Ivan Porres

Difference and Union of Models

This paper discusses the difference and union of models in the context of a version control system. We show three metamodel-independent algorithms that calculate the difference between two models, merge a model with the difference of two models and calculate the union of two models. We show how to detect union conflicts and how they can be resolved either automatically or manually. We present an application of these algorithms in a version control system for MOF-based models.

Using Ant Colony System to Consolidate VMs for Green Cloud Computing

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mode. On the other hand, achieving the desired level of Quality of Service (QoS) between cloud providers and their users is critical. Therefore, the main challenge is to reduce energy consumption of data centers while satisfying QoS requirements. In this paper, we present a distributed system architecture to perform dynamic VM consolidation to reduce energy consumption of cloud data centers while maintaining the desired QoS. Since the VM consolidation problem is strictly NP-hard, we use an online optimization metaheuristic algorithm called Ant Colony System (ACS). The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function. Experimental results on real workload traces show that ACS-VMC reduces energy consumption while maintaining the required performance levels in a cloud data center. It outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.

Model Refactorings as Rule-Based Update Transformations

A model refactoring is a model transformation that improves the design described in the model. A refactoring should only affect a previously chosen subset of the original model. In this paper, we discuss how to define and execute model refactorings as rule-based transformations. We also present an experimental tool to execute these transformations.

Prediction-Based Dynamic Resource Allocation for Video Transcoding in Cloud Computing

This paper presents prediction-based dynamic resource allocation algorithms to scale video transcoding service on a given Infrastructure as a Service cloud. The proposed algorithms provide mechanisms for allocation and deallocation of virtual machines (VMs) to a cluster of video transcoding servers in a horizontal fashion. We use a two-step load prediction method, which allows proactive resource allocation with high prediction accuracy under real-time constraints. For cost-efficiency, our work supports transcoding of multiple on-demand video streams concurrently on a single VM, resulting in a reduced number of required VMs. We use video segmentation at group of pictures level, which splits video streams into smaller segments that can be transcoded independently of one another. The approach is demonstrated in a discrete-event simulation and an experimental evaluation involving two different load patterns.

Authoring and verification of clinical guidelines: A model driven approach

OBJECTIVES The goal of this research is to provide a framework to enable authoring and verification of clinical guidelines. The framework is part of a larger research project aimed at improving the representation, quality and application of clinical guidelines in daily clinical practice. METHODS The verification process of a guideline is based on (1) model checking techniques to verify guidelines against semantic errors and inconsistencies in their definition, (2) combined with Model Driven Development (MDD) techniques, which enable us to automatically process manually created guideline specifications and temporal-logic statements to be checked and verified regarding these specifications, making the verification process faster and cost-effective. Particularly, we use UML statecharts to represent the dynamics of guidelines and, based on this manually defined guideline specifications, we use a MDD-based tool chain to automatically process them to generate the input model of a model checker. The model checker takes the resulted model together with the specific guideline requirements, and verifies whether the guideline fulfils such properties. RESULTS The overall framework has been implemented as an Eclipse plug-in named GBDSSGenerator which, particularly, starting from the UML statechart representing a guideline, allows the verification of the guideline against specific requirements. Additionally, we have established a pattern-based approach for defining commonly occurring types of requirements in guidelines. We have successfully validated our overall approach by verifying properties in different clinical guidelines resulting in the detection of some inconsistencies in their definition. CONCLUSIONS The proposed framework allows (1) the authoring and (2) the verification of clinical guidelines against specific requirements defined based on a set of property specification patterns, enabling non-experts to easily write formal specifications and thus easing the verification process.

Rule-based update transformations and their application to model refactorings

A rule-based update transformation is a model transformation where a single model is transformed in place. A model refactoring is a model transformation that improves the design described in the model. A refactoring should only affect a previously chosen subset of the original model. In this paper, we discuss how to define and execute model refactorings as rule-based transformations in the context of the UML and MOF standards. We also present an experimental tool to execute this kind of transformation.

A metamodeling language supporting subset and union properties

In this article, we describe successive versions of a metamodeling language using a set-theoretic formalization. We focus on language extension mechanisms, particularly on the relatively new subset and union properties of MOF 2.0 and the UML 2.0 Infrastructure. We use Liskov substitutability as the rationale for our formalization. We also show that property redefinitions are not a safe language extension mechanism. Each language version provides new features, and we note how such features cannot be mixed arbitrarily. Instead, constraints over the metamodel and model structures must be established. We expect that this article provides a better understanding of the foundations of MOF 2.0, which is necessary to define new extensions, model transformation languages and tools.

Energy-Aware Dynamic VM Consolidation in Cloud Data Centers Using Ant Colony System

As the scale of a cloud data center becomes larger and larger, the energy consumption of the data center also grows rapidly. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy by turning off unused Physical Machines (PMs) in data centers. In this paper, we present a distributed controller to perform dynamic VM consolidation to improve the resource utilizations of PMs and to reduce their energy consumption. Moreover, we use the ant colony system to find a near-optimal VM placement solution based on the specified objective function. Experimental results on the real workload traces from more than a thousand PlanetLab VMs show that the proposed approach reduces energy consumption and maintains required performance levels in a large-scale data center.

Stream-Based Admission Control and Scheduling for Video Transcoding in Cloud Computing

This paper presents a novel approach for stream-based admission control and job scheduling for video transcoding called SBACS (Stream-Based Admission Control and Scheduling). SBACS uses queue waiting time of transcoding servers to make admission control decisions for incoming video streams. It implements stream-based admission control with per stream admission. To ensure efficient utilization of the transcoding servers, video streams are segmented at the Group of Pictures level. In addition to the traditional rejection policy, SBACS also provides a stream deferment policy, which exploits cloud elasticity to allow temporary deferment of the incoming video streams. In other words, the admission controller can decide to admit, defer, or reject an incoming stream and hence reduce rejection rate. In order to prevent transcoding jitters in the admitted streams, we introduce a job scheduling mechanism, which drops a small proportion of video frames from a video segment to ensure continued delivery of video contents to the user. The approach is demonstrated in a discrete-event simulation with a series of experiments involving different load patterns and stream arrival rates.

A Session-Based Adaptive Admission Control Approach for Virtualized Application Servers

This paper presents a session-based adaptive admission control approach for virtualized application servers called ACVAS (adaptive Admission Control for Virtualized Application Servers). ACVAS uses measured and predicted resource utilizations of a server to make admission control decisions for new user sessions. Instead of using the traditional on-off control, it implements per session admission control, which reduces the risk of over-admission. Moreover, instead of relying only on rejection of new sessions, ACVAS takes benefit of the cloud elasticity, which allows dynamic provisioning of cloud resources. It also implements a simple session deferment mechanism that reduces the number of rejected sessions while increasing session throughput. Thus, each admission control decision has three possible outcomes: admit, defer, or reject. Performance under varying user load is guaranteed by automatic adjustment and tuning of the admission control mechanism. The proposed approach is demonstrated in a discrete-event simulation.