A Scalable Cluster-based Infrastructure for Edge-computing Services

In this paper we present a scalable and dynamic intermediary infrastruc- ture, SEcS (acronym of BScalable Edge computing Services’’), for developing and deploying advanced Edge computing services, by using a cluster of heterogeneous machines. Our goal is to address the challenges of the next-generation Internet services: scalability, high availability, fault-tolerance and robustness, as well as programmability and quick prototyping. The system is written in Java and is based on IBM’s Web Based Intermediaries (WBI) [71] developed at IBM Almaden Research Center

QoS-Based Web Service Discovery in Mobile Ad Hoc Networks Using Swarm Strategies

Mobile ad hoc networks are noncentralised, multihop, wireless networks that lack a common infrastructure and hence require self-organisation. Their infrastructureless and dynamic nature entails the implementation of a new set of networking technologies in order to provide efficient end-to-end communication according to the principles of the standard TCP/IP suite. Routing, IP address autoconfiguration and Web service discovery are among the most challenging tasks in the ad hoc network domain. Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviours of insects, such as ants and bees. Self-organization, decentralization, adaptivity, robustness, and scalability make swarm intelligence a successful design paradigm for the above-mentioned problems. In this paper we proposeBeeAdHocServiceDiscovery, a new service discovery algorithm based on the bee metaphor, which also takes into account quality metrics estimates. The protocol has been specifically designed to work in mobile ad hoc network scenarios operating withBeeadhoc, a well-known routing algorithm inspired by nature. We present both the protocol strategy and the formal evaluation of the discovery overhead and route optimality metrics showing thatBeeAdHocServiceDiscoveryguarantees valuable performances even in large scale ad hoc wireless networks. Eventually, future research suggestions are sketched

Approximate TF–IDF based on topic extraction from massive message stream using the GPU

The Web is a constantly expanding global information space that includes disparate types of data and resources. Recent trends demonstrate the urgent need to manage the large amounts of data stream, especially in specific domains of application such as critical infrastructure systems, sensor networks, log file analysis, search engines and more recently, social networks. All of these applications involve large-scale data-intensive tasks, often subject to time constraints and space complexity. Algorithms, data management and data retrieval techniques must be able to process data stream, i.e., process data as it becomes available and provide an accurate response, based solely on the data stream that has already been provided. Data retrieval techniques often require traditional data storage and processing approach, i.e., all data must be available in the storage space in order to be processed. For instance, a widely used relevance measure is Term Frequency–Inverse Document Frequency (TF–IDF), which can evaluate how important a word is in a collection of documents and requires to a priori know the whole dataset. To address this problem, we propose an approximate version of the TF–IDF measure suitable to work on continuous data stream (such as the exchange of messages, tweets and sensor-based log files). The algorithm for the calculation of this measure makes two assumptions: a fast response is required, and memory is both limited and infinitely smaller than the size of the data stream. In addition, to face the great computational power required to process massive data stream, we present also a parallel implementation of the approximate TF–IDF calculation using Graphical Processing Units (GPUs). This implementation of the algorithm was tested on generated and real data stream and was able to capture the most frequent terms. Our results demonstrate that the approximate version of the TF–IDF measure performs at a level that is comparable to the solution of the precise TF–IDF measure