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... New design for 5G network which is a combining MEC and network slicing for autonomous and connected vehicles proposed in [16]. Study on different classes of genome processing services results in a new solution which leverage network replacement caching, therefore, cause scalability of service deployment for all optimization strategies [17]. ...
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One of the core elements for the upcoming generation of wireless cellular networks is the availability of network service access continuity in addition to high-speed internet and low latency. The forthcoming fifth generation (5G) greatly improves users’ demand in terms of faster download rates, exceptional system availability, superb end to end coverage with exceptionally low latency and ultra reliability. One of the solutions to provide end to end low latency is the utilization of Mobile Edge Computing (MEC) in the network. MEC provides cloud advantages to users by setting up a small cloud server in the edge node (i.e. close to the end-user), which decreases the amount of latency in network connections, in this regard, service migration has required as users migrate to the new location. Optimal migration decisions are challenging because they depend on the cloud environment, or edge nodes belong to different orchestrators, and security issues in the migration process must also be resolved in order to prevent unreliable requests. This study provides different approaches to address these challenges by identifying the security implications of migration methods based on the blockchain integration.
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The management of network infrastructures has become increasingly complex over time, which is mainly attributed to the introduction of new functionality to support emerging services and applications. To address this important issue, research efforts in the last few years focused on developing software-defined networking solutions. While initial work proposed centralized architectures, their scalability limitations have led researchers to investigate a distributed control plane. Controller placement algorithms and mechanisms for building a logically centralized network view are some examples of challenges addressed in this context. A critical issue that requires specific attention concerns the communication between distributed entities involved in decision-making processes. To this end, we propose SigMA, a signaling framework that supports communication between the different entities of a decentralized management and control system. We also define the communication primitives and interfaces involved in such a decentralized environment. The benefits of SigMA are illustrated through three realistic network resource management use cases with different communication requirements. Based on simulation, we demonstrate the flexibility and extensibility of our solution in satisfying these requirements, thus effectively supporting advanced decentralized decision-making processes.
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Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a "four-headed beast"-it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the "genomical" challenges of the next decade.
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Increasingly complex biomedical data from diverse sources demands large storage, efficient software and high performance computing for the data’s computationally intensive analysis. Cloud technology provides flexible storage and data processing capacity to aggregate and analyze complex data; facilitating knowledge sharing and integration from different disciplines in a collaborative research environment. The ICTBioMed collaborative is a team of internationally renowned academic and medical research institutions committed to advancing discovery in biomedicine. In this work we describe the cloud framework design, development, and associated software platform and tools we are working to develop, federate and deploy in a coordinated and evolving manner to accelerate research developments in the biomedical field. Further, we highlight some of the essential considerations and challenges to deploying a complex open architecture cloud-based research infrastructure with numerous software components, internationally distributed infrastructure and a diverse user base.
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Software-Defined Networking (SDN) is an emerging paradigm that promises to change the state of affairs of current networks, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. Today, SDN is both a hot research topic and a concept gaining wide acceptance in industry, which justifies the comprehensive survey presented in this paper. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbounds APIs, network virtualization layers, network operating systems, network programming languages, and management applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.
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This paper shows the network solutions proposed and implemented in the framework of the project ARES. The strategic objective of ARES is to create an advanced CDN, accessible through a cloud interface, supporting medical and research systems making a large use of genomic data. The expected achievements consist of identifying suitable management policies of genomic contents in a cloud environment, in terms of efficiency, resiliency, scalability, and QoS in a distributed fashion using a multi-user CDN approach. The experimental architecture envisages the use of the following key elements: a wideband networking infrastructure, a resource virtualization software platform, a distributed service architecture, including suitable computing and storage infrastructure, a distributed software platform able to ingest, validate, and analyze high volumes of data, a software engine executing a sophisticated intelligence for workload distribution and CDN provisioning.
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As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics.
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In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.
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Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.
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Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist - such as cloud and heterogeneous computing - to successfully tackle our big data problems.
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Experts have warned that processing of genetic data will soon exceed the computing needs of Twitter and YouTube. This is due to the drop of the costs for sequencing DNA of any living creature and its huge impact in many application areas. Designing suitable network architectures for distributing such data is therefore of paramount importance. Management of genomic data sets is a typical big data problem, characterized not only by a huge volume, but also by the large size of each genomic file. Since it is unthinkable that any professional who needs to process genomes can own the infrastructure for massive genome analysis, a cloud-based access to genomic services is envisaged. This will have a significant impact on the underlying networks, which could become the system bottleneck. In this paper, we propose Genome Centric Networking (GCN), a novel network function virtualization framework for cloud-based genomic data management, designed with the aim of limiting the exchanged traffic by using distributed caching. The key element of GCN is a novel signaling protocol, which allows both discovering network resources and managing caches. We evaluated GCN on a real testbed. GCN allows halving the exchanged traffic and reducing the transfer time of genomic datasets significantly.
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This paper deals with the one-dimensional integer cutting stock problem, which consists of cutting a set of available objects in stock in order to produce ordered smaller items in such a way as to minimize the waste of material. The case in which there are various types of objects available in stock in limited quantities is studied. A new heuristic method based on the evolutionary algorithm concept is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and the results are compared with other methods from the literature.
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To date, realistic ISP topologies have not been accessible to the research community, leaving work that depends on topology on an uncertain footing. In this paper, we present new Internet mapping techniques that have enabled us to measure router-level ISP topologies. Our techniques reduce the number of required traces compared to a brute-force, all-to-all approach by three orders of magnitude without a significant loss in accuracy. They include the use of BGP routing tables to focus the measurements, the elimination of redundant measurements by exploiting properties of IP routing, better alias resolution, and the use of DNS to divide each map into POPs and backbone. We collect maps from ten diverse ISPs using our techniques, and find that our maps are substantially more complete than those of earlier Internet mapping efforts. We also report on properties of these maps, including the size of POPs, distribution of router outdegree, and the interdomain peering structure. As part of this work, we release our maps to the community.
Network Functions Virtualisation (NFV); Management and Orchestration
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ETSI ISG NFV, "Network Functions Virtualisation (NFV); Management and Orchestration", ETSI GS NFV-MAN 001 V1.1.1, Dec. 2014.
1000 Genomes Project. Mapping structural variation at fine scale by population scale genome sequencing
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Federated Clouds for Biomedical Research: Integrating OpenStack for ICTBioMed
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C. Mazurek et al., "Federated Clouds for Biomedical Research: Integrating OpenStack for ICTBioMed", IEEE CloudNet 2014, 2014.
Software-Defined Networking: A Comprehensive Survey
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