Enterprise IT Operations

Over the past decade or so, for most enterprises, information technology (IT) has shifted from being a support function to be a synonym for business wellness. During the same period, though, the scale and complexity of IT for running business has grown significantly; today, performing any business function requires complex interplay of many, often invisible and dynamically changing, technology components. This is making design resilient and interruption-free IT a significant challenge. This chapter discusses limitations of traditional approaches for managing enterprise IT operations; introduces the concept of cognitive automation, a novel approach that blends intelligence with automation to transform enterprise IT operations; and describes the design of ignio™, a cognitive automation platform for enterprises. The author concludes by highlighting the challenges in driving cognitive transformation of enterprise operations and providing some suggestions for embarking upon this journey.

A Survey of AIOps Methods for Failure Management

Modern society is increasingly moving toward complex and distributed computing systems. The increase in scale and complexity of these systems challenges O&M teams that perform daily monitoring and repair operations, in contrast with the increasing demand for reliability and scalability of modern applications. For this reason, the study of automated and intelligent monitoring systems has recently sparked much interest across applied IT industry and academia. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to Machine Learning, AI, and Big Data. However, AIOps as a research topic is still largely unstructured and unexplored, due to missing conventions in categorizing contributions for their data requirements, target goals, and components. In this work, we focus on AIOps for Failure Management (FM), characterizing and describing 5 different categories and 14 subcategories of contributions, based on their time intervention window and the target problem being solved. We review 100 FM solutions, focusing on applicability requirements and the quantitative results achieved, to facilitate an effective application of AIOps solutions. Finally, we discuss current development problems in the areas covered by AIOps and delineate possible future trends for AI-based failure management.

Interstellar Neutral He Parameters from Crossing Parameter Tubes with the Interstellar Mapping and Acceleration Probe Informed by 10 yr of Interstellar Boundary Explorer Observations

The Sun's motion through the interstellar medium leads to an interstellar neutral (ISN) wind through the heliosphere. Several ISN species, including He, moderately depleted by ionization are observed with pickup ions and directly imaged. Since 2009, analyzed Interstellar Boundary Explorer (IBEX) observations returned a precise 4D parameter tube associated with the bulk velocity vector and the temperature of ISN flow distribution. This 4D parameter tube is typically expressed in terms of the ISN speed, the inflow latitudinal direction, and the temperature as a function of the inflow longitudinal direction and the local flow Mach number. We have used IBEX observations and those from other spacecraft to reduce statistical parameter uncertainties: V ISN ∞ = 25.99 ± 0.18 km s−1, λ ISN ∞ = 75 .° 28 ± 0 .° 13 , β ISN ∞ = −5 .° 200 ± 0 .° 075 , and T ISN ∞ = 7496 ± 172 K. IBEX ISN viewing is restricted almost perpendicular to the Earth–Sun line, which limits observations in ecliptic longitude to ∼130° ± 30° and results in relatively small uncertainties across the IBEX parameter tube but large uncertainties along it. Operations over the last three years enabled the IBEX spin axis to drift to the maximum operational offset (7°) west of the Sun, helping to break the ISN parameter degeneracy by weakly crossing the IBEX parameter tubes: the range of possible inflow longitudes extends over the range λ ISN ∞ = 75 .° 28 − 2.21 + 2.27 and the corresponding range of other ISN parameters is V ISN ∞ = 25.99 − 1.76 + 1.86 km s−1, β ISN ∞ = −5 .° 200 − 0.085 + 0.093 , and T ISN ∞ = 7496 − 1528 + 1274 K. This enhances the full χ 2 analysis of ISN parameters through comparison with detailed models. The next-generation IBEX-Lo sensor on IMAP will be mounted on a pivot platform, enabling IMAP-Lo to follow the ISN flow over almost the entire spacecraft orbit around the Sun. A near-continuous set of 4D parameter tube orientations on IMAP will be observed for He and for O, Ne, and H that cross at varying angles to substantially reduce the ISN flow parameter uncertainties and mitigate systematic uncertainties (e.g., from ionization effects and the presence of secondary components) to derive the precise parameters of the primary and secondary local interstellar plasma flows.

The impact of outsourcing information technology services on business operations

Background: Organisations outsource Information Technology (IT) services in order to keep up with the IT evolution and to remain competitive. Although the IT operations department is responsible to manage service provider service quality, they are not in a position to evaluate IT services performance because of the loss of control and capability in the IT environment.Objectives: This article investigated the impact of outsourcing in-house IT services on the performance of IT operations and how it affects the performance of the organisation.Methods: The qualitative approach was used in this research. Data was collected using structured interviews and were analysed using thematic analysis.Results: Outsourcing of IT services was found to have negative and positive impact on the organisation. Knowledge of the operating environment proved to be significant in the provision of relevant services that add value to the organisation. The results of the analysis revealed that in-house IT and the quality of IT services provided have a direct impact on the performance of the organisation.Conclusion: The study demonstrated that outsourcing IT services impacts the quality and the performance of the organisation positively and negatively. The study recommended that in-house IT and outsource service providers need to collaborate to ensure smooth service delivery, process alignment and also to equip in-house IT with skills to handle first-line support.

Incentive-Based Scheduling for Green Computational Grid

In the computing grid environment, jobs scheduling is fundamentally the process of allocating computing jobs with choices relevant to the available resources. As the scale of grid computing system grows in size over time, exponential increase in energy consumption is foreseen. As such, large data centers (DC) are embarking on green computing initiatives to address the IT operations impact on the environment. The main component within a computing system consuming the most electricity and generating the most heat is the microprocessor. The heat generated by these high-performance microprocessors is emitting high CO2 footprint. Therefore, jobs scheduling with thermal considerations (thermal-aware) to the microprocessors is important in DC grid operations. An approach for jobs scheduling is proposed in this chapter for reducing electricity usage (green computing) in DC grid. This approach is the outcome of the R&D works based on the DC grid environment in Universiti Teknologi PETRONAS, Malaysia.