4,247 research outputs found
DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
Multi-tenancy in resource-constrained environments is a key challenge in Edge
computing. In this paper, we develop 'DYVERSE: DYnamic VERtical Scaling in
Edge' environments, which is the first light-weight and dynamic vertical
scaling mechanism for managing resources allocated to applications for
facilitating multi-tenancy in Edge environments. To enable dynamic vertical
scaling, one static and three dynamic priority management approaches that are
workload-aware, community-aware and system-aware, respectively are proposed.
This research advocates that dynamic vertical scaling and priority management
approaches reduce Service Level Objective (SLO) violation rates. An online-game
and a face detection workload in a Cloud-Edge test-bed are used to validate the
research. The merits of DYVERSE is that there is only a sub-second overhead per
Edge server when 32 Edge servers are deployed on a single Edge node. When
compared to executing applications on the Edge servers without dynamic vertical
scaling, static priorities and dynamic priorities reduce SLO violation rates of
requests by up to 4% and 12% for the online game, respectively, and in both
cases 6% for the face detection workload. Moreover, for both workloads, the
system-aware dynamic vertical scaling method effectively reduces the latency of
non-violated requests, when compared to other methods
Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling
Shelf and coastal sea processes extend from the atmosphere through the water
column and into the sea bed. These processes are driven by physical, chemical,
and biological interactions at local scales, and they are influenced by
transport and cross strong spatial gradients. The linkages between domains and
many different processes are not adequately described in current model systems.
Their limited integration level in part reflects lacking modularity and
flexibility; this shortcoming hinders the exchange of data and model components
and has historically imposed supremacy of specific physical driver models. We
here present the Modular System for Shelves and Coasts (MOSSCO,
http://www.mossco.de), a novel domain and process coupling system
tailored---but not limited--- to the coupling challenges of and applications in
the coastal ocean. MOSSCO builds on the existing coupling technology Earth
System Modeling Framework and on the Framework for Aquatic Biogeochemical
Models, thereby creating a unique level of modularity in both domain and
process coupling; the new framework adds rich metadata, flexible scheduling,
configurations that allow several tens of models to be coupled, and tested
setups for coastal coupled applications. That way, MOSSCO addresses the
technology needs of a growing marine coastal Earth System community that
encompasses very different disciplines, numerical tools, and research
questions.Comment: 30 pages, 6 figures, submitted to Geoscientific Model Development
Discussion
E-mail and Direct Participation in Decision Making: A Literature Review
This paper reviews the literature on the effects of the use of e-mail on direct participation in decision making (PDM) in organisations. After a brief review of the organisational literature on participation the paper distinguishes e-mail theories on direct participation in three different theoretical perspectives. Then the paper focuses the attention on the role of e-mail in affecting task type, vertical and horizontal communication and their consequences for PDM. Finally the paper presents indications and open questions for future research.email, e-mail, decision making, participation in decision making, literature review,
Image analysis and statistical inference in neuroimaging with R
R is a language and environment for statistical computing and graphics.
It can be considered an alternative implementation of the S language
developed in the 1970s and 1980s for data analysis and graphics (Becker and
Chambers, 1984; Becker et al., 1988). The R language is part of the GNU
project and offers versions that compile and run on almost every major
operating system currently available. We highlight several R packages built
specifically for the analysis of neuroimaging data in the context of
functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI.
We review their methodology and give an overview of their capabilities for
neuroimaging. In addition we summarize some of the current activities in the
area of neuroimaging software development in R
ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY
Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs.
In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities.
We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries.
When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling
A QoS registry for adaptive real-time service-oriented applications
Real-time service-oriented applications are charac- terized by Quality of Service (QoS) requirements that cannot be properly managed by using classical real-time systems methodologies. In dynamic environments the QoS management can effectively leverage adaptive techniques, that provide flexibility and do not require a complex offline analysis. In turn, such techniques make a massive use of on-line collected data regarding the application performance and the resource requirements. Moreover, a common issue for adaptive systems is the one of deciding the initial configuration of the application and/or the run-time environment at the time of service instantiation. In this paper, we propose a QoS registry for coping with these issues and supporting the configuration of proper scheduling parameters in real-time Service Oriented Architectures (SOAs). The registry permits to gather QoS data related to different functional behaviors of applications, to predict the future trend based on data already collected and to permanently store such data for an effective reuse at the time of future re-instantiations. We have also built an implementation of such registry, computed its overhead costs and performed some experiments for showing the effectiveness in auto-tuning resource allocations for providing QoS guarantees in a real-time SOA
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
- …