Browsing by Author "Trivedi, Kishor S"

Attack tree (AT) is one of the widely used non-statespace

models in security analysis. The basic formalism of AT

does not take into account defense mechanisms. Defense trees

(DTs) have been developed to investigate the effect of defense

mechanisms usinghg measures such as attack cost, security

investment cost, return on attack (ROA) and return on investment

(ROI). DT, however, places defense mechanisms only at the

leaf nodes and the corresponding ROI/ROA analysis does not

incorporate the probabilities of attack. In attack response tree

(ART), attack and response are both captured but ART suffers

from the problem of state-space explosion, since solution of

ART is obtained by means of a state space model. In this

paper, we present a novel attack tree paradigm called attack

countermeasure tree (ACT) which avoids the generation and

solution of the state-space model and takes into account attacks as

well as countermeasures (in the form of detection and mitigation

events). In ACT, detection and mitigation are allowed not just at

the leaf node but also at the intermediate nodes while at the same

time the state-space explosion problem is avoided in its analysis.

We use single and multiobjective optimization to find optimal

countermeasures under different constraints. We illustrate the

features of ACT using several case studies.

Decisions in health care must often be made under inherent uncertainty; from treating patients, to provisioning medical devices, to operational decisions at an outpatient clinic. The outcomes depend on the health of patients as well as the availability of health care professionals and resources. Complex models of clinic performance allow for experiments with new schedules and resource levels without the time, cost, unfeasibility, or risk of testing new policies in real clinics. Model-based methods quantify the effect of various uncertain factors such as the availability of personnel on health care quality indicators like patient wait times in a clinic.

Despite their purported value, few opportunities have existed to test models from data collection through optimization. This dissertation develops a clinic model from end-to-end, beginning with a description of the medical practice, to data collection, to model validation, to optimization. Specialty medical practice is abstracted into treatment steps, measured electronically, and verified through systematic observation. These data are anonymized and made available for researchers. A validation framework uses the data to develop and test candidate models, selecting one that maximizes predictive accuracy while retaining interpretability and reproducibility. The resulting model is used in improving schedules via heuristic optimization. Clustering the results reveals clinic performance groups that represent different goals in clinic quality.

Inter-Vehicle Communication (IVC) is a vital part of Intelligent Transportation System (ITS), which has been extensively researched in recent years. Dedicated Short Range Communication (DSRC) is being seriously considered by automotive industry and government agencies as a promising wireless technology for enhancing transportation safety and efficiency of road utilization. In the DSRC based vehicular ad hoc networks (VANETs), the transportation safety is one of the most crucial features that needs to be addressed. Safety applications usually demand direct vehicle-to-vehicle ad hoc communication due to a highly dynamic network topology and strict delay requirements. Such direct safety communication will involve a broadcast service because safety information can be beneficial to all vehicles around a sender. Broadcasting safety messages is one of the fundamental services in DSRC. In order to provide satisfactory quality of services (QoS) for various safety applications, safety messages need to be delivered both timely and reliably. To support the stringent delay and reliability requirements of broadcasting safety messages, researchers have been seeking to test proposed DSRC protocols and suggesting improvements. A major hurdle in the development of VANET for safety-critical services is the lack of methods that enable one to determine the effectiveness of VANET design mechanism for predictable QoS and allow one to evaluate the tradeoff between network parameters. Computer simulations are extensively used for this purpose. A few analytic models and experiments have been developed to study the performance and reliability of IEEE 802.11p for safety-related applications. In this thesis, we propose to develop detailed analytic models to capture various safety message dissemination features such as channel contention, backoff behavior, concurrent transmissions, hidden terminal problems, channel fading with path loss, multi-channel operations, multi-hop dissemination in 1-Dimentional or 2-Dimentional traffic scenarios. MAC-level and application-level performance metrics are derived to evaluate the performance and reliability of message broadcasting, which provide insights on network parameter settings. Extensive simulations in either Matlab or NS2 are conducted to validate the accuracy of our proposed models.

A blockchain is an immutable record of transactions (called ledger ) between a distributed set of mutually untrusting peers. Although blockchain networks provide tremendous benefits, there are concerns about whether their performance would be a hindrance to its adoption. Our research is focused on Hyperledger Fabric (HLF), which is an open-source implementation of a distributed ledger platform for running smart contracts in a modular architecture. This thesis presents our research on performance modeling of Hyperledger Fabric using a Stochastic Petri Nets modeling formalism known as Stochastic Reward Nets (SRN). We capture the key system operations and complex interactions between them. We focus on two different releases of HLF, viz. v0.6 and v1.0+ (V1). HLF v0.6 follows a traditional state-machine replication architecture followed by many other blockchain platforms, whereas HLF V1 follows a novel execute-order-validate architecture. We parameterize and validate our models with data collected from a real-world Fabric network setup. Our models provide a quantitative framework that helps compare different deployment configurations of Fabric and make design trade-off decisions. It also enables us to compute performance for a system with proposed architectural improvements before they are implemented. From our analysis, we recommend design improvements along with the estimates of performance improvement. Overall, our models provide a stepping stone to the Hyperledger Fabric community towards achieving optimal performance of Fabric in the real-world deployments.

Cloud computing appears to be a paradigm shift in service oriented computing. Massively scalable Cloud architectures are spawned by new business and social applications as well as Internet driven economics. Besides being inherently large scale and highly distributed, Cloud systems are almost always virtualized and operate in automated shared environments. The deployed Cloud services are still in their infancy and a variety of research challenges need to be addressed to predict their long-term behavior. Performance and dependability of Cloud services are in general stochastic in nature and they are affected by a large number of factors, e.g., nature of workload and faultload, infrastructure characteristics and management policies. As a result, developing scalable and predictive analytics for Cloud becomes difficult and non-trivial. This dissertation presents the research framework needed to develop high fidelity stochastic models for large scale enterprise systems using Cloud computing as an example. Throughout the dissertation, we show how the developed models are used for: (i) performance and availability analysis, (ii) understanding of power-performance trade-offs, (ii) resiliency quantification, (iv) cost analysis and capacity planning, and (v) risk analysis of Cloud services. In general, the models and approaches presented in this thesis can be useful to a Cloud service provider for planning, forecasting, bottleneck detection, what-if analysis or overall optimization during design, development, testing and operational phases of a Cloud.

Storage systems play a vital part in modern IT systems. As the volume of data grows explosively and greater requirement on storage performance and reliability is put forward, effective and efficient design and operation of storage systems become increasingly complicated.

Such efforts would benefit significantly from the availability of quantitative analysis techniques that facilitate comparison of different system designs and configurations and provide projection of system behavior under potential operational scenarios. The techniques should be able to capture the system details that are relevant to the system measures of interest with adequate accuracy, and they should allow efficient solution so that they can be employed for multiple scenarios and for dynamic system reconfiguration.

This dissertation develops a set of quantitative analysis methods for modern storage systems using stochastic modeling techniques. The presented models cover several of the most prevalent storage technologies, including RAID, cloud storage and replicated storage, and investigate some major issues in modern storage systems, such as storage capacity planning, provisioning and backup planning. Quantitative investigation on important system measures such as reliability, availability and performance is conducted, and for this purpose a variety of modeling formalisms and solution methods are employed based on the matching of the underlying model assumptions and nature of the system aspects being studied. One of the primary focuses of the model development is on solution efficiency and scalability of the models to large systems. The accuracy of the developed models are validated through extensive simulation.