No description available.

The research project reported in this thesis considers Multiple Distributed Replications in Parallel (MDRIP), a hybrid approach to parallelisation of quantitative stochastic discrete-event simulation. Parallel Discrete-Event Simulation (PDES) generally covers distributed simulation or simulation with replicated trials. Distributed simulation requires model partitioning and synchronisation among submodels. Simulation with replicated trials can be executed on-line by applying Multiple Replications in Parallel (MRIP). MDRIP has been proposed for overcoming problems related to the large size of simulated models and their complexity, as well as with the problem of controlling the accuracy of the final simulation results. A survey of PDES investigates several primary issues which are directly related to the parallelisation of DES. A secondary issue related to implementation efficiency is also covered. Statistical analysis as a supporting issue is described. The AKAROA2 package is an implementation of making such supporting issue effortless. Existing solutions proposed for PDES have exclusively focused on collecting of output data during simulation and conducting analysis of these data when simulation is finished. Such off-line statistical analysis of output data offers no control of statistical errors of the final estimates. On-line control of statistical errors during simulation has been successfully implemented in AKAROA2, an automated controller of output data analysis during simulation executed in MRIP. However, AKAROA2 cannot be applied directly to distributed simulation. This thesis reports results of a research project aimed at employing AKAROA2 for launching multiple replications of distributed simulation models and for on-line sequential control of statistical errors associated with a distributed performance measure; i.e. with a performance measure which depends on output data being generated by a number of submodels of distributed simulation. We report changes required in the architecture of AKAROA2 to make MDRIP possible. A new MDRIP-related component of AKAROA2, a distributed simulation engine mdrip engine, is introduced. Stochastic simulation in its MDRIP version, as implemented in AKAROA2, has been tested in a number of simulation scenarios. We discuss two specific simulation models employed in our tests: (i) a model consisting of independent queues, and (ii) a queueing network consisting of tandem connection of queueing systems. In the first case, we look at the correctness of message orderings from the distributed messages. In the second case, we look at the correctness of output data analysis when the analysed performance measures require data from all submodels of a given (distributed) simulation model. Our tests confirm correctness of our mdrip engine design in the cases considered; i.e. in models in which causality errors do not occur. However, we argue that the same design principles should be applicable in the case of distributed simulation models with (potential) causality errors.

No description available.

Advances in computing and communication technologies are blurring the distinction between today's PCs and mobile phones. With expected smart phones sales to skyrocket, lack of awareness regarding securing them, and access to personal and proprietary information, has resulted in the recent surge of mobile malware. In addition to using traditional social-engineering techniques such as email and file-sharing, malware unique to Bluetooth, Short Messaging Service (SMS) and Multimedia Messaging Service (MMS) messages are being used. Large scale simulations of malware on wireless networks have becomes important and studying them under realistic device deployments is important to obtain deep insights into their dynamics and devise ways to control them. In this dissertation, we present EpiNet: an individual-based scalable high-performance oriented modeling environment for simulating the spread of mobile malware over large, dynamic networks. EpiNet can be used to undertake comprehensive studies during both planning and response phase of a malware epidemic in present and future generation wireless networks. Scalability is an important design consideration and the current EpiNet implementation can scale to 3-5 million device networks and case studies show that large factorial designs on million device networks can be executed within a day on 100 node clusters. Beyond compute time, EpiNet has been designed for analysts to easily represent a range of interventions and evaluating their efficacy. The results indicate that Bluetooth malware with very low initial infection size will not result in a major wireless epidemic. The dynamics are dependent on the network structure and, activity-based mobility models or their variations can yield realistic spread dynamics. Early detection of the malware is extremely important in controlling the spread. Non-adaptive response strategies using static graph measures such as degree and betweenness are not effective. Device-based detection mechanisms provide a much better means to control the spread and only effective when detection occurs early on. Automatic signature generation can help in detecting newer strains of the malware and signature distributions through a central server results in better control of the spread. Centralized dissemination of patches are required to reach a large proportion of devices to be effective in slowing the spread. Non-adaptive dynamic graph measures such as vulnerability are found to be more effective. Our studies of SMS and hybrid malware show that SMS-only malware spread slightly faster than Bluetooth-only malware and do not spread to all devices. Hybrid malware spread orders of magnitude faster than either SMS-only or Bluetooth-only malware and can cause significant damage. Bluetooth-only malware spread faster than SMS-only malware in cases where density of devices in the proximity of an infected device is higher. Hybrid malware can be much more damaging than Bluetooth-only or SMS-only malware and we need mechanisms that can prevent such an outbreak. EpiNet provide a means to propose, implement and evaluate the response mechanisms in realistic and safe settings. / Ph. D.

In the past decade or so, significant advances were made in the field of Future Internet Architecture (FIA) design. Undoubtedly, the size of Future Internet will increase tremendously, and so will the complexity of its users’ behaviors. This advancement means most of future Internet applications and services can only achieve and demonstrate full potential on a large-scale basis. The development of network testbeds that can validate key design decisions and expose operational issues at scale is essential to FIA research. In conjunction with the development and advancement of FIA, cyber-infrastructure testbeds have also achieved remarkable progress. For meaningful network studies, it is indispensable to utilize cyber-infrastructure testbeds appropriately in order to obtain accurate experiment results. That said, existing current network experimentation is intrinsically deficient. The existing testbeds do not offer scalability, flexibility, and realism at the same time. This dissertation aims to construct a hybrid system of conducting at-scale network studies and experiments by exploiting the distributed computing ability of current testbeds. First, this work presents a synchronization of parallel discrete event simulation that offers the simulation with transparent scalability and performance on various high-end computing platforms. The parallel simulator that we implement is configured so that it can self-adapt for the performance while running on supercomputers with disparate architectures. The simulator could be used to handle models of different sizes, varying modeling details, and different complexity levels. Second, this works addresses the issue of researching network design and implementation realistically at scale, through the use of distributed cyber-infrastructure testbeds. An existing symbiotic approach is applied to integrate emulation with simulation so that they can overcome the limitations of physical setup. The symbiotic method is used to improve the capabilities of a specific emulator, Mininet. In this case, Mininet can be used to run applications directly on the virtual machines and software switches, with network connectivity represented by detailed simulation at scale. We also propose a method for using the symbiotic approach to coordinate separate Mininet instances, each representing a different set of the overlapping network flows. This approach provides a significant improvement to the scalability of the network experiments.

Discrete event simulation is the most suitable type of simulation for analyzing a complex system where changes happen at discrete time instants. Discrete event simulation is a major experimental methodology in several scientific and engineering domains. Unfortunately, a conventional discrete event simulator cannot meet with increasing demands of computational or the structural complexities of modern systems such as peer-to-peer (P2P) systems; therefore parallel discrete event simulation has been a focus of researchers for several decades. Unfortunately, no simulator is regarded as a standard which can satisfy the demands of all kinds of applications. Thus while given a simulator yields good performance for a specific kind of applications, it may failed to be efficient for other kinds of applications. Furthermore, although technological advancements have been made in the multi-core computing hardware, none of the mainstream P2P discrete event simulators is designed to support parallel simulation that exploits multi-core architectures. The proposed HydraNetSim parallel discrete event simulator (PDES) is a step toward addressing these issues. Developing a simulator which can support very large numbers of nodes to realize a massive P2P system, and can also execute in parallel is a non-trivial task. The literature review in this thesis gives a broad overview of prevailing approaches to dealing with the tricky problems of simulating a massive, large, and rapidly changing system, and provides a foundation for adopting a suitable architecture for developing a PDES. HydraNetSim is a discrete event simulator which allows parallel simulation and exploits the capabilities of parallelization of modern computing hardware. It is based on a novel master/slave paradigm. It divides the simulation model into a number of specific slaves (a cluster of processes) considering the number of cores provided by the underlying computing hardware. Each slave can be assigned to a specific CPU on a different core. Synchronization of the slaves is achieved by proposing a variant of the classic Null-Message Algorithm (NMA) with a focus on keeping the synchronization overhead as low as possible. Furthermore, HydraNetSim provides log information for debugging purposes and introduces a new mechanism of gathering and writing simulation results to a database. The experimental results show that the sequential counterpart of HydraNetSim (SDES) takes 41.6% more time than HydraNetSim-2Slave and 23.6% than HydraNetSim-3Slave. HydraNetSim-2Slave is 1.42 times faster, consumes 1.18 times more memory, and supports 2.02 times more nodes than a sequential discrete event simulator (SDES). Whereas, HydraNetSim-3Slave executes 1.24 times faster, consumes 2.08 times more memory, and supports 3.04 times more nodes than SDES. The scaling factor of HydraNetSim is ⌈(β-1)*102.04%⌉ of the maximum numbered of nodes supported by SDES; where β is the number of slaves. / Diskret händelsesimulering är den mest passande typen av simulering för att analysera ett komplext system där förändringar sker i diskreta tidpunkter. Diskret händelsesimulering är en stor experimentell metod i flera vetenskapliga och tekniska områden. Tyvärr kan en konventionell diskret händelse simulator uppfyller inte med ökande krav på beräkningsprogram eller strukturella komplexiteten av moderna system som peer-to-peer (P2P) system, och därför parallellt diskret händelse simulering har varit ett fokus för forskare under flera årtionde. Tyvärr ingen simulator ansåg som en standard som kan uppfylla kraven på alla typer av applikationer. Så samtidigt få en simulator ger bra prestanda för en specifik typ av applikationer kan det inte vara effektivt för andra typer av applikationer. Även om tekniska framsteget har gjorts i multi-core datorhårdvara, är ingen av de vanliga P2P händelsestyrd simulatorer för att stödja parallella simulering som utnyttjar flera kärnor arkitekturer. Den föreslagna HydraNetSim parallella diskret händelse simulator (PDES) är ett steg mot att fokusera på dessa frågor. Utveckla en simulator som kan stödja ett mycket stort antal noder för att realisera en massiv P2P-system, och kan även utföra parallellt är en icke-trivial uppdrag. Litteraturstudien i denna tesen ger en bred översikt över aktuell metoder för att hantera de svåra problem som simulerar en massiv, stor och snabbt ändra system och ger en grund för att adoptera en passande struktur för att utveckla ett PDES. HydraNetSim är en diskret händelse simulator som gör det möjligt parallellt simulering och utnyttjar funktionerna i parallellisering av modern datorhårdvara. Det är baserat på en ny master / slav paradigm. Den delar simuleringsmodellen i ett antal specifika slavar (ett kluster av processer) med tanke på antalet kärnor som tillhandahålls av den underliggande datorhårdvara. Varje slav kan tilldelas en specifik CPU på en annan kärna. Synkronisering av slavarna uppnås genom att föreslå en variant av det klassiska Null-Message Algorithm (NMA) med fokus på att hålla simuleringen overhead så lågt som möjligt. Dessutom ger HydraNetSim log information för felsökning ändamål och inför en ny mekanism för att samla in och skriva simuleringar resultat till en databas. De experimentella resultaten visar att den sekventiella motsvarigheten till HydraNetSim (SDES) tar 41,6% mer tid än HydraNetSim-2Slave och 23,6% mindre än HydraNetSim-3Slave. HydraNetSim-2Slave är 1,42 gånger snabbare, förbrukar 1,18 gånger mer minne, och stöder 2.02 gånger fler noder än en sekventiell händelsestyrd simulator (SDES). I HydraNetSim-3Slave kör 1.24 gånger snabbare, förbrukar 2,08 gånger mer minne, och stöder 3,04 gånger fler noder än SDES. Skalfaktorn av HydraNetSim är ⌈(β-1)*102.04%⌉ av den maximala numrerade noder som stöds av SDES; där β är antalet slavar.

No description available.

No description available.

No description available.

No description available.