Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture

Local Increase of Arginase Activity in Lesions of Patients with Cutaneous Leishmaniasis in Ethiopia

The leishmaniases are a complex of diseases caused by Leishmania parasites. Currently, the diseases affect an estimated 12 million people in 88 countries, and approximately 350 million more people are at risk. The leishmaniases belong to the most neglected tropical diseases, affecting the poorest populations, for whom access to diagnosis and effective treatment are often not available. Leishmania parasites infect cells of the immune system called macrophages, which have the capacity to eliminate the intracellular parasites when they receive the appropriate signals from other cells of the immune system. In nonhealing persistent leishmaniasis, lymphocytes are unable to transmit the signals to macrophages required to kill the intracellular parasites. The local upregulation of the enzyme arginase has been shown to impair lymphocyte effector functions at the site of pathology. In this study, we tested the activity of this enzyme in skin lesions of patients presenting with localized cutaneous leishmaniasis. Our results show that arginase is highly upregulated in these lesions. This increase in arginase activity coincides with lower expression of a signalling molecule in lymphocytes, which is essential for efficient activation of these cells. These results suggest that increased arginase expression in the localized cutaneous lesions might contribute to persistent disease in patients presenting with cutaneous leishmaniasis

Autonomous Management for Pervasive Computing

Network complexity will increase dramatically over the next 5 years as will the amount of devices inhabiting these networks. Ad-hoc and active paradigms will make the already onerous task of network management increasingly problematic. An approach to managing such networks based on bacterial colony behaviour is discussed, offering innate abilities for essential tasks such as software proliferation, load balancing and differing but distinct qualities of service. Robustness to fractal request streams is also demonstrated using real world requests as a source of simulated network load. The ‘hands off’ element of the adaptive algorithm is a major asset for any configuration and optimisation task. This biologically inspired adaptive management solution could be the ideal approach to managing the behaviour of complex data networks of the future

Biologically Inspired Models for Sensor Network Design

This paper discusses the purposes, requirements and challenges of creating pervasive networks of devices that have sensor technology and embedded computation. The demands placed on these sensor networks suggest that they have to function in a similar way to self-organised biological systems so concepts can be taken from them to design appropriate models for how sensor networks could work. This paper focuses on the reasons why this is possible and gives examples of previously studied biological system

The role of complex systems in the management of pervasive computing.

Network complexity will increase dramatically over the next 5 years as will the amount of devices inhabiting these networks. Ad-hoc and active paradigms will make the already onerous task of network management increasingly problematic. An approach to managing such networks based on bacterial colony behaviour is discussed, offering innate abilities for essential tasks such as software proliferation, load balancing and differing but distinct qualities of service. Robustness to fractal request streams is also demonstrated using real world requests as a source of simulated network load. The ‘hands off’ element of the adaptive algorithm is a major asset for any configuration and optimisation task. This biologically inspired adaptive management solution could be the ideal approach to managing the behaviour of complex data networks of the future