4,887 research outputs found
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
Visual Localisation of Mobile Devices in an Indoor Environment under Network Delay Conditions
Current progresses in home automation and service robotic environment have
highlighted the need to develop interoperability mechanisms that allow a
standard communication between the two systems. During the development of the
DHCompliant protocol, the problem of locating mobile devices in an indoor
environment has been investigated. The communication of the device with the
location service has been carried out to study the time delay that web services
offer in front of the sockets. The importance of obtaining data from real-time
location systems portends that a basic tool for interoperability, such as web
services, can be ineffective in this scenario because of the delays added in
the invocation of services. This paper is focused on introducing a web service
to resolve a coordinates request without any significant delay in comparison
with the sockets
Social-aware hybrid mobile offloading
Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co- located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user's participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users' social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user's present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild
Network-aware Evaluation Environment for Reputation Systems
Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the
most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation
systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run. We present a software tool (NEVER) for network-aware evaluation of reputation systems and their rapid prototyping through experiments performed according to user-specified parameters. To demonstrate effectiveness of NEVER, we analyse reputation models based on the beta distribution and the maximum likelihood estimation
Measuring and Managing Answer Quality for Online Data-Intensive Services
Online data-intensive services parallelize query execution across distributed
software components. Interactive response time is a priority, so online query
executions return answers without waiting for slow running components to
finish. However, data from these slow components could lead to better answers.
We propose Ubora, an approach to measure the effect of slow running components
on the quality of answers. Ubora randomly samples online queries and executes
them twice. The first execution elides data from slow components and provides
fast online answers; the second execution waits for all components to complete.
Ubora uses memoization to speed up mature executions by replaying network
messages exchanged between components. Our systems-level implementation works
for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the
EasyRec Recommendation Engine, and the OpenEphyra question answering system.
Ubora computes answer quality much faster than competing approaches that do not
use memoization. With Ubora, we show that answer quality can and should be used
to guide online admission control. Our adaptive controller processed 37% more
queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor
Vision: a Lightweight Computing Model for Fine-Grained Cloud Computing
Cloud systems differ fundamentally in how they offer and
charge for resources. While some systems provide a generic
programming abstraction at coarse granularity, e.g., a virtual machine rented by the hour, others offer specialized
abstractions with fine-grained accounting on a per-request
basis. In this paper, we explore Tasklets, an abstraction for
instances of short-duration, generic computations that migrate from a host requiring computation to hosts that are
willing to provide computation. Tasklets enable fine-grained
accounting of resource usage, enabling us to build infrastructure that supports trading computing resources according to
various economic models. This computation model is especially attractive in settings where mobile devices can utilize
resources in the cloud to mitigate local resource constraints
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