The escalating demand for data and computational power has led to the rapid expansion of data centres, significantly increasing their environmental footprint. Data centres now account for an estimated 2% to 4% of global electricity consumption. Although various best practices and key performance indicators (KPIs) have been developed to mitigate this impact, creating a comprehensive framework for evaluating the environmental sustainability of data centres remains challenging. Existing certification schemes, such as Leadership in Energy and Environmental Design (LEED) and the Building Research Establishment Environmental Assessment Method (BREEAM), were originally designed for traditional building infrastructure and are increasingly applied to data centres. However, these frameworks primarily focus on building-related aspects, inadequately addressing the unique mechanical, electrical, and IT dimensions critical to data centres. This study introduces a comprehensive evaluation model applied to the LEED framework, identifying significant gaps and proposing enhancements. The findings highlight notable discrepancies and limitations within the current LEED version, emphasising the need for tailored approaches that accurately reflect the distinct characteristics and operational demands of data centres.

Demand for digital services is increasing significantly. Addressing energy efficiency at the data center mechanical and electrical infrastructure level is starting to suffer from the law of diminishing returns. IT equipment, specifically servers, account for a significant part of the overall facility energy consumption and environmental impact, and thus, present a major opportunity, not the least from a circular economy perspective. To reduce the environmental impact of servers, it is important to realize the effect of manufacturing, operating, and disposing of servers on the environment. This work presents new insights into the effect of refreshing servers with remanufactured and refurbished servers on energy efficiency and the environment. The research takes into consideration the latest changes in CPU design trends and Moore's law. The study measures and analyzes the use phase energy consumption of remanufactured servers vs new servers with various hardware configurations. Case studies are used to evaluate the potential impact of refurbished server refresh from an economic as well as environmental perspectives.

Data centers have been highlighted as a major energy consumer and there has been an increasing trend towards the consolidation of smaller data centers into larger facilities. Yet, small data centers exist for a variety of reasons and account for a significant portion of the total number of servers. Frequent refreshes of IT hardware have emerged as a trend in hyper-scale data centers but little attention has been paid to how these savings can be achieved in smaller facilities. This work provides a comprehensive framework for the energy saving opportunities while determining when a return on investment can be achieved to enable small data center operators to create credible business cases for hardware refreshes. Various data center deployment scenarios are used as case studies (based on real-life datasets) to validate the proposed concepts. Our results show that a return on investment can be achieved for organizations with small data center in less than two years and that these organizations can save more than three times their annual electricity cost over five years.

Rapid changes in security threat landscapes cause uncertainty for IT operations and security professionals and may force changes to organizations' security strategy. Decision support data that help reduce ambiguity or even predict future developments in this regard can thus be of economic value. Based on over 200 security predictions published in 2015, we use a topic modeling approach to identify 17 underlying predicted threat developments. To verify the extent to which these predicted threat topics were realized in 2016, we solicited opinions from respondents with varying experience of IT and information security. In addition, we reviewed secondary sources to corroborate the survey results. Based on the presented findings, we conclude that the security predictions for 2016 did foresee notable developments in that year. The identified latent predictions were related to hacking political campaigns, large-scale data breaches of personal data and health records, increasing threats from various types of malware, specifically ransomware, and large-scale DDoS attacks. The findings of this research are relevant to IT executives and security professionals, who can apply this approach to improve the effectiveness of organizations' technology and cyber security strategy.

With the increased trend of moving towards the consolidation of computing in larger facilities, and with the rise of paradigms such as Cloud, Smart Cities, and IoT, data centers have been highlighted as a major energy consumer. Yet, data centers exist purely to host IT services, which on average tend to account for the largest part of the overall facility energy consumption. Frequent refresh of IT hardware has emerged as a trend in hyper-scale data centers. However, the wider environmental impact of hardware refresh has become a concern. This work provides a comprehensive framework that helps identify the energy saving opportunities, while demonstrating the overall environmental impact related to hardware refresh. The work sheds new light on key relationships such as the one between hardware utilization and Power Usage Effectiveness (PUE) to drive efficiency. Various data center deployment scenarios are used as case studies (based on real-life datasets) to validate the proposed concepts.

Cloud gaming where computer games are executed on cloud based resources is a new service which can be extremely challenging for service providers. Unlike other web and video streaming applications it requires both a relatively high constant downlink bandwidth and low latency simultaneously. Hence, cloud gaming providers recommend that clients be relatively close to data centers to ensure reasonable Quality of Service. Latency, however, is also dependent on the compression type used for the video streaming and there is a trade-off between compression efficiency and complexity which affects client computer capability. In this paper we propose the Strife system which examines the processing capabilities of the client as well as the network conditions between the client and data center to select the compression type that maximizes the size of the client base for cloud gaming services for which reasonable Quality of Service can be provided. We model a cloud gaming service using data which reflects gaming hardware in common use, the bandwidth available in the regions and optimize the number of users that can receive reasonable Quality of Service. Finally, we compare other schemes with the Strife system to evaluate its performance.

With the rising energy demand in ICT services and its associated environmental impact, the need for energy efficient Enterprise ICT solutions is growing. As data centres account for a large part of energy consumption in ICT, data centre operators strive to create opportunities to put more emphasis on reducing energy consumption. However, creating ICT Systems that are energy efficient by design remains a key challenge. In this paper, we identify and map contextual energy information about data centre operations in order to model their power related components. This contextual modelling is then mapped to deployment diagram where we introduce greenUML (gUML), an extension to UML diagrams to improve energy efficiency through energy analysis at design time. gUML will allow system architects to reason about the energy footprint of their applications at design time.

Monitoring energy consumption of applications is crucial for energy optimisation and improvements in software systems. With the recent emphasis on energy efficiency, it is vital that software engineers have an understanding of the energy consumed by the code they write. In this paper, we present Jolinar, a tool that bridges the gap between energy measurements and accessibility to software engineers and even end-users. The tool builds on top of recent energy models to provide an accurate, light and easy-to-use interface for energy measurements. The target audience of Jolinar is both software engineers and non-technical end-users who want to monitor their applications' energy footprint. We show that end-users can use Jolinar's GUI to determine the energy consumed by the software they are using, and software engineers can use the tool to analyse energy consumption of systems to make energy-conscious decisions.

With the proliferation of Software systems and the rise of paradigms such the Internet of Things, Cyber-Physical Systems and Smart Cities to name a few, the energy consumed by software applications is emerging as a major concern. Hence, it has become vital that software engineers have a better understanding of the energy consumed by the code they write. At software level, work so far has focused on measuring the energy consumption at function and application level. In this paper, we propose a novel approach to measure energy consumption at a feature level, cross-cutting multiple functions, classes and systems. We argue the importance of such measurement and the new insight it provides to non-traditional stakeholders such as service providers. We then demonstrate, using an experiment, how the measurement can be done with a combination of tools, namely our program slicing tool (PORBS) and energy measurement tool (Jolinar).

Variability management is one of the main activities in the Software Product Line Engineering process. Common and varied features of related products are modelled along with the dependencies and relationships among them. With the increase in size and complexity of product lines and the more holistic systems approach to the design process, managing the ever-growing variability models has become a challenge. In this paper, we present MUSA, a tool for managing variability and features in large-scale models. MUSA adopts the Separation of Concerns design principle by providing multiple perspectives to the model, each conveying different set of information. The demonstration is conducted using a real-life model (comprising of 1000+ features) particularly showing the Structural View, which is displayed using a mind-mapping visualisation technique (hyperbolic trees), and the Dependency View, which is displayed graphically using logic gates.

Purpose – This study aims to examine the influence of one or more information security breaches on an organisation’s stock market value as a way to benchmark the wider economic impact of such events. Design/methodology/approach – An event studies-based approach was used where a measure of the event’s economic impact can be constructed using security prices observed over a relatively short period of time. Findings – Based on the results, it is argued that, although no strong conclusions could be made given the current data constraints, there was enough evidence to show that such correlation exists, especially for recurring security breaches. Research limitations/implications – One of the main limitations of this study was the quantity and quality of published data on security breaches, as organisations tend not to share this information. Practical implications – One of the challenges in information security management is assessing the wider economic impact of security breaches. Subsequently, this helps drive investment decisions on security programmes that are usually seen as cost rather than moneymaking initiatives. Originality/value – This study envisaged that as more breach event data become more widely available because of compliance and regulatory changes, this approach has the potential to emerge as an important tool for information security managers to help support investment decisions.

Product line software engineering depends on capturing the commonality and variability within a family of products, typically using feature modeling, and using this information to evolve a generic reference architecture for the family. For embedded systems, possible variability in hardware and operating system platforms is an added complication. The design process can be facilitated by first exploring the behavior associated with features. In this paper we outline a bidirectional feature modeling scheme that supports the capture of commonality and variability in the platform environment as well as within the required software. Additionally, 'behavior' associated with features can be included in the overall model. This is achieved by integrating the UCM path notation in a way that exploits UCM's static and dynamic stubs to capture behavioral variability and link it to the feature model structure. The resulting model is a richer source of information to support the architecture development process

Summary form only given. With the rapid expansion of the Internet and the increasing demand on Web servers, many techniques were developed to overcome the servers' hardware performance limitation. Mirrored Web servers is one of the techniques used where a number of servers carrying the same "mirrored" set of services are deployed. Client access requests are then distributed over the set of mirrored servers to even up the load. In this paper, we present a generic reference software architecture for load balancing over mirrored Web servers. The architecture was designed adopting the latest NaSr architectural style and described using the ADLARS architecture description language. With minimal effort, different tailored product architectures can be generated from the reference architecture to serve different network protocols and server operating systems. An example product system is described and a sample Java implementation is presented.

Software product line (SPL) engineering has emerged to become a mature domain for maximizing reuse within the context of a family of related software products. Within the process of SPL, the variability and commonality among the different products within the scope of a family is captured and modeled into a system's `feature model'. Currently, there are no architecture description languages (ADLs) that support the relationship between the feature model domain and the system architecture domain, leaving a gap which significantly increases the complexity of analyzing the system's architecture and insuring that it complies with its set feature model and variability requirements. In this paper we present ADLARS, an architecture description language that supports the relationship between the system's feature model and the architectural structures in an attempt to alleviate the aforementioned problem. The link between the two spaces also allows the automatic generation of product architectures from the family reference architecture

Software product-line engineering aims to maximize reuse by exploiting the commonality within families of related systems. Its success depend on capturing the commonality and variability, and using this to evolve a reference architecture for the product family. With embedded system families, the possibility of variability in hardware and operating system platforms is an added complication. In this paper we outline a strategy for evolving reference architectures from bi-directional feature models. The proposed strategy complements information provided by the feature model with scenarios that help to elaborate feature behavior.

Inter-component communication has always been of great importance in the design of software architectures and connectors have been considered as first-class entities in many approaches by R. Allen and D. Garlan (1994), M. Shaw et al., (1995), and D. Batory and S. O'Malley (1992). We present a novel architectural style that is derived from the well-established domain of computer networks. The style adopts the inter-component communication protocol in a novel way that allows large scale software reuse. It mainly targets real-time, distributed, concurrent, and heterogeneous systems.