Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers

Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms

Preoccupation with a restricted pattern of interest in modelling autistic learning

Autism is a developmental disorder in which attention shifting is known to be restricted. Self-organization of neural networks, conditioned by different attention shifting characteristics is investigated for higher-dimensional stimuli presented to the network from different sources. The attention shifting modes are 1) novelty seeking, 2) attention shift impairment (attention is shifted but with a low probability) and 3) attention is shifted with a preference for a source which has become familiar to the map. The feature maps resulting from self-organization are much the same for modes 1 and 2 but distinctly different for mode 3, where the maps learn the stimuli from the source with the lowest variability in great detail, at the expense of the other source(s). Detailed learning in narrow fields is a known characteristic of autismValiderad; 2003; 20100924 (andbra)</p

Attention shift impairments and novelty avoidance : effects of characteristics of autism on the self-organization of an artificial neural network

We discuss application of Artificial Neural Networks (ANN) in simulation of attention shift impairments and novelty avoidance, common deficits in autism. It has been theorized that cortical feature maps in individuals with autism are inadequate for forming abstract codes and representations, explaining the importance paid to detail, rather than salient features. ANNs known as the Self-Organization Maps (SOM) offer insights into the development of cortical feature maps. We present results of the formation of SOMs in response to stimuli from two sources in four modes, namely, novelty seeking (normal learning), attention shift impairment, novelty avoidance and novelty avoidance in conjunction with attention shift impairment. The SOMs resulting from learning with novelty seeking and with attention shift impairment were, perhaps surprisingly, identical. In the case of learning with novelty avoidance the resulting SOMs were adapted to one of the sources at the expense of the other. The SOMs resulting from learning with novelty avoidance in conjunction with attention shift impairment were strikingly different, ranging from almost normal to poor from one simulation to the next, even with identical initial conditions. Such learning, in many different maps, would result in very uneven capacities, common in individuals with autism.Godkänd; 2001; 20061101 (ysko

An attempt in modelling early intervention in autism using neural networks

We present a solution to a problem of early intervention in autistic learning. This is an addition to our model of autism which is based on Kohonen self-organizing maps extended with the source familiarity filter and the attention shift mechanism. In particular we study the feature map formation when attention shift is restricted by familiarity preference. The network learns the stimuli from the source with the lowest variability in great detail at the expense of the other source. The early intervention neural controller modifies the probabilities of presenting stimuli from a given source in response to the attention shift acceptance/rejection signals.Godkänd; 2004; 20060929 (ysko

An attempt in modelling autism using self-organizing maps

Autism is a developmental disorder in which attention shift impairment and strong familiarity preference are considered to be prime deficiencies. We model these two characteristics of autistic behaviour using Self-Organizing Maps (SOFM).Godkänd; 2002; 20061031 (ysko

Bimodal integration of phonemes and letters : an application of multimodal self-organizing networks

Multimodal integration of sensory information has clear advantages for survival: events that can be sensed in more than one modality are detected more quickly and accurately, and if the sensory information is corrupted by noise the classification of the event is more robust in multimodal percepts than in the unisensory information. It is shown that using a Multimodal Self-Organizing Network (MuSON), consisting of several interconnected Kohonen Self-Organizing Maps (SOM), bimodal integration of phonemes, auditory elements of language, and letters, visual elements of language, can be simulated. Robustness of the bimodal percepts against noise in both the auditory and visual modalities is clearly demonstrated.Godkänd; 2006; 20061228 (ysko

Self-organization of an artificial neural network subjected to attention shift impairments and familiarity preference : characteristics studied in autism

Autism is a developmental disorder with possibly multiple pathophysiologies. It has been theorized that cortical feature maps in individuals with autism are inadequate for forming abstract codes and representations. Cortical feature maps make it possible to classify stimuli, such as phonemes of speech, disregarding incidental detail. Hierarchies of such maps are instrumental in creating abstract codes and representations of objects and events. Self-Organizing Maps (SOMs) are artificial neural networks that offer insights into the development of cortical feature maps. Attentional impairment is prevalent in autism, but whether it is caused by attention-shift impairment or strong familiarity preference or negative response to novelty is a matter of debate. We model attention shift during self-organization by presenting a SOM with stimuli from two sources in four different modes, namely, novelty seeking (regarded as normal learning), attention-shift impairment (shifts are made with a low probability), familiarity preference (shifts made with a lower probability to the source that is the less familiar to the SOM of the two sources), and familiarity preference in conjunction with attention-shift impairment. The resulting feature maps from learning with novelty seeking and with attention-shift impairment are much the same except that learning with attention-shift impairment often yields maps with a somewhat better discrimination capacity than learning with novelty seeking. In contrast, the resulting maps from learning with strong familiarity preference are adapted to one of the sources at the expense of the other, and if one of the sources has a set of stimuli with smaller variability, the resulting maps are adapted to stimuli from that source. When familiarity preference is less pronounced, the resulting maps may become normal or fully restricted to one of the sources, and in that case, always the source with smaller variability if such a source is present. Such learning, in a system with many different maps, will result in very uneven capacities. Learning with familiarity preference in conjunction with attention-shift impairment surprisingly has higher probability for the development of normal maps than learning with familiarity preference alone.Validerad; 2004; 20060922 (ysko)</p