10,287 research outputs found
On Optimal and Fair Service Allocation in Mobile Cloud Computing
This paper studies the optimal and fair service allocation for a variety of
mobile applications (single or group and collaborative mobile applications) in
mobile cloud computing. We exploit the observation that using tiered clouds,
i.e. clouds at multiple levels (local and public) can increase the performance
and scalability of mobile applications. We proposed a novel framework to model
mobile applications as a location-time workflows (LTW) of tasks; here users
mobility patterns are translated to mobile service usage patterns. We show that
an optimal mapping of LTWs to tiered cloud resources considering multiple QoS
goals such application delay, device power consumption and user cost/price is
an NP-hard problem for both single and group-based applications. We propose an
efficient heuristic algorithm called MuSIC that is able to perform well (73% of
optimal, 30% better than simple strategies), and scale well to a large number
of users while ensuring high mobile application QoS. We evaluate MuSIC and the
2-tier mobile cloud approach via implementation (on real world clouds) and
extensive simulations using rich mobile applications like intensive signal
processing, video streaming and multimedia file sharing applications. Our
experimental and simulation results indicate that MuSIC supports scalable
operation (100+ concurrent users executing complex workflows) while improving
QoS. We observe about 25% lower delays and power (under fixed price
constraints) and about 35% decrease in price (considering fixed delay) in
comparison to only using the public cloud. Our studies also show that MuSIC
performs quite well under different mobility patterns, e.g. random waypoint and
Manhattan models
Towards agent-based crowd simulation in airports using games technology
We adapt popular video games technology for an agent-based crowd simulation in an airport terminal. To achieve this, we investigate the unique traits of airports and implement a virtual crowd by exploiting a scalable layered intelligence technique in combination with physics middleware and a socialforces approach. Our experiments show that the framework runs at interactive frame-rate and evaluate the scalability with increasing number of agents demonstrating
navigation behaviour
Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things
The number of connected sensors and devices is expected to increase to billions in the near
future. However, centralised cloud-computing data centres present various challenges to meet the
requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput
and bandwidth constraints. Edge computing is becoming the standard computing paradigm for
latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related
to centralised cloud-computing models. Such a paradigm relies on bringing computation close to
the source of data, which presents serious operational challenges for large-scale cloud-computing
providers. In this work, we present an architecture composed of low-cost Single-Board-Computer
clusters near to data sources, and centralised cloud-computing data centres. The proposed
cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT
workload requirements while keeping scalability. We include an extensive empirical analysis to
assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data
centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud
architectures, and evaluate them through extensive simulation. We finally show that acquisition costs
can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Developing Real-Time Emergency Management Applications: Methodology for a Novel Programming Model Approach
The last years have been characterized by the arising of highly distributed computing
platforms composed of a heterogeneity of computing and communication resources including
centralized high-performance computing architectures (e.g. clusters or large shared-memory
machines), as well as multi-/many-core components also integrated into mobile nodes
and network facilities. The emerging of computational paradigms such as Grid and Cloud
Computing, provides potential solutions to integrate such platforms with data systems, natural
phenomena simulations, knowledge discovery and decision support systems responding to a
dynamic demand of remote computing and communication resources and services.
In this context time-critical applications, notably emergency management systems, are
composed of complex sets of application components specialized for executing specific
computations, which are able to cooperate in such a way as to perform a global goal in a
distributed manner. Since the last years the scientific community has been involved in facing
with the programming issues of distributed systems, aimed at the definition of applications
featuring an increasing complexity in the number of distributed components, in the spatial
distribution and cooperation between interested parties and in their degree of heterogeneity.
Over the last decade the research trend in distributed computing has been focused on
a crucial objective. The wide-ranging composition of distributed platforms in terms of
different classes of computing nodes and network technologies, the strong diffusion of
applications that require real-time elaborations and online compute-intensive processing as
in the case of emergency management systems, lead to a pronounced tendency of systems
towards properties like self-managing, self-organization, self-controlling and strictly speaking
adaptivity.
Adaptivity implies the development, deployment, execution and management of applications
that, in general, are dynamic in nature. Dynamicity concerns the number and the specific
identification of cooperating components, the deployment and composition of the most
suitable versions of software components on processing and networking resources and
services, i.e., both the quantity and the quality of the application components to achieve
the needed Quality of Service (QoS). In time-critical applications the QoS specification
can dynamically vary during the execution, according to the user intentions and the
Developing Real-Time Emergency
Management Applications: Methodology for
a Novel Programming Model Approach
Gabriele Mencagli and Marco Vanneschi
Department of Computer Science, University of Pisa, L. Bruno Pontecorvo, Pisa
Italy
2
2 Will-be-set-by-IN-TECH
information produced by sensors and services, as well as according to the monitored state
and performance of networks and nodes.
The general reference point for this kind of systems is the Grid paradigm which, by
definition, aims to enable the access, selection and aggregation of a variety of distributed and
heterogeneous resources and services. However, though notable advancements have been
achieved in recent years, current Grid technology is not yet able to supply the needed software
tools with the features of high adaptivity, ubiquity, proactivity, self-organization, scalability
and performance, interoperability, as well as fault tolerance and security, of the emerging
applications.
For this reason in this chapter we will study a methodology for designing high-performance
computations able to exploit the heterogeneity and dynamicity of distributed environments
by expressing adaptivity and QoS-awareness directly at the application level. An effective
approach needs to address issues like QoS predictability of different application configurations
as well as the predictability of reconfiguration costs. Moreover adaptation strategies need to
be developed assuring properties like the stability degree of a reconfiguration decision and the
execution optimality (i.e. select reconfigurations accounting proper trade-offs among different
QoS objectives). In this chapter we will present the basic points of a novel approach that lays
the foundations for future programming model environments for time-critical applications
such as emergency management systems.
The organization of this chapter is the following. In Section 2 we will compare the existing
research works for developing adaptive systems in critical environments, highlighting their
drawbacks and inefficiencies. In Section 3, in order to clarify the application scenarios that
we are considering, we will present an emergency management system in which the run-time
selection of proper application configuration parameters is of great importance for meeting the
desired QoS constraints. In Section 4we will describe the basic points of our approach in terms
of how compute-intensive operations can be programmed, how they can be dynamically
modified and how adaptation strategies can be expressed. In Section 5 our approach will
be contextualize to the definition of an adaptive parallel module, which is a building block
for composing complex and distributed adaptive computations. Finally in Section 6 we will
describe a set of experimental results that show the viability of our approach and in Section 7
we will give the concluding remarks of this chapter
Implanting Life-Cycle Privacy Policies in a Context Database
Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies
- …