Showing papers in "Future Generation Computer Systems in 2014"

TL;DR: A green energy-efficient scheduling algorithm using the DVFS technique for Cloud computing datacenters with a dynamic voltage frequency scaling technique that can efficiently increase resource utilization and decrease the energy consumption for executing jobs.

TL;DR: A Cloud management platform to optimize VM consolidation along three main dimensions, namely power consumption, host resources, and networking is proposed for the open-source OpenStack Cloud.

TL;DR: CAPIM is a platform designed to automate the process of collecting and aggregating context information on a large scale that provides support for intelligent Smart City applications, for actively and autonomously adaptation and smart provision of services and content, using the advantages of contextual information.

TL;DR: BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware that provides programming abstractions that allow the rapid development of community BSN applications.

TL;DR: Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.

TL;DR: This survey presents significant insights to the state-of-the-art research conducted pertaining to the DCN domain along with a detailed discussion of the energy efficiency aspects of the DCNs.

TL;DR: This paper presents an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services, allowing them to scale their applications only at bottleneck tiers, and presents the design of an intelligent platform that automates the scaling process.

TL;DR: This paper proposes an alternative service which uses the elastic capacities of Cloud Computing to escape the limitations of the desktop and produce accurate results more rapidly, and improves risk and investment analysis and maintaining accuracy and efficiency whilst improving performance over desktops.

TL;DR: This work is the first that focuses on generating small trusted graphs for large online social networks, and it explores the stable and objective information (such as domain) for inferring trust.

TL;DR: The architecture of Tibidabo is introduced, the first large-scale HPC cluster built from ARM multicore chips, and a detailed performance and energy efficiency evaluation, and the lessons learned for the design and improvement in energy efficiency are presented.

TL;DR: This work proposes a new method to generate suboptimal or sufficiently good schedules for smooth multitask workflows on cloud platforms and proves the suboptimality through mathematical analysis.

TL;DR: The issues of privacy and security in the domain of mobile telecare and Cloud computing are addressed and a model that will allow patients to share their health information with other doctors, nurses or medical professional in a secure and confidential manner is presented.

TL;DR: ESGF is presented as a successful example of integration of disparate open source technologies into a cohesive, wide functional system to serve the needs of the global climate science community.

TL;DR: A CLOUD Resource Broker (CLOUDRB) for efficiently managing cloud resources and completing jobs for scientific applications within a user-specified deadline is designed and integrated with a Deadline-based Job Scheduling and Particle Swarm Optimization-based Resource Allocation mechanism.

TL;DR: The TRAILER project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out, by providing a technological framework using cloud services, a workflow, and a methodology.

TL;DR: A model for estimating the energy consumption of each virtual machine without dedicated measurement hardware is suggested and a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of eachvirtual machine is proposed.

TL;DR: This work distinguishes itself from previous methods by considering AL-DDoS attack detection in heavy backbone traffic and integrates the above detection principles into a modularized defense architecture, which consists of a head-end sensor, a detection module and a traffic filter.

TL;DR: A dedicated modeling language and an application are presented, showing first how it is possible to ease the modeling process and second how the semantic gap between modeling logic and the domain can be reduced, by means of vertical multiformalism modeling.

TL;DR: A new Pareto-based multi-objective workflow scheduling algorithm as an extension to an existing state-of-the-art heuristic capable of computing a set of tradeoff optimal solutions in terms of makespan and energy efficiency is presented.

TL;DR: This paper suggests a cost model for the most general form of a cloud, namely federated hybrid clouds, which is composed of a private cloud and a number of interoperable public clouds and shows that the service placement algorithm with the cost model minimizes the spending for computational services.

TL;DR: A novel pattern mining algorithm is proposed to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications.

TL;DR: This paper proposes a flexible multi-keyword query scheme, called MKQE, which greatly reduces the maintenance overhead during the keyword dictionary expansion and takes keyword weights and user access history into consideration when generating the query result.

TL;DR: The proposed general method to generate temporal semantic annotation of a semantic relation between entities by constructing its connection entities, lexical syntactic patterns, context sentences, context graph, and context communities is proposed.

TL;DR: A novel high-order fuzzy time series model which overcomes the drawback of fuzzification and applies an artificial neural network to compute the complicated fuzzy logical relationships and uses the adaptive expectation model to adjust the forecasting during the defuzzification procedure.

TL;DR: This paper presents an extended access control model based on attributes associated with objects and subjects that incorporates trust and privacy issues in order to make access control decisions sensitive to the cross-organizational collaboration context.

TL;DR: A manual analysis performed over a set of real-world scientific workflows from Taverna, Wings, Galaxy and Vistrails has resulted in set of scientific workflow motifs that are helpful to identify the functionality of the steps in a given workflow, to develop best practices for workflow design, and to develop approaches for automated generation of workflow abstractions.

TL;DR: A QoS-Aware Resource Elasticity (QRE) framework is proposed that allows service providers to make an assessment of the application behavior and develop mechanisms that enable dynamic scalability of cloud resources hosting the application components.

TL;DR: A hybrid ontological and temporal approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach to provide powerful representation capabilities for activity modelling is introduced.

TL;DR: A data-intensive computer system for tree-based mining of frequent itemsets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data is proposed.

TL;DR: It is demonstrated in this paper that the combination of negotiation, brokering and deployment using SLA-aware extensions and autonomic computing principles are required for achieving reliable and efficient service operation in distributed environments.