There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Matrix Factorization Techniques for Recommender Systems

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A Break in the Clouds: Towards a Cloud Definition

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change. Application of this technology relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as "Virtual Network Embedding (VNE)" algorithms. This paper presents a survey of current research in the VNE area. Based upon a novel classification scheme for VNE algorithms a taxonomy of current approaches to the VNE problem is provided and opportunities for further research are discussed.

Virtual Network Embedding: A Survey

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

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Search-based software engineering: Trends, techniques and applications

In light of rapid increase of electricity cost, many business organizations have to find new ways to cut the electricity bill. This paper studies how to reduce the electricity cost in geographical inter-domain virtual network embedding, which embeds virtual networks requested by users to multiple geographically distributed substrate networks run by an infrastructure provider. Previous researches have primarily focused on finding embedding methods to increase revenues by accommodating more virtual network requests, with little attention to reducing the electricity cost. To bridge this gap, we formulate an electricity cost model and design an efficient cost-aware virtual network embedding algorithm by exploiting the location-varying and time-varying diversities of the electricity price and optimizing the energy consumption. Through extensive simulations, we show that our algorithm can significantly reduce the electricity cost by up to 21% over the existing cost-oblivious algorithm, while maintaining nearly the same revenues for the infrastructure provider.

Minimizing electricity cost in geographical virtual network embedding

In this paper, we study the location-aware targeted influence maximization problem in social networks, which finds a seed set to maximize the influence spread over the targeted users. In particular, we consider those users who have both topic and geographical preferences on promotion products as targeted users. To efficiently solve this problem, one challenge is how to find the targeted users and compute their preferences efficiently for given requests. To address this challenge, we devise a TR-tree index structure, where each tree node stores users' topic and geographical preferences. By traversing the TR-tree in depth-first order, we can efficiently find the targeted users. Another challenge of the problem is to devise algorithms for efficient seeds selection. We solve this challenge from two complementary directions. In one direction, we adopt the maximum influence arborescence (MIA) model to approximate the influence spread, and propose two efficient approximation algorithms with 1−1/e approximation ratio, which prune some candidate seeds with small influences by precomputing users' initial influences offline and estimating the upper bound of their marginal influences online. In the other direction, we propose a fast heuristic algorithm to improve efficiency. Experiments conducted on real-world data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

Location‐aware targeted influence maximization in social networks

Matchmaking is an important aspect of Web Services interactions. In a dynamic environment such as the Internet, matchmaking services require rich and flexible metadata as well as efficient matching algorithms. The Semantic Web technologies, especially OWL and OWL-S, are gaining momentum and generating suitable technologies and tools to cover both the metadata and the algorithmic aspects. In this paper, we introduce a step-by-step matching algorithm that can be used to determine the semantic similarity of two Web Services. The matching algorithm is based on the Description Logics reasoning services and operates on service descriptions in OWL and OWL-S, with the matching results ranked. The algorithm is implemented in a prototype system, based on the well-known Racer description logics system.

Service Matching Based on Semantic Descriptions

Feature interaction has been first formally identified as a problem in the telecommunication domain. With the rapid growth and application of Web Services, feature interaction problem also arises in the dynamic Web Service interactions. Race conditions, one type of functional feature interactions in Web Services, will lead to the unexpected system behavior in the composite Web Services system. This paper proposed a Petri net-based method to detect the race conditions in Web Services. The method can be divided into two phases: modeling Web Services and detecting the race conditions. On the basis of modeling Web Services, the Petri net analysis methods are employed in the detection algorithm. This static detection method will help avoid race conditions in the composite Web Services.

Detecting Race Conditions in Web Services

Web Services with distributed and dynamic characteristics need efficient and decentralized discovery infrastructure supporting the semantic descriptions and discovery. In this paper, a novel peer-to-peer indexing system with related P2P registries is proposed to support the completely decentralized discovery capabilities. In the presented system, with the ontology encoding scheme, the semantic service description is distributed into a distributed trie index on the structured P2P network to allow requesters to lookup services with semantic requirements. Finally, experimental result shows that the presented system is efficient and scalable.

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Sen Su

Papers

Minimizing electricity cost in geographical virtual network embedding

Location‐aware targeted influence maximization in social networks

Service Matching Based on Semantic Descriptions

Detecting Race Conditions in Web Services

A peer-to-peer approach to semantic web services discovery