Accelerating Deep Learning with Shrinkage and Recall

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data sets. Results show that the speedup using shrinking Deep Learning with recall (sDLr) can reach more than 2.0 while still giving competitive classification performance.Comment: The 22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS 2016

Optimal VM placement in data centres with architectural and resource constraints

Recent advance in virtualisation technology enables service provisioning in a flexible way by consolidating several virtual machines (VMs) into a single physical machine (PM). The inter-VM communications are inevitable when a group of VMs in a data centre provide services in a collaborative manner. With the increasing demands of such intra-data-centre traffics, it becomes essential to study the VM-to-PM placement such that the aggregated communication cost within a data centre is minimised. Such optimisation problem is proved NP-hard and formulated as an integer programming with quadratic constraints in this paper. Different from existing work, our formulation takes into consideration of data-centre architecture, inter-VM traffic pattern, and resource capacity of PMs. Furthermore, a heuristic algorithm is proposed and its high efficiency is extensively validated

Dynamic scheduling using CPU oversubscription in the ALICE Grid

The ALICE Grid is designed to perform a realtime comprehensive monitoring of both jobs and execution nodes in order to maintain a continuous and consistent status of the Grid infrastructure. An extensive database of historical data is available and is periodically analyzed to tune the workflows and data management to optimal performance levels. This data, when evaluated in real time, has the power to trigger decisions for efficient resource management of the currently running payloads, for example to enable the execution of a higher volume of work per unit of time. In this article, we consider scenarios in which, through constant interaction with the monitoring agents, a dynamic adaptation of the running workflows is performed. The target resources are memory and CPU with the objective of using them in their entirety and ensuring optimal utilization fairness between executing jobs. Grid resources are heterogeneous and of different generations, which means that some of them have better hardware characteristics than the minimum required to execute ALICE jobs. Our middleware, JAliEn, works on the basis of having at least 2 GB of RAM allocated per core (allowing up to 8 GB of virtual memory when including swap). Many of the worker nodes have higher memory per core ratios than these basic limits and in terms of available memory they therefore have free resources to accommodate extra jobs. The running jobs may have different behaviors and unequal resource usages depending on their nature. For example, analysis tasks are I/O bound while Monte-Carlo tasks are CPU intensive. Running additional jobs with complementary resource usage patterns on a worker node has a great potential to increase its total efficiency. This paper presents the methodology to exploit the different resource usage profiles by oversubscribing the worker nodes with extra jobs taking into account their CPU resource usage levels and memory capacity

Balancing the use of batteries and opportunistic scheduling policies for maximizing renewable energy consumption in a Cloud data center

International audienceThe fast growth of cloud computing considerably increases the energy consumption of cloud infrastructures, especially , data centers. To reduce brown energy consumption and carbon footprint, renewable energy such as solar/wind energy is considered recently to supply new green data centers. As renewable energy is intermittent and fluctuates from time to time, this paper considers two fundamental approaches for improving the usage of renewable energy in a small/medium-sized data center. One approach is based on opportunistic scheduling: more jobs are performed when renewable energy is available. The other approach relies on Energy Storage Devices (ESDs), which store renewable energy surplus at first and then, provide energy to the data center when renewable energy becomes unavailable. In this paper, we explore these two means to maximize the utilization of on-site renewable energy for small data centers. By using real-world job workload and solar energy traces, our experimental results show the energy consumption with varying battery size and solar panel dimensions for opportunistic scheduling or ESD-only solution. The results also demonstrate that opportunistic scheduling can reduce the demand for ESD capacity. Finally, we find an intermediate solution mixing both approaches in order to achieve a balance in all aspects, implying minimizing the renewable energy losses. It also saves brown energy consumption by up to 33% compared to ESD-only solution

A Vector-Based Approach to Virtual Machine Arrangement

Cloud based data centres benefit from minimizing operating costs and service level agreement violations. Vector-based data centre management policies have been shown to assist with these goals. Vector-based data centre management policies arrange virtual machines in a data centre to minimize the number of hosts being used which translates to greater power efficiency and reduced costs for the data centre overall. I propose an improved vector-based virtual machine arrangement algorithm with two novel additions, namely a technique that changes what it means for a host to be balanced and a concept that excludes undesirable target hosts, thereby improving the arrangement process. Experiments conducted with a simulated data centre demonstrate the effectiveness of this algorithm and compares it to existing algorithms

Contribution au pronostic de défaut dans les systèmes complexes par les techniques intelligentes

Nous avons présenté une nouvelle approche basée sur l'utilisation d'une méthode guidée par les données pour le pronostic des défauts. Cette méthode requiert des données décrivant le processus de dégradation. Lorsque les données sont insuffisantes, la prédiction des états devient difficile avec les modèles profonds de type mémoire à long terme (LSTM), qui nécessitent une quantité importante de données d'apprentissage. Pour résoudre ce problème de rareté des données dans la prédiction de la durée de vie restante (RUL), nous proposons d'adopter une stratégie d'augmentation des données. Les résultats obtenus sont démontrent que l'application d'une stratégie d'augmentation des données, peut améliorer les performances de prédiction de la RUL en utilisant les techniques LSTM. Nous avons validé cette approche en utilisant les données de la NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPPS)