3,756 research outputs found
Exploring the inner mechanisms of 5G networks for orchestrating container-based applications in edge data centers
One of the novel new features of mobile 5G networks is what is commonly known as "Ultra Reliable Low Latency" communication.
To achieve the "Low Latency" part, it is necessary to introduce processing and storage capabilities closer to the radio access network, thus introducing Edge data centers.
An Edge data center will be capable of hosting third-party applications and a user of these applications can access them using the cellular mobile network.
This makes the network path between the user equipment (UE) and the application short in terms of physical distance and network hops, thus reducing the latency dramatically.
This thesis looks into these new features of the 5th-generation mobile networks to establish if, and how they can be used to orchestrate container-based applications deployed at edge data centers.
The orchestration mechanism suggested will be described in more detail in the thesis body but as an overview, it involves using the user's positions and the knowledge about which applications the users are accessing and information about where these applications reside to move applications between edge data centers.
One of the 5G exploration findings was that the location of users in a 5G network can be determined using the Network Exposure Function (NEF) API.
The NEF is one of the new 5G network functions and enables trusted third-party actors to interact with the 5G core through a publisher-subscriber-oriented API.
The proposed orchestration strategy involves calculating the ``weighted average location'' of 5G users who have accessed the specific application residing in the Edge within a specified time frame.
A live 5G network with a stand-alone (SA) core was not available at the time of writing and part of the thesis work has therefore been to identify if there exist network emulators with the functionality needed to reach the goal of this thesis, i.e. design and implement the orchestrator based on interaction with the network.
More specifically: can we find a NEF emulator that can be configured to give us network data related to user equipment location? Unfortunately, the three alternatives considered: Open5Gs, NEF\_emulator, and Nokia's Open5Glab do not fully meet our requirements for generating user events.
Open5Gs an open source 5G network implementation lacks the whole NEF north-bridge implementation, NEF\_emulator has limited implementation and integration complexities, and Nokia's Open5Glab's simulated users are inactive and thus do not generate sufficient data.
Given the absence of suitable emulators to generate the needed data, the thesis pivoted to also include the design and implementation of a mobile network emulator with the following key components: a mobile network abstraction that encompasses crucial elements from 5G, such as users and radio access nodes, allowing users to connect to the mobile network; a network abstraction that hosts emulated edge data centers and the corresponding applications accessible to connected users; and mobile network exposure that exposes mobile network core events through a simplified NEF north-bound API implementation.
Finally, the thesis concludes by implementing the proposed orchestration strategy using the mobile network emulator, demonstrating that orchestrating can effectively reduce the end-to-end latency from users to applications, as evidenced by the obtained results
Adaptive Microarchitectural Optimizations to Improve Performance and Security of Multi-Core Architectures
With the current technological barriers, microarchitectural optimizations are increasingly important to ensure performance scalability of computing systems. The shift to multi-core architectures increases the demands on the memory system, and amplifies the role of microarchitectural optimizations in performance improvement. In a multi-core system, microarchitectural resources are usually shared, such as the cache, to maximize utilization but sharing can also lead to contention and lower performance. This can be mitigated through partitioning of shared caches.However, microarchitectural optimizations which were assumed to be fundamentally secure for a long time, can be used in side-channel attacks to exploit secrets, as cryptographic keys. Timing-based side-channels exploit predictable timing variations due to the interaction with microarchitectural optimizations during program execution. Going forward, there is a strong need to be able to leverage microarchitectural optimizations for performance without compromising security. This thesis contributes with three adaptive microarchitectural resource management optimizations to improve security and/or\ua0performance\ua0of multi-core architectures\ua0and a systematization-of-knowledge of timing-based side-channel attacks.\ua0We observe that to achieve high-performance cache partitioning in a multi-core system\ua0three requirements need to be met: i) fine-granularity of partitions, ii) locality-aware placement and iii) frequent changes. These requirements lead to\ua0high overheads for current centralized partitioning solutions, especially as the number of cores in the\ua0system increases. To address this problem, we present an adaptive and scalable cache partitioning solution (DELTA) using a distributed and asynchronous allocation algorithm. The\ua0allocations occur through core-to-core challenges, where applications with larger performance benefit will gain cache capacity. The\ua0solution is implementable in hardware, due to low computational complexity, and can scale to large core counts.According to our analysis, better performance can be achieved by coordination of multiple optimizations for different resources, e.g., off-chip bandwidth and cache, but is challenging due to the increased number of possible allocations which need to be evaluated.\ua0Based on these observations, we present a solution (CBP) for coordinated management of the optimizations: cache partitioning, bandwidth partitioning and prefetching.\ua0Efficient allocations, considering the inter-resource interactions and trade-offs, are achieved using local resource managers to limit the solution space.The continuously growing number of\ua0side-channel attacks leveraging\ua0microarchitectural optimizations prompts us to review attacks and defenses to understand the vulnerabilities of different microarchitectural optimizations. We identify the four root causes of timing-based side-channel attacks: determinism, sharing, access violation\ua0and information flow.\ua0Our key insight is that eliminating any of the exploited root causes, in any of the attack steps, is enough to provide protection.\ua0Based on our framework, we present a systematization of the attacks and defenses on a wide range of microarchitectural optimizations, which highlights their key similarities.\ua0Shared caches are an attractive attack surface for side-channel attacks, while defenses need to be efficient since the cache is crucial for performance.\ua0To address this issue, we present an adaptive and scalable cache partitioning solution (SCALE) for protection against cache side-channel attacks. The solution leverages randomness,\ua0and provides quantifiable and information theoretic security guarantees using differential privacy. The solution closes the performance gap to a state-of-the-art non-secure allocation policy for a mix of secure and non-secure applications
Mobility classification of cattle with micro-Doppler radar
Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification.
The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17).
The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data
Security Elites in Egypt and Jordan after the Arab Spring : A Case Study on Securocracies’ Role on National Security, Domestic Power Politics, Regional Order and Middle Eastern Alliance Making between 2011 and 2021
The doctoral dissertation studied changes in the balance of power, alliance making and the hegemonic struggles of security elites within a Middle Eastern regional context over a ten year reference period between 2011 and 2021. The study focused on two case study countries: Egypt and Jordan. The results were compared within a historical context to the pre-Arab Spring era. The theoretical approach combined the English School of Thought and Middle Eastern Studies with a conceptual model of securocracy developed by the author.
The primary contribution of the research is the realization of the core importance of securocracy within autocratic state systems. Inside securocracies there exists rivalling groups and organisations that counterbalance each other. The study points to the fact that the power struggle between executive powers – either purely domestic one or supported by foreign involvement, is the main driver behind why case study countries were in varying degrees dragged into instability and turmoil in the aftermath of the Arab Spring. Securocracies can be divided into two main types: centralised and decentralised. The centralised model occurs when different elites groups have the same ”distance” to the ruler while having equal privileges and equal access to political power. The model predicts durability and stability of the regime (status quo). In the de-centralised model, there is an ongoing struggle amongst elite groups and “distances” to ruler are not equal, neither are the privileges.
In Egypt the hegemonic struggle amongst elites took precedence over the interests and stability of the state after the Arab Spring and has continued since then. The situation at the end of 2021 is a de-centralised model where all executive powers are concentrated within President al-Sisi’s family dynasty (palace) and the leadership of military intelligence. This de-centralised type of securocracy makes Egypt’s situation fragile. Any impact from the outside, such as the Biden administration’s decision to impose additional conditions on U.S. financial military aid, could lead to a new hegemonic struggle challenging al-Sisi’s power. The securocracy’s survival strategy found in the study was the use of vertical power at all levels of the state hierarchy (” the winner takes it all”). In the situation of a power struggle, the ruler uses omni-balancing i.e., alliance making with powerful foreign states in order to gain an advantage against domestic rivals and revisionist regional states. The Egyptian example is al-Sisi’s rapprochement with Russia’s President Putin and his distancing of Egypt from its previous role of being the United States’ loyal Middle Eastern ally. The Jordanian example however, is the opposite, resulting in even closer relations with the United States since January 2021 when the two countries signed an updated Status of Forces Agreement (SOFA).
The study also highlights that decisions concerning ruler succession in authoritarian states take place behind-the-scenes amongst the securocracy as it, per rule, prefers to choose a member inside its own interest group or alternatively a political figurehead that commits to protect securocracy’s privileged interests in exchange of their own power position.
Within the Middle East, the recent U.S. pivot to Asia-Pacific created an opportunity for Russia to make a come-back. Russia, however, does not have the resources to compensate for the loss of U.S. financial military aid to the security elites. This in turn, and with Russia’s consent, has given space for regional state actors, particularly, the United Arab Emirates and Saudi-Arabia, to increase their influence. Gulf support to the regional clients is not free of charge: they request their clients adopt their own threat perceptions, take sides in armed conflicts and contribute to military capabilities which support the sponsors’ regional foreign and security policy goals.Väitöstutkimuksessa tarkasteltiin kymmenen vuoden ajanjaksolla voimatasapainon muutosta Lähi-idän alueellisessa valtarakenteessa, liittolaissuhteiden muutoksia sekä turvallisuuseliittien roolia maan sisäisessä valtataistelussa. Tuloksia verrattiin historiallisessa kontekstissa arabikevättä edeltävään aikaan kahdessa tapaustutkimusmaassa: Egyptissä ja Jordaniassa. Teoriaviitekehyksenä sovellettiin Englantilaisen koulukunnan ja Lähi-idän tutkimuksen teoriamalleja, sekä tutkijan kehittelemää sekurokratian konseptuaalista mallia. Tutkimuksen keskeinen tulos on havainto sekurokratian merkittävästä roolista osana autoritaarista valtiomallia. Sekurokratian sisälle on luotu useita toinen toistaan tasapainottavia ryhmittymiä. Tapaustutkimusmaiden arabikevään jälkeisen turvallisuustilanteen muutoksia selittävien tekijöiden joukossa turvallisuuseliittien valtakamppailu nousi merkittävään rooliin. Valtakamppailua käytiin eliittien kesken joko pelkästään maan sisällä tai vaihtoehtoisesti osin myös valtion ulkopuolisten voimien tukemana. Tutkimuksen perusteella sekurokratiat voidaan jakaa kahteen päätyyppiin: keskitettyyn ja hajautettuun malliin. Jos eri turvallisuuseliitti-ryhmien edut, vallankäyttö ja etäisyys vallan keskipisteeseen ovat tasapainossa puhutaan keskitetyn sekurokratian mallista, mikä ennustaa vallassa olevan regiimin pysyvyyttä ja vakautta. Jos taas sekurokratian rakenne on hajautetun mallin mukainen, sen valtakamppailu voi johtaa yhden osan pyrkimyksiin hegemonia-asemasta.
Egyptin tapauksessa arabikevään jälkeinen turvallisuuseliittien valtakamppailu asetettiin maan vakauden edelle ja eliittien valtakamppailu on jatkunut tähän päivään. Tilanteessa vuoden 2021 lopussa valta on al-Sisin perhedynastialla ja sotilastiedustelun eliitillä (hajautettu malli). Hajautettu malli ei ennusta pitkäaikaista vallassa pysymistä; vahva ulkopuolinen heräte, esimerkiksi Bidenin hallinnon sotilaallisen talousavun lopettaminen voisi johtaa uuteen valtakamppailuun ja al-Sisin valta-aseman haastamiseen. Tutkimustulokset osoittavat, että sekurokratoiden selviytymisstrategiana on vallanvertikaalin käyttö valtiohallinnon eri tasoilla. Valtakamppailun tilanteessa käytetään tasapainotusstrategiaa (omni-balancing), missä alueellisia vahvoja valtioita ja suurvaltoja pyritään yhdistämään hallitsijan puolelle kilpailevia eliittiryhmittymiä tai revisionistisia ulkovaltoja vastaan. Egyptissä presidentti al-Sisin valtaannousu johti maan lähentymiseen presidentti Putinin Venäjän kanssa sekä etääntymiseen aiemmasta Yhdysvalloille uskollisen Lähi-idän liittolaisen roolista. Jordaniassa puolestaan maa on nyt entistä tiiviimmin liittoutunut Yhdysvaltojen kanssa. Esimerkkinä tästä on tammikuussa 2021 maiden kesken solmittu sotilasyhteistyötä ja jordanialaisten tukikohtien käyttöä säätelevä isäntämaatuki-sopimus.
Tutkimustulosten valossa autoritaariselle vallanperimykselle tyypillistä on se, että julkisuuteen näkymättömän sisäisen valtakamppailun jälkeen uudeksi valtionpäämieheksi pyritään nostamaan sekurokratian sisältä sen oman intressiryhmän edustaja, tai vaihtoehtoisesti sekurokratian valitsema ulkopuolinen poliitikko, jonka vastuulle korporaation intressien vaaliminen lankeaa vastapalveluksena sekurokratian tuesta keulakuva-poliitikon vallassa pitämiseksi. Alueellisen turvallisuusjärjestyksen osalta tutkimuksen tulokset osoittavat sen, että Yhdysvaltojen painopisteen siirto Tyynellemerelle vii ja Aasiaan on antanut Venäjälle mahdollisuuden palauttaa vaikutusvaltaansa Lähiitään. Venäjällä ei kuitenkaan ole resursseja kompensoida Yhdysvaltojen arabivaltioiden turvallisuuseliiteille allokoimaa taloudellista tukea. Tämä on antanut tilaa alueellisten toimijoiden kuten Yhdistyneiden arabiemiirikuntien ja Saudi-Arabian vaikutusvallan kasvattamiselle - tosin Venäjän hyväksynnällä. Tuki ei myöskään tule ilmaiseksi, sillä sponsorit edellyttävät, että niille alisteisessa asemassa olevat maat omaksuvat tukijavaltioidensa uhkakuvat, sekä konfliktitilanteissa kontribuoivat sotilaallisia kyvykkyyksiä näiden valtioiden ulko- ja turvallisuuspoliittisten päämäärien saavuttamiseksi
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
An integrated computational and collaborative approach for city resilience planning
Given the rise in climate change-related extreme events, there is an urgent need for cities and regions to implement resilience plans based on data and evidence and developed in collaboration with key stakeholders. However, current planning and decision-making processes rely on limited data and modelling. Moreover, stakeholder engagement is significantly inhibited by social, political, and technological barriers. The research presented in this thesis aims to enhance resilience planning practice through the development and evaluation of an integrated computational and collaborative scenario planning approach.
The scenario planning approach is tested within a geodesign framework and supported by several planning support systems (PSS), including urban growth models. These PSS tools are made accessible to key stakeholders through dedicated planning support theatres, enabling participants to collaborate both in-person and online. Through two empirical case studies conducted in Australian regions, this research integrates data-driven modelling (computational) with people-led geodesign (collaborative) approaches for scenario forecasting and planning. The first case study explores anticipatory/normative scenarios, while the second focuses on exploratory scenario planning, with both aiming to enhance city and regional resilience.
This thesis examines the roles played by both simple digital tools and purpose-built planning support theatres in scenario planning processes with key stakeholders. The research investigates the utility of data-driven models in supporting collaborative scenario planning. Both integration experiments received positive feedback from most participants. However, to truly improve the process, there is a need for widely available high-quality spatial and temporal datasets, including localised climate change impact data. In summary, an integrated computational and collaborative approach, augmented by data and technology, can provide an evidence base for decision-making towards a resilient future, fostering deeper engagement of the local community and across-government collaboration in scenario planning
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