@article {Longo20152506, title = {Variable operating conditions in distributed systems: Modeling and evaluation}, journal = {Concurrency Computation Practice and Experience}, volume = {27}, number = {10}, year = {2015}, note = {cited By 1}, pages = {2506-2530}, publisher = {John Wiley and Sons Ltd}, abstract = {

SummaryPerformance and dependability evaluation plays a key role in the design of a broad range of systems, especially when strict requirements need to be met. This is particularly challenging in distributed contexts, where several components may interact among themselves by influencing each other. In this paper, we present an analytical method that allows the study of a class of systems where different operating conditions alternate by changing the stochastic behavior of the system components but still preserving the continuity of the performance and dependability quantities to investigate. The proposed solution technique, based on phase type distributions, Kronecker algebra, and ad-hoc fitting algorithms, can be applied for the analytical evaluation of a wide class of distributed systems. Examples are provided to show the usefulness and the applicability of the methodology, characterizing and investigating different performance and dependability aspects of three distributed computing systems, that is, a connection-oriented network, an Internet of Things application, and an Infrastructure-as-a-Service Cloud. Copyright {\textcopyright} 2014 John Wiley \& Sons, Ltd.

}, keywords = {Algebra, cloud computing, Conservative systems, Dependability, Distributed computer systems, Internet, Internet of Things, Kronecker algebra, Markov processes, Non-Markovian, Performance, phase type distributions, Stochastic systems}, issn = {15320626}, doi = {10.1002/cpe.3419}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84932604392\&partnerID=40\&md5=b8f197a50fa177e85f0c1ac2a93fe392}, author = {Francesco Longo and Dario Bruneo and Salvatore Distefano and Marco Scarpa} } @proceedings {Distefano201377, title = {Investigating mobile crowdsensing application performance}, journal = {Proceedings of the 3rd ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet), Co-located with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)}, year = {2013}, note = {cited By 0; Conference of 3rd ACM Int. Symp. on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2013, Held in Conjunction with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2013 ; Conference Date: 3 November 2013 Through 8 November 2013; Conference Code:101342}, pages = {77-83}, publisher = {ACM}, address = {Barcelona, Spain, 3-8 November 2013}, abstract = {

Mobile Crowdsensing (MCS) is an emerging distributed paradigm lying at the intersection between the Internet of Things and the volunteer/crowd-based approach. MCS applications are usually deployed on contributing nodes such as smart devices and mobiles, equipped by sensing resources that sample the physical environment and provide the sensed data, once filtered, aggregated and preprocessed, to the MCS application server. The MCS opportunistic approach unlocks new form of pervasive, participatory sensing applications, acquiring interests also in business contexts that call for adequate techniques and tools to drive architects and developers in MCS application design. Aim of this paper is to evaluate the performance of an MCS application though a stochastic model able to stochastically represent the overall MCS environment, thus providing a valid support to MCS application development. The Petri nets formalism is used due to its expressiveness and the capabilities to represent complex, dependent, non-Markovian, phenomena usually characterizing MCS environments. A specific MCS application is then evaluated to demonstrate the effectiveness of the proposed technique on a real case study. {\textcopyright} 2013 ACM.

}, keywords = {Application development, Application performance, Complex networks, crowdsensing, Design, Digital storage, Internet of Things (IOT), Participatory sensing applications, Performance, Petri nets, Physical environments, Stochastic models, Techniques and tools}, isbn = {9781450323581}, doi = {10.1145/2512921.2512931}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84889685098\&partnerID=40\&md5=15df22b992482173437dcd8080bbbc94}, author = {Salvatore Distefano and Francesco Longo and Marco Scarpa} } @article {Ghosh20131216, title = {Modeling and performance analysis of large scale IaaS clouds}, journal = {Future Generation Computer Systems - Elsevier}, volume = {29}, number = {5}, year = {2013}, note = {cited By 10}, pages = {1216-1234}, abstract = {

For Cloud based services to support enterprise class production workloads, Mainframe like predictable performance is essential. However, the scale, complexity, and inherent resource sharing across workloads make the Cloud management for predictable performance difficult. As a first step towards designing Cloud based systems that achieve such performance and realize the service level objectives, we develop a scalable stochastic analytic model for performance quantification of Infrastructure-as-a-Service (IaaS) Cloud. Specifically, we model a class of IaaS Clouds that offer tiered services by configuring physical machines into three pools with different provisioning delay and power consumption characteristics. Performance behaviors in such IaaS Clouds are affected by a large set of parameters, e.g., workload, system characteristics and management policies. Thus, traditional analytic models for such systems tend to be intractable. To overcome this difficulty, we propose a multi-level interacting stochastic sub-models approach where the overall model solution is obtained iteratively over individual sub-model solutions. By comparing with a single-level monolithic model, we show that our approach is scalable, tractable, and yet retains high fidelity. Since the dependencies among the sub-models are resolved via fixed-point iteration, we prove the existence of a solution. Results from our analysis show the impact of workload and system characteristics on two performance measures: mean response delay and job rejection probability. {\textcopyright} 2012 Elsevier B.V. All rights reserved.

}, keywords = {Analytic modeling, Analytical models, Clouds, CTMC, Fixed-point iterations, IaaS, Infrastructure as a service (IaaS), Performance, Provisioning, Stochastic models, Stochastic systems, Submodels}, issn = {0167739X}, doi = {10.1016/j.future.2012.06.005}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84887062784\&partnerID=40\&md5=5aa7bb3aa9d27ba52b585a03501c8e18}, author = {Rahul Ghosh and Francesco Longo and Vijay K. Naik and Kishor S. Trivedi} } @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} }