Optimizing Resource Availability in Composable Data Center Infrastructures

The exponential growth of cloud computing and hybrid enterprise IT infrastructures has significantly transformed how organizations store, process, and transmit data. As reliance on distributed systems deepens, ensuring the confidentiality, integrity, and availability of sensitive information has become a paramount concern especially in the face of emerging quantum computing capabilities. Quantum computers, once fully realized, will render many current public-key cryptographic systems such as RSA, ECC, and DH obsolete due to their vulnerability to quantum algorithms like Shor’s and Grover’s. This looming threat demands a paradigm shift toward quantum-resistant cryptographic protocols that can safeguard digital assets across dynamic and heterogeneous environments. This study presents a comprehensive analysis of quantum-resistant cryptographic schemes, focusing on lattice-based, hash-based, multivariate polynomial, and code-based approaches. These protocols are critically evaluated in terms of computational efficiency, scalability, resistance to side-channel attacks, and adaptability to multi-tenant cloud architectures. Special emphasis is placed on their integration into hybrid enterprise IT ecosystems, which often comprise on-premises servers, private clouds, and public cloud services each with unique security and compliance requirements. Additionally, the research proposes a layered security model that leverages post-quantum encryption for data-at-rest, hybrid key exchange mechanisms for secure data-in-transit, and secure bootstrapping protocols for cross-domain identity verification. Through simulations and policy analysis, this work demonstrates how quantum-resilient cryptography can be effectively deployed without compromising system performance or interoperability. Ultimately, the findings aim to guide enterprises, cloud providers, and cybersecurity professionals in building forward-compatible, future-proof data protection strategies.

A multi-view similarity measure framework for trouble ticket mining

Service-oriented Architecture (SOA) is becoming the prevalent architectural style for the creation of agile and flexible enterprise IT infrastructures. Services, as the key elements of Service-oriented Computing (SOC), can be composed in very efficient way to support flexible business processes and to adapt application systems quickly to new business requirements. Virtualization technologies offer cost-effective and rapid resource scalability as well as greater flexibility in providing hardware resources to service demands. However, also service-oriented applications need to be tested, monitored and maintained; thus the management aspect of Service-oriented infrastructures is often neglected. We propose a Service Management System that enhances the management of infrastructures for SOC and describes a first step towards the self-management of services in infrastructures with virtualized hardware resources. This paper provides a core set of components and required management functions that must be considered in providing a Service Management System for SOC.

As Enterprise IT infrastructure broadens beyond the traditional core data center and as new workloads move into the virtual realm, new opportunities and challenges emerge. In this talk we will focus on the changes being wrought in the virtual world by ...

So many of an enterprise's security problems aren't about technology. They're about organisation. They're about bureaucratic blind spots. They're about not being able to see the forest for the trees. The kinds of artificial barriers that separate different parts of the enterprise IT infrastructure may have once been useful, but they're increasingly getting in the way of scale and the ability to deliver business outcomes.

Enterprise IT infrastructures experience a transformation because of rapid cloud computing adoption, especially Amazon Web Services (AWS), which delivers scalability, flexibility, and cost efficiency. AWS users encounter difficulties optimizing their performance because they face problematic situations combining workload variability with complex resource distribution systems and delay issues. The implementation of Artificial Intelligence constructs an evaluation process to boost performance optimization in Amazon Web Services (AWS) cloud systems. AI-powered insights implemented through machine learning algorithms with predictive analytics capabilities and automation functionality improve workload distribution and enhance auto-scaling efficiency while reducing operational expenses. Processor analysis of historical data combined with real-time metrics allows AI algorithms to make proactive decisions about resource administration, detect anomalies, and perform innovative management. The research evaluates performance-altering AI-based approaches that enhance AWS services, including EC2, Lambda, and Sage Maker. Implementing AI-based optimizations generates copious improvements in computational efficiency and cost performance based on experimental data. Thanks to AI, cloud performance management strategies will fundamentally transform since they endorse efficient AWS environments that align with business requirements.

Driven by the advances in hardware technologies, smart environments (or “pervasive computing”) already penetrate many spaces of our daily live. Smart Homes, Smart Buildings and larger ensembles like airports, hospitals or university campuses are already equipped with a multitude of mobile terminals, embedded devices as well as connected sensors and actuators. Some activities already envision the “Smart City” which uses the opportunities provided by pervasive computing technologies to the benefits of their inhabitants. In such a setting, smart environments are expected to play a crucial role for coping with the challenges of urbanization and demographic chance e.g. regarding sustainability, energy distribution, mobility, health or public safety/security.While “smartness” is often centered on a user perspective, we give the business perspective of a large industrial supplier of infrastructures and solutions. This includes application scenarios for smart cities and an outline of the involved business- and research challenges. A particular focus will be the setup and energy efficient operation of smart infrastructures and data centers. While “autonomic computing principles” like self-configuration, self-healing, self-protection, and self-optimization are well understood for enterprise IT infrastructures, their application to highly-distributed, heterogeneous pervasive computing systems is less straightforward. A particular concern is the incorporation of energy efficiency in such settings. This involves a reduction of the rapidly raising IT energy costs and concepts for making software applications and services more aware of their energy consumption. It is therefore the basis for the identification of IT energy hotspots in software and IT system architecture and therefore for a sustainable development and operation of smart environmentsKeywordsSmart CitiesPervasive ComputingSustainabilitySystem ManagementGreen ITCloud ComputingPlatformsInfrastructure

After almost 20 years, DAM still awaits its mass-market adoption. Two factors are now challenging the stand-alone DAM business: the enterprise content management (ECM) approach to orchestrate the data from all business applications, and Microsoft's recent SharePoint release in particular. DAM systems adopting the likely success of ECM might finally see broader market penetration and stand the chance to become a core component of the overall Enterprise IT Infrastructure. This paper looks at the impact of SharePoint on DAM and the resulting opportunities. The workflow technology SharePoint offers and the capability to efficiently integrate DAM with major line of business applications, or other specific technology components, indicate vast potential to increase the effect of DAM. DAM business is likely to change and customers might soon be able to select DAM components instead of locking themselves into the corner of a single DAM vendor.

The need to protect big data, particularly those relating to information security maintenance (ISM) of an enterprise's IT infrastructure (ITI), and their processing is shown. Related worldwide experience of addressing big data ISM issues is summarized. An attempt to formulate a big data ISM problem statement is undertaken. An infrastructure for big data ISM is proposed. The importance of big data visualization is discussed.

The surging demand for inexpensive and scalable IT infrastructures has led to the widespread adoption of Cloud computing architectures. These architectures have therefore reached their momentum due to inherent capacity of simplification in IT infrastructure building and maintenance, by making related costs easily accountable and paid on a pay-per-use basis. Cloud providers strive to host as many service providers as possible to increase their economical income and, toward that goal, exploit virtualization techniques to enable the provisioning of multiple virtual machines (VMs), possibly belonging to different service providers, on the same host. At the same time, virtualization technologies enable runtime VM migration that is very useful to dynamically manage Cloud resources. Leveraging these features, data center management infrastructures can allocate running VMs on as few hosts as possible, so to reduce total power consumption by switching off not required servers. This chapter presents and discusses management infrastructures for power-efficient Cloud architectures. Power efficiency relates to the amount of power required to run a particular workload on the Cloud and pushes toward greedy consolidation of VMs. However, because Cloud providers offer Service-Level Agreements (SLAs) that need to be enforced to prevent unacceptable runtime performance, the design and the implementation of a management infrastructure for power-efficient Cloud architectures are extremely complex tasks and have to deal with heterogeneous aspects, e.g., SLA representation and enforcement, runtime reconfigurations, and workload prediction. This chapter aims at presenting the current state of the art of power-efficient management infrastructure for Cloud, by carefully considering main realization issues, design guidelines, and design choices. In addition, after an in-depth presentation of related works in this area, it presents some novel experimental results to better stress the complexities introduced by power-efficient management infrastructure for Cloud.

The robustness of mission-critical database platforms has become a key issue of concern to the enterprises that rely on constant availability of transactional and analytical data to run their day-to-day operations. Not only does SAP HANA as the most popular in-memory enterprise resource planning and real-time analytics platform need to have high performance, but also fault tolerance, scalability, and predictable lifecycle management. With such a dramatic change in the business IT infrastructure, organizations are still struggling with the very thorny issues of downtime, performance loss and complex upgrade processes, which kill the continuity of the business and elevates operational risk. The losses incurred in the event of down time in both financial and reputational terms have increased the need to have frameworks that inculcate resiliency to all phases of the deployment and governance of SAP HANA. This paper formulates an evidence-based approach to designing resilient SAP HANA systems combining implementation procedures, performance optimization approaches, and lifecycle maintenance. Based solely on the vendor documentation, technical notes and best-practice reports, which were published in December 2022, the framework proposes to pull together the fragmented guidance and condense it into a roadmap of resilience. The approach puts a strong focus on pre-deployment testing and certified platform testing, wherein operating system, database and virtualization compatibility matrices are used to protect against unsupported configuration. The migration pathways, i.e. phased, near-zero-downtime pathways are analyzed against their effects on service level agreements and exposure to risk. This research has a contribution to the fact that it has consolidated resilience strategies to a prescriptive and practical model that fits with the realities of enterprise IT landscapes. It synthesizes deployment rigor, tuning precision and governance continuity and thus forms a roadmap that does not just provide greater technical resilience but also augurs business requirements of continuity, compliance and optimization of performance. The results are an addition to the academic literature on the topic of enterprise database resilience, as well as a practical guide to IT leaders, architects, and administrators who must ensure the safety of mission-critical SAP HANA infrastructures

Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). It provides an on-demand, pay-as-you-go computing resources and had become an alternative to traditional IT Infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of SLA templates need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. SLA template contains many parameters like cloud's resources (physical memory, main memory, processor speed etc.) and properties (availability, response time etc.). This work addresses the issue of matching SLA parameters to find suitable cloud provider for particular application.

A key feature of service-oriented models of information technology is the promise of prespecified quality levels enforceable via service level agreements (SLAs). This poses difficult management problems when considerable variability exists in user preferences and service demand within any organization. Because variance in expectations impact service levels, effective pricing and resource allocation mechanisms are needed to deliver services at the promised quality level. In this paper, we propose a mechanism for SLA formulation that is responsive to demand fluctuations and user preference variance, with the objective of maximizing organizational welfare of the participants. This formulation features a dynamic priority based price-penalty scheme targeted to individual users. An analytical model is presented and evaluated for effectiveness of a proposed dynamic priority-based pricing scheme vis-à-vis a baseline fixed-price single-quality level SLA. Simulations using data from an existing SLA is used to provide evidence that the proposed dynamic pricing scheme is likely to be more effective than a fixed-price approach from a system welfare perspective.

Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

Over the last few years, we have seen the transformation of the traditional monolithic enterprise, in which all operations were performed in-house, to the extended enterprise, which consists of a network of collaborating entities. Global operations, outsourcing, and increasing specialization have all contributed to this trend. One challenge facing the extended enterprise is how to reconnect the information flows and business processes that were disconnected as the enterprise disaggregated. The emergence of web services, service-oriented architectures, and business process modeling and execution standards are helping to address this challenge. Our contention is that the next phase of evolution is the rise of the intelligent enterprise, which is characterized by being able to adapt quickly to changes in its operating environment. The intelligent enterprise monitors its own business processes and its interactions with customers, partners, suppliers, and collaborators; it understands how this information relates to its business objectives; and it acts to control and optimize its operations to meet its business objectives. Decisions are made quickly and accurately to modify business processes on the fly, dynamically allocate resources, or change business partners (e.g., suppliers, service providers) and partnerships (e.g., establish new service level agreements). This talk will describe challenges in managing the business operations of an intelligent enterprise. While a plethora of tools exist for managing the IT infrastructure (servers, storage, and network resources) of the enterprise, there is little systematic support today for the closed loop management and control of business operations. We will describe technology approaches to intelligent business operations management that we are pursuing at HP Labs., the progress we have made, and some open research questions.