Purpose The purpose of this paper is to present a review of existing models and tools for evaluating the adaptability of buildings. A baseline of the current state of the art in adaptability evaluation and adaptation decision support is established; from this baseline, gaps for future research are recommended. Design/methodology/approach A literature review was conducted to identify papers describing adaptability models and tools. The identified models were characterized based on their focus (new buildings, existing buildings, building life cycle), considered variables (physical and/or context features) and degree/type of validation. Findings Models can be grouped as those focusing on: evaluating adaptation decisions for existing buildings; the design of new buildings for future adaptation; and understanding adaptation throughout a building life cycle. Models focusing on existing building evaluation are further in development and validation than the other model types; as such, they are more suitable for use by practitioners. Another finding is that modeling of adaptability in buildings is still in its nascent stage and that data-driven quantitative modeling is a prime area for future research. Originality/value This paper is the first comprehensive review of models and tools for evaluating adaptability. Other works have evaluated the topic of adaptability more broadly, but this is the first paper to systematically characterize existing models and tools. Based on the review future, research topics are recommended.

No abstract

Unprecedented and accelerating trends in technology, climate change, and urbanization are changing the functional and physical demands placed on buildings. Buildings that cannot be readily changed are susceptible to obsolesce, one of the most common but least understood "hazards" facing the built environment. In contrast, adaptable buildings are resistant to obsolesce; they can be easily adapted in order to meet new demands. This paper presents a "Learning Buildings Framework" (LBF) for quantifying the adaptability of buildings. Currently available models can be used for assessing the adaptability of existing buildings; however, the LBF focuses on new construction and is based on decisions and strategies that are within the domain of building designers. The LBF quantifies a building's adaptability as the summation of adaptability for individual building systems. Scores for each system are weighted according to their likelihood of needing adaptation, cost, and the degree to which adaptability strategies are applied. This paper focuses on the theory and development of the LBF. Validation and calibration of the LBF are the objectives of ongoing research.

No abstract

Obtaining optimal data transfer performance is of utmost importance to today's data-intensive distributed applications and wide-area data replication services.Doing so necessitates effectively utilizing available network bandwidth and resources, yet in practice transfers seldom reach the levels of utilization they potentially could. Tuning protocol parameters such as pipelining, parallelism, and concurrency can significantly increase utilization and performance, however determining the best settings for these parameters is a difficult problem, as network conditions can vary greatly between sites and over time. Nevertheless, it is an important problem, since poor tuning can cause either under-or over-utilization of network resources and thus degrade transfer performance. In this paper, we present three algorithms for application-level tuning of different protocol parameters for maximizing transfer throughput in wide-area networks.Our algorithms dynamically tune the number of parallel data streams per file (for large file optimization), the level of control channel pipelining (for small file optimization), and the number of concurrent file transfers to increase I/O throughput (a technique useful for all types of files). The proposed heuristic algorithms improve the transfer throughput up to 10x compared to the baseline and 7x compared to the state of the art solutions.