15864534
Real-Time Disk Scheduling with On-Disk Cache Conscious
Previous real-time disk scheduling algorithms assume that each disk request incurs a disk mechanical operation and only consider how to move the disk head under real-time constraints. However, with the increased capacity of on-disk cache, modern disk drives read-ahead data aggressively. Thus, the on-disk cache may service lots of requests without incurring physical disk access. By exploring the design methodology of on-disk cache, in this paper, we propose CARDS: a cache-aware real-time disk scheduling algorithm that takes the on-disk cache into consideration during scheduling. Therefore, the scheduling algorithm can help to minimize the cache miss ratio. Besides, the service timing estimation is more accurate in schedulability analysis since the cache effect is considered. A simulation-based evaluation shows CARDS to be highly successful as compared to the classical real-time disk scheduling algorithms. For example, under sequential workload with 10 sequential streams, the data throughput of CARDS is 1.1 times of DM-SCAN.
35414635
Cache-Aware Real-Time Disk Scheduling
Previous real-time disk scheduling algorithms assume that each disk request incurs a physical disk mechanical operation and only consider how to move the disk head under real-time constraints. However, with the increased capacity of on-disk cache, modern disk drives read-ahead data aggressively. Thus, the on-disk cache may service many disk requests without incurring physical disk access. By exploring the design methodology of on-disk cache, in this paper, we propose cacheaware real-time disk scheduling algorithms that take the on-disk cache into consideration during scheduling. Therefore, the scheduling algorithm can help the cache replacement scheme to minimize the cache miss ratio. Besides, the service timing estimation is more accurate in schedulability analysis since the cache effect is considered during scheduling. A simulation-based evaluation shows the proposed scheduling algorithms to be highly successful as compared with the classical real-time disk scheduling algorithms. For example, under sequential workload with 10 sequential streams, the data throughput of our scheme is 1.1 times that of DM-SCAN.
1945331
From Components to Autonomic Elements Using Negotiable Contracts
Autonomic computing aims at producing software systems that can manage themselves. As component-based development also partly addresses the complexity of large applications, we propose to combine the benefits of both approaches by using components equipped with negotiable contracts. These contracts specify the correct behavior of the components and play a central role in feedback control loops to enforce some autonomic features on components. In this paper, we present ConFract, a contract-based framework for hierarchical components in which contracts are runtime objects that are dynamically built from specifications, and automatically updated according to dynamic reconfigurations. Moreover, contracts clearly define the responsibilities (guarantor, beneficiaries) between their participating components which are exploited by some negotiations to automatically adapt components and contracts, and revalidate the system. The generic negotiation mechanism and an associated concession-based policy are presented.
2231141
Locally linear metric adaptation for semi-supervised clustering
Many supervised and unsupervised learning algorithms are very sensitive to the choice of an appropriate distance metric. While classification tasks can make use of class label information for metric learning, such information is generally unavailable in conventional clustering tasks. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a linear transformation. In this paper, we propose a new metric learning method which performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present two methods for solving it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some real data sets.
8605759
Fine-grained contract negotiation for hierarchical software components
Component-based development addresses the complexity of large applications by building software systems from reusable software components. To support reuse and successfully combine units of software, the contractual approach turns out to be well-suited to specify and to verify components and their interactions. However, as contracts must support functional and extra-functional aspects, they are frequently challenged by fluctuations in extra-functional properties and dynamic reconfigurations of components. In this paper, we propose a negotiation model in which components have clearly identified roles and interact in order to automatically restore the validity of contracts. The negotiation model currently supports a concession-based negotiation policy and is well-suited to behavioral contracts based on executable assertions. This model is integrated into ConFract, a contracting system for the fractal hierarchical component model.
1730596
A Scalable Kernel-Based Algorithm for Semi-Supervised Metric Learning
In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale up well with the data set size. In this paper, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.
2846430
Super-resolution through neighbor embedding
In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold teaming methods, particularly locally linear embedding (LLE). Specifically, small image patches in the lowand high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results.