Reservation-based Resource-Brokering for Grid Computing

In this paper we present the design and implementation of the Migol brokering framework. Migol is a Grid middleware, which addresses the fault-tolerance of long-running and compute-intensive applications. The framework supports e. g. the automatic and transparent recovery respectively the migration of applications. Another core feature of Migol is the discovery, selection, and allocation of resources using advance reservation. Grid broker systems can significantly benefit from advance reservation. With advance reservation brokers and users can obtain execution guarantees from local resource management systems (LRM) without requiring detailed knowledge of current and future workloads or of the resource owner’s policies. Migol’s Advance Reservation Service (ARS) provides an adapter layer for reservation capabilities of different LRMs, which is currently not provided by existing Grid middleware platforms. Further, we propose a shortest expected delay (SED) strategy for scheduling of advance reservations within the Job Broker Service. SED needs information about the earliest start time of an application. This is currently not supported by LRMs. We added this feature for PBSPro. Migol depends on Globus and its security infrastructure. Our performance experiments show the substantial overhead of this serviceoriented approach

Synthetic Data Generation for the Internet of Things

The concept of Internet of Things (IoT) is rapidly moving from a vision to being pervasive in our everyday lives. This can be observed in the integration of connected sensors from a multitude of devices such as mobile phones, healthcare equipment, and vehicles. There is a need for the development of infrastructure support and analytical tools to handle IoT data, which are naturally big and complex. But, research on IoT data can be constrained by concerns about the release of privately owned data. In this paper, we present the design and implementation results of a synthetic IoT data generation framework. The framework enables research on synthetic data that exhibit the complex characteristics of original data without compromising proprietary information and personal privacy

Exact and Approximate Probabilistic Symbolic Execution

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes

W(h)ither Fossils? Studying Morphological Character Evolution in the Age of Molecular Sequences

A major challenge in the post-genomics era will be to integrate molecular sequence data from extant organisms with morphological data from fossil and extant taxa into a single, coherent picture of phylogenetic relationships; only then will these phylogenetic hypotheses be effectively applied to the study of morphological character evolution. At least two analytical approaches to solving this problem have been utilized: (1) simultaneous analysis of molecular sequence and morphological data with fossil taxa included as terminals in the analysis, and (2) the molecular scaffold approach, in which morphological data are analyzed over a molecular backbone (with constraints that force extant taxa into positions suggested by sequence data). The perceived obstacles to including fossil taxa directly in simultaneous analyses of morphological and molecular sequence data with extant taxa include: (1) that fossil taxa are missing the molecular sequence portion of the character data; (2) that morphological characters might be misleading due to convergence; and (3) character weighting, specifically how and whether to weight characters in the morphological partition relative to characters in the molecular sequence data partition. The molecular scaffold has been put forward as a potential solution to at least some of these problems. Using examples of simultaneous analyses from the literature, as well as new analyses of previously published morphological and molecular sequence data matrices for extant and fossil Chiroptera (bats), we argue that the simultaneous analysis approach is superior to the molecular scaffold approach, specifically addressing the problems to which the molecular scaffold has been suggested as a solution. Finally, the application of phylogenetic hypotheses including fossil taxa (whatever their derivation) to the study of morphological character evolution is discussed, with special emphasis on scenarios in which fossil taxa are likely to be most enlightening: (1) in determining the sequence of character evolution; (2) in determining the timing of character evolution; and (3) in making inferences about the presence or absence of characteristics in fossil taxa that may not be directly observable in the fossil record. Published By: Missouri Botanical Garde

Stratified Abstraction of Access Control Policies

The shift to cloud-based APIs has made application security critically depend on understanding and reasoning about policies that regulate access to cloud resources. We present stratified predicate abstraction, a new approach that summarizes complex security policies into a compact set of positive and declarative statements that precisely state who has access to a resource. We have implemented stratified abstraction and deployed it as the engine powering AWS’s IAM Access Analyzer service, and hence, demonstrate how formal methods and SMT can be used for security policy explanation

Chromosomal localization of the large subunit of mouse replication factor C in the mouse and human

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47015/1/335_2004_Article_BF00350900.pd

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines

<p>Abstract</p> <p>Background</p> <p>Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts.</p> <p>Results</p> <p>To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (<it>e.g</it>., for biomolecular sequences, alignments, structures) and functionality (<it>e.g</it>., to parse/write standard file formats).</p> <p>Conclusions</p> <p>PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at <url>http://muralab.org/PaPy</url>, and includes extensive documentation and annotated usage examples.</p

Chemical Magnetoreception: Bird Cryptochrome 1a Is Excited by Blue Light and Forms Long-Lived Radical-Pairs

Cryptochromes (Cry) have been suggested to form the basis of light-dependent magnetic compass orientation in birds. However, to function as magnetic compass sensors, the cryptochromes of migratory birds must possess a number of key biophysical characteristics. Most importantly, absorption of blue light must produce radical pairs with lifetimes longer than about a microsecond. Cryptochrome 1a (gwCry1a) and the photolyase-homology-region of Cry1 (gwCry1-PHR) from the migratory garden warbler were recombinantly expressed and purified from a baculovirus/Sf9 cell expression system. Transient absorption measurements show that these flavoproteins are indeed excited by light in the blue spectral range leading to the formation of radicals with millisecond lifetimes. These biophysical characteristics suggest that gwCry1a is ideally suited as a primary light-mediated, radical-pair-based magnetic compass receptor