6,866 research outputs found
Investigation into Mobile Learning Framework in Cloud Computing Platform
Abstract—Cloud computing infrastructure is increasingly
used for distributed applications. Mobile learning
applications deployed in the cloud are a new research
direction. The applications require specific development
approaches for effective and reliable communication. This
paper proposes an interdisciplinary approach for design and
development of mobile applications in the cloud. The
approach includes front service toolkit and backend service
toolkit. The front service toolkit packages data and sends it
to a backend deployed in a cloud computing platform. The
backend service toolkit manages rules and workflow, and
then transmits required results to the front service toolkit.
To further show feasibility of the approach, the paper
introduces a case study and shows its performance
A general purpose programming framework for ubiquitous computing environments
It is important to note that the need to support ad-hoc and potentially mobile arrangements of devices in ubiquitous environments does not fit well within the traditional client/server architecture. We believe peer-to-peer communication offers a preferable alternative due to its decentralised nature, removing dependence on individual nodes. However, this choice adds to the complexity of the developers task. In this paper, we describe a two-tiered approach to address this problem: A lower tier employing peer-to-peer interactions for managing the network infrastructure and an upper tier providing a mobile agent based programming framework. The result is a general purpose framework for developing ubiquitous applications and services, where the underlying complexity is hidden from the developer. This paper discusses our on-going work; presenting our design decisions, features supported by our framework, and some of the challenges still to be addressed in a complex programming environment
Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches
Background: Large-scale biological jobs on high-performance computing systems
require manual intervention if one or more computing cores on which they
execute fail. This places not only a cost on the maintenance of the job, but
also a cost on the time taken for reinstating the job and the risk of losing
data and execution accomplished by the job before it failed. Approaches which
can proactively detect computing core failures and take action to relocate the
computing core's job onto reliable cores can make a significant step towards
automating fault tolerance.
Method: This paper describes an experimental investigation into the use of
multi-agent approaches for fault tolerance. Two approaches are studied, the
first at the job level and the second at the core level. The approaches are
investigated for single core failure scenarios that can occur in the execution
of parallel reduction algorithms on computer clusters. A third approach is
proposed that incorporates multi-agent technology both at the job and core
level. Experiments are pursued in the context of genome searching, a popular
computational biology application.
Result: The key conclusion is that the approaches proposed are feasible for
automating fault tolerance in high-performance computing systems with minimal
human intervention. In a typical experiment in which the fault tolerance is
studied, centralised and decentralised checkpointing approaches on an average
add 90% to the actual time for executing the job. On the other hand, in the
same experiment the multi-agent approaches add only 10% to the overall
execution time.Comment: Computers in Biology and Medicin
Fault Tolerant Adaptive Parallel and Distributed Simulation through Functional Replication
This paper presents FT-GAIA, a software-based fault-tolerant parallel and
distributed simulation middleware. FT-GAIA has being designed to reliably
handle Parallel And Distributed Simulation (PADS) models, which are needed to
properly simulate and analyze complex systems arising in any kind of scientific
or engineering field. PADS takes advantage of multiple execution units run in
multicore processors, cluster of workstations or HPC systems. However, large
computing systems, such as HPC systems that include hundreds of thousands of
computing nodes, have to handle frequent failures of some components. To cope
with this issue, FT-GAIA transparently replicates simulation entities and
distributes them on multiple execution nodes. This allows the simulation to
tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some
protection against Byzantine failures, since interaction messages among the
simulated entities are replicated as well, so that the receiving entity can
identify and discard corrupted messages. Results from an analytical model and
from an experimental evaluation show that FT-GAIA provides a high degree of
fault tolerance, at the cost of a moderate increase in the computational load
of the execution units.Comment: arXiv admin note: substantial text overlap with arXiv:1606.0731
Goal accomplishment tracking for automatic supervision of plan execution
It is common practice to break down plans into a series of goals or sub-goals in order to facilitate plan execution, thereby only burdening the individual agents responsible for their execution with small, easily achievable objectives at any one time, or providing a simple way of sharing these objectives amongst a group of these agents. Ensuring that plans are executed correctly is an essential part of any team management. To allow proper tracking of an agent's progress through a pre-planned set of goals, it is imperative to keep track of which of these goals have already been accomplished. This centralised approach is essential when the agent is part of a team of humans and/or robots, and goal accomplishment is not always being tracked at a low level. This paper presents a framework for an automated supervision system to keep track of changes in world states so as to chart progress through a pre-planned set of goals. An implementation of this framework on a mobile service robot is presented, and applied in an experiment which demonstrates its feasibility
Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems
This paper describes a comprehensive prototype of large-scale fault adaptive
embedded software developed for the proposed Fermilab BTeV high energy physics
experiment. Lightweight self-optimizing agents embedded within Level 1 of the
prototype are responsible for proactive and reactive monitoring and mitigation
based on specified layers of competence. The agents are self-protecting,
detecting cascading failures using a distributed approach. Adaptive,
reconfigurable, and mobile objects for reliablility are designed to be
self-configuring to adapt automatically to dynamically changing environments.
These objects provide a self-healing layer with the ability to discover,
diagnose, and react to discontinuities in real-time processing. A generic
modeling environment was developed to facilitate design and implementation of
hardware resource specifications, application data flow, and failure mitigation
strategies. Level 1 of the planned BTeV trigger system alone will consist of
2500 DSPs, so the number of components and intractable fault scenarios involved
make it impossible to design an `expert system' that applies traditional
centralized mitigative strategies based on rules capturing every possible
system state. Instead, a distributed reactive approach is implemented using the
tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th
Annual IEEE International Conference and Workshop on the Engineering of
Computer Based Systems (ECBS), Washington, DC, April, 200
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