29,084 research outputs found

    Software development in the post-PC era : towards software development as a service

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    PhD ThesisEngineering software systems is a complex task which involves various stakeholders and requires planning and management to succeed. As the role of software in our daily life is increasing, the complexity of software systems is increasing. Throughout the short history of software engineering as a discipline, the development practises and methods have rapidly evolved to seize opportunities enabled by new technologies (e.g., the Internet) and to overcome economical challenges (e.g., the need for cheaper and faster development). Today, we are witnessing the Post-PC era. An era which is characterised by mobility and services. An era which removes organisational and geographical boundaries. An era which changes the functionality of software systems and requires alternative methods for conceiving them. In this thesis, we envision to execute software development processes in the cloud. Software processes have a software production aspect and a management aspect. To the best of our knowledge, there are no academic nor industrial solutions supporting the entire software development process life-cycle(from both production and management aspects and its tool-chain execution in the cloud. Our vision is to use the cloud economies of scale and leverage Model-Driven Engineering (MDE) to integrate production and management aspects into the development process. Since software processes are seen as workflows, we investigate using existing Workflow Management Systems to execute software processes and we find that these systems are not suitable. Therefore, we propose a reference architecture for Software Development as a Service (SDaaS). The SDaaS reference architecture is the first proposal which fully supports development of complex software systems in the cloud. In addition to the reference architecture, we investigate three specific related challenges and propose novel solutions addressing them. These challenges are: Modelling & enacting cloud-based executable software processes. Executing software processes in the cloud can bring several benefits to software develop ment. In this thesis, we discuss the benefits and considerations of cloud-based software processes and introduce a modelling language for modelling such processes. We refer to this language as EXE-SPEM. It extends the Software and Systems Process Engineering (SPEM2.0) OMG standard to support creating cloudbased executable software process models. Since EXE-SPEM is a visual modelling language, we introduce an XML notation to represent EXE-SPEM models in a machine-readable format and provide mapping rules from EXE-SPEM to this notation. We demonstrate this approach by modelling an example software process using EXE-SPEM and mapping it to the XML notation. Software process models expressed in this XML format can then be enacted in the proposed SDaaS architecture. Cost-e cient scheduling of software processes execution in the cloud. Software process models are enacted in the SDaaS architecture as workflows. We refer to them sometimes as Software Workflows. Once we have executable software process models, we need to schedule them for execution. In a setting where multiple software workflows (and their activities) compete for shared computational resources (workflow engines), scheduling workflow execution becomes important. Workflow scheduling is an NP-hard problem which refers to the allocation of su cient resources (human or computational) to workflow activities. The schedule impacts the workflow makespan (execution time) and cost as well as the computational resources utilisation. The target of the scheduling is to reduce the process execution cost in the cloud without significantly a ecting the process makespan while satisfying the special requirements of each process activity (e.g., executing on a private cloud). We adapt three workflow scheduling algorithms to fit for SDaaS and propose a fourth one; the Proportional Adaptive Task Schedule. The algorithms are then evaluated through simulation. The simulation results show that the our proposed algorithm saves between 19.74% and 45.78% of the execution cost, provides best resource (VM) utilisation and provides the second best makespan compared to the other presented algorithms. Evaluating the SDaaS architecture using a case study from the safety-critical systems domain. To evaluate the proposed SDaaS reference architecture, we instantiate a proof-of-concept implementation of the architecture. This imple mentation is then used to enact safety-critical processes as a case study. Engineering safety-critical systems is a complex task which involves multiple stakeholders. It requires shared and scalable computation to systematically involve geographically distributed teams. In this case study, we use EXE-SPEM to model a portion of a process (namely; the Preliminary System Safety Assessment - PSSA) adapted from the ARP4761 [2] aerospace standard. Then, we enact this process model in the proof-of-concept SDaaS implementation. By using the SDaaS architecture, we demonstrate the feasibility of our approach and its applicability to di erent domains and to customised processes. We also demonstrate the capability of EXE-SPEM to model cloud-based executable processes. Furthermore, we demonstrate the added value of the process models and the process execution provenance data recorded by the SDaaS architecture. This data is used to automate the generation of safety cases argument fragments. Thus, reducing the development cost and time. Finally, the case study shows that we can integrate some existing tools and create new ones as activities used in process models. The proposed SDaaS reference architecture (combined with its modelling, scheduling and enactment capabilities) brings the benefits of the cloud to software development. It can potentially save software production cost and provide an accessible platform that supports collaborating teams (potentially across di erent locations). The executable process models support unified interpretation and execution of processes across team(s) members. In addition, the use of models provide managers with global awareness and can be utilised for quality assurance and process metrics analysis and improvement. We see the contributions provided in this thesis as a first step towards an alternative development method that uses the benefits of cloud and Model-Driven Engineering to overcome existing challenges and open new opportunities. However, there are several challenges that are outside the scope of this study which need to be addressed to allow full support of the SDaaS vision (e.g., supporting interactive workflows). The solutions provided in this thesis address only part of a bigger vision. There is also a need for empirical and usability studies to study the impact of the SDaaS architecture on both the produced products (in terms of quality, cost, time, etc.) and the participating stakeholders

    ERA: A Framework for Economic Resource Allocation for the Cloud

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    Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations

    Algorithms for advance bandwidth reservation in media production networks

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    Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

    A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

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    Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

    Digital Preservation Services : State of the Art Analysis

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    Research report funded by the DC-NET project.An overview of the state of the art in service provision for digital preservation and curation. Its focus is on the areas where bridging the gaps is needed between e-Infrastructures and efficient and forward-looking digital preservation services. Based on a desktop study and a rapid analysis of some 190 currently available tools and services for digital preservation, the deliverable provides a high-level view on the range of instruments currently on offer to support various functions within a preservation system.European Commission, FP7peer-reviewe

    Cloud engineering is search based software engineering too

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    Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE
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