Bridging the Vendor-User Gap in Enterprise Cloud Software Development through Data-Driven Requirements Engineering

The shift from on-premise to cloud software has fundamentally changed the interactions between enterprise software vendors and their users. Where user involvement has traditionally been a challenge, increasingly large amounts of user input now allow for data-driven requirements engineering (RE). Research has paid little attention so far to the changes entailed by data-driven RE and addressed neither technical nor empirical perspectives of data-driven RE in enterprise software development. We aim to understand how the increasing availability of large amounts of user input impact RE in enterprise cloud software development. We provide a conceptualization of the newly available user input and how it changes traditional RE. We collect and analyze rich data from multiple product units at a leading enterprise software company and examine the integration of user input into RE; specifically requirements discovery, prioritization, experimentation, and specification. We thereby aim to contribute to non-normative and empirical work on RE

Multi-disciplinary Green IT Archival Analysis: A Pathway for Future Studies

With the growth of information technology (IT), there is a growing global concern about the environmental impact of such technologies. As such, academics in several research disciplines consider research on green IT a vibrant theme. While the disparate knowledge in each discipline is gaining substantial momentum, we need a consolidated multi-disciplinary view of the salient findings of each research discipline for green IT research to reach its full potential. We reviewed 390 papers published on green IT from 2007 to 2015 in three disciplines: computer science, information systems and management. The prevailing literature demonstrates the value of this consolidated approach for advancing our understanding on this complex global issue of environmental sustainability. We provide an overarching theoretical perspective to consolidate multi-disciplinary findings and to encourage information systems researchers to develop an effective cumulative tradition of research

Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

Strategies to succeed in an emerging market: a study of Australian service sector MNEs in India

This study focuses on post entry strategies of Multinational enterprises (MNEs) from a developedeconomy (Australia) operating in an emerging market (India). The research is organised around two core questions: (a) What are the post entry institutional challenges for service sector MNEs operating in an emerging market and (b) How do firms respond to institutional distance following their initial entry into an emerging economy? These questions are closely allied to the fundamental questions confronting international business (IB) such as “What drives firm strategy in international business (IB)?” and “What determines the success and failure of firms around the world?” (Peng et al., 20081920). This study draws on institutional theory (IT) and the network perspective to identify challenges faced by Australian service MNEs in India and the strategies they adapt to mitigate the liability of foreignness (LOF). The institutions in emerging economies are quite different from those in the developed economies, and recent research suggests that both formal and informal institutions, more commonly known as the "rules of the game" (North 1990) have an impact on the strategy and operations of MNEs (Hoskisson, et al., 2000; Wright, et al., 2005). Operating in emerging markets is challenging as the rule of law is poorly enforced (Hoskisson et al., 2000), proprietary technology and knowledge cannot be protected through enforceable legal mechanisms (Delios and Henisz, 2000) and there are large differences in culture and business norms. Contemporary institutional theory (Scott, 1995) indicates that, in order to survive, organisations must conform to the rules and belief systems prevailing in the environment (DiMaggio and Powell, 1983; Meyer and Rowan, 1977). Scott (1995) defined the institutional environment in terms of three pillars - regulative, normative and culturalcognitive. These unfamiliar institutions in the emerging markets often lead to unforeseen transaction costs as a result of which MNEs struggle to implement successful strategies in emerging markets. This thesis describes how the Australian service MNEs adaptively curb these costs by developing strategies that suit emerging markets as they acquire relevant local knowledge, re-script mental models and adapt plans and tactics. This study investigates key areas of concern to MNEs seeking competitive advantage in emerging markets through the development of superior strategies post-entry within the institutional framework of India. The AT. Kearney FDl Confidence Index 2007 rates India as the second most attractive destination for FDI. Its dynamic environment provides a rich context for examining the success strategies of Australian service MNEs. Finding effective strategies that can overcome the challenges of operating in an emerging market is the central issue in this thesis