Automating Resource Selection and Configuration in Inter-clouds through a Software Product Line Method

Abstract

International audienceNowadays, cloud users face three important problems: (a) choosing one or more appropriate cloud provider(s) to run their application(s), (b) selecting appropriate cloud resources, which implies having enough information about the available resources, including their characteristics and constraints, and (c) configuring the cloud resources. These problems are mostly due to the wide range of resources. These resources usually have distinct dependencies, and they are offered at various clouds' layers. In this complex scenario, the users often have to handle cloud resources and their dependencies manually. This is an error-prone and time-consuming activity, even for skilled cloud users and system administrators. In this context, this paper proposes a software product line engineering (SPLE) method and a tool to deal with these issues. Our SPL-based engineering method enables a declarative and goal-oriented strategy. Furthermore, it allows resource selection and configuration in inter-cloud environments. In our proposal, the cloud users specify their applications and requirements, and our tool automatically selects and configures a suitable computing environment, taking into account temporal and functional dependencies. Experimental results on Amazon EC2 and Google Compute Engine (GCE) show that our approach enables unskilled users to have access to advanced inter-cloud computing configurations, without being concerned with the characteristics of each cloud

GoalD: A Goal-Driven deployment framework for dynamic and heterogeneous computing environments

“…In that case, the alternative is marked as not resolved (line 11). Otherwise, the checkCtx returns TRUE then the alternative dependencies are checked (lines [14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

“…Leite et al [14] propose an approach for autonomous deployment on intercloud environments, based on DSPL as well. It relies on abstract and concrete features models and constraint satisfaction problem solver to create a computing environment using resources distributed across various clouds.…”

“…Moreover, business process based solutions will be able to either orchestrate or choreograph once the components are already deployed, since it will only be able to manage the executable methods, services and events to the corresponding workflow engines for execution [12] once the required and suitable component infrastructure is deployed. There are also proposals to address autonomous deployment in the realm of dynamic software product lines [13,14,15] where the focus is on devising adaptation strategies based on feature configurations for deployment. Nevertheless, configuration and behavior are related and it is not always possible to change one without changing the other:…”

Constraint programming heuristics for configuring optimal products in multi product lines

“…Current CP-based approaches support the configuration of products for a PL by considering single-objective optimization [8,9,20,24,41] , and multi-objective optimization [16,29,35] . These approaches have evaluated performance and/or scalability concerns, by considering the number of configuration constraints and the size of the related FM.…”

“…This is especially important when the introduced non-functional requirements are translated into optimization problems where a predefined set of numerical non-functional values related to product features should be minimized or maximized. To face this challenge, some approaches based on constraint programming have been developed to solve single-objective optimization [8,9,20,24,41] , and multi-objective optimization [16,29,35] problems. These approaches provide a product line configuration in between 1.8 s [20] and 90 min [9] depending on the product line size (models with up to 50 0 0 features), the number of optimization objectives (e.g., 1 to 5), and other confounding variables that are meant to be explored and discovered by researchers in the field (e.g., type of involved constraints).…”