Samir Tata

A verification and deployment approach for elastic component-based applications

Cloud environments are being increasingly used for the deployment and execution of complex applications and particularly component-based ones. They are expected to provide elasticity, among other characteristics, in order to allow a deployed application to rapidly change the amount of its allocated resources in order to meet the variation in demand while ensuring a given Quality of Service (QoS). However, establishing a correct elastic component-based application is not guaranteed in Cloud. Indeed, applying elasticity mechanisms should preserve functional properties and improve non-functional properties related to QoS, performance and resource consumption. In this paper, we propose an approach for the verification and deployment of elastic component-based applications. Our approach is based on the Event-B formal method. In fact, we formally model the component artifacts using Event-B and we define the Event-B events that model the elasticity mechanisms (scaling up and down) for component-based applications. Furthermore, we formally verify that our approach preserves the semantics of the component-based applications by using the proof obligations and the ProB animator. Once the elastic component-based applications are validated, they can be deployed in a Cloud environment using an elastic deployment framework which we have developed.

The rSLA Framework: Monitoring and Enforcement of Service Level Agreements for Cloud Services

Managing service quality in heterogeneous Cloud environments is complex: different Cloud providers expose different management interfaces. To manage Service Level Agreements (SLAs) in this context, we have developed the rSLA framework that enables fast setup of SLA monitoring in dynamic and heterogeneous Cloud environments. The rSLA framework is made up of three main components: the rSLA language to formally represent SLAs, the rSLA Service, which interprets the SLAs and implements the behavior specified in them, and a set of Xlets - lightweight, dynamically bound adapters to monitoring and controlling interfaces. In this paper, we present the rSLA framework, and describe how it enables the monitoring and enforcement of service level agreements for heterogeneous Cloud services.

Cloud to Edge: Distributed Deployment of Process-Aware IoT Applications

The Internet of Things (IoT) integrates a large number of heterogeneous and pervasive objects that continuously generate information about the physical world. These objects, through standard communication protocols and unique addressing schemes provide services to the final users or systems. IoT is envisioned to bring together billions of devices, also denoted as smart objects, by connecting them in an Internet-like structure, allowing them to communicate and exchange information and to enable new forms of interaction among things and people. Given the distributed nature of IoT applications, it is often the case that the application is modeled, developed and tested on each compute node, and connected to other nodes in the application network for achieving the end result. Therefore the deployment of application is also, more or less, done on each individual compute node. In this paper we propose an approach where the IoT application can be modeled in one place, where after modeling, the different pieces of application are annotated with location information, and based on this annotation, the application is decomposed into fragments that are deployed to corresponding individual compute nodes, automatically generating code to remotely connect the application fragments to other application fragments on other compute nodes in the edge or in the cloud.

STRATModel: Elasticity Model Description Language for Evaluating Elasticity Strategies for Business Processes

Nowadays, Cloud Computing is receiving more and more attention from IT companies as a new computing paradigm for executing and handling their Business Processes in an efficient and cost-effective way. One of the most important features behind this attention is the Cloud Computing’s elasticity which became the focus of many research works. Its management has been considered as a pivotal issue among IT community that works on finding the right tradeoffs between QoS levels and operational costs by developing novel methods and mechanisms. Elasticity controller has been used in many research works to automate the provisioning of cloud resources and control cloud applications elasticity. However, most of the previous works have been proposed based on a specific elasticity model for either vertical or horizontal elasticity. In this paper, we propose an elasticity model description language for Service-based Business processes (SBP), called StratModel. It allows business process holders to define different elasticity models with different elasticity capabilities by providing their elasticity mechanisms through set of examples and automatically generate their associated elasticity controllers. The generated elasticity controllers are used for evaluating elasticity strategies before using them in real cloud environments. Based on StratModel, we present our elasticity strategies evaluation framework that facilitates the description and evaluation of elasticity strategies for SBPs according to a customized elasticity model. Our contributions and developments provide Cloud tenants with facilities to choose elasticity strategies that fit to their business processes and usage behaviors using a customized elasticity controller.

Simulation Extension for Cloud Standard OCCIware

This paper presents OCCI simulation extension, a generic simulator for any kind of resources. To achieve that, we first study the position of cloud simulator entities against OCCI model. Afterward, we define an OCCI extension simulation. In order to study the usefulness of this extension, we implement in the second time a module for Eclipse IDE to define OCCI configuration graphically. This tool uses the proposed extension. From the generated OCCI configuration, cloud resources are extracted and used by Cloud Sim simulator upon OCCI metamodel. Multiple experiments with different configuration were conducted to validate the generality of the proposed simulation extension.

Data Provenance Model for Internet of Things (IoT) Systems

Internet of Things (IoT) systems and applications are increasingly deployed for critical use cases and therefore exhibit an increasing need for dependability. Data provenance deals with the recording, management and retrieval of information about the origin and history of data. We propose that the introduction of data provenance concepts into the IoT domain can help create dependable and trustworthy IoT systems by recording the lineage of data from basic sensor readings up to complex derived information created by software agents. In this paper, we present a data provenance model for IoT systems that is geared towards providing a generic mechanism for assuring the correctness and integrity of IoT applications and thereby reinforcing their trustworthiness and dependability for critical use cases.

Optimization and Approximate Placement of Autonomic Resources for the Management of Service-Based Applications in the Cloud

Cloud Computing is a new distributed computing paradigm that consists in provisioning of infrastructure, software and platform resources as services. This paradigm is being increasingly used for the deployment and execution of service-based applications. To efficiently manage them according the autonomic computing approach, service-based applications can be associated with autonomic managers that monitor them, analyze monitoring data, plan and execute configuration action on them. Although, in these last years, autonomic management of cloud services has received an increasing attention, optimization of autonomic managers (AMs) assigned to cloud services and their placement in the cloud remain not well explored. In fact, almost all the existing solutions on autonomic computing have been interested in modeling and implementing of autonomic environments without paying attention on optimization. To address this issue, we present in this paper a novel approach to optimize autonomic management of service-based applications that consists in minimizing both the cost of allocated AMs while avoiding bottlenecks in management and the cost of their placement in the cloud (the inter-virtual machine communication cost). We propose two algorithms: (i) an algorithm that determines the optimal number of AMs to be assigned to services of a managed service-based application, and (ii) an algorithm that approximates the optimal placement of AMs in the cloud. Experiments conducted show the efficiency of our finding.

Optimizing cloud solutioning design

Abstract The economics of the cloud model has been encouraging IT enterprises to migrate from on-premise environments to public, private, or hybrid cloud solutions. To perform such a migration, a cloud offering needs to be chosen and a cloud solution needs to be built. In industrial settings, cloud designers may spend days or even weeks to come up with an acceptable cloud solution at a low cost/price. Like any manual process, it is obvious that such a cloud solution design process is error prone, time consuming, and does not guarantee an optimal output, e.g. a solution with a minimum cost/price. Different from existing works that solve the problem from the user’s angle, we solve it from the cloud provider’s prospective, who aims at offering customized cloud solutions for different user requirements at low costs. Such difference requires a unique way of problem modeling. Through analyzing real business data, we abstract the problem into a general attribute–value combinations and formulate a powerful integer programming optimization model to solve it. The general form of the optimization model allows various definitions of customer requirements as well as cloud offerings. Our novel optimization approach for cloud solution design satisfies client requirements, cloud offering constraints, and produces a solution at a minimum cost in a short time, if one exists. We evaluated our solution on realistic data against two baseline approaches. The numerical results show both the effectiveness and efficiency of our approach as well as its practical potential.

Monitoring services in the Internet of Things: an optimization approach

Devices in Internet of Things (IoT) often offer services that allow tenants to access data of different metrics collected from sensors. These sensors can be built-in or remotely connected to such devices. Given that such monitoring services are usually invoked within devices that have limited IT resource capacities, it is impossible to collect data of all metrics in the application’s context with a very high frequency. In this paper, we propose a framework that determines which metrics to monitor, monitoring start times, the optimal allocation of metrics to devices, and the optimal monitoring frequency of these metrics, without exceeding different device-specific time-varying resource capacities. Our approach is also adaptive; it gives updated solutions whenever a trigger happens in the system necessitating the need for a change in the previous optimal decisions. We provide an implementation of our approach and present numerical results showing its usage and limitations. At the heart of our approach is an integer programming optimization model that might be hard to solve for large-sized IoT systems. Thus, we present another predictive model that predicts for the user whether our optimization-based approach would be appropriate for her system or not. That is, whether the optimization model is predicted to give optimal solutions within some user-given optimality gaps in a time less than or equal to some user-given maximum allowed time.

Cognitive Determination of Policies for Data Management in IoT Systems

Internet of Things (IoT) has emerged as a very hot area in the past few years. Managing data in IoT systems is still a challenging research topic, particularly when one aims at determining the correct set of decisions to take given some trigger events in an IoT system. The state of the art in determining such actions and corresponding policies is ad-hoc, based on significant human intervention. In this work, we propose a cognitive automated approach for policy and action determination in IoT systems that uses historical data to learn the best set of actions to take and involves an mathematical optimization module that chooses the optimal set of actions to pursue given the limited resource capacity in the system. Our system requires minimal human intervention and thus could be very beneficial in today’s IoT frameworks.