@proceedings {608, title = {A full Secondary User model for Cognitive Radio in a GSM-900 scenario}, journal = {2019 International Conference on Computing, Networking and Communications (ICNC)}, year = {2019}, month = {Feb. 2019}, pages = {344-349}, publisher = {IEEE publisher}, address = {18-21 Feb. 2019, Honolulu, Hawaii, USA}, abstract = {

In this paper, we propose a Petri Net based model able to characterize the QoS of a Secondary User network in a cognitive radio environment operating in the GSM900 band. The proposed model is quite flexible as that it can consider several Primary User network loads, several Secondary User types of services (that involve semantic transparency or time transparency) and it{\textquoteright}s able to take into account mistakes of sensing spectrum algorithm used by Secondary Users. Specifically, we derive the response time distribution of Secondary User from which it is possible to obtain an estimation of both the maximum throughput and jitter. The proposed cognitive radio scenario considers a Secondary User synchronized access to the channel with the GSM frame structure.

}, keywords = {cellular radio, Cognitive Radio, cognitive radio environment, full secondary user model, GSM, GSM frame structure, GSM-900 scenario, jitter, jitter estimation, maximum throughput estimation, Non Markovian Stochastic Petri Net, Petri net, Petri nets, primary user network loads, QoS, Quality of service, radio spectrum management, response time distribution, secondary user network, semantic transparency, sensing spectrum algorithm, signal detection, time transparency}, isbn = {978-1-5386-9224-0}, issn = {978-1-5386-9223-3}, doi = {10.1109/ICCNC.2019.8685497}, author = {M. Scarpa and S. Serrano} } @article {8928171, title = {Using Deep Reinforcement Learning for Application Relocation in Multi-Access Edge Computing}, journal = {IEEE Communications Standards Magazine}, volume = {3}, number = {3}, year = {2019}, month = {Sep.}, pages = {71-78}, keywords = {5G mobile communication, Base stations, Computer architecture, Edge computing, Long Term Evolution, Quality of service, Reinforcement learning}, issn = {2471-2833}, doi = {10.1109/MCOMSTD.2019.1900011}, author = {F. De Vita and G. Nardini and A. Virdis and D. Bruneo and A. Puliafito and G. Stea} } @conference {8597889, title = {A Deep Reinforcement Learning Approach For Data Migration in Multi-Access Edge Computing}, booktitle = {2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K)}, year = {2018}, month = {Nov}, pages = {1-8}, keywords = {5G, 5G mobile communication, cloud computing, computational resources, data migration, Deep Reinforcement Learning, deep reinforcement learning approach, distributed services, Edge computing, Keras machine learning, Keras machine learning framework, learning (artificial intelligence), Long Term Evolution, LTE, MEC scenarios, Multi-access Edge Computing, multiaccess edge computing, network performance, OMNeT++/SimuLTE simulator, parameter settings, push data, Quality of service, Reinforcement learning, Resource allocation, resource utilization, Servers, SimuLTE}, doi = {10.23919/ITU-WT.2018.8597889}, author = {F. D. Vita and D. Bruneo and A. Puliafito and G. Nardini and A. Virdis and G. Stea} } @proceedings {Bruneo2015797, title = {Analytical Modeling of Reactive Autonomic Management Techniques in IaaS Clouds}, journal = {Proceedings - 2015 IEEE 8th International Conference on Cloud Computing, CLOUD 2015}, year = {2015}, note = {cited By 2; Conference of 8th IEEE International Conference on Cloud Computing, CLOUD 2015 ; Conference Date: 27 June 2015 Through 2 July 2015; Conference Code:116940}, pages = {797-804}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {New York (USA)}, abstract = {

Cloud computing infrastructures provide services to a wide number of users whose behavior can deeply change at the occurrence of particular events. To correctly handle such situations a cloud infrastructure have to be reconfigured in a way that does not cause degradation in the overall performance. Otherwise, the quality of service specified in the service level agreement could be violated. To prevent such situations, the infrastructure could be organized as an autonomic system where self-adaptation and self-configuration techniques are implemented. Appropriate design choices become important in order not to fail in this goal. We propose a technique, based on a Petri net model and a specific analytical analysis approach, to represent Infrastructure-as-a-Service (IaaS) systems in the case in which the load conditions can suddenly change and reactive autonomic management techniques are applied to mitigate the consequences of the change. The model we propose is able to appropriately evaluate performance metrics in such critical situations making it suitable as a design tool for IaaS cloud systems. {\textcopyright} 2015 IEEE.

}, keywords = {Autonomic management, cloud computing, Cloud computing infrastructures, Cloud infrastructures, Iaas clouds, Industrial management, Infrastructure as a service (IaaS), Performance metrics, Petri nets, Quality of service, Resiliency, Service Level Agreements, Stochastic reward nets, Stochastic systems}, isbn = {9781467372879}, doi = {10.1109/CLOUD.2015.110}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960155425\&doi=10.1109\%2fCLOUD.2015.110\&partnerID=40\&md5=2aa09923889aea0bc667deb1d6d250d8}, author = {Dario Bruneo and Francesco Longo and Rahul Ghosh and Marco Scarpa and Antonio Puliafito and Kishor Trivedi} } @article {Longo2015280, title = {Dependability modeling of Software Defined Networking}, journal = {Computer Networks}, volume = {83}, year = {2015}, note = {cited By 6}, pages = {280-296}, publisher = {Elsevier}, abstract = {

Software Defined Networking (SDN) is a new network design paradigm that aims at simplifying the implementation of complex networking infrastructures by separating the forwarding functionalities (data plane) from the network logical control (control plane). Network devices are used only for forwarding, while decisions about where data is sent are taken by a logically centralized yet physically distributed component, i.e., the SDN controller. From a quality of service (QoS) point of view, an SDN controller is a complex system whose operation can be highly dependent on a variety of parameters, e.g., its degree of distribution, the corresponding topology, the number of network devices to control, and so on. Dependability aspects are particularly critical in this context. In this work, we present a new analytical modeling technique that allows us to represent an SDN controller whose components are organized in a hierarchical topology, focusing on reliability and availability aspects and overcoming issues and limitations of Markovian models. In particular, our approach allows to capture changes in the operating conditions (e.g., in the number of managed devices) still allowing to represent the underlying phenomena through generally distributed events. The dependability of a use case on a two-layer hierarchical SDN control plane is investigated through the proposed technique providing numerical results to demonstrate the feasibility of the approach. {\textcopyright} 2015 Elsevier B.V.

}, keywords = {Availability, Complex networks, Controllers, Degree of distributions, Distributed components, Electric network topology, Information dissemination, Markov processes, Networking infrastructure, Non-Markovian, Quality control, Quality of service, Random processes, Reliability, Reliability and availability, Software defined networking (SDN), Software reliability, Software-defined networkings, Stochastic models, Stochastic systems, Topology, Type expansions}, issn = {13891286}, doi = {10.1016/j.comnet.2015.03.018}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946489290\&doi=10.1016\%2fj.comnet.2015.03.018\&partnerID=40\&md5=b4f32c89d2b7b79fefcaf97082764960}, author = {Francesco Longo and Salvatore Distefano and Dario Bruneo and Marco Scarpa} } @proceedings {Bruneo2016175, title = {Multi-level adaptations in a CloudWave infrastructure: A telco use case}, journal = {European Conference on Service-Oriented and Cloud Computing}, volume = {567}, year = {2015}, note = {cited By 1; Conference of Workshops on CLIoT, WAS4FI, SeaClouds, CloudWay, IDEA, FedCloudNet 2015 held in conjunction with European Conference on Service-Oriented and Cloud Computing, ESOCC 2015 ; Conference Date: 15 September 2015 Through 17 September 2015; Conference Code:174149}, pages = {175-183}, publisher = {Springer Verlag}, address = {Taormina (Italy)}, abstract = {

CloudWave is a FP7 EU project whose aim is delivering novel technologies and methods for improving both the development of Cloud services and the management of their operation and execution. Such goal is reached by providing mechanisms and policies for coordinating multiple adaptations both at the level of the Cloud infrastructure and at the level of the hosted applications. In this paper, we describe the CloudWave Telco application use case and we provide a proof of concept discussing how the QoS experienced by the application users can be improved thanks to the technologies provided by CloudWave. {\textcopyright} Springer International Publishing Switzerland 2016.

}, keywords = {cloud computing, Cloud infrastructures, Cloud services, Eu projects, Multilevels, Multiple adaptation, Proof of concept, Quality of service}, isbn = {9783319333120}, issn = {18650929}, doi = {10.1007/978-3-319-33313-7_13}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966667439\&doi=10.1007\%2f978-3-319-33313-7_13\&partnerID=40\&md5=00474a6170ba9f89b97c8a7ef8c59fce}, author = {Dario Bruneo and Francesco Longo and Boris Moltchanov} } @article {Distefano2015629, title = {QoS Assessment of Mobile Crowdsensing Services}, journal = {Journal of Grid Computing}, volume = {13}, number = {4}, year = {2015}, note = {cited By 2}, pages = {629-650}, publisher = {Springer Netherlands}, abstract = {

The wide spreading of smart devices drives to develop distributed applications of increasing complexity, attracting efforts from both research and business communities. Recently, a new volunteer contribution paradigm based on participatory and opportunistic sensing is affirming in the Internet of Things scenario: Mobile Crowdsensing (MCS). A typical MCS application considers smart devices as contributing sensors able to produce geolocalized data about the physical environment, then collected by a remote application server for processing. The growing interest on MCS allows to think about its possible exploitation in commercial context. This calls for adequate methods able to support MCS service providers in design choices, implementing mechanisms for the quality of service (QoS) assessment while dealing with complex time-dependent phenomena and churning issues due to contributors that unpredictably join and leave the MCS system. In this paper, we propose an analytical modeling framework based on stochastic Petri nets to evaluate QoS metrics of a class of MCS services. This method requires to extend the Petri net formalism by specifying a marking dependency semantics for non-exponentially distributed transitions. The approach is then applied to an MCS application example deriving some QoS measures that can drive quantitative evaluation and characterization of the {\textquotedblleft}crowd{\textquotedblright} behavior. {\textcopyright} 2015, Springer Science+Business Media Dordrecht.

}, keywords = {crowdsensing, Digital storage, Distributed applications, Marking dependency, Non-Markovian, Performability, Petri nets, Quality of service, Quality of service (QoS) assessments, Quantitative evaluation, Semantics, Stochastic systems, Time dependent phenomena}, issn = {15707873}, doi = {10.1007/s10723-015-9338-7}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958155341\&doi=10.1007\%2fs10723-015-9338-7\&partnerID=40\&md5=3bd7e36a37ab06a3acabb37a21b90ab1}, author = {Salvatore Distefano and Francesco Longo and Marco Scarpa} } @proceedings {Bruneo201384, title = {Analytical evaluation of resource allocation policies in green IaaS clouds}, journal = {Proceedings of the IEEE 3rd International Conference on Cloud and Green Computing (CGC) Co-located with the IEEE 3rd International Conference on Social Computing and Its Applications (SCA)}, year = {2013}, note = {cited By 1; Conference of 3rd IEEE International Conference on Cloud and Green Computing, CGC 2013, Held Jointly with the 3rd IEEE International Conference on Social Computing and Its Applications, SCA 2013 ; Conference Date: 30 September 2013 Through 2 October 2013; Conference Code:102391}, pages = {84-91}, publisher = {IEEE Computer Society}, address = {Karlsruhe, Germany, 30 September - 2 October 2013}, abstract = {

Cloud systems represent the new ICT frontier where computing utilities are offered in terms of virtual instances, following the so called as-a-service philosophy. Different commercial solutions have been already put in place but several aspects need to be faced in order to provide high-value services able to meet business requirements. In particular, performance evaluation plays a critical role being strictly related to data center optimization and user satisfaction. Moreover, the use of large data centers able to respond to the high service demand has increased the attention to power efficiency, thus calling for green solutions and energy-aware strategies that jointly consider environmental and economical aspects. In order to design powerful strategies able to meet the quality of service requirements still reducing the energy costs, performance analysis frameworks are needed. In this paper, we present an analytical model, based on stochastic reward nets, that is able to easily implement resource allocation strategies in a green infrastructure as-a-service cloud. The model is organized into layers that represent the virtual resource pool and the physical machines. Different allocation algorithms (i.e., scattering, saturation) have been implemented by properly managing the coordination between the model layers. Numerical results are provided that demonstrate the effectiveness of the proposed approach. {\textcopyright} 2013 IEEE.

}, keywords = {Analytical evaluation, cloud computing, Distributed computer systems, Green Computing, Performance analysis, Performance evaluation, Quality of service, Resource allocation, Resource allocation policy, Resource allocation strategies, Service requirements, Stochastic reward nets, Stochastic systems}, isbn = {9780769551142}, doi = {10.1109/CGC.2013.21}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84893329592\&partnerID=40\&md5=369f37efc4aabea38300336d819db864}, author = {Dario Bruneo and Audric Lhoas and Francesco Longo and Antonio Puliafito} } @article {Bruneo20132090, title = {Stochastic evaluation of QoS in service-based systems}, journal = {IEEE Transactions on Parallel and Distributed Systems - IEEE Computer Society}, volume = {24}, number = {10}, year = {2013}, note = {cited By 7}, pages = {2090-2099}, abstract = {

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. {\textcopyright} 1990-2012 IEEE.

}, keywords = {Business Process, Business relationships, Design, Information services, Non functional properties, Performance, Quality of service, Random access storage, Reliability, Service level agreement (SLAs), Service oriented architecture (SOA), Service time distribution, Stochastic Petri Nets, Web services, WS-BPEL}, issn = {10459219}, doi = {10.1109/TPDS.2012.313}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84883410035\&partnerID=40\&md5=cdebb3fe5a36e131fd3a9485d01fe3c6}, author = {Dario Bruneo and Salvatore Distefano and Francesco Longo and Marco Scarpa} } @article {Distefano20123701, title = {Investigating dynamic reliability and availability through state-space models}, journal = {Computers and Mathematics with Applications - Elsevier Ltd}, volume = {64}, number = {12}, year = {2012}, note = {cited By 7}, pages = {3701-3716}, abstract = {

Quality standards impose increasingly stringent requirements and constraints on quality of service attributes and measures. As a consequence, aspects, phenomena, and behaviors, hitherto approximated or neglected, have to be taken into account in quantitative assessment in order to provide adequate measures satisfying smaller and smaller confidence intervals and tolerances. With specific regards to reliability and availability, this means that interferences and dependencies involving the components of a system can no longer be neglected. Therefore, in order to support such a trend, specific techniques and tools are required to adequately deal with dynamic aspects in reliability and availability assessment. The main goal of this paper is to demonstrate how state-space based techniques can satisfy such a demand. For this purpose some examples of specific dynamic reliability behaviors, such as common cause failure and load sharing, are considered applying state-space based techniques to study the corresponding reliability models. Different repair policies in availability contexts are also explored. Both Markovian and non-Markovian models are studied via phase type expansion and renewal theory in order to adequately represent and evaluate the considered dynamic reliability aspects in case of generally distributed lifetimes and times to repair. {\textcopyright} 2012 Elsevier Ltd. All rights reserved.

}, keywords = {Availability, Common cause failure, Confidence interval, Dynamic aspects, Dynamic reliability, Load sharing, Markov processes, Markov regenerative process, Markovian, Non-Markovian, Quality of service, Quality standard, Quantitative assessments, Reliability, Reliability and availability, Reliability model, Renewal theory, Repair policy, Semi Markov model, State-space, State-space models, Stringent requirement}, issn = {08981221}, doi = {10.1016/j.camwa.2012.02.038}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870249108\&partnerID=40\&md5=9ecb143c24c7493a16292faa4f50d175}, author = {Salvatore Distefano and Francesco Longo and Kishor S. Trivedi} } @proceedings {Bruneo2012277, title = {Modeling energy-aware cloud federations with SRNs}, journal = {32nd International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets)}, volume = {7400 Lecture Notes in Computer Science}, year = {2012}, note = {cited By 4; Conference of 32nd International Conference on Application and Theory of Petri Nets and Concurrency, Petri Nets 2011 ; Conference Date: 20 June 2011 Through 24 June 2011; Conference Code:94089}, pages = {277-307}, publisher = {Springer-Verlag}, address = {Newcastle upon Tyne, United Kingdom, 20-24 June 2011}, abstract = {

Cloud computing is a challenging technology that promises to strongly modify the way computing and storage resources will be accessed in the near future. However, it may demand huge amount of energy if adequate management policies are not put in place. In particular, in the context of Infrastructure as a Service (IaaS) Cloud, optimization strategies are needed in order to allocate, migrate, consolidate virtual machines, and manage the switch on/switch off period of a data centre. In this paper, we present a methodology based on stochastic reward nets (SRNs) to investigate the more convenient strategies to manage a federation of two or more private or public IaaS Clouds. Several policies are presented and their impact is evaluated, thus contributing to a rational and efficient adoption of the Cloud computing paradigm. {\textcopyright} 2012 Springer-Verlag.

}, keywords = {cloud computing, Computing paradigm, Data centres, Digital storage, Energy aware, Energy conservation, Management policy, Optimization strategy, Performance evaluation, Petri nets, Quality of service, Stochastic reward nets, Stochastic systems, Storage resources, Switch-on, virtual machines}, isbn = {9783642351785}, issn = {03029743}, doi = {10.1007/978-3-642-35179-2_12}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870045120\&partnerID=40\&md5=b1e106331e417cc512bcbc713cc582de}, author = {Dario Bruneo and Francesco Longo and Antonio Puliafito} } @proceedings {Bruneo2011, title = {Evaluating energy consumption in a cloud infrastructure}, journal = {2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)}, year = {2011}, note = {cited By 4; Conference of 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2011 ; Conference Date: 20 June 2011 Through 23 June 2011; Conference Code:86401}, publisher = {IEEE Computer Society}, address = {Lucca, Italy, 20-23 June 2011}, abstract = {

Cloud computing is a challenging technology that promises to strongly modify the way computing and storage resources will be accessed in the near future. Clouds may demand huge amount of energy if adequate management policies are not put in place. Optimization strategies are needed in order to allocate, migrate, consolidate virtual machines and manage the switch on/switch off period of a data center. In this paper, we present a modeling approach based on Stochastic reward nets to investigate the more convenient strategies to manage a federation of Clouds, having in mind the final goal to reduce the overall energy consumption. Several policies are presented and their impact is evaluated, thus contributing to a rational and efficient adoption of the Cloud computing paradigm. {\textcopyright} 2011 IEEE.

}, keywords = {cloud computing, Clouds, computer systems, Computing paradigm, Data centers, Energy utilization, Management policy, Modeling approach, Multimedia systems, Optimization strategy, Performance evaluation, Quality control, Quality of service, Stochastic reward nets, Stochastic systems, Storage resources, Switch-on, virtual machines, Wireless networks}, isbn = {9781457703515}, doi = {10.1109/WoWMoM.2011.5986479}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052709389\&partnerID=40\&md5=9d12783598805d3b668813d0d5b70057}, author = {Dario Bruneo and Francesco Longo and Antonio Puliafito} } @article {Bruneo20111213, title = {Performance analysis of job dissemination techniques in Grid systems}, journal = {Concurrency Computation Practice and Experience - John Wiley \& Sons, Inc.}, volume = {23}, number = {11}, year = {2011}, note = {cited By 2}, pages = {1213-1235}, abstract = {

In the last few years, remarkable efforts have been made to extend the Grid paradigm to commercial solutions. Business-oriented grids call for effective Quality of Service strategies able to adapt to different user requirements and to address Service Level Agreements. Performance analysis and prediction with respect to different load conditions or management policies are required to define such strategies. However, the highly distributed nature of Grid systems and the presence of distinct administrative domains make it difficult to carry out performance estimations. In fact, several parameters are involved and the autonomy of each site could make it complex to set them in a proper way. In this paper, we present a non-Markovian Stochastic Petri Net methodology that allows to conduct performance analysis of Grid systems focusing on aspects related to the Virtual Organization as a whole. In particular, different job allocation techniques can be evaluated with respect to both user and provider points-of-view. The influence of different information update policies on the accuracy of the allocation schemes can also be investigated, highlighting the costs/benefits in terms of job waiting time, service availability, and system utilization. The proposed methodology is designed to be as general as possible and it can be applied to analyze a gLite Grid infrastructure taken as case study. {\textcopyright} 2011 John Wiley \& Sons, Ltd.

}, keywords = {Grid computing, Grid infrastructures, Grid systems, Information updates, Job allocation, Load condition, Management policy, Non-Markovian, Performance analysis, Performance estimation, performance measurements, Petri nets, Quality of service, Random access storage, Service availability, Service Level Agreements, Stochastic Petri Nets, Stochastic systems, System utilization, User requirements, Virtual organization, Waiting-time}, issn = {15320626}, doi = {10.1002/cpe.1697}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960133545\&partnerID=40\&md5=a557a0ec6dd02249f31a6a0960e2bc92}, author = {Dario Bruneo and Francesco Longo and Marco Scarpa and Antonio Puliafito} } @proceedings {Bruneo2010, title = {QoS assessment of WS-BPEL processes through non-Markovian stochastic Petri nets}, journal = {Proceedings of the 24th IEEE International Symposium on Parallel and Distributed Processing (IPDPS)}, year = {2010}, note = {cited By 11; Conference of 24th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2010 ; Conference Date: 19 April 2010 Through 23 April 2010; Conference Code:80843}, publisher = {IEEE Computer Society}, address = {Atlanta, GA, United States, 19-23 April 2010}, abstract = {

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 ofQoS guaranteed composed Web services still requires several efforts due to the highly distributed nature ofsuch 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. {\textcopyright} 2010 IEEE.

}, keywords = {Automatic tools, Business relationships, Computer software, Design, Design phase, Distributed parameter networks, Effective tool, Formal definition, Graph theory, Industrial standards, Information services, Internet-based services, Non-Markovian, Novel techniques, Operating condition, Petri nets, Quality of service, Service oriented architecture (SOA), Service reliability, Service time distribution, Software applications, Software engineers, Software paradigm, Software solution, Stochastic Petri Nets, Stochastic systems, Value added service, Web services, WS-BPEL}, isbn = {9781424464432}, doi = {10.1109/IPDPS.2010.5470391}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77954020712\&partnerID=40\&md5=70790f3d893defcf4e9c792d8b58c5da}, author = {Dario Bruneo and Salvatore Distefano and Francesco Longo and Marco Scarpa} } @proceedings {Ghosh2010343, title = {Quantifying resiliency of IaaS cloud}, journal = {Proceedings of the 29th IEEE Symposium on Reliable Distributed Systems (SRDS)}, year = {2010}, note = {cited By 16; Conference of 29th IEEE Symposium on Reliable Distributed Systems, SRDS 2010 ; Conference Date: 31 October 2010 Through 3 November 2010; Conference Code:82914}, pages = {343-347}, publisher = {IEEE Computer Society}, address = {New Delhi, India, 31 October - 3 November 2010}, abstract = {

Cloud based services may experience changes - internal, external, large, small - at any time. Predicting and quantifying the effects on the quality-of-service during and after a change are important in the resiliency assessment of a cloud based service. In this paper, we quantify the resiliency of infrastructure-as-a-service (IaaS) cloud when subject to changes in demand and available capacity. Using a stochastic reward net based model for provisioning and servicing requests in a IaaS cloud, we quantify the resiliency of IaaS cloud w.r.t. two key performance measures - job rejection rate and provisioning response delay. {\textcopyright} 2010 IEEE.

}, keywords = {Available capacity, Infrastructure as a services, Performance measure, Quality of service, Rejection rates, Response delays, Stochastic models, Stochastic reward nets}, isbn = {9780769542508}, issn = {10609857}, doi = {10.1109/SRDS.2010.49}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650562592\&partnerID=40\&md5=6542308f50d349fc7456919e658e6840}, author = {Rahul Ghosh and Francesco Longo and Vijay K. Naikz and Kishor S. Trivedi} } @proceedings {Bruneo2010243, title = {VO-level performance analysis of gLite Grids}, journal = {Proceedings of the 19th IEEE Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)}, year = {2010}, note = {cited By 0; Conference of 19th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2010 ; Conference Date: 28 June 2010 Through 30 June 2010; Conference Code:81490}, pages = {243-248}, publisher = {IEEE Computer Society}, address = {Larissa, Greece, 28-30 June 2010}, abstract = {

Business oriented grids call for effective Quality of Service strategies able to adapt to different user requirements. Performance analyses and predictions with respect to different load conditions or management policies are essential instruments to define such strategies. In this paper, we present a non-Markovian Stochastic Petri Net model that allows to conduct performance analyses of Grid systems focusing on aspects related to the Virtual Organization as a whole. Different job allocation techniques will be evaluated with respect to both user and provider point-of-views. We will also investigate the influence of different information update policies on the accuracy of the allocation schemes, highlighting the costs/benefits in terms of job waiting time, service availability, and system utilization. {\textcopyright} 2010 IEEE.

}, keywords = {Business-oriented, gLite middleware, Graph theory, Grid computing, Grid systems, Information updates, Job allocation, Load condition, Management policy, middleware, Non-Markovian, Performance analysis, performance measurements, Petri nets, Quality of service, Random access storage, Service availability, Stochastic models, Stochastic Petri Nets, Stochastic systems, System utilization, User requirements, Virtual organization, Waiting-time}, isbn = {9780769540634}, issn = {15244547}, doi = {10.1109/WETICE.2010.45}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955917215\&partnerID=40\&md5=5f89b9d07327325a09d78d1af77f0519}, author = {Dario Bruneo and Francesco Longo and Marco Scarpa and Antonio Puliafito} }