The applicability of a use value-based file retention method

The determination of the relative value of files is important for an organization while determining a retrieval service level for its files and a corresponding file retention policy. This paper discusses via a literature review methods for developing file retention policies based on the use values of files. On basis of these results we propose an enhanced version of one of them. In a case study, we demonstrate how one can develop a customized file retention policy by testing causal relations between file parameters and the use value of files. This case shows that, contrary to suggestions of previous research, the file type has no significant relation with the value of a file and thus should be excluded from a retention policy in this case. The case study also shows a strong relation between the position of a file user and the value of this file. Furthermore, we have improved the Information Value Questionnaire (IVQ) for subjective valuation of files. However, the resulting method needs software to be efficient in its application. Therefore, we developed a prototype for the automatic execution of a file retention policy. We conclude with a discussio

StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Evaluating the Applicability of a Use Value-Based File Retention Method

A well constructed file retention policy can help a company determine the relative value and the corresponding retrieval service level of the different files it owns. Though such a retention policy is useful, the method one can use to arrive at such a policy is under-researched. This paper discusses how one can arrive at a method (based on a systematic literature review) for developing file retention policies based on use values of files. In the case study, we demonstrate how one can develop a file retention policy by testing of causal relations between file retention policy parameters and the use value of files. This case study shows that, contrary to suggestions of previous research, the file type has no significant causal relation with the value of a file and thus should be excluded from a retention policy in this case. The case study also shows that there is a strong causal relation between the position of a user of a file and the value of this file. Furthermore, we have amended an existing subjective file valuation method, namely, the Information Value Questionnaire (IVQ). However, to make file retention methods effective and reliable a substantially more case experiences need to be collected

Wirtschaftlichkeitsanalyse der automatisierten Verwaltung unstrukturierter Daten im Information Lifecycle Management

Ziel von Information Lifecycle Management (ILM) ist die Klassifizierung und kostengünstige Verwaltung von Informationen. Informationen, auf die in einem Unternehmen oft zugegriffen wird, können von Informationen mit geringerer Zugriffshäufigkeit getrennt und den Unternehmensanforderungen entsprechend auf dem jeweils sinnvollsten Speichermedium bereitgestellt und verwaltet werden. Das rasche Datenwachstum, hohe Speicher-, Administrations- und Betriebskosten sowie zahlreiche rechtliche Anforderungen sind die wesentlichen Gründe für die Entstehung von ILM. Zur Analyse des Kosten- und Nutzenverhältnisses, das bei der Implementierung und Anwendung von ILM entsteht, wird in dieser Arbeit ein Verfahren für die Wirtschaftlichkeitsanalyse zur Verwaltung unstrukturierter Daten im ILM konstruiert, implementiert, demonstriert und evaluiert. Es werden Klassifizierungs-, Verlagerungs- und Kostenfunktionen implementiert, um die Entstehung von Kosten und Kostensenkungspotentialen für verschiedene Nutzungsgrade von Informationen zu simulieren und auszuwerten. Es wird untersucht, welchen Einfluss die automatisierte Klassifizierung und Verwaltung unterschiedlich großer Datenmengen mit variierenden Zugriffshäufigkeiten und Nutzungsgraden auf die Wirtschaftlichkeit des ILM hat. Neben unternehmensinternen Speichermedien wird die Verwaltung von Daten im Cloud Computing in die Betrachtungen einbezogen. Die vorliegende Arbeit liefert eine Literaturanalyse zu den Konzepten ILM und Cloud Computing sowie einen wissenschaftlichen Beitrag dazu, sowohl rechtliche als auch funktionale und technische Anforderungen an ILM und Cloud Computing zu erörtern. Beendet wird die Arbeit durch eine Zusammenfassung sowie eine kritische Würdigung der Ergebnisse. Ein Ausblick gibt Hinweise und Verbesserungsvorschläge hinsichtlich der Wirtschaftlichkeitsanalyse unstrukturierter Daten im Information Lifecycle Management.The aim of Information Lifecycle Management (ILM) is the classification and cost-effective management of information. Highly accessed information should be separated from information with low access rates with the goal to use enterprise storage according to usage and business needs. The rapid growth of information, increasing storage-, administration- and operating costs as well as legal requirements are the main reasons for the emergence of ILM. To analyze the cost-benefit ratio resulting from implementing and running ILM, a method for the economic analysis for managing unstructured data in ILM is designed, implemented, demonstrated and evaluated. The author is implementing classification, displacement and cost functions to simulate the formation of costs and cost reduction potentials for different access rates and lifecycles of information. Automated classification is used for different volumes of data with varying access frequencies and degrees of utilization. Cloud Computing is included in the considerations as a way to store and manage data in addition to internal enterprise storage. The present study provides a literature review on ILM concepts and cloud computing. Legal, functional and technical requirements for ILM and Cloud Computing are discussed. A summary and a critical assessment of the results is finishing the work. A lookout gives hints and suggestions for improving the economic analysis of unstructured data in Information Lifecycle Management

File Classification in Self-* Storage Systems

To tune and manage themselves, file and storage systems must understand key properties (e.g., access pattern, lifetime, size) of their various files. This paper describes how systems can automatically learn to classify the properties of files (e.g., read-only access pattern, short-lived, small in size) and predict the properties of new files, as they are created, by exploiting the strong associations between a file's properties and the names and attributes assigned to it. These associations exist, strongly but differently, in each of four real NFS environments studied. Decision tree classifiers can automatically identify and model such associations, providing prediction accuracies that often exceed 90%. Such predictions can be used to select storage policies (e.g., disk allocation schemes and replication factors) for individual files. Further, changes in associations can expose information about applications, helping autonomic system components distinguish growth from fundamental change

File Classification in Self-* Storage Systems (CMU-PDL-04-101)

To tune and manage themselves, file and storage systems must understand key properties (e.g., access pattern, lifetime, size) of their various files. This paper describes how systems can automatically learn to classify the properties of files (e.g., read-only access pattern, short-lived, small in size) and predict the properties of new files, as they are created, by exploiting the strong associations between a file’s properties and the names and attributes assigned to it. These associations exist, strongly but differently, in each of four real NFS environments studied. Decision tree classifiers can automatically identify and model such associations, providing prediction accuracies that often exceed 90%. Such predictions can be used to select storage policies (e.g., disk allocation schemes and replication factors) for individual files. Further, changes in associations can expose information about applications, helping autonomic system components distinguish growth from fundamental change