Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

/pdf/a-set-theory-based-similarity-measure-for-text-clustering-2nvist37eo.pdf

A set theory based similarity measure for text clustering and classification

The article deals with the creation of virtual research teams of scientists from various geographically distributed organizations united for joint interdisciplinary researches. Library social institutions are the satellites of virtual research teams and have to implement information and communication support of scientific researches. The use of cloud managers by academic libraries is proposed as platforms to facilitate remote collaborative work of the participants of the virtual research teams. The research of number of free cloud managers and their capabilities was held. The most successful cloud manager for supporting the scientific work of virtual research teams was selected by using hierarchy analysis method.

/pdf/the-information-support-of-virtual-research-teams-by-means-26zohlbze9.pdf

The Information Support of Virtual Research Teams by Means of Cloud Managers

Performance and dependability levels of cloud-based computations are difficult to guarantee by-design due to segregation of visibility and control between applications, data owners, and cloud providers. Lack of predictability increases users’ uncertainty about the service levels they will actually achieve. Cloud tenants compete for shared resources/services at all layers of the cloud stack, and pose heterogeneous and conflicting non-functional requirements over them. These requirements have implications for platform and infrastructure layers, which have to be configured to satisfy inter-tenants requirements. We argue that adaptation techniques can play a crucial role in providing a reliable cloud, supporting definite behavior of applications and stable quality of service. Existing adaptation techniques however are unsuitable for cloud use, since they mostly focus on single tenancy, performance requirements, and are based on unverifiable evidence, which is collected in an untrusted way. In this paper, we propose a multi-tenant, general-purpose adaptation technique for the cloud, based on evidence collected by means of a trustworthy certification process. We depart from traditional heavy and comprehensive certification processes, such as ISO/IEC 27017, and consider a flexible and lightweight certification process for the cloud. It is based on authentic evidence and provides accountable validation on the compliance of a cloud-based system. Our approach adapts the cloud at all layers to maintain stable non-functional properties in certificates over time, by continuously verifying certificate validity. We assess the performance and quality of our adaptation approach in a wide range of settings.

/pdf/certification-based-cloud-adaptation-4fszwbobb2.pdf

Certification-Based Cloud Adaptation

When moving large and heterogeneous data collections to the cloud, a key requirement concerns the selection of the most suitable (set of) cloud service(s) for outsourcing. Not only can different resources have different characteristics and requirements, but different cloud providers can also offer different services and security guarantees, and can have different costs. Selecting a single service for outsourcing an entire data collection can result in a non-optimal solution, as a single service satisfying, at reasonable costs, all the requirements specified by the data owner might not exist. Selecting a set of services could instead ensure the satisfaction of the requirements, possibly with economic advantages. In this article, we address this problem and present a flexible and expressive, yet simple model for supporting data owners in identifying a proper allocation of their resources to a set of cloud services. Our model allows data owners to specify in an easy and intuitive way protection requirements operating at the granularity level of single resource (or class thereof), and representing the minimum security guarantees that a cloud service must offer to store resources. Resources can be outsourced in plaintext or encrypted form, depending on their requirements and on what is the most convenient allocation. Data owners can then also specify global allocation requirements that apply to the overall allocation, to reduce the burden on their side and to avoid excessive fragmentation of the resource collection. We solve the problem of finding an allocation that satisfies both the protection and the global allocation requirements, while minimizing economic costs, by formulating it as a binary programming problem, thus allowing the use of existing techniques for its efficient solution.

/pdf/security-aware-data-allocation-in-multicloud-scenarios-4bmo0yuyka.pdf

Security-Aware Data Allocation in Multicloud Scenarios

https://air.unimi.it/bitstream/2434/685774/2/main.pdf

Cost-effective deployment of certified cloud composite services

An important problem when moving an application to the cloud consists in selecting the most suitable cloud plan (among those available from cloud providers) for the application deployment, with the goal of finding the best match between application requirements and plan characteristics. If a user wishes to move multiple applications at the same time, this task can be complicated by the fact that different applications might have different (and possibly contrasting) requirements. In this paper, we propose an approach enabling users to select a cloud plan that best balances the satisfaction of the requirements of multiple applications. Our solution operates by first ranking the available plans for each application (matching plan characteristics and application requirements) and then by selecting, through a consensus-based process, the one that is considered more acceptable by all applications.

/pdf/a-consensus-based-approach-for-selecting-cloud-plans-1ppsk02swg.pdf

A consensus-based approach for selecting cloud plans

Clouds provide an illusion of an infinite amount of resources and enable elastic services and applications that are capable to scale up and down (grow and shrink by requesting and releasing resources) in response to changes in its environment, workload, and Quality of Service (QoS) requirements. Elasticity allows to achieve required QoS at a minimal cost in a Cloud environment with its pay-as-you-go pricing model. In this paper, we present our experience in designing a feedback elastically controller for a key-value store. The goal of our research is to investigate the feasibility of the control theoretic approach to the automation of elasticity of Cloud-based key-value stores. We describe design steps necessary to build a feedback controller for a real system, namely Voldemort, which we use as a case study in this work. The design steps include defining touchpoints (sensors and actuators), system identification, and controller design. We have designed, developed, and implemented a prototype of the feedback elasticity controller for Voldemort. Our initial evaluation results show the feasibility of using feedback control to automate elasticity of distributed key-value stores.

Elasticity Controller for Cloud-Based Key-Value Stores

A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key performance indicators. The DORAM solution has been defined and developed in the MOSAIC research and development project with ALSTOM and other companies. The DORAM solution is validated by using real data and conditions in the Tuscany region.

/pdf/analyzing-demand-with-respect-to-offer-of-mobility-1gw4x7f4.pdf

Analyzing Demand with Respect to Offer of Mobility

Modularization is one of the important subjects in the software design area which leads to increasing the level of quality attributes such as maintainability, portability, reusability, interoperability and flexibility. Therefore, measuring the modularity of a designed architecture is a vital issue to obtain software with a high quality level. Moreover, low coupling between modules, high cohesion of a fine-grained module is two major criteria that could lead to more advanced standard design. In this paper, we introduce an analytical method to calculate modularity considering coupling, granularity and cohesion. To assess the comprehensiveness of the proposed method, the degree of modularity is calculated in a case study using two different architectural designs which shows the architecture's desired quality characteristics in designing the software. The assessment implies that our approach offers a holistic, flexible method considering the type of software application .

/pdf/towards-an-analytical-approach-to-measure-modularity-in-16xbtxcp2l.pdf

Towards an Analytical Approach to Measure Modularity in Software Architecture Design

Cloud users usually have different preferences over their applications that outsource to the cloud, based on the financial profit of each application's execution. Moreover, various types of virtual machines are offered by a cloud service provider with distinct characteristics , such as rental prices, availab ility levels , each with a different probability of occurrence and a penalty, which is paid to the user in case the virtual machine is not available. Therefore, the problem of applicat ion scheduling in cloud computing environments, considering the risk of financial loss of application-to-VM assignment becomes a challenging issue. In this paper, we propose a riskaware scheduling model, using risk analysis to allocate the applications to the virtual machines , so that, the expected total pay-off o f an application is maximized, by taking into account of the priority of applications. A running example is used through the paper to better illustrate the model and its application to improve the efficiency of resource assignment in cloud computing scenarios.

/pdf/a-risk-aware-application-scheduling-model-in-cloud-computing-2ylbcgoibt.pdf

A Risk-Aware Application Scheduling Model in Cloud Computing Scenarios

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