17,195 research outputs found
A model and framework for reliable build systems
Reliable and fast builds are essential for rapid turnaround during
development and testing. Popular existing build systems rely on correct manual
specification of build dependencies, which can lead to invalid build outputs
and nondeterminism. We outline the challenges of developing reliable build
systems and explore the design space for their implementation, with a focus on
non-distributed, incremental, parallel build systems. We define a general model
for resources accessed by build tasks and show its correspondence to the
implementation technique of minimum information libraries, APIs that return no
information that the application doesn't plan to use. We also summarize
preliminary experimental results from several prototype build managers
MorphStream: Scalable Processing of Transactions over Streams on Multicores
Transactional Stream Processing Engines (TSPEs) form the backbone of modern
stream applications handling shared mutable states. Yet, the full potential of
these systems, specifically in exploiting parallelism and implementing dynamic
scheduling strategies, is largely unexplored. We present MorphStream, a TSPE
designed to optimize parallelism and performance for transactional stream
processing on multicores. Through a unique three-stage execution paradigm
(i.e., planning, scheduling, and execution), MorphStream enables dynamic
scheduling and parallel processing in TSPEs. Our experiment showcased
MorphStream outperforms current TSPEs across various scenarios and offers
support for windowed state transactions and non-deterministic state access,
demonstrating its potential for broad applicability
EDI and intelligent agents integration to manage food chains
Electronic Data Interchange (EDI) is a type of inter-organizational information system, which permits the automatic and structured communication of data between organizations. Although EDI is used for internal communication, its main application is in facilitating closer collaboration between organizational entities, e.g. suppliers, credit institutions, and transportation carriers. This study illustrates how agent technology can be used to solve real food supply chain inefficiencies and optimise the logistics network. For instance, we explain how agribusiness companies can use agent technology in association with EDI to collect data from retailers, group them into meaningful categories, and then perform different functions. As a result, the distribution chain can be managed more efficiently. Intelligent agents also make available timely data to inventory management resulting in reducing stocks and tied capital. Intelligent agents are adoptive to changes so they are valuable in a dynamic environment where new products or partners have entered into the supply chain. This flexibility gives agent technology a relative advantage which, for pioneer companies, can be a competitive advantage. The study concludes with recommendations and directions for further research
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Dynamic asset (and liability) management under market and credit risk
We introduce a modelling paradigm which integrates credit risk and market
risk in describing the random dynamical behaviour of the underlying fixed income assets.
We then consider an asset and liability management (ALM) problem and develop a mul-
tistage stochastic programming model which focuses on optimum risk decisions. These
models exploit the dynamical multiperiod structure of credit risk and provide insight
into the corrective recourse decisions whereby issues such as the timing risk of default is
appropriately taken into consideration. We also present a index tracking model in which
risk is measured (and optimised) by the CVaR of the tracking portfolio in relation to the
index. Both in- and out-of-sample (backtesting) experiments are undertaken to validate
our approach. In this way we are able to demonstrate the feasibility and flexibility of
the chosen framework
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Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints
The problem of optimal allocation of monitoring resources for tracking transactions progressing through a distributed system, modeled as a queueing network, is considered. Two forms of monitoring information are considered, viz., locally unique transaction identifiers, and arrival and departure timestamps of transactions at each processing queue. The timestamps are assumed to be available at all the queues but in the absence of identifiers, only enable imprecise tracking since parallel processing can result in out-of-order departures. On the other hand, identifiers enable precise tracking but are not available without proper instrumentation. Given an instrumentation budget, only a subset of queues can be selected for the production of identifiers, while the remaining queues have to resort to imprecise tracking using timestamps. The goal is then to optimally allocate the instrumentation budget to maximize the overall tracking accuracy. The challenge is that the optimal allocation strategy depends on accuracies of timestamp-based tracking at different queues, which has complex dependencies on the arrival and service processes, and the queueing discipline. We propose two simple heuristics for allocation by predicting the order of timestamp-based tracking accuracies of different queues. We derive sufficient conditions for these heuristics to achieve optimality through the notion of the stochastic comparison of queues. Simulations show that our heuristics are close to optimality, even when the parameters deviate from these conditions
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