A QoS Aware Resource Placement Approach Inspired on the Behavior of the Social Spider Mating Strategy in the Cloud Environment

Wireless Personal Communications - The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of...     

Escherichia coli,Salmonella spp., and Staphylococcus aureus susceptibility to antimicrobials of human and veterinary importance in poultry sector of India

In India, little attention has been paid on antimicrobial resistance (AMR) in the context of developing “One Health” approach. Hence, utilizing multi-disciplinary approach, we assess the AMR level and dynamics/pattern of multi-drug resistance (MDR) in Escherichia coli, Salmonella spp., and Staphylococcus aureus circulating over the different stages of poultry in India. A total of 342 isolates including E. coli (n = 143), Salmonella spp. (n = 104), and S. aureus (n = 95) were recovered from fecal (n = 80) and cecal (n = 80) samples of chicken, collected across the different poultry-retail shops and poultry-farms located at urban and rural areas of Rajasthan, India, respectively. High rates of AMR to drugs that are critically/highly important both in human and veterinary medicine were observed among all the isolates. Upward trends in AMR prevalence was observed in poultry-retail shops than in poultry-farms. Notably, >90% of all the isolates were MDR, of particular, pattern/prevalence of MDR was substantially varied across the poultry-farms vs. poultry-retail shops. Our results indicate AMR including MDR to be common in E. coli, Salmonella spp., and S. aureus distributed frequently in poultry. The study encourages the formulation of national policy, programmes and further research with a “One Health” approach that can benefits to the human/animal and the environment.     

Conceptual and Implementation Models for the Grid

The Grid is rapidly emerging as the dominant paradigm for wide area distributed application systems. As a result, there is a need for modeling and analyzing the characteristics and requirements of Grid systems and programming models. This work adopts the well-established body of models for distributed computing systems, which are based upon carefully stated assumptions or axioms, as a basis for defining and characterizing Grids and their programming models and systems. The requirements of programming Grid applications and the resulting requirements on the underlying virtual organizations and virtual machines are investigated. The assumptions underlying some of the programming models and systems currently used for Grid applications are identified and their validity in Grid environments is discussed. A more in-depth analysis of two programming systems, the Imperial College E-Science Networked Infrastructure (ICENI) and Accord, using the proposed definitions' structure is presented.     

Instrumental characterization of clay by XRF,XRD and FTIR

Instrumental characterizations of the clay were performed by different techniques such as XRF, XRD and FTIR. XRF shows the chemical compositions of the clay where Al-oxide and silica oxide are present in major quantity whereas XRD confirms the presence of these minerals in clay. FTIR studies show the presence of quartz, alumina, haematite and different mineral matters.     

Hybrid Runtime Management of Space-Time Heterogeneity for Parallel Structured Adaptive Applications

Structured adaptive mesh refinement (SAMR) techniques provide an effective means for dynamically concentrating computational effort and resources to appropriate regions in the application domain. However, due to their dynamism and space-time heterogeneity, scalable parallel implementation of SAMR applications remains a challenge. This paper investigates hybrid runtime management strategies and presents an adaptive hierarchical multipartitioner (AHMP) framework. AHMP dynamically applies multiple partitioners to different regions of the domain, in a hierarchical manner, to match the local requirements of the regions. Key components of the AHMP framework include a segmentation-based clustering algorithm (SBC) that can efficiently identify regions in the domain with relatively homogeneous partitioning requirements, mechanisms for characterizing the partitioning requirements of these regions, and a runtime system for selecting, configuring, and applying the most appropriate partitioner to each region. Further, to address dynamic resource situations for long-running applications, AHMP provides a hybrid partitioning strategy (HPS) that involves application-level pipelining, trading space for time when resources are sufficiently large and underutilized, and an application-level out-of-core strategy (ALOC), trading time for space when resources are scarce in order to enhance the survivability of applications. The AHMP framework has been implemented and experimentally evaluated on up to 1,280 processors of the IBM SP4 cluster at the San Diego Supercomputer Center.     

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.     

ISHair: Importance Sampling for Hair Scattering

We present an importance sampling method for the bidirectional scattering distribution function (bsdf) of hair. Our method is based on the multi‐lobe hair scattering model presented by Sadeghi et al. [ [SPJT10] ]. We reduce noise by drawing samples from a distribution that approximates the bsdf well. Our algorithm is efficient and easy to implement, since the sampling process requires only the evaluation of a few analytic functions, with no significant memory overhead or need for precomputation. We tested our method in a research raytracer and a production renderer based on micropolygon rasterization. We show significant improvements for rendering direct illumination using multiple importance sampling and for rendering indirect illumination using path tracing.     

Proactive thermal management in green datacenters

The increasing demand for faster computing and high storage capacity has resulted in an increase in energy consumption and heat generation in datacenters. Because of the increase in heat generation, cooling requirements have become a critical concern, both in terms of growing operating costs as well as their environmental and societal impacts. Presently, thermal management techniques make an effort to thermally profile and control datacenters’ cooling equipment to increase their efficiency. In conventional thermal management techniques, cooling systems are triggered by the temperature crossing predefined thresholds. Such reactive approaches result in delayed response as the temperature may already be too high, which can result in performance degradation of hardware.     

A Peer-to-Peer Approach to Web Service Discovery

Web Services are emerging as a dominant paradigm for constructing and composing distributed business applications and enabling enterprise-wide interoperability. A critical factor to the overall utility of Web Services is a scalable, flexible and robust discovery mechanism. This paper presents a Peer-to-Peer (P2P) indexing system and associated P2P storage that supports large-scale, decentralized, real-time search capabilities. The presented system supports complex queries containing partial keywords and wildcards. Furthermore, it guarantees that all existing data elements matching a query will be found with bounded costs in terms of number of messages and number of nodes involved. The key innovation is a dimension reducing indexing scheme that effectively maps the multidimensional information space to physical peers. The design and an experimental evaluation of the system are presented.