20 research outputs found
An agent-based intelligent environmental monitoring system
Fairly rapid environmental changes call for continuous surveillance and
on-line decision making. There are two main areas where IT technologies can be
valuable. In this paper we present a multi-agent system for monitoring and
assessing air-quality attributes, which uses data coming from a meteorological
station. A community of software agents is assigned to monitor and validate
measurements coming from several sensors, to assess air-quality, and, finally,
to fire alarms to appropriate recipients, when needed. Data mining techniques
have been used for adding data-driven, customized intelligence into agents. The
architecture of the developed system, its domain ontology, and typical agent
interactions are presented. Finally, the deployment of a real-world test case
is demonstrated.Comment: Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive
Agents, Air-Quality Monitorin
Agentless robust load sharing strategy for utilising hetero-geneous resources over wide area network
Resource monitoring and performance prediction services have always been regarded as important keys to improving the performance of load sharing strategy. However, the traditional methodologies usually require specific performance information, which can only be collected by installing proprietary agents on all participating resources. This requirement of implementing a single unified monitoring service may not be feasible because of the differences in the underlying systems and organisation policies. To address this problem, we define a new load sharing strategy which bases the load decision on a simple performance estimation that can be measured easily at the coordinator node. Our proposed strategy relies on a stage-based dynamic task allocation to handle the imprecision of our performance estimation and to correct load distribution on-the-fly. The simulation results showed that the performance of our strategy is comparable or better than traditional strategies, especially when the performance information from the monitoring service is not accurate
Adaptive monitoring: A systematic mapping
Context:
Adaptive monitoring is a method used in a variety of domains for responding to changing conditions. It has been applied in different ways, from monitoring systems’ customization to re-composition, in different application domains. However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches.
Objective:
To characterize the current state of the art on adaptive monitoring, specifically to: (a) identify the main concepts in the adaptive monitoring topic; (b) determine the demographic characteristics of the studies published in this topic; (c) identify how adaptive monitoring is conducted and evaluated by the different approaches; (d) identify patterns in the approaches supporting adaptive monitoring.
Method:
We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices. We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers. Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies. Finally, we have applied data mining techniques for identifying patterns in the solutions.
Results:
We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring. By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies’ demographic data and arranged the studies into approaches; (3) characterized the main approaches’ contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions.
Conclusions
This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field. Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.Peer ReviewedPostprint (author's final draft
Uniform resource visualization
Computing environments continue to increase in scale, heterogeneity, and hierarchy, with resource usage varying dynamically during program execution. Computational and data grids and distributed collaboration environments are examples. To understand performance and gain insights into developing applications that efficiently use system resources, performance visualization has proven useful. Performance visualization tools, however, often are specific to a particular resource at a certain level of the system, possibly with fixed views. Thus, they limit a user\u27s ability to observe a performance problem associated with multiple resources across system levels and platforms. To address this limitation, information integration is necessary. In this research, we propose a new performance visualization framework, Uniform Resource Visualization (URV), focusing on integration of performance information into system-level visualizations. The goal of URV research is to systemize the performance visualization of resources with reusable and composable visualizations
Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System
Resource Discovery (RD) is a key issue in Internet-based distributed sytems such as
grid. RD is about locating an appropriate resource/service type that matches the user's
application requirements. This is very important, as resource reservation and task
scheduling are based on it. Unfortunately, RD in grid is very challenging as resources
and users are distributed, resources are heterogeneous in their platforms, status of the
resources is dynamic (resources can join or leave the system without any prior notice)
and most recently the introduction of a new type of grid called intergrid (grid of grids)
with the use of multi middlewares. Such situation requires an RD system that has rich
interoperability, scalability, decentralization and dynamism features. However,
existing grid RD systems have difficulties to attain these features. Not only that, they
lack the review and evaluation studies, which may highlight the gap in achieving the
required features. Therefore, this work discusses the problem associated with intergrid
RD from two perspectives. First, reviewing and classifying the current grid RD
systems in such a way that may be useful for discussing and comparing them. Second,
propose a novel RD framework that has the aforementioned required RD features. In
the former, we mainly focus on the studies that aim to achieve interoperability in the
first place, which are known as RD systems that use semantic information (semantic
technology). In particular, we classify such systems based on their qualitative use of
the semantic information. We evaluate the classified studies based on their degree of
accomplishment of interoperability and the other RD requirements, and draw the
future research direction of this field. Meanwhile in the latter, we name the new
framework as semantic-based scalable decentralized dynamic RD. The framework
further contains two main components which are service description, and service
registration and discovery models. The earlier consists of a set of ontologies and
services. Ontologies are used as a data model for service description, whereas the
services are to accomplish the description process. The service registration is also based on ontology, where nodes of the service (service providers) are classified to
some classes according to the ontology concepts, which means each class represents a
concept in the ontology. Each class has a head, which is elected among its own class
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nodes/members. Head plays the role of a registry in its class and communicates with
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the other heads of the classes in a peer to peer manner during the discovery process.
We further introduce two intelligent agents to automate the discovery process which
are Request Agent (RA) and Description Agent (DA). Eaclj. node is supposed to have
both agents. DA describes the service capabilities based on the ontology, and RA
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carries the service requests based on the ontology as well. We design a service search
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algorithm for the RA that starts the service look up from the class of request origin
first, then to the other classes.
We finally evaluate the performance of our framework ~ith extensive simulation
experiments, the result of which confirms the effectiveness of the proposed system in
satisfying the required RD features (interoperability, scalability, decentralization and
dynamism). In short, our main contributions are outlined new key taxonomy for the
semantic-based grid RD studies; an interoperable semantic description RD component
model for intergrid services metadata representation; a semantic distributed registry
architecture for indexing service metadata; and an agent-qased service search and
selection algorithm.
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