A Catalog of Architectural Tactics for Cyber-Foraging

Mobile devices have become for many the preferred way of interacting with the Internet, social media and the enterprise. However, mobile devices still do not have the computing power or battery life that will allow them to perform effectively over long periods of time or for executing applications that require extensive communication or computation, or low latency. Cyber-foraging is a technique enabling mobile devices to extend their computing power and storage by offloading computation or data to more powerful servers located in the cloud or in single-hop proximity. This paper presents a catalog of architectural tactics for cyber-foraging that was derived from the results of a systematic literature review on architectures for cyber-foraging systems. Elements of the architectures identified in the primary studies were codified in the form of Architectural Tactics for Cyber-Foraging. These tactics will help architects extend their design reasoning towards cyber-foraging as a way to support the mobile applications of the present and the future

A tale of three systems : case studies on the application of architectural tactics for cyber-foraging

Cyber-foraging is a technique to enable mobile devices to extend their computing power and storage by offloading computation or data to more powerful servers located in the cloud or in single-hop proximity. In previous work, we developed a set of reusable architectural tactics for cyber-foraging systems. We define architectural tactics as design decisions that influence the achievement of a system quality. In this article we present the results of three case studies to validate the application of the tactics to promote their intended functional and non-functional requirements. The first two case studies focus on the identification of architectural tactics in existing cyber-foraging systems. The third case study focuses on the development of a new cyber-foraging system using the architectural tactics. The results of the case studies are an initial demonstration of the validity of the tactics, and the potential for taking a tactics-driven approach to fulfill functional and non-functional requirements for cyber-foraging systems. (C) 2019 Elsevier B.V. All rights reserved

Empirical Validation of Cyber-Foraging Architectural Tactics for Surrogate Provisioning

Background Cyber-foraging architectural tactics are used to build mobile applications that leverage proximate, intermediate cloud surrogates for computation offload and data staging. Compared to direct access to cloud resources, the use of intermediate surrogates improves system qualities such as response time, energy efficiency, and resilience. However, the state-of-the-art mostly focuses on introducing new architectural tactics rather than quantitatively comparing the existing tactics, which can help software architects and software engineers with new insights on each tactic. Aim Our work aims at empirically evaluating the architectural tactics for surrogate provisioning, specifically with respect to resilience and energy efficiency. Method We follow a systematic experimentation framework to collect relevant data on Static Surrogate Provisioning and Dynamic Surrogate Provisioning tactics. Our experimentation approach can be reused for validation of other cyber-foraging tactics. We perform statistical analysis to support our hypotheses, as compared to baseline measurements with no cyber-foraging tactics deployed. Results Our findings show that Static Surrogate Provisioning tactics provide higher resilience than Dynamic Surrogate Provisioning tactics for runtime environmental changes. Both surrogate provisioning tactics perform with no significant difference with respect to their energy efficiency. We observe that the overhead of the runtime optimization algorithm is similar for both tactic types. Conclusions The presented quantitative evidence on the impact of different tactics empowers software architects and software engineers with the ability to make more conscious design decisions. This contribution, as a starting point, emphasizes the use of quantifiable metrics to make better-informed trade-offs between desired quality attributes. Our next step is to focus on the impact of runtime programmable infrastructure on the quality of cyber-foraging systems

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research

Addressing Energy Efficiency in System Design: A Journey FromArchitecture to Operation

Digital-transformation initiatives have led to major efficiencies and cost savings but at the cost of consuming nearly 10 percent of the world’s electricity. Energy consumption research has increased datacentre, network, and hardware efficiency, but a neglected aspect of energy research has been the energy consumption of the software applications that underpin digital transformation. To date, software architects have lacked the knowledge, guidance, and tools to allow them to understand the energy properties of their systems. The research reported in this thesis begins to address this situation by developing practical knowledge, techniques, and tools to allow software architects to play their part in controlling the energy consumption of our modern digital world. The work commences with an investigation into formal architectural description languages, through a literature review and a case study, resulting in two research contributions, namely a comprehensive systematic survey of architecture description languages from 1991 to 2015, and a case study of practical ADL use at scale in industry. The second part of the research investigates how to assist architects in prioritising energy efficiency through a study of how experienced architects focus their attention for maximum effectiveness, which leads to the development of a model to guide architecture practitioners, which is validated and refined through a large survey of practising software architects. The research contribution is a refined and validated model for architectural effort prioritisation. The third aspect of the research examines the energy-related guidance available to architects and having found little generally applicable advice, analyses a significant industrial case study to understand how leading-edge practitioners addressed energy efficiency, contributing a set of three energy-related architectural principles, which can be used to guide architects in improving application energy efficiency. Finally, we consider the practical problem of understanding the runtime energy properties of a system, and designed a novel approach to estimate the energy consumption of execution scenarios via application execution tracing and a cost-based energy model. We created a proof of concept implementation of the approach and validated its consistency and correctness through practical testing. The contribution of this work was twofold, namely the design of a practical system for allocating energy to application execution scenarios, and a tested, open-source, proof-of-concept implementation of the system. Hence, the result of this work is six distinct contributions to knowledge in the area of ADLs (the survey and practical case study), architectural practice (the prioritisation model and the architectural principles for energy efficiency) and application energy efficiency (the design of the energy allocation system and the proof-of-concept implementation), which collectively can help architects to treat energy efficiency as a first class architectural concern in their work

An exploratory case study on reusing architecture decisions in software-intensive system projects

Reusing architecture decisions from previous projects promises to support architects when taking decisions. However, little is known about the state of art of decision-reuse and the benefits and challenges associated with reusing decisions. Therefore, we study how software architects reuse architecture decisions, the stakeholders and their concerns related to decision-reuse, and how architects perceive the ideal future state of decision-reuse. We conducted a qualitative explorative case study in the software-intensive systems industry. The study has shown that architects frequently reuse decisions but are confined to decisions they already know or have heard about. The results also suggest that architects reuse decisions in an ad-hoc manner. Moreover this study presents a conceptual model of decision-reuse and lists stakeholder concerns with regards to decision-reuse. The results of this study indicate that improving the documentation and discoverability of decisions holds a large potential to increase reuse of decisions and that decision documentation is not only important for system understanding or in the context of architecture reviews but also to support architects in upcoming projects

A Value-Driven Framework for Software Architecture

Software that is not aligned with the business values of the organization for which it was developed does not entirely fulfill its raison d’etre. Business values represent what is important in a company, or organization, and should influence the overall software system behavior, contributing to the overall success of the organization. However, approaches to derive a software architecture considering the business values exchanged between an organization and its market players are lacking. Our quest is to address this problem and investigate how to derive value-centered architectural models systematically. We used the Technology Research method to address this PhD research question. This methodological approach proposes three steps: problem analysis, innovation, and validation. The problem analysis was performed using systematic studies of the literature to obtain full coverage on the main themes of this work, particularly, business value modeling, software architecture methods, and software architecture derivation methods. Next, the innovation step was accomplished by creating a framework for the derivation of a software reference architecture model considering an organization’s business values. The resulting framework is composed of three core modules: Business Value Modeling, Agile Reference Architecture Modeling, and Goal-Driven SOA Architecture Modeling. While the Business value modeling module focuses on building a stakeholder-centric business specification, the Agile Reference Architecture Modeling and the Goal-Driven SOA Architecture Modeling modules concentrate on generating a software reference architecture aligned with the business value specification. Finally, the validation part of our framework is achieved through proof-of-concept prototypes for three new domain specific languages, case studies, and quasi-experiments, including a family of controlled experiments. The findings from our research show that the complexity and lack of rigor in the existing approaches to represent business values can be addressed by an early requirements specification method that represents the value exchanges of a business. Also, by using sophisticated model-driven engineering techniques (e.g., metamodels, model transformations, and model transformation languages), it was possible to obtain source generators to derive a software architecture model based on early requirements value models, while assuring traceability throughout the architectural derivation process. In conclusion, despite using sophisticated techniques, the derivation process of a software reference architecture is helped by simple to use methods supported by black box transformations and guidelines that facilitate the activities for the less experienced software architects. The experimental validation process used confirmed that our framework is feasible and perceived as easy to use and useful, also indicating that the participants of the experiments intend to use it in the future

Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move