Feature placement algorithms for high-variability applications in cloud environments

While the use of cloud computing is on the rise, many obstacles to its adoption remain. One of the weaknesses of current cloud offerings is the difficulty of developing highly customizable applications while retaining the increased scalability and lower cost offered by the multi-tenant nature of cloud applications. In this paper we describe a Software Product Line Engineering (SPLE) approach to the modelling and deployment of customizable Software as a Service (SaaS) applications. Afterwards we define a formal feature placement problem to manage these applications, and compare several heuristic approaches to solve the problem. The scalability and performance of the algorithms is investigated in detail. Our experiments show that the heuristics scale and perform well for systems with a reasonable load

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Orthogonal variability modeling to support multi-cloud application configuration

Cloud service providers benefit from a vast majority of customers due to variability and making profit from commonalities between the cloud services that they provide. Recently, application configuration dimensions has been increased dramatically due to multi-tenant, multi-device and multi-cloud paradigm. This challenges the configuration and customization of cloud-based software that are typically offered as a service due to the intrinsic variability. In this paper, we present a model-driven approach based on variability models originating from the software product line community to handle such multi-dimensional variability in the cloud. We exploit orthogonal variability models to systematically manage and create tenant-specific configuration and customizations. We also demonstrate how such variability models can be utilized to take into account the already deployed application parts to enable harmonized deployments for new tenants in a multi-cloud setting. The approach considers application functional and non-functional requirements to provide a set of valid multi-cloud configurations. We illustrate our approach through a case study