Feature placement algorithms for high-variability applications in cloud environments

Scheduling processes have been applied to a wide range of application areas, such as scheduling tasks in operating systems, scheduling facilities at airports, scheduling assembly lines in production, scheduling resources in project management, timetabling in public transportation, and scheduling activities in cyber-physical systems. In general, scheduling problems are not trivial to solve effectively and efficiently. Design and implementation of scheduling software is expensive and time-consuming. This dissertation makes three main contributions: First, we have adopted a feature-oriented Software Product Line Engineering (SPLE) approach. If applied correctly, the SPLE approach provides more economic solutions in case a family of products is developed instead of one product. Companies that develop scheduling systems generally implement product families. After an extensive domain analysis of the theory, the common and variable “features” have been defined. By choosing and configuring the right set of “features”, the designers can efficiently define scheduling systems according to their needs. As a second contribution, to convert the abstract definitions into executable programs, we have designed and implemented an “application framework” called First Scheduling Framework (FSF). We consider reusability and dynamic extensibility as two quality attributes of the framework. To this end, generic “constraint-solvers” are adopted to translate the predefined “scheduling constraints” into the “constraint-language” of the corresponding solver and to use them to solve the translated problem. We have extended our implementation using MDE (Model-Driven Engineering) techniques so that “feature-oriented” models can be easily converted to the abstractions of the “application framework”. As a third contribution, we have expanded our “feature-oriented” approach to a general “model optimization framework”. Scheduling systems can be influenced by many contextual factors, such as the desire for lower energy consumption, more precise computation, and dealing with certain hardware limitations. Due to contextual factors, it can be very difficult to define an optimal system that gives the best compromise for such multiple concerns. For this purpose, we propose OptML Framework, which accepts different models representing the contextual factors in the language of ECORE in the MDE environment and calculates the optimal models that meet user-defined requirements.

Version control (VC) and Software Product Line Engineering (SPLE) are two software engineering disciplines to manage variability in time and variability in space. In this paper, a thorough comparison of VC and SPLE is provided, showing that both disciplines imply a number of desirable properties. As a proof of concept for the combination of VC and SPLE, we present SuperMod, a tool realizes an existing conceptual framework that transfers the iterative VC editing model to SPLE. The tool allows to develop a software product line in a single-version workspace step by step, while variability management is completely automated. It offers familiar version control metaphors such as check-out and commit, and in addition uses the SPLE concepts of feature models and feature configuration the definition of logical variability and to define the logical scope of a change. SuperMod has been implemented in a model-driven way and primarily targets EMF models as software artifacts. We successfully apply the tool to a standard SPLE example.

With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self-Tuning Particle Swarm Optimization approach (called STPSO) is proposed to configure the cloud environment. STPSO can automatically adjust the arbitrary configuration to a valid configuration. To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.

Context. From recent works on product properties resulting from configurations and the optimisation of these properties, one comes quickly to more complex challenges such as multi-objective optimisation, conflicting objectives, multiple stakeholders, and conflict resolution. The intuition is that Software Product Line Engineering (SPLE) can draw from other disciplines that deal with decision spaces and complex decision scenarios.Objectives. We aim to (1) explore links to such disciplines, (2) systematise and compare concepts, and (3) identify opportunities, where SPLE approaches can be enriched.Method. We undertake an exploratory study: Starting from common SPLE activities and artefacts, we identify aspects where we expect to find corresponding counterparts in other disciplines. We focus on Multiple Criteria Decision Analysis (MCDA), Multi-Objective Optimisation (MOO), and Design Space Exploration (DSE), and perform a comparison of the key concepts.Results. The resulting comparison relates SPLE activities and artefacts to concepts from MCDA, MOO, and DSE and identifies areas where SPLE approaches can be enriched. We also provide examples of existing work at the intersections of SPLE with the other fields. These findings are aimed to foster the conversation on research opportunities where SPLE can draw techniques from other disciplines dealing with complex decision scenarios.

In the face of development of a software-intensive products family in a domain, Software Product Line Engineering (SPLE) itself is a best practice that has been representatively demonstrated by the cases inducted into Software Product Line Hall of Fame. SPLE best practice is supported by various and vivid meta best practices generated from concrete activities through solution finding processes. This paper describes the evolution of the FISCAN MTMSIS software product line, nominated to the Hall of Fame at SPLC 2010. FISCAN applies SPLE in the security inspection domain and has identified SPLE best practices through years of experience in the past. This paper discusses some representative best practices and the lessons we learned during the process. More work, both development and research, are still in progress on the platform of the MTMSIS software product line and FISCAN will continue to apply SPLE in the future.

It is our claim that the adoption of software engineering reuse techniques can leverage MAS development, mostly when we consider similar applications belonging to the same domain. MAS-Product Line (MAS-PL) raises as an interesting approach that uses Software Product Line Engineering (SPLE) techniques and AOSE to manage the commonalities (similarities) and variabilities (differences) of such MAS applications. Although MAS present specific characteristics that could be considered when describing the system variability, existing work on MAS-PL is devoted to deal with MAS variability considering only domain-specific issues. Moreover, the adoption of variability models such as feature models should be considered for describing both Generic and Specific MAS variability. We propose a MAS-PL approach to address the aforementioned issues by representing Generic MAS variability according to MAS concepts such as agents, environment, interaction and organization, and Specific MAS variability according to a specific application domain.

Cloud computing has caused a revolution in our way of using software. Software development and deployment based on the new models of Software as a Service (SaaS) and Service-Oriented Architecture (SOA) are expected to bring a lot of benefits for users. However, software developers and software service providers will have to address new challenging issues before such benefits can be realized. The issues mainly arise from two characteristics of service-based applications: software based on distributed service flows and online concurrent user access. This paper deals with one of the important issues concerning resource allocation and dynamic provisioning for the deployed services in a cloud environment. We propose a service deployment strategy based on the structural property of application service flows and a dynamic resource provisioning mechanism utilizing the estimation of future service requests. A series of experiments were conducted on the CloudSim platform to evaluate the proposed mechanisms. The experimental results indicate that the proposed methods significantly outperform the commonly used primitive approaches, achieving up to 79.9% performance improvement.

In recent years, there has been a growing interest in cloud technologies. Using current cloud solutions, it is however difficult to create customizable multi-tenant applications, especially if the application must support varying Quality of Service (QoS) guarantees. Software Product Line Engineering (SPLE) and feature modeling techniques are commonly used to address these issues in non-cloud applications, but these techniques cannot be ported directly to a cloud context, as the common approaches are geared towards customization of on-premise deployed applications, and do not support multi-tenancy. In this paper, we propose an architecture for the development and management of customizable Software as a Service (SaaS) applications, built using SPLE techniques. In our approach, each application is a composition of services, where individual services correspond to specific application functionalities, referred to as features. A feature-based methodology is described to abstract and convert the application information required at different stages of the application life-cycle: development, customization and deployment. We specifically focus on how development feature models can be adapted ensuring a one-to-one correspondence between features and services exists, ensuring the composition of services yields an application containing the corresponding features. These runtime features can then be managed using feature placement techniques. The proposed approach enables developers to define significantly less features, while limiting the amount of automatically generated features in the application runtime stage. Conversion times between models are shown to be in the order of milliseconds, while execution times of management algorithms are shown to improve by 5 to 17% depending on the application case.

PLUM[1] is a tool suite for the design, implementation, and management of product lines that follow a Model-Driven Software Development approach. PLUM is a domain agnostic tool suite. It aims to provide an integrated set of tools which support adopting Product Line Engineering (PLE) approach in any domain.

Software Product Line Engineering (SPLE) is an approach for software reuse. It concerns to produce customized software products as atomic or composite services to be reused in SOA-based applications. A common set of artifacts is used to build these services in a planned and managed way. The main purpose of SPLE is to explore commonalities and variabilities. The SPLE approach provides a strategic software reuse that can produce quality Software as a Service (SaaS) while cutting cost and reducing time-to-market. This paper proposes a process to construct services as Software Product Lines by using Model Driven techniques. The process combines the use of maps, visual techniques for SPL modeling, especially features diagrams and MD techniques. In addition to the process, we have developed a tool to support map, feature, and class diagrams modeling.

In this paper we identify key challenges a medium-sized software organization is facing in migrating towards Software Product Line Engineering (SPLE). The software engineering context of the company is characterized by a two-fold access to the market - core customer driven product enhancement and product development for a broader, anonymous market - and the embedding of software engineering in multi-disciplinary systems and solutions engineering.Based on a characterization of the business, the software product subject to migration towards SPLE, and the goals and background of the SPLE initiative, seven key challenges with respect to the migration are identified. These challenges relate to process diversity in the face of multiple reuse approaches; the management of requirements and variability; the integration of requirements traceability and variability management; legacy software and discipline vs. software-specific modularization; integration with systems engineering; costing and pricing models; and project vs. product documentation.

Software Product Line Engineering (SPLE) has drawn large amounts of attention during the last two decades as it offers the benefits of reducing cost and time to market by reusing requirements and components. Recently, more and more large scale industries start to implement SPLE in their domains (combining SPLE with model-based modelling methods). However, the problem of how to combine SPLE with Model-Based System Engineering is still a challenge, as systems are much broader than the software domain. Unlike software engineering, system engineering has to consider the physical resources aspect. This paper classifies typical types of physical variability and provides general modelling solutions for each type of physical variation at the system design stage. Specifically, this approach combines a variability model with a SysML Block Definition Diagram and an Internal Block Diagram to model the contextual variability, architectural variability, connector variability, instance number variability, component variability, location variability and evolutional variability of physical designs. Variability is modelled separately to help reduce the complexity of design models. Last but not least, the proposed method is illustrated by an aircraft system case study.

Software Product Line Engineering (SPLE) is a software development paradigm that aims at reducing the development effort and shorting time-to-market through systematic software reuse. While this paradigm has been successfully applied for the development of embedded systems in various domains, new challenges have emerged from the development of safety critical systems that require certification against a specific standard. Existing SPLE approaches do not explicitly consider the various certification standards or levels that products should satisfy. In this paper, we focus on several practical issues involved in the SPLE process, establishing an infrastructure of a product line engineering for certified products. A metamodel is proposed to capture the entities involved in SPL certification and the relationships among them. ProLiCES, which is a model-driven process for the development of SPLs, was modified to serve as an example of our approach, in the context of the UAV (Unmanned Aerial Vehicle) domain.

Software product family or line is a software engineering paradigm that systematizes reuse. In software product line engineering, two phases are distinguished: domain engineering which is in charge of developing a common infrastructure and assets and application engineering which makes use of those assets to generate the products. One of the key aspects of product lines is variability and its management. However, the main focus has been on functional variability and quality variability in software product lines has not received so much attention by researchers. In a product line different members of the line may require different levels of a quality requirement, for instance they could differ in terms of their availability, security, reliability, etc. Due to this variability, quality evaluation in software product lines is much more complicated that in single-systems. One alternative is to evaluate all the products of a line but it is very expensive and ways of reducing evaluation efforts are necessary. In this direction, the paper presents a method for facilitating cost-effective quality evaluation of a product line taking into consideration variability on quality attributes.

Due to ever-evolving software developments processes, companies are motivated to develop desired quality products quickly and effectively. Industries are now focusing on the delivery of configurable systems to provide several services to a wide range of customers by making different configurations in a single largest system. Nowadays, component-based systems are highly demanded due to their capability of reusability and restructuring of existing components to develop new systems. Moreover, product line engineering is the major branch of the component-based system for developing a series of systems. Software product line engineering (SPLE) provides the ability to design several software modifications according to customer needs in a cost-effective manner. Researchers are trying to tailor the software product line (SPL) process that integrates agile development technologies to overcome the issues faced during the execution of the SPL process such as delay in product delivery, restriction to requirements change, and exhaustive initial planning. The selection of suitable components, the need for documentation, and tracing back the user requirements in the agile-integrated product line (APL) models still need to improve. Furthermore, configurable systems demand the selected features to be the least dependent. In this paper, a hybrid APL model, quality enhanced application product line engineering (QeAPLE) is proposed that provides support for highly configurable systems (HCS) by evaluating the dependency of features before making the final selection. It also has a documentation and requirement traceability function to ensure that the product meets the desired quality. Two-fold assessments are undertaken to validate the suggested model, with the proposed model being deployed on an active project. After that, we evaluated the proposed model performance and effectiveness using after implementing it in a real-world environment and compared the results with an existing method using statistical analysis. The results of the experimental study proofs that the proposed model is practically and statistically significant as compared to the existing method in terms of effectiveness and participants’ performance. Hence, the statistical results of the comparative analysis show that the proposed model improved ease of understanding and adaptability, required effort, high-quality achievement, and version management are significant i.e., more the 50% as compared to the exiting method i.e., less than 50%. The proposed model offers to assist in the development of a highly configurable system that achieves the needed quality. Therefore, the proposed model manages the variation identification, versions control, components dependency for correct selection of components, and validation activities from domain engineering to application engineering.