Shadow replication: An energy-aware, fault-tolerant computational model for green cloud computing

As the demand for cloud computing continues to increase, cloud service providers face the daunting challenge to meet the negotiated SLA agreement, in terms of reliability and timely performance, while achieving cost-effectiveness. This challenge is increasingly compounded by the increasing likelihood of failure in large-scale clouds and the rising impact of energy consumption and CO2 emission on the environment. This paper proposes Shadow Replication, a novel fault-tolerance model for cloud computing, which seamlessly addresses failure at scale, while minimizing energy consumption and reducing its impact on the environment. The basic tenet of the model is to associate a suite of shadow processes to execute concurrently with the main process, but initially at a much reduced execution speed, to overcome failures as they occur. Two computationally-feasible schemes are proposed to achieve Shadow Replication. A performance evaluation framework is developed to analyze these schemes and compare their performance to traditional replication-based fault tolerance methods, focusing on the inherent tradeoff between fault tolerance, the specified SLA and profit maximization. The results show that Shadow Replication leads to significant energy reduction, and is better suited for compute-intensive execution models, where up to 30% more profit increase can be achieved due to reduced energy consumption

CHAP : Enabling efficient hardware-based multiple hash schemes for IP lookup

Building a high performance IP lookup engine remains a challenge due to increasingly stringent throughput requirements and the growing size of IP tables. An emerging approach for IP lookup is the use of set associative memory architecture, which is basically a hardware implementation of an open addressing hash table with the property that each row of the hash table can be searched in one memory cycle. While open addressing hash tables, in general, provide good average-case search performance, their memory utilization and worst-case performance can degrade quickly due to bucket overflows. This paper presents a new simple hash probing scheme called CHAP (Content-based HAsh Probing) that tackles the hash overflow problem. In CHAP, the probing is based on the content of the hash table, thus avoiding the classical side effects of probing. We show through experimenting with real IP tables how CHAP can effectively deal with the overflow. © IFIP International Federation for Information Processing 2009

Operation of graphene quantum Hall resistance standard in a cryogen-free table-top system

We demonstrate quantum Hall resistance measurements with metrological accuracy in a small cryogen-free system operating at a temperature of around 3.8K and magnetic fields below 5T. Operating this system requires little experimental knowledge or laboratory infrastructure, thereby greatly advancing the proliferation of primary quantum standards for precision electrical metrology. This significant advance in technology has come about as a result of the unique properties of epitaxial graphene on SiC.Comment: 15 pages, 9 figure

Cascaded Segmentation of Brain Tumors Using Multi-Modality MR Profiles

The accurate identification of the brain tumor boundary and its components is crucial for their effective treatment, but is rendered challenging due to the large variations in tumor size, shape and location, and the inherent inhomogeneity, presence of edema, and infiltration into surrounding tissue. Most of the existing tumor segmentation methods use supervised or unsupervised tissue classification based on the conventional T1 and/or T2 enhanced images and show promising results in differentiating tumor and normal tissues [1-3]. However, perhaps due to the lack of enough MR modalities that could provide a more distinctive appearance signature of each tissue type, these methods have difficulty in differentiating tumor components (enhancing or non-enhancing) and edema. These issues are alleviated by the framework proposed in this paper, that incorporates multi-modal MR images, including the conventional structural MR images and the diffusion tensor imaging (DTI) related maps to create tumor tissue profiles that provide better differentiation between tumor components, edema, and normal tissue types. Tissue profiles are created using pattern classification techniques that learn the multimodal appearance signature of each tissue type by training on expert identified training samples from several patients. The novel use of DTI in the multi-modality framework, helps incorporate the information that tumors grow along white matter tracts [4]. In addition to distinguishing between enhancing and non-enhancing tumors, our framework is also able to identify edema as a separate class, contributing to the solution of tumor boundary detection problem. Tumor segmentation and probabilistic tissue maps generated as a result of applying the classifiers on a new patient reflect the subtle characterizations of tumors and surrounding tissues, and thus could be used to aid tumor diagnosis, tumor boundary identification and tumor surgery planning

Probabilistic Segmentation of Brain Tumors Based on Multi-Modality Magnetic Resonance Images

In this paper, multi-modal Magnetic Resonance (MR) images are integrated into a tissue profile that aims at differentiating tumor components, edema and normal tissue. This is achieved by a tissue classification technique that learns the appearance models of different tissue types based on training samples identified by an expert and assigns tissue labels to each voxel. These tissue classifiers produce probabilistic tissue maps reflecting imaging characteristics of tumors and surrounding tissues that may be employed to aid in diagnosis, tumor boundary delineation, surgery and treatment planning. The main contributions of this work are: 1) conventional structural MR modalities are combined with diffusion tensor imaging data to create an integrated multimodality profile for brain tumors, and 2) in addition to the tumor components of enhancing and non-enhancing tumor types, edema is also characterized as a separate class in our framework. Classification performance is tested on 22 diverse tumor cases using cross-validation

Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images

Rationale and Objectives: Treatment of brain neoplasms can greatly benefit from better delineation of bulk neoplasm boundary and the extent and degree of more subtle neoplastic infiltration. MRI is the primary imaging modality for evaluation before and after therapy, typically combining conventional sequences with more advanced techniques like perfusion-weighted imaging and diffusion tensor imaging (DTI). The purpose of this study is to quantify the multi-parametric imaging profile of neoplasms by integrating structural MRI and DTI via statistical image analysis methods, in order to potentially capture complex and subtle tissue characteristics that are not obvious from any individual image or parameter. Materials and Methods: Five structural MR sequences, namely, B0, Diffusion Weighted Images, FLAIR, T1-weighted, and gadolinium-enhanced T1-weighted, and two scalar maps computed from DTI, i.e., fractional anisotropy and apparent diffusion coefficient, are used to create an intensity-based tissue profile. This is incorporated into a non-linear pattern classification technique to create a multi-parametric probabilistic tissue characterization, which is applied to data from 14 patients with newly diagnosed primary high grade neoplasms who have not received any therapy prior to imaging. Results: Preliminary results demonstrate that this multi-parametric tissue characterization helps to better differentiate between neoplasm, edema and healthy tissue, and to identify tissue that is likely progress to neoplasm in the future. This has been validated on expert assessed tissue. Conclusion: This approach has potential applications in treatment, aiding computer-assisted surgery by determining the spatial distributions of healthy and neoplastic tissue, as well as in identifying tissue that is relatively more prone to tumor recurrence

Mobilized Peripheral Blood Stem Cells Versus Unstimulated Bone Marrow As a Graft Source for T-Cell-Replete Haploidentical Donor Transplantation Using Post-Transplant Cyclophosphamide.

Purpose T-cell-replete HLA-haploidentical donor hematopoietic transplantation using post-transplant cyclophosphamide was originally described using bone marrow (BM). With increasing use of mobilized peripheral blood (PB), we compared transplant outcomes after PB and BM transplants. Patients and Methods A total of 681 patients with hematologic malignancy who underwent transplantation in the United States between 2009 and 2014 received BM (n = 481) or PB (n = 190) grafts. Cox regression models were built to examine differences in transplant outcomes by graft type, adjusting for patient, disease, and transplant characteristics. Results Hematopoietic recovery was similar after transplantation of BM and PB (28-day neutrophil recovery, 88% v 93%, P = .07; 100-day platelet recovery, 88% v 85%, P = .33). Risks of grade 2 to 4 acute (hazard ratio [HR], 0.45; P \u3c .001) and chronic (HR, 0.35; P \u3c .001) graft-versus-host disease were lower with transplantation of BM compared with PB. There were no significant differences in overall survival by graft type (HR, 0.99; P = .98), with rates of 54% and 57% at 2 years after transplantation of BM and PB, respectively. There were no differences in nonrelapse mortality risks (HR, 0.92; P = .74) but relapse risks were higher after transplantation of BM (HR, 1.49; P = .009). Additional exploration confirmed that the higher relapse risks after transplantation of BM were limited to patients with leukemia (HR, 1.73; P = .002) and not lymphoma (HR, 0.87; P = .64). Conclusion PB and BM grafts are suitable for haploidentical transplantation with the post-transplant cyclophosphamide approach but with differing patterns of treatment failure. Although, to our knowledge, this is the most comprehensive comparison, these findings must be validated in a randomized prospective comparison with adequate follow-up