A trustworthy mobile agent infrastructure for network management

Despite several advantages inherent in mobile-agent-based approaches to network management as compared to traditional SNMP-based approaches, industry is reluctant to adopt the mobile agent paradigm as a replacement for the existing manager-agent model; the management community requires an evolutionary, rather than a revolutionary, use of mobile agents. Furthermore, security for distributed management is a major concern; agent-based management systems inherit the security risks of mobile agents. We have developed a Java-based mobile agent infrastructure for network management that enables the safe integration of mobile agents with the SNMP protocol. The security of the system has been evaluated under agent to agent-platform and agent to agent attacks and has proved trustworthy in the performance of network management tasks

The SCC and the SICSA multi-core challenge

Two phases of the SICSA Multi-core Challenge have gone past. The first challenge was to produce concordances of books for sequences of words up to length N; and the second to simulate the motion of N celestial bodies under gravity. We took both challenges on the SCC, using C and the Linux Shell. This paper is an account of the experiences gained. It also gives a shorter account of the performance of other systems on the same set of problems, as they provide benchmarks against which the SCC performance can be compared with

DEBS Grand Challenge: Glasgow Automata Illustrated

The challenge is solved using Glasgow automata, concise complex event processing engines executable in the context of a topic-based publish/subscribe cache of event streams and relations. The imperative programming style of the Glasgow Automaton Programming Language (GAPL) enables multiple, efficient realisations of the two challenge queries

Assessing the Impact of the Latest Deregulatory Developments in the EU28 Transport Industry Production: a Critical Review Based on Empirical Data

The European Transport Industry has been heavily deregulated during the past 30 years, through initiatives that increase competition among incumbents primarily by reducing the entry restrictions and by opening the market(s) for non-state owned companies. This paper discusses the impact the latest interventions by the European Commission in terms of further opening the transport industry have had. By analyzing current data, the impact of economic deregulation on the volume of transport industry production is studied. The empirical evidence confirm the positive impact on the system wide level suggesting the further use of deregulatory tools in order to support the industry growth. Additionally, recommendations for further research are made in order to understand more in depth the indirect effects of deregulation

UDRF: Multi-resource Fairness for Complex Jobs with Placement Constraints

In this paper, we study the problem of multi- resource fairness in systems running complex jobs that consist of multiple interconnected tasks. A job is considered finished when all its corresponding tasks have been executed in the system. Tasks can have different resource requirements. Because of special demands on particular hardware or software, tasks may have placement constraints limiting the type of machines they can run on. We develop User-Dependence Dominant Resource Fairness (UDRF), a generalized version of max-min fairness that combines graph theory and the notion of dominant re- source shares to ensure multi-resource fairness between complex workflows. UDRF satisfies several desirable properties including strategy proofness, which ensures that users do not benefit from misreporting their true resource demands. We propose an offline algorithm that computes optimal UDRF allocation. But optimality comes at a cost, especially for systems where schedulers need to make thousands of online scheduling decisions per second. Therefore, we develop a lightweight online algorithm that closely approximates UDRF. Besides that, we propose a simple mechanism to decentralize the UDRF scheduling process across multiple schedulers. Large-scale simulations driven by Google cluster-usage traces show that UDRF achieves better resource utilization and throughput compared to the current state-of-the-art in fair resource allocation

Strategic correlations for maritime clusters

Maritime clusters formulate appealing objects of study, for many viewpoints. At the same time, the theory is not homogenous nor compartmentalized, although some main themes do seem to be prevalent. The latter include innovation, competitiveness, strategy, and policy. Through an inclusive analysis of the literature, data mining is attempted within this body of knowledge. A dominant instance within the literature is the existence of a strategic case, along with the fact that this is rooted within a recurring constellation of topics vested within strategic management. These occurrences are categorized per generic premise, according to a coding protocol. The data is then adjusted into dichotomous variables, to investigate dependent samples’ correlation. The aim of this methodology is to examine association between the categorical variables of academic impact and the presence of a strategic case. The results of the analysis are statistically significant. This research can provoke novel directions with respect to strategic and tactical decision making, for academia and practice. In addition, this work provides a rudimentary inventory of the literature of maritime clusters, that can aid the formulation and investigation of further statistical hypotheses

Saber: window-based hybrid stream processing for heterogeneous architectures

Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes