A quantitative comparison of load balancing approaches in distributed object computing systems

Several load balancing schemes are recently proposed for distributed object computing systems, which are widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. However, while the rationales and mechanisms employed are dramatically different, the relative strengths and weaknesses of these approaches are unknown, making it difficult for a practitioner to choose an appropriate approach for the problem at hand. In this paper, we describe in detail three representative approaches, which are all practicable, and present a quantitative comparison using our experimental distributed object computing platform. Among these three approaches, namely, JavaSpaces based, request redirection based, and fuzzy decision based, we find that the fuzzy decision based algorithm outperforms the other two considerably.published_or_final_versio

A Pragmatic Approach for the Semantic Description and Matching of Pervasive Resources

The increasing popularity of personal wireless devices has raised new demands for the efficient discovery of heterogeneous devices and services in pervasive environments. With the advancement of the electronic world, the diversity of available services is increasing rapidly. %This raises new demands for the efficient discovery and location of heterogeneous services and resources in dynamically changing environments. Traditional approaches for service discovery describe services at a syntactic level and the matching mechanisms available for these approaches are limited to syntactic comparisons based on attributes or interfaces. In order to overcome these limitations, there has been an increased interest in the use of semantic description and matching techniques to support effective service discovery. In this paper, we present a semantic matching approach to facilitate the discovery of device-based services in pervasive environments. The approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request during the matching process. The solution has been systematically evaluated for its retrieval effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. Another important practical concern is the efficiency and the scalability of the semantic matching solution. Therefore, we have evaluated the scalability of the proposed solution by investigating the variation in matching time in response to increasing numbers of advertisements and increasing request sizes, and have presented the empirical results

Autonomic Thread Load Balancing Using Fuzzy Logic

In today’s inter-networked and information savvy world, developers of large-scale applications are experiencing an increase in complexity of their software systems due to the continual integration of new services, software, and hardware. The concept of multithreading and, consequently, multi-threaded software, have become the norm, with most software applications today consisting of multiple threads to achieve improved performance and concurrency. However, this performance gain comes at the cost of an increase in the complexity of applications to such an extent that it has begun to exceed the human ability to design, manage, and secure these applications. This complexity affects not only the developers but also the administrators who manage the software systems and the hardware. The recognition of this problem has instigated various research efforts for simplifying and easing the management and maintenance of multithreaded software. This thesis presents one such solution to address this problem of increased maintenance complexity in the form of a novel approach to autonomously balance thread loads. The two primary design paradigms used in this research include autonomie computing, which is a vision that strives for system self-management by making quick and smart decisions, and fuzzy logic, which provides the ability to analyze multiple crisp inputs to make a single crisp output based on a set of pre-defined rules. This research integrates the best features of both paradigms and the resultant simulation exhibits properties such as self-optimization where in the fuzzy logic reasoning engine determines the appropriateness of existence and execution of a thread on a machine, and selfconfiguration, where inappropriate threads are relocated to other systems for improved performance. The simulation has been implemented in Java 1.5 and a case for the validation of the proposed approach is presented through a detailed discussion of three scenarios covering a range of system and network conditions, along with detailed results presented as evidence for the feasibility of the approach

Analysis Of Aircraft Arrival Delay And Airport On-time Performance

While existing grid environments cater to specific needs of a particular user community, we need to go beyond them and consider general-purpose large-scale distributed systems consisting of large collections of heterogeneous computers and communication systems shared by a large user population with very diverse requirements. Coordination, matchmaking, and resource allocation are among the essential functions of large-scale distributed systems. Although deterministic approaches for coordination, matchmaking, and resource allocation have been well studied, they are not suitable for large-scale distributed systems due to the large-scale, the autonomy, and the dynamics of the systems. We have to seek for nondeterministic solutions for large-scale distributed systems. In this dissertation we describe our work on a coordination service, a matchmaking service, and a macro-economic resource allocation model for large-scale distributed systems. The coordination service coordinates the execution of complex tasks in a dynamic environment, the matchmaking service supports finding the appropriate resources for users, and the macro-economic resource allocation model allows a broker to mediate resource providers who want to maximize their revenues and resource consumers who want to get the best resources at the lowest possible price, with some global objectives, e.g., to maximize the resource utilization of the system

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Intensional Cyberforensics

This work focuses on the application of intensional logic to cyberforensic analysis and its benefits and difficulties are compared with the finite-state-automata approach. This work extends the use of the intensional programming paradigm to the modeling and implementation of a cyberforensics investigation process with backtracing of event reconstruction, in which evidence is modeled by multidimensional hierarchical contexts, and proofs or disproofs of claims are undertaken in an eductive manner of evaluation. This approach is a practical, context-aware improvement over the finite state automata (FSA) approach we have seen in previous work. As a base implementation language model, we use in this approach a new dialect of the Lucid programming language, called Forensic Lucid, and we focus on defining hierarchical contexts based on intensional logic for the distributed evaluation of cyberforensic expressions. We also augment the work with credibility factors surrounding digital evidence and witness accounts, which have not been previously modeled. The Forensic Lucid programming language, used for this intensional cyberforensic analysis, formally presented through its syntax and operational semantics. In large part, the language is based on its predecessor and codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective Lucid, and JOOIP bound by the underlying intensional programming paradigm.Comment: 412 pages, 94 figures, 18 tables, 19 algorithms and listings; PhD thesis; v2 corrects some typos and refs; also available on Spectrum at http://spectrum.library.concordia.ca/977460

Semantic description and matching of services for pervasive environments

With the evolution of the World Wide Web and the advancement of the electronic world, the diversity of available services is increasing rapidly.This raises new demands for the efficient discovery and location of heterogeneous services and resources in dynamically changing environments. The traditional approaches for service discovery such as UDDI, Salutation, SLP etc. characterise the services by using predefined service categories and fixed attribute value pairs and the matching techniques in these approaches are limited to syntactic comparisons based on attributes or interfaces. More recently with the popularity of Semantic Web technologies, there has been an increased interest in the application of reasoning mechanisms to support discovery and matching. These approaches provide important directions in overcoming the limitations present in the traditional approaches to service discovery. However, these still have limitations and have overlooked issues that need to be addressed; particularly these approaches do not have an effective ranking criterion to facilitate the ordering of the potential matches, according to their suitability to satisfy the request under concern. This thesis presents a semantic matching framework to facilitate effective discovery of device based services in pervasive environments. This offers a ranking mechanism that will order the available services in the order of their suitability and also considers priorities placed on individual requirements in a request during the matching process. The proposed approach has been implemented in a pervasive scenario for matching device-based services. The Device Ontology which has been developed as part of this research, has been used to describe the devices and their services. The retrieval effectiveness of this semantic matching approach has been formally investigated through the use of human participant studies and the experimental results have indicated that the results correlate well with human perception. The performance of the solution has also been evaluated, to explore the effects of employing reasoning mechanisms on the efficiency of the matching process. Specifically the scalability of the solution has been investigated with respect to the request size and the number of advertisements involved in matching.EThOS - Electronic Theses Online ServiceGBUnited Kingdo