization. We want to minimize the total energy consumption of the placements of tasks. We propose here a genetic algorithm (GA) to solve the PATS problem. The GA is developed with two versions: (1) BKGPUGA, which is an adaptively implemented using NVIDIA's Compute Unified Device Architecture (CUDA) framework; and (2) SGA, which is a serial GA version on CPU. The experimental results show the BKGPUGA program that executed on a single NVIDIA® TESLA™ M2090 GPU (512 cores) card obtains significant speedups in comparing to the SGA program executing on Intel® Xeon TM E5-2630 (2.3 GHz) on same input problem size. Both versions share the same GA's parameters (e.g. number of generations, crossover and mutation probability, etc.) and a relative small (10 -11 ) on difference of two finesses between BKGPUGA and SGA. Moreover, the proposed BKGPUGA program can handle large-scale task scheduling problems with scalable speedup under limitations of GPU device (e.g. GPU's device memory, number of GPU cores, etc.).

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A Taxonomy of Evolutionary Inspired Solutions for Energy Management in Green Computing: Problems and Resolution Methods

Cited by 9 publications

References 29 publications

A survey on text mining in social networks

A survey on text mining in social networks

Hierarchical genetic-based grid scheduling with energy optimization

A GPU-Based Enhanced Genetic Algorithm for Power-Aware Task Scheduling Problem in HPC Cloud

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