Searching for SNPs with cloud computing

As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow is a cloud-computing software tool that combines the aligner Bowtie and the SNP caller SOAPsnp. Executing in parallel using Hadoop, Crossbow analyzes data comprising 38-fold coverage of the human genome in three hours using a 320-CPU cluster rented from a cloud computing service for about $85. Crossbow is available from .     

The case for cloud computing in genome informatics

With DNA sequencing now getting cheaper more quickly than data storage or computation, the time may have come for genome informatics to migrate to the cloud.     

Adaptive Combiner for MapReduce on cloud computing

MapReduce is a programming model to process a massive amount of data on cloud computing. MapReduce processes data in two phases and needs to transfer intermediate data among computers between phases. MapReduce allows programmers to aggregate intermediate data with a function named combiner before transferring it. By leaving programmers the choice of using a combiner, MapReduce has a risk of performance degradation because aggregating intermediate data benefits some applications but harms others. Now, MapReduce can work with our proposal named the Adaptive Combiner for MapReduce (ACMR) to automatically, smartly, and trainer for getting a better performance without any interference of programmers. In experiments on seven applications, MapReduce can utilize ACMR to get the performance comparable to the system that is optimal for an application.     

Use of cloud computing in biomedicine

Nowadays, biomedicine is characterised by a growing need for processing of large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides many advantages, such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing and the related use of this concept in the area of biomedicine. Authors offer a comprehensive analysis of the implementation of the cloud computing approach in biomedical research, decomposed into infrastructure, platform and service layer, and a recommendation for processing large amounts of data in biomedicine. Firstly, the paper describes the appropriate forms and technological solutions of cloud computing. Secondly, the high-end computing paradigm of cloud computing aspects is analysed. Finally, the potential and current use of applications in scientific research of this technology in biomedicine is discussed.     

Design a cloud storage platform for pervasive computing environments

An increasing number of personal electronic handheld devices (e.g., SmartPhone, netbook, MID and etc.), which make up the personal pervasive computing environments, are playing an important role in our daily lives. Data storage and sharing is difficult for these devices due to the data inflation and the natural limitations of mobile devices, such as the limited storage space and the limited computing capability. Since the emerging cloud storage solutions can provide reliable and unlimited storage, they satisfy to the requirement of pervasive computing very well. Thus we designed a new cloud storage platform which includes a series of shadow storage services to address these new data management challenges in pervasive computing environments, which called as “SmartBox”. In SmartBox, each device is associated its shadow storage with a unique account, and the shadow storage acts as backup center as well as personal repository when the device is connected. To facilitate file navigation, all datasets in shadow storage are organized based on file attributes which support the users to seek files by semantic queries. We implemented a prototype of SmartBox focusing on pervasive environments being made up of Internet accessible devices. Experimental results with the deployments confirm the efficacy of shadow storage services in SmartBox.     

A hierarchical method for molecular docking using cloud computing

Discovering small molecules that interact with protein targets will be a key part of future drug discovery efforts. Molecular docking of drug-like molecules is likely to be valuable in this field; however, the great number of such molecules makes the potential size of this task enormous. In this paper, a method to screen small molecular databases using cloud computing is proposed. This method is called the hierarchical method for molecular docking and can be completed in a relatively short period of time. In this method, the optimization of molecular docking is divided into two subproblems based on the different effects on the protein–ligand interaction energy. An adaptive genetic algorithm is developed to solve the optimization problem and a new docking program (FlexGAsDock) based on the hierarchical docking method has been developed. The implementation of docking on a cloud computing platform is then discussed. The docking results show that this method can be conveniently used for the efficient molecular design of drugs.     

Software architecture definition for on-demand cloud provisioning

Cloud computing is a promising paradigm for the provisioning of IT services. Cloud computing infrastructures, such as those offered by the RESERVOIR project, aim to facilitate the deployment, management and execution of services across multiple physical locations in a seamless manner. In order for service providers to meet their quality of service objectives, it is important to examine how software architectures can be described to take full advantage of the capabilities introduced by such platforms. When dealing with software systems involving numerous loosely coupled components, architectural constraints need to be made explicit to ensure continuous operation when allocating and migrating services from one host in the Cloud to another. In addition, the need for optimising resources and minimising over-provisioning requires service providers to control the dynamic adjustment of capacity throughout the entire service lifecycle. We discuss the implications for software architecture definitions of distributed applications that are to be deployed on Clouds. In particular, we identify novel primitives to support service elasticity, co-location and other requirements, propose language abstractions for these primitives and define their behavioural semantics precisely by establishing constraints on the relationship between architecture definitions and Cloud management infrastructures using a model denotational approach in order to derive appropriate service management cycles. Using these primitives and semantic definition as a basis, we define a service management framework implementation that supports on demand cloud provisioning and present a novel monitoring framework that meets the demands of Cloud based applications.     

RHAS: robust hybrid auto-scaling for web applications in cloud computing

Cluster Computing - The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives...     

A framework for SLA management in cloud computing for informed decision making

In cloud computing, service providers offer cost-effective and on-demand IT services to service users on the basis of Service Level Agreements (SLAs). However the effective management of SLAs in cloud computing is essential for the service users to ensure that they achieve the desired outcomes from the formed service. In this paper, we introduce a SLA management framework that will enable service users to select the best available service provider on the basis of its reputation and then monitor the run time performance of the service provider to determine whether or not it will fulfill its promise defined in the SLA. Such analysis will assist the service user to make an informed decision about the continuation of service with the service provider.     

Improving load balancing for data-duplication in big data cloud computing networks

Data transmission and retrieval in a cloud computing environment are usually handled by storage device providers or physical storage units leased by third parties. Improving network performance considering power connectivity and resource stability while ensuring workload balance is a hot topic in cloud computing. In this research, we have addressed the data duplication problem by providing two dynamic models with two variant architectures to investigate the strengths and shortcomings of architectures in Big Data Cloud Computing Networks. The problems of the data duplication process will be discussed accurately in each model. Attempts have been made to improve the performance of the cloud network by taking into account and correcting the flaws of the previously proposed algorithms. The accuracy of the proposed models have been investigated by simulation. Achieved results indicate an increase in the workload balance of the network and a decrease in response time to user requests in the model with a grouped architecture for all the architectures. Also, the proposed duplicate data model with peer-to-peer network architecture has been able to increase the cloud network optimality compared to the models presented with the same architecture.

     

Simultaneous authentication and secrecy in identity-based data upload to cloud

Most existing works to secure cloud devote to remote integrity check, search and computing on encrypted data. In this paper, we deal with simultaneous authentication and secrecy when data are uploaded to cloud. Observing that cloud is most interesting to companies in which multiple authorized employees are allowed to upload data, we propose a general framework for secure data upload in an identity-based setting. We present and employ identity-based signcryption (IBSC) to meet this goal. As it is shown that it is challenging to construct IBSC scheme in the standard model and most IBSC schemes are realized in the random oracle model which is regarded weak to capture the realistic adversaries, we propose a new IBSC scheme simultaneously performing encryption and signature with cost less than the signature-then-encryption approach. The identity based feature eliminates the complicated certificates management in signcryption schemes in the traditional public-key infrastructure (PKI) setting. Our IBSC scheme exploits Boneh et al.’s strongly unforgeable signature and Paterson et al.’s identity-based signature. The scheme is shown to satisfy semantic security and strong unforgeability. The security relies on the well-defined bilinear decision Diffie-Hellman (BDDH) assumption and the proof is given in the standard model. With our IBSC proposal, a secure data upload scheme is instantiated with simultaneous authentication and secrecy in a multi-user setting.     

Outsourcing computation of modular exponentiations in cloud computing

Cloud computing is an emerging computing paradigm in which IT resources and capacities are provided as services over the Internet. Promising as it is, this paradigm also brings forth new challenges for security when users want to securely outsource the computation of cryptographic operations to the untrusted cloud servers. As we know, modular exponentiation is one of the basic operations among most of current cryptosystems. In this paper, we present the generic secure outsourcing schemes enabling users to securely outsource the computations of exponentiations to the untrusted cloud servers. With our techniques, a batch of exponentiations (e.g. t exponentiations) can be efficiently computed by the user with only O(n+t) multiplications, where n is the number of bits of the exponent. Compared with the state-of-the-art algorithm, the proposed schemes are superior in both efficiency and verifiability. Furthermore, there are not any complicated pre-computations on the user side. Finally, the schemes are proved to be secure under the Subset Sum Problem.     

Mutual authentication scheme of IoT devices in fog computing environment

The radical shift in the technology with the advent of connected things has led to the significant proliferation in demand for IoT devices, commonly called ‘smart devices’. These devices are capable of data collection, which can help in umpteen applications, particularly in healthcare. With the tremendous growth in these resource-constrained end devices, there has been a substantial increase in the number of attack varieties. Since these end devices deal with the sensitive data that might cause severe damage if not handled properly. Hence, defending its integrity, preserving its privacy, and maintaining its confidentiality as well as availability is of utmost importance. However, there are many protocols, models, architecture tools, etc. proposed to provide security. Nevertheless, almost every solution propound so far is not fully resilient and lacks in giving full protection to the system in some way or the other. So here, we have proposed a lightweight anonymous mutual authentication scheme for end devices and fog nodes.

     

Kani: a QoS-aware hypervisor-level scheduler for cloud computing environments

Cloud computing environments (CCEs) are expected to deliver their services with qualities in service level agreements. On the other hand, they typically employ virtualization technology to consolidate multiple workloads on the same physical machine, thereby enhancing the overall utilization of physical resources. Most existing virtualization technologies are, however, unaware of their delivered quality of services (QoS). For example, the Xen hypervisor merely focuses on fair sharing of processor resources. We believe that CCEs have got married with traditional virtualization technologies without many traits in common. To bridge the gap between these two technologies, we have designed and implemented Kani, a QoS-aware hypervisor-level scheduler. Kani dynamically monitors the quality of delivered services to quantify the deviation between desired and delivered levels of QoS. Using this information, Kani determines how to allocate processor resources among running VMs so as to meet the expected QoS. Our evaluations of Kani scheduler prototype in Xen show that Kani outperforms the default Xen scheduler namely the Credit scheduler. For example, Kani reduces the average response time to requests to an Apache web server by up to \(93.6\,\%\); improves its throughput by up to \(97.9\,\%\); and mitigates the call setup time of an Asterisk media server by up to \(96.6\,\%\).     

Towards effective science cloud provisioning for a large-scale high-throughput computing

The science cloud paradigm has been actively developed and investigated, but still requires a suitable model for science cloud system in order to support increasing scientific computation needs with high performance. This paper presents an effective provisioning model of science cloud, particularly for large-scale high throughput computing applications. In this model, we utilize job traces where a statistical method is applied to pick the most influential features to improve application performance. With these features, a system determines where VM is deployed (allocation) and which instance type is proper (provisioning). An adaptive evaluation step which is subsequent to the job execution enables our model to adapt to dynamical computing environments. We show performance achievements by comparing the proposed model with other policies through experiments and expect noticeable improvements on performance as well as reduction of cost from resource consumption through our model.     

Primary deuterium isotope effects for the 3-methylaspartase-catalyzed deamination of (2S)-aspartic acid, (2S,3S)-3-methylaspartic acid, and (2S,3S)-3-ethylaspartic acid

3-Methylaspartate ammonia-lyase catalyzes the deamination of (2S)-aspartic acid 137 times more slowly than the deamination of (2S,3S)-3-methylaspartic acid but catalyzes the amination of fumaric acid 1.8 times faster than the amination of mesaconic acid [Botting, N.P., Akhtar, M., Cohen, M. A., & Gani, D. (1988) Biochemistry (preceding paper in this issue)]. In order to understand the mechanistic basis for these observations, the deamination reaction was examined kinetically with (2S)-aspartic acid, (2S,3S)-3-methylaspartic acid, (2S,3S)-3-ethylaspartic acid, and the corresponding C-3-deuteriated isotopomers. Comparison of the double-reciprocal plots of the initial reaction velocities for each of the three pairs of substrates revealed that the magnitude of the primary isotope effect on both Vmax and V/K varied with the substituent at C-3 of the substrate. 3-Methylaspartic acid showed the largest isotope effect (1.7 on Vmax and V/K), 3-ethylaspartic acid showed a smaller isotope effect (1.2 on Vmax and V/K), and aspartic acid showed no primary isotope effect at all. These results, which are inconsistent with earlier reports that there is no primary isotope effect for 3-methylaspartic acid [Bright, H. J. (1964) J. Biol. Chem. 239, 2307], suggest that for both 3-methylaspartic acid and 3-ethylaspartic acid elimination occurs via a predominantly concerted mechanism whereas for aspartic acid an E1cb mechanism prevails.(ABSTRACT TRUNCATED AT 250 WORDS)     

RALBA: a computation-aware load balancing scheduler for cloud computing

Cloud computing serves as a platform for remote users to utilize the heterogeneous resources in data-centers to compute High-Performance Computing jobs. The physical resources are virtualized in Cloud to entertain user services employing Virtual Machines (VMs). Job scheduling is deemed as a quintessential part of Cloud and efficient utilization of VMs by Cloud Service Providers demands an optimal job scheduling heuristic. An ideal scheduling heuristic should be efficient, fair, and starvation-free to produce a reduced makespan with improved resource utilization. However, static heuristics often lead to inefficient and poor resource utilization in the Cloud. An idle and underutilized host machine in Cloud still consumes up to 70% of the energy required by an active machine (Ray, in Indian J Comput Sci Eng 1(4):333–339, 2012). Consequently, it demands a load-balanced distribution of workload to achieve optimal resource utilization in Cloud. Existing Cloud scheduling heuristics such as Min–Min, Max–Min, and Sufferage distribute workloads among VMs based on minimum job completion time that ultimately causes a load imbalance. In this paper, a novel Resource-Aware Load Balancing Algorithm (RALBA) is presented to ensure a balanced distribution of workload based on computation capabilities of VMs. The RABLA framework comprises of two phases: (1) scheduling based on computing capabilities of VMs, and (2) the VM with earliest finish time is selected for jobs mapping. The outcomes of the RALBA have revealed that it provides substantial improvement against traditional heuristics regarding makespan, resource utilization, and throughput.