Dario Bruneo

A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems

Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of cloud computing infrastructures is required to predict and quantify the cost-benefit of a strategy portfolio and the corresponding quality of service (QoS) experienced by users. Such analyses are not feasible by simulation or on-the-field experimentation, due to the great number of parameters that have to be investigated. In this paper, we present an analytical model, based on stochastic reward nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions.

Workload-Based Software Rejuvenation in Cloud Systems

Cloud computing is a promising paradigm able to rationalize the use of hardware resources by means of virtualization. Virtualization allows to instantiate one or more virtual machines (VMs) on top of a single physical machine managed by a virtual machine monitor (VMM). Similarly to any other software, a VMM experiences aging and failures. Software rejuvenation is a proactive fault management technique that involves terminating an application, cleaning up the system internal state, and restarting it to prevent the occurrence of future failures. In this work, we propose a technique to model and evaluate the VMM aging process and to investigate the optimal rejuvenation policy that maximizes the VMM availability under variable workload conditions. Starting from dynamic reliability theory and adopting symbolic algebraic techniques, we investigate and compare existing time-based VMM rejuvenation policies. We also propose a time-based policy that adapts the rejuvenation timer to the VMM workload condition improving the system availability. The effectiveness of the proposed modeling technique is demonstrated through a numerical example based on a case study taken from the literature.

Communication paradigms for mobile grid users

This paper wishes to investigate the converging field of mobile and Grid computing by defining an architecture for the provision of Grid services, which Is based on standards, robust and useful across application domains. We propose using the mobile agent paradigm in order to develop a middleware layer that takes care of all the details to allow mobile users to access distributed resources in a transparent, secure and effective way. Our purpose Is also that of Identifying the environmental situations In which such paradigm should be preferred or adopted in conjunction with more traditional communication paradigms (i.e. client/server, Remote Evaluation). For this purpose, we provide an experimental and analytical evaluation of the Client-Server, Remote Evaluation and Mobile Agent communication paradigms.

Stack4Things: Integrating IoT with OpenStack in a Smart City context

As the adoption of embedded systems, mobiles and other smart devices keeps rising, and the scope of their involvement broadens, for instance in the enablement of Smart City-like scenarios, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad-hoc solutions, while at the same time taking into account valid options with regards to infrastructure management, and other more advanced functionalities. In this sense, a widely used and competitive framework for Infrastructure as a Service such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks like fitting the bill. This work thus describes rationale, efforts, and results so far achieved, for an integration of IoT paradigms and resource ecosystems with such a kind of Cloud-oriented environment, by focusing on a Smart City scenario, and featuring data collection and visualization as example use cases of such integration.

CloudWave: Where adaptive cloud management meets DevOps

The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. Moreover, the design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another.This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles.

Evaluating wireless sensor node longevity through Markovian techniques

Wireless sensor networks are constituted of a large number of tiny sensor nodes randomly distributed over a geographical region. In order to reduce power consumption, nodes undergo active-sleep periods that, on the other hand, limit their ability to send/receive data. The aim of this paper is to analyze the longevity of a battery-powered sensor node. A battery discharge model able to capture both linear and non linear discharge processes is presented. Then, two different models are proposed to investigate the longevity, in terms of reliability, of sensor nodes with active-sleep cycles. The first model, well known in the literature, is based on the Markov reward theory and on the evaluation of the accumulated reward distribution. The second model, based on continuous phase type distributions and Kronecker algebra, represents the main contribution of the present work, since it allows to relax some assumptions of the Markov reward model, thus increasing its applicability to more concrete use cases. In the final part of the paper, the results obtained by applying the two techniques to a case study are compared in order to validate and highlight the benefits of our approach and demonstrate the utility of the proposed model in a quite complex and real scenario.

A Smart City Lighting Case Study on an OpenStack-Powered Infrastructure

The adoption of embedded systems, mobile devices and other smart devices keeps rising globally, and the scope of their involvement broadens, for instance, in smart city-like scenarios. In light of this, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad hoc solutions, while at the same time taking into account valid options with regard to infrastructure management and other more advanced functionalities. Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop. A new, more flexible, cloud-based approach, able to provide device-focused workflows, is required. In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one. This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration.

Stochastic Evaluation of QoS in Service-Based Systems

WS-BPEL language has become the industrial standard to design and orchestrate modular applications, formalizing service compositions and business relationships among providers and consumers. Once service level agreements (SLAs) among the parties are established, effective tools for evaluating appropriate measurements have to be developed to meet the requirements. However, the design of quality of service (QoS)-guaranteed composed Web services (WSes) still requires several efforts. This work aims at proposing a complete method to study the QoS of a composed WS at design time, i.e., when the process is specified by using WS-BPEL. Starting from the nonfunctional properties of the WS to compose, we propose a technique to derive non-Markovian stochastic Petri net (NMSPN) models from WS-BPEL processes, with the final goal of evaluating parameters such as the service time distribution and the service reliability. To demonstrate the effectiveness of the proposed method and to validate the obtained model, a nontrivial example implementing a travel agency flight reservation process, exposed as a synchronous composed WS, is investigated.

QoS assessment of WS-BPEL processes through non-Markovian stochastic Petri nets

Service Oriented Architecture (SOA) is the most important and effective software paradigm to design Internet-based services. Using the SOA technology, value-added services can be easily deployed as a combination of existing Web services. In this context, WS-BPEL language has become the SOA industrial standard. To allow services to be composed, business relationships between providers and consumers have to be adequately managed. This implies that a formal definition of Quality of Service (QoS) is agreed and that effective tools for its measurement have to be developed. However, the design of QoS guaranteed composed Web services still requires several efforts due to the highly distributed nature of such software applications. This work aims at proposing a methodology to evaluate Web service performance at the earliest design phase. We present a novel technique to translate WS-BPEL processes into non-Markovian stochastic Petri nets with the final goal to evaluate parameters such as service time distribution and service reliability. The obtained model can be numerically solved through automatic tools, allowing to investigate the service behavior under different operating conditions and thus helping software engineers to develop QoS-guaranteed software solutions.

Performance Evaluation of gLite Grids through GSPNs

Grid Computing supports the shared and coordinated use of several resources in dynamic Virtual Organizations. In the last few years, it is evolving into a business-innovating technology that is driving commercial adoption. Such a new scenario calls for powerful strategies able to guarantee stringent QoS requirements in order to meet Service Level Agreements (SLAs) between customers and providers. For this reason, it is necessary to analyze and predict performance with respect to different load conditions or management strategies. In this paper, we present a methodology to analyze performance in gLite Grids through the use of Generalized Stochastic Petri Nets (GSPNs). We introduce a cluster-level model of a typical gLite site taking into account the coexistence between normal and MPI-based jobs. We investigate the influence of different strategies (e.g., scheduling) on the performance of the whole site, highlighting aspects related to both customer and provider point of views. We also provide a business-oriented performance analysis introducing two different SLA typologies and highlighting how the site configuration may influence the expected profit of the service provider.