1,082,388 research outputs found
Next challenges for adaptive learning systems
Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p
Using a desktop grid to support simulation modelling
Simulation is characterized by the need to run multiple sets of computationally intensive experiments. We argue that Grid computing can reduce the overall execution time of such experiments by tapping into the typically underutilized network of departmental desktop PCs, collectively known as desktop grids. Commercial-off-the-shelf simulation packages (CSPs) are used in industry to simulate models. To investigate if Grid computing can benefit simulation, this paper introduces our desktop grid, WinGrid, and discusses how this can be used to support the processing needs of CSPs. Results indicate a linear speed up and that Grid computing does indeed hold promise for simulation
Performance Analysis of Publish/Subscribe Systems
The Desktop Grid offers solutions to overcome several challenges and to
answer increasingly needs of scientific computing. Its technology consists
mainly in exploiting resources, geographically dispersed, to treat complex
applications needing big power of calculation and/or important storage
capacity. However, as resources number increases, the need for scalability,
self-organisation, dynamic reconfigurations, decentralisation and performance
becomes more and more essential. Since such properties are exhibited by P2P
systems, the convergence of grid computing and P2P computing seems natural. In
this context, this paper evaluates the scalability and performance of P2P tools
for discovering and registering services. Three protocols are used for this
purpose: Bonjour, Avahi and Free-Pastry. We have studied the behaviour of
theses protocols related to two criteria: the elapsed time for registrations
services and the needed time to discover new services. Our aim is to analyse
these results in order to choose the best protocol we can use in order to
create a decentralised middleware for desktop grid
Distributed Exact Shortest Paths in Sublinear Time
The distributed single-source shortest paths problem is one of the most
fundamental and central problems in the message-passing distributed computing.
Classical Bellman-Ford algorithm solves it in time, where is the
number of vertices in the input graph . Peleg and Rubinovich (FOCS'99)
showed a lower bound of for this problem, where
is the hop-diameter of .
Whether or not this problem can be solved in time when is
relatively small is a major notorious open question. Despite intensive research
\cite{LP13,N14,HKN15,EN16,BKKL16} that yielded near-optimal algorithms for the
approximate variant of this problem, no progress was reported for the original
problem.
In this paper we answer this question in the affirmative. We devise an
algorithm that requires time, for , and time, for larger . This
running time is sublinear in in almost the entire range of parameters,
specifically, for . For the all-pairs shortest paths
problem, our algorithm requires time, regardless of
the value of .
We also devise the first algorithm with non-trivial complexity guarantees for
computing exact shortest paths in the multipass semi-streaming model of
computation.
From the technical viewpoint, our algorithm computes a hopset of a
skeleton graph of without first computing itself. We then conduct
a Bellman-Ford exploration in , while computing the required edges
of on the fly. As a result, our algorithm computes exactly those edges of
that it really needs, rather than computing approximately the entire
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
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