Performance Analysis of a Fibre Channel Switch supporting Node Port Identifier Virtualization

The server virtualization architecture encompassing sharing of storage subsystems among virtual machines using fibre channel fabrics, to improve server utilization and reduce the total cost of ownership, was pioneered by IBM through their System z9 mainframe and its predecessors. With the advent of sharing small computer system interface storage subsystems among host servers through fibre channel based storage area networks, has cropped up new set of security and associated performance issues when the host servers are virtual machines on a single physical server. To address the security issues and reduce the total cost of ownership, IBM introduced new storage virtualization architecture known as node port identifier virtualization enabling thousands of virtual machines on a server to share storage subsystems through a few numbers of host bus adapters.In this paper, we introduce the node port identifier virtualization architecture and the associated fibre channel switch latency performance issue that would affect virtual machine instantiation when supporting thousands of virtual machines. We first show the architectural problem in hard zoning mechanism contributing to the large fibre channel switch latency by actual performance measurements on a switch using hardware simulators. Next, we suggest a modification to the hard zoning mechanism to reduce the fabric channel switch latency significantly and demonstrate the reduction using hardware simulators. The performance issue we have identified and addressed will allow a single fibre channel switch to support thousands of virtual machines on a server using only a few numbers of host bus adapters

Performance analysis of an iSCSI block device in virtualized environment

Virtualization is new to telecom but it has been already implemented in IT sectors. Thus its benefits are already proven, which drags other sectors attention towards it. Now the telecom organizations are also focusing on virtualization to reap the full benefits of it. The main focus of this thesis is to conduct a performance analysis of a block storage device in a virtualization environment. Storage performance plays vital role in telecom sector. The performance and the reliability of the storage device is more important factor to fulfill the client request with minimum latency. This thesis is comprised of three main areas. The first literature part is to study the different storage networking possibilities and the different storage protocol practice to establish communication between server and the storage in the storage area network. The study indicated that Internet Small Computer System Interface (iSCSI) has more advantages than other practices in the storage area network. The second part covers the design of storage area network (SAN) solution. The storage is offered by an iSCSI storage server. It offers a block level storage device access to the compute server. Different iSCSI targets are available in market, performance of those were compared. Linux-IO Target was concluded as better iSCSI target with better performance and reliability. The Storage server was implemented as a virtual machine for better resource utilization, thus there was a study about the hypervisor and the different networking options for the virtual machines were compared. The final part is to optimize the SAN solution. Multipathing, different caching options and different driver options provided by the kernel virtual machine (KVM)/ Quick emulators (QEMU) were considered for optimization

Storage Area Networks

This tutorial compares Storage area Network (SAN) technology with previous storage management solutions with particular attention to promised benefits of scalability, interoperability, and high-speed LAN-free backups. The paper provides an overview of what SANs are, why invest in them, and how SANs can be managed. The paper also discusses a primary management concern, the interoperability of vendor-specific SAN solutions. Bluefin, a storage management interface and interoperability solution is also explained. The paper concludes with discussion of SAN-related trends and implications for practice and research

Data center resilience assessment : storage, networking and security.

Data centers (DC) are the core of the national cyber infrastructure. With the incredible growth of critical data volumes in financial institutions, government organizations, and global companies, data centers are becoming larger and more distributed posing more challenges for operational continuity in the presence of experienced cyber attackers and occasional natural disasters. The main objective of this research work is to present a new methodology for data center resilience assessment, this methodology consists of: • Define Data center resilience requirements. • Devise a high level metric for data center resilience. • Design and develop a tool to validate and the metric. Since computer networks are an important component in the data center architecture, this research work was extended to investigate computer network resilience enhancement opportunities within the area of routing protocols, redundancy, and server load to minimize the network down time and increase the time period of resisting attacks. Data center resilience assessment is a complex process as it involves several aspects such as: policies for emergencies, recovery plans, variation in data center operational roles, hosted/processed data types and data center architectures. However, in this dissertation, storage, networking and security are emphasized. The need for resilience assessment emerged due to the gap in existing reliability, availability, and serviceability (RAS) measures. Resilience as an evaluation metric leads to better proactive perspective in system design and management. The proposed Data center resilience assessment portal (DC-RAP) is designed to easily integrate various operational scenarios. DC-RAP features a user friendly interface to assess the resilience in terms of performance analysis and speed recovery by collecting the following information: time to detect attacks, time to resist, time to fail and recovery time. Several set of experiments were performed, results obtained from investigating the impact of routing protocols, server load balancing algorithms on network resilience, showed that using particular routing protocol or server load balancing algorithm can enhance network resilience level in terms of minimizing the downtime and ensure speed recovery. Also experimental results for investigating the use social network analysis (SNA) for identifying important router in computer network showed that the SNA was successful in identifying important routers. This important router list can be used to redundant those routers to ensure high level of resilience. Finally, experimental results for testing and validating the data center resilience assessment methodology using the DC-RAP showed the ability of the methodology quantify data center resilience in terms of providing steady performance, minimal recovery time and maximum resistance-attacks time. The main contributions of this work can be summarized as follows: • A methodology for evaluation data center resilience has been developed. • Implemented a Data Center Resilience Assessment Portal (D$-RAP) for resilience evaluations. • Investigated the usage of Social Network Analysis to Improve the computer network resilience

Fiber Channel Vs. Internet Scsi On Storage Area Networks For Disaster Recovery Operations

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2006Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2006Bu tez çalışmasında iSCSI tabanlı veri depolama ağlarının performansının iyileştirilmesi için iSCSI ve TCP katmalarının birbiriyle etkileşimi incelenmektedir. Bu inceleme neticesinde en uygun iSCSI ve TCP parametre değerleri belirlenmeye çalışılmıştır. Uygun parametre değerleri kullanılarak optimize edilmiş bir iSCSI veri depolama çözümünün Fiber Kanal tabanlı veri depolama çözümlerine alternatif olabileceği gösterilmeye çalışılmıştır.This thesis examines the interactions between the iSCSI and TCP layer in order to improve the performance of iSCSI-based storage system. As a result of this study, the most proper iSCSI and TCP parameter values were supposed to be determined. By using these proper parameter values, it was tried to be shown that an optimized iSCSI-based storage solution with suitable parameters can be an alternative to FC-based storage solutions.Yüksek LisansM.Sc

Design and implementation of an object storage system

Master'sMASTER OF ENGINEERIN

High Availability and Scalability of Mainframe Environments using System z and z/OS as example

Mainframe computers are the backbone of industrial and commercial computing, hosting the most relevant and critical data of businesses. One of the most important mainframe environments is IBM System z with the operating system z/OS. This book introduces mainframe technology of System z and z/OS with respect to high availability and scalability. It highlights their presence on different levels within the hardware and software stack to satisfy the needs for large IT organizations

Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze