Exploiting Queuing Networks to Model and Assess the Performance of Self-Adaptive Software Systems: A Survey

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Learning Queuing Networks by Recurrent Neural Networks

It is well known that building analytical performance models in practice is difficult because it requires a considerable degree of proficiency in the underlying mathematics. In this paper, we propose a machine-learning approach to derive performance models from data. We focus on queuing networks, and crucially exploit a deterministic approximation of their average dynamics in terms of a compact system of ordinary differential equations. We encode these equations into a recurrent neural network whose weights can be directly related to model parameters. This allows for an interpretable structure of the neural network, which can be trained from system measurements to yield a white-box parameterized model that can be used for prediction purposes such as what-if analyses and capacity planning. Using synthetic models as well as a real case study of a load-balancing system, we show the effectiveness of our technique in yielding models with high predictive power

Performance Analysis of a Medical Record Exchanges Model(SCI)

[[abstract]]Electronic medical record exchange among hospitals can provide more information for physician diagnosis and reduce costs from duplicate examinations. In this paper, we proposed and implemented a medical record exchange model. According to our study, exchange interface servers (EISs) are designed for hospitals to manage the information communication through the intra and interhospital networks linked with a medical records database. An index service center can be given responsibility for managing the EIS and publishing the addresses and public keys. The prototype system has been implemented to generate, parse, and transfer the health level seven query messages. Moreover, the system can encrypt and decrypt a message using the public-key encryption algorithm. The queuing theory is applied to evaluate the performance of our proposed model. We estimated the service time for each queue of the CPU, database, and network, and measured the response time and possible bottlenecks of the model. The capacity of the model is estimated to process the medical records of about 4000 patients/h in the 1-MB network backbone environments, which comprises about the 4% of the total outpatients in Taiwan. Performance Analysis of a Medical Record Exchanges Model (PDF Download Available). Available from: https://www.researchgate.net/publication/51375541_Performance_Analysis_of_a_Medical_Record_Exchanges_Model [accessed Jan 15, 2016]

Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation

Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson’s networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10%. Finally, we show the usefulness of the model for performing the dynamic provisioning of the vMME experimentally.This work has been partially funded by the H2020 research and innovation project 5G-CLARITY (Grant No. 871428)National research project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of Education, Culture and Sport (FPU Grant 13/04833). We would also like to thank the reviewers for their valuable feedback to enhance the quality and contribution of this wor

A Combined Analytical Modeling Machine Learning Approach for Performance Prediction of MapReduce Jobs in Hadoop Clusters

Nowadays MapReduce and its open source implementation, Apache Hadoop, are the most widespread solutions for handling massive dataset on clusters of commodity hardware. At the expense of a somewhat reduced performance in comparison to HPC technologies, the MapReduce framework provides fault tolerance and automatic parallelization without any efforts by developers. Since in many cases Hadoop is adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted jobs, for instance when SLAs are established with end-users. In this work, we propose and validate a hybrid approach exploiting both queuing networks and support vector regression, in order to achieve a good accuracy without too many costly experiments on a real setup. The experimental results show how the proposed approach attains a 21% improvement in accuracy over applying machine learning techniques without any support from analytical models

Performance and Security Trade-offs in High-Speed Networks. An investigation into the performance and security modelling and evaluation of high-speed networks based on the quantitative analysis and experimentation of queueing networks and generalised stochastic Petri nets.

Most used security mechanisms in high-speed networks have been adopted without adequate quantification of their impact on performance degradation. Appropriate quantitative network models may be employed for the evaluation and prediction of ¿optimal¿ performance vs. security trade-offs. Several quantitative models introduced in the literature are based on queueing networks (QNs) and generalised stochastic Petri nets (GSPNs). However, these models do not take into consideration Performance Engineering Principles (PEPs) and the adverse impact of traffic burstiness and security protocols on performance. The contributions of this thesis are based on the development of an effective quantitative methodology for the analysis of arbitrary QN models and GSPNs through discrete-event simulation (DES) and extended applications into performance vs. security trade-offs involving infrastructure and infrastructure-less high-speed networks under bursty traffic conditions. Specifically, investigations are carried out focusing, for illustration purposes, on high-speed network routers subject to Access Control List (ACL) and also Robotic Ad Hoc Networks (RANETs) with Wired Equivalent Privacy (WEP) and Selective Security (SS) protocols, respectively. The Generalised Exponential (GE) distribution is used to model inter-arrival and service times at each node in order to capture the traffic burstiness of the network and predict pessimistic ¿upper bounds¿ of network performance. In the context of a router with ACL mechanism representing an infrastructure network node, performance degradation is caused due to high-speed incoming traffic in conjunction with ACL security computations making the router a bottleneck in the network. To quantify and predict the trade-off of this degradation, the proposed quantitative methodology employs a suitable QN model consisting of two queues connected in a tandem configuration. These queues have single or quad-core CPUs with multiple-classes and correspond to a security processing node and a transmission forwarding node. First-Come-First-Served (FCFS) and Head-of-the-Line (HoL) are the adopted service disciplines together with Complete Buffer Sharing (CBS) and Partial Buffer Sharing (PBS) buffer management schemes. The mean response time and packet loss probability at each queue are employed as typical performance metrics. Numerical experiments are carried out, based on DES, in order to establish a balanced trade-off between security and performance towards the design and development of efficient router architectures under bursty traffic conditions. The proposed methodology is also applied into the evaluation of performance vs. security trade-offs of robotic ad hoc networks (RANETs) with mobility subject to Wired Equivalent Privacy (WEP) and Selective Security (SS) protocols. WEP protocol is engaged to provide confidentiality and integrity to exchanged data amongst robotic nodes of a RANET and thus, to prevent data capturing by unauthorised users. WEP security mechanisms in RANETs, as infrastructure-less networks, are performed at each individual robotic node subject to traffic burstiness as well as nodal mobility. In this context, the proposed quantitative methodology is extended to incorporate an open QN model of a RANET with Gated queues (G-Queues), arbitrary topology and multiple classes of data packets with FCFS and HoL disciplines under bursty arrival traffic flows characterised by an Interrupted Compound Poisson Process (ICPP). SS is included in the Gated-QN (G-QN) model in order to establish an ¿optimal¿ performance vs. security trade-off. For this purpose, PEPs, such as the provision of multiple classes with HoL priorities and the availability of dual CPUs, are complemented by the inclusion of robot¿s mobility, enabling realistic decisions in mitigating the performance of mobile robotic nodes in the presence of security. The mean marginal end-to-end delay was adopted as the performance metric that gives indication on the security improvement. The proposed quantitative methodology is further enhanced by formulating an advanced hybrid framework for capturing ¿optimal¿ performance vs. security trade-offs for each node of a RANET by taking more explicitly into consideration security control and battery life. Specifically, each robotic node is represented by a hybrid Gated GSPN (G-GSPN) and a QN model. In this context, the G-GSPN incorporates bursty multiple class traffic flows, nodal mobility, security processing and control whilst the QN model has, generally, an arbitrary configuration with finite capacity channel queues reflecting ¿intra¿-robot (component-to-component) communication and ¿inter¿-robot transmissions. Two theoretical case studies from the literature are adapted to illustrate the utility of the QN towards modelling ¿intra¿ and ¿inter¿ robot communications. Extensions of the combined performance and security metrics (CPSMs) proposed in the literature are suggested to facilitate investigating and optimising RANET¿s performance vs. security trade-offs. This framework has a promising potential modelling more meaningfully and explicitly the behaviour of security processing and control mechanisms as well as capturing the robot¿s heterogeneity (in terms of the robot architecture and application/task context) in the near future (c.f. [1]. Moreover, this framework should enable testing robot¿s configurations during design and development stages of RANETs as well as modifying and tuning existing configurations of RANETs towards enhanced ¿optimal¿ performance and security trade-offs.Ministry of Higher Education in Libya and the Libyan Cultural Attaché bureau in Londo

Envisioning Model-Based Performance Engineering Frameworks.

Abstract Our daily activities depend on complex software systems that must guarantee certain performance. Several approaches have been devised in the last decade to validate software systems against performance requirements. However, software designers still encounter problems in the interpretation of performance analysis results (e.g., mean values, probability distribution functions) and in the definition of design alternatives (e.g., to split a software component in two and redeploy one of them) aimed at fulfilling performance requirements. This paper describes a general model-based performance engineering framework to support designers in dealing with such problems aimed at enhancing the system. The framework relies on a formalization of the knowledge needed in order to characterize performance flaws and provide alternative system design. Such knowledge can be instantiated based on the techniques devised for interpreting performance analysis results and providing feedback to designers. Three techniques are considered in this paper for instantiating the framework and the main challenges to face during such process are pointed out and discussed

Estimation of vehicle queuing lengths at metering roundabouts

© 2017 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Signalized metering roundabouts are equipped with advanced loop detectors and traffic signals that can reduce vehicle queuing lengths, especially on the dominant approach, when unbalanced traffic flow conditions occur. At a metering roundabout, changeable queuing lengths and the location of detectors determine signal phase times, which in turn affect queuing length on each approach. To date, most studies have focused on performance comparisons between normal and metered roundabouts, but have failed to evaluate the effect of detector locations on queuing formations. In addition, no guidelines have been developed to enable practitioners to select the appropriate detector location that would lead to optimum roundabout performance. This study, therefore, formulated a numerical model for the estimation of queuing length at a metering roundabout. The model consists of advance vehicle detectors on two approaches and one traffic signal. In order to calibrate and verify the model, queuing lengths were recorded using two drones for the Old Belair Road metering roundabout in Adelaide, South Australia. In order to assess the fitness of the model, an R2 test was conducted, and the results showed that the numerical model can predict queuing lengths on the controlling and metered approaches with up to 83% of R2 value. Moreover, the estimated queuing lengths were compared against those predicted by the software AIMSUN for the same location and under the same conditions. It is expected that the model will assist and guide practitioners in determining the best detector locations for metering roundabouts

Performance requirements verification during software systems development

Requirements verification refers to the assurance that the implemented system reflects the specified requirements. Requirement verification is a process that continues through the life cycle of the software system. When the software crisis hit in 1960, a great deal of attention was placed on the verification of functional requirements, which were considered to be of crucial importance. Over the last decade, researchers have addressed the importance of integrating non-functional requirement in the verification process. An important non-functional requirement for software is performance. Performance requirement verification is known as Software Performance Evaluation. This thesis will look at performance evaluation of software systems. The performance evaluation of software systems is a hugely valuable task, especially in the early stages of a software project development. Many methods for integrating performance analysis into the software development process have been proposed. These methodologies work by utilising the software architectural models known in the software engineering field by transforming these into performance models, which can be analysed to gain the expected performance characteristics of the projected system. This thesis aims to bridge the knowledge gap between performance and software engineering domains by introducing semi-automated transformation methodologies. These are designed to be generic in order for them to be integrated into any software engineering development process. The goal of these methodologies is to provide performance related design guidance during the system development. This thesis introduces two model transformation methodologies. These are the improved state marking methodology and the UML-EQN methodology. It will also introduce the UML-JMT tool which was built to realise the UML-EQN methodology. With the help of automatic design models to performance model algorithms introduced in the UML-EQN methodology, a software engineer with basic knowledge of performance modelling paradigm can conduct a performance study on a software system design. This was proved in a qualitative study where the methodology and the tool deploying this methodology were tested by software engineers with varying levels of background, experience and from different sectors of the software development industry. The study results showed an acceptance for this methodology and the UML-JMT tool. As performance verification is a part of any software engineering methodology, we have to define frame works that would deploy performance requirements validation in the context of software engineering. Agile development paradigm was the result of changes in the overall environment of the IT and business worlds. These techniques are based on iterative development, where requirements, designs and developed programmes evolve continually. At present, the majority of literature discussing the role of requirements engineering in agile development processes seems to indicate that non-functional requirements verification is an unchartered territory. CPASA (Continuous Performance Assessment of Software Architecture) was designed to work in software projects where the performance can be affected by changes in the requirements and matches the main practices of agile modelling and development. The UML-JMT tool was designed to deploy the CPASA Performance evaluation tests