DESIGNING A FRAMEWORK FOR RESTFUL MULTI-AGENT SYSTEMS

Nowadays there are many systems that require some degree of automation. To attain this automation, agent technology has generally been found to be a promising approach. An agent is a piece of software that does activities on behalf of a user or another program. However, designing and deploying an agent infrastructure that achieves scalability is still a major challenge. In this thesis, a pattern for designing agents following RESTful principles is proposed in an effort to address the aforementioned challenges. In addition, the pattern will follow the FIPA Abstract Architecture; which is aimed at developing intelligent agents and supporting interoperability among agents and agent-based systems. Furthermore, an evaluation is done to investigate the scalability of the deployment of a RESTful multi-agent system

Pure functional epidemics

Agent-Based Simulation (ABS) is a methodology in which a system is simulated in a bottom-up approach by modelling the micro interactions of its constituting parts, called agents, out of which the global system behaviour emerges. So far mainly object-oriented techniques and languages have been used in ABS. Using the SIR model of epidemiology, which simulates the spreading of an infectious disease through a population, we demonstrate how to use pure Functional Reactive Programming to implement ABS. With our approach we can guarantee the reproducibility of the simulation at compile time and rule out specific classes of run-time bugs, something that is not possible with traditional object-oriented languages. Also, we found that the representation in a purely functional format is conceptually quite elegant and opens the way to formally reason about ABS

Multiparty session types for dynamic verification of distributed systems

In large-scale distributed systems, each application is realised through interactions among distributed components. To guarantee safe communication (no deadlocks and communication mismatches) we need programming languages and tools that structure, manage, and policy-check these interactions. Multiparty session types (MPST), a typing discipline for structured interactions between communicating processes, offers a promising approach. To date, however, session types applications have been limited to static verification, which is not always feasible and is often restrictive in terms of programming API and specifying policies. This thesis investigates the design and implementation of a runtime verification framework, ensuring conformance between programs and specifications. Specifications are written in Scribble, a protocol description language formally founded on MPST. The central idea of the approach is a dynamic monitor, which takes a form of a communicating finite state machine, automatically generated from Scribble specifications, and a communication runtime stipulating a message format. We extend and apply Scribble-based runtime verification in manifold ways. First, we implement a Python library, facilitated with session primitives and verification runtime. We integrate the library in a large cyber-infrastructure project for oceanography. Second, we examine multiple communication patterns, which reveal and motivate two novel extensions, asynchronous interrupts for verification of exception handling behaviours, and time constraints for enforcement of realtime protocols. Third, we apply the verification framework to actor programming by augmenting an actor library in Python with protocol annotations. For both implementations, measurements show Scribble-based dynamic checking delivers minimal overhead and allows expressive specifications. Finally, we explore a static analysis of Scribble specifications as to efficiently compute a safe global state from which a monitored system of interacting processes can be recovered after a failure. We provide an implementation of a verification framework for recovery in Erlang. Benchmarks show our recovery strategy outperforms a built-in static recovery strategy, in Erlang, on a number of use cases.Open Acces

INTEREST-BASED FILTERING OF SOCIAL DATA IN DECENTRALIZED ONLINE SOCIAL NETWORKS

In Online Social Networks (OSNs) users are overwhelmed with huge amount of social data, most of which are irrelevant to their interest. Due to the fact that most current OSNs are centralized, people are forced to share their data with the site, in order to be able to share it with their friends, and thus they lose control over it. Decentralized Online Social Networks have been proposed as an alternative to traditional centralized ones (such as Facebook, Twitter, Google+, etc.) to deal with privacy problems and to allow users to maintain control over their data. This thesis presents a novel peer-to-peer architecture for decentralized OSN and a mechanism that allows each node to filter out irrelevant social data, while ensuring a level of serendipity (serendipitous are social data which are unexpected since they do not belong in the areas of interest of the user but are desirable since they are important or popular). The approach uses feedback from recipient users to construct a model of different areas of interest along the relationships between sender and receiver, which acts as a filter while propagating social data in this area of interest. The evaluation of the approach, using an Erlang simulation shows that it works according to the design specification: with the increasing number of social data passing through the network, the nodes learn to filter out irrelevant data, while serendipitous important data is able to pass through the network

Towards Automated Network Configuration Management

Modern networks are designed to satisfy a wide variety of competing goals related to network operation requirements such as reachability, security, performance, reliability and availability. These high level goals are realized through a complex chain of low level configuration commands performed on network devices. As networks become larger, more complex and more heterogeneous, human errors become the most significant threat to network operation and the main cause of network outage. In addition, the gap between high-level requirements and low-level configuration data is continuously increasing and difficult to close. Although many solutions have been introduced to reduce the complexity of configuration management, network changes, in most cases, are still manually performed via low--level command line interfaces (CLIs). The Internet Engineering Task Force (IETF) has introduced NETwork CONFiguration (NETCONF) protocol along with its associated data--modeling language, YANG, that significantly reduce network configuration complexity. However, NETCONF is limited to the interaction between managers and agents, and it has weak support for compliance to high-level management functionalities. We design and develop a network configuration management system called AutoConf that addresses the aforementioned problems. AutoConf is a distributed system that manages, validates, and automates the configuration of IP networks. We propose a new framework to augment NETCONF/YANG framework. This framework includes a Configuration Semantic Model (CSM), which provides a formal representation of domain knowledge needed to deploy a successful management system. Along with CSM, we develop a domain--specific language called Structured Configuration language to specify configuration tasks as well as high--level requirements. CSM/SCL together with NETCONF/YANG makes a powerful management system that supports network--wide configuration. AutoConf supports two levels of verifications: consistency verification and behavioral verification. We apply a set of logical formalizations to verifying the consistency and dependency of configuration parameters. In behavioral verification, we present a set of formal models and algorithms based on Binary Decision Diagram (BDD) to capture the behaviors of forwarding control lists that are deployed in firewalls, routers, and NAT devices. We also adopt an enhanced version of Dyna-Q algorithm to support dynamic adaptation of network configuration in response to changes occurred during network operation. This adaptation approach maintains a coherent relationship between high level requirements and low level device configuration. We evaluate AutoConf by running several configuration scenarios such as interface configuration, RIP configuration, OSPF configuration and MPLS configuration. We also evaluate AutoConf by running several simulation models to demonstrate the effectiveness and the scalability of handling large-scale networks

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Modeling adaptation with a tuple-based coordination language

In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present a preliminary investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and usage of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some adaptation techniques, namely the MAPE-K loop, aspect- and context-oriented programming

Call Center Experience Optimization: A Case for a Virtual Predictive Queue

The evolution of the call center into contact centers and the growth of their use in providing customer-facing service by many companies has brought considerable capabilities in maintaining customer relationships but it also has brought challenges in providing quality service when call volumes are high. Limited in their ability to provide service at all times to all customers, companies are forced to balance the costs associated with hiring more customer service representatives and the quality of service provided by a fewer number. A primary challenge when there are not enough customer service representatives to engage the volume of callers in a timely manner is the significant wait times that can be experienced by many customers. Normally, callers are handled in accordance with a first-come, first-served policy with exceptions being skill-based routing to those customer service representatives with specialized skills. A proposed call center infrastructure framework called a Virtual Predictive Queue (VPQ) can allow some customers to benefit from a shorter call queue wait time. This proposed system can be implemented within a call center’s Automatic Call Distribution (ACD) device associated with computer telephony integration (CTI) and theoretically will not violate a first-come, first served policy