Performance Prediction of Cloud-Based Big Data Applications

Big data analytics have become widespread as a means to extract knowledge from large datasets. Yet, the heterogeneity and irregular- ity usually associated with big data applications often overwhelm the existing software and hardware infrastructures. In such con- text, the exibility and elasticity provided by the cloud computing paradigm o er a natural approach to cost-e ectively adapting the allocated resources to the application’s current needs. However, these same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a key step to proper management and planning. This paper explores three modeling approaches for performance prediction of cloud-based big data applications. We evaluate two queuing-based analytical models and a novel fast ad hoc simulator in various scenarios based on di erent applications and infrastructure setups. The three ap- proaches are compared in terms of prediction accuracy, nding that our best approaches can predict average application execution times with 26% relative error in the very worst case and about 7% on average

Testing IP differentiated services implementations

Diffserv architecture is pointed out as a promising solution for the provision of QoS in the Internet in a scalable manner. The main objective of this work is to test, evaluate and discuss, from a practical point of view, two platforms for implementing Diffserv services: one developed at ICA/EPFL for the Linux OS and the other based on Cisco Systems routers. After comparing the configuration strategy in each platform, QoS related functionalities (e.g. classification, marking, policing, shaping) are tested and assessed. In particular, the implementation of EF and AF PHBs is analysed. The capacity of providing bandwidth guarantees and performing adequate traffic conditioning is evaluated as well as the impact background traffic has on delaying high priority traffic. Moreover, the computational effort Diffserv puts on routers is also measured in terms of CPU utilisation.EURESCOM P1006 - DISCMAN Projec

Quality-of-service management in IP networks

Quality of Service (QoS) in Internet Protocol (IF) Networks has been the subject of active research over the past two decades. Integrated Services (IntServ) and Differentiated Services (DiffServ) QoS architectures have emerged as proposed standards for resource allocation in IF Networks. These two QoS architectures support the need for multiple traffic queuing systems to allow for resource partitioning for heterogeneous applications making use of the networks. There have been a number of specifications or proposals for the number of traffic queuing classes (Class of Service (CoS)) that will support integrated services in IF Networks, but none has provided verification in the form of analytical or empirical investigation to prove that its specification or proposal will be optimum. Despite the existence of the two standard QoS architectures and the large volume of research work that has been carried out on IF QoS, its deployment still remains elusive in the Internet. This is not unconnected with the complexities associated with some aspects of the standard QoS architectures. [Continues.

Tuning active monitoring in multiservice IP networks

Comunicação apresentada "Performance Modelling and Evaluation of Heterogeneous Networks (HET-Nets 04), 2, Ilkley, U.K., July 2004.This paper explores the use of edge-to-edge active monitoring to control simultaneously multiple QoS parameters in multi-service IP networks, while reducing the effects of intrusion on real traffic. Considering a multi-class domain where traffic is controlled at network boundaries based on feedback from on-line measurements, the present work is centered on obtaining adequate per class in-band probing streams so that each class behaviour is correctly captured, even if more than a QoS metric is under control. In this way, we investigate distinct properties of probing patterns and cross-check probing and passive measurement results in order to assess and tune probing effectiveness. To enhance probing ability to sense multiple metrics, we explore Active Queue Management effects on probes and their different probability of reaching the network boundary. The results show that, while IPTD can be easily captured using a very low probing rate, matching ipdv and IPLR is not straightforward. However, we found that choosing a convenient drop precedence for probing packets, the simultaneous estimation of these QoS metrics can be significantly improved

Data-Driven Simulation Modeling of Construction and Infrastructure Operations Using Process Knowledge Discovery

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality

A hierarchical approach to multi-project planning under uncertainty

We survey several viewpoints on the management of the planning complexity of multi-project organisations under uncertainty. A positioning framework is proposed to distinguish between different types of project-driven organisations, which is meant to aid project management in the choice between the various existing planning approaches. We discuss the current state of the art of hierarchical planning approaches both for traditional manufacturing and for project environments. We introduce a generic hierarchical project planning and control framework that serves to position planning methods for multi-project planning under uncertainty. We discuss multiple techniques for dealing with the uncertainty inherent to the different hierarchical stages in a multi-project organisation. In the last part of this paper we discuss two cases from practice and we relate these practical cases to the positioning framework that is put forward in the paper

Organizing Multidisciplinary Care for Children with Neuromuscular Diseases

The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the `Children's Muscle Center Amsterdam' (CMCA). The CMCA diagnoses and treats children with neuromuscular diseases. These patients require care from a variety of clinicians. Through the establishment of the CMCA, children and their parents will generally visit the hospital only once a year, while previously they visited on average six times a year. This is a major improvement, because the hospital visits are both physically and psychologically demanding for the patients. This article describes how quantitative modelling supports the design and operations of the CMCA. First, an integer linear program is presented that selects which patients to invite for a treatment day and schedules the required combination of consultations, examinations and treatments on one day. Second, the integer linear program is used as input to a simulation to study to estimate the capacity of the CMCA, expressed in the distribution of the number patients that can be seen on one diagnosis day. Finally, a queueing model is formulated to predict the access time distributions based upon the simulation outcomes under various demand scenarios