This paper investigates strategies to limit the cost of switching the cache in the context of an optimistic discrete caching problem. We have chosen as a starting point the current state-of-the-art in optimistic discrete caching, the Optimistic Follow-The-Perturbed-Leader (OFTPL) algorithm. We propose two strategies that attempt to limit this cost. The first approach sets a lower bound for the perturbation factor, thus including a mandatory amount of noise in the optimization of the cache state. The second approach investigates dynamically updating the perturbation factor to increase the switching cost, hence allowing the algorithm to adapt to the scenario at hand. Therefore, to the best of our knowledge, we design the first optimistic discrete caching algorithm that attempts to optimize the switching cost. Finally, we experiment and evaluate our solutions while highlighting their strengths and limitations.

This paper presents an adaptive per-file caching policy designed to dynamically adjust caching decisions based on the importance of the requested files. It relies on the Online Gradient Ascent (OGA) algorithm, which treats the caching problem as an online optimization problem. This methodology ensures minimal regret by continuously optimizing caching configurations in response to real-time request sequences. The caching configurations are optimized after every request using a constant learning rate. Because the trends of requested files can change, we will introduce two new algorithms that change the learning rate at every request to increase the adaptability. We will present two new algorithms, the Universally Adaptive Caching (UAC) algorithm and the Adaptive Per File Caching Algorithm (APFC), and will present scenarios to highlight their performances.

In recent years, the novel framing of the caching problem as an Online Convex Optimisation (OCO) problem has led to the introduction of several online caching policies. These policies are proven optimal with regard to regret for any arbitrary request pattern, including that of adversarial origin. Nevertheless, their performance is primarily affected by the tuning of their so-called hyperparameters (e.g. learning rate), which is often done under the presumption of adversarial conditions. Consequently, as not all request patterns encountered in practice are adversarial, this suggests potential for further improvements. Unfortunately, the tuning of these hyperparameters to achieve optimal performance across non-adversarial request sequences remains dubious and poses a new challenge. This paper proposes an online meta-learner that combines several instances of the OGA caching policy, each distinct in their learning rate, framing the problem as an expert advice decision problem. The proposed meta-learner dynamically shifts between caching experts and achieves a regret upper bound similar to that of the best-performing caching expert. The penalty introduced for learning the best expert is limited to a logarithmic dependence on the number of experts, which improves upon previous works. Numerical evaluations composed of synthetic request traces demonstrate consistent improvements when the caching experts' relative performance varies over time.

With the strong demand for computing capacity in industrial applications and the rapid development of the hardware industry in recent years, multiprocessor platforms have been widely used in real-time embedded systems. The quest for performance has led to existing multiprocessor platforms often featuring complex interconnected hardware components and multiple levels of cache. This brings negative interference to the execution of tasks and challenges the predictability of real-time systems. However, existing non-preemptive execution models are an effective solution to eliminate these negative effects. In addition to this, the tasks that modern real-time systems process are becoming increasingly complex. Traditional sequential task models cannot cope with this situation and a stronger expressive model is required. A directed acyclic graph (DAG) that can express the complexity and parallelism of these tasks is a suitable model.

In this thesis, we focus on priority-based scheduling algorithms for multiple parallel DAG tasks on non-preemptive multiprocessor platforms, investigating, analysing, and improving existing global and partitioned scheduling algorithms.

We propose the concept of stacks to simulate a multi-processor platform and apply release-time tuning techniques based on its simulation of a task set in a hyperperiod. This allows us to impose tighter constraints on the execution of each job released from a task set in a fine-grained manner. We improved the existing priority-based global scheduling algorithm through the release-time tuning technique mentioned above. We also tried to construct a partitioned scheduling algorithm using the simulation results of stacks, i.e. so that all jobs are restricted to run on only one processor, consistent with the simulation results. Furthermore, to compare different scheduling algorithms more efficiently and to facilitate future researchers, we have developed the evaluation framework, a customisable experimental platform that includes DAG generation, application of scheduling algorithms, generation and analysis of the test results, allowing users to specify their experiments according to their goals.

Experiments with randomly generated workloads show that our improved algorithm consistently outperforms the state-of-the-art priority-based scheduling algorithm for different task graph structures and parameter configurations.

This paper explores algorithms to optimize networked caching, where requests for files can be handled by a local cache instead of a remote server. Caches work collaboratively to prevent redundant caching, and each new batch of file requests is used to update the entire network. Data is cached in an integral manner, meaning that only discrete files or chunks can be stored, not fractions of them. Bipartite networks are studied, though the proposed model supports arbitrary network topologies. The approach is based upon an Online Mirror Descent (OMD) policy, which has been shown to have sublinear regret in single cache scenarios.

Caching is a widely relevant problem in the world of ever-growing online traffic. In recent years, on-line learning methods have inspired algorithms that outperform more traditional, widely used policies such as LRU and LFU. Furthermore, some of these newly proposed policies have proven upper regret bounds, offering guarantees on arbitrary request se-quences. In this paper, we inspect one such policy named Follow the Perturbed Leader (FTPL), which we found to perform reasonably well among dif-ferent traces but showcases poor ability to adapt to changing file popularities. We propose a modifi-cation to the algorithm which addresses this issue, and although it comes with no performance guar-antees, we leverage the expert problem to maintain such a guarantee. More precisely, we utilize dif-ferent configurations of FTPL, including the one with a tight upper bound, as experts in an Incre-mentally Adaptive Weighted Majority (IAWM) al-gorithm. Our findings include simulation results on the MovieLens trace, as well as multiple syn-thetic traces, some with adversarial properties. We record these in a setting where a single user is con-nected to a single cache, which is then connected to a server, but also in a bipartite network where mul-tiple clients execute traces against multiple caches.

The advent of wireless networks such as content distribution networks and edge computing networks calls for more effective online caching policies. Traditional policies lose performance since these new networks deal with highly non-stationary requests and frequent popularity shifts. Consequently, a new framework called Online Convex Optimization (OCO), which does not assume the request pattern, has recently been used to tackle the online caching problem. Besides, in many practical scenarios, a request prediction of unknown quality is available. This paper will leverage that and proposes a new online caching policy that uses these predictions. This policy will use the Optimistic Online Mirror Descent (OOMD) algorithm to solve the OCO problem. The policy will still obtain the same regret bound as its non-optimistic counterpart up to some constant even if the predictions are not accurate. The performance of the proposed policy is evaluated and compared with previous OCO-based policies with the use of trace-driven numerical tests.

The increasing global food demand, accompanied by the decreasing number of expert growers, brings the need for more sustainable and efficient solutions in horticulture. Consultancy company Delphy aims to face this challenge by taking a more data-driven approach, by means of autonomous growing inside the greenhouse. The controlled environment of greenhouses enable data collection and precise control. Delphy's current solutions focus on the needs of the crop, but a grower also needs to consider the economic aspect of taking certain decisions on the greenhouse climate. A potential method for solving this complex problem is Constraint Programming (CP). In this work, the applicability of CP for the greenhouse economic optimal control problem will be studied. The contributions of this work are threefold; First, the greenhouse climate is modelled with Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) machine learning models. Secondly, an LSTM model is embedded into a CP model. Lastly, the profit of the grower is optimised through this CP decision support system (DSS). The performed experiments show that both types of time-based machine learning models can model greenhouse temperature and humidity deficit with reasonable accuracy, while light and CO₂ are harder to predict. The correctness of the LSTM-in-CP embedding is validated. The implemented DSS is not yet practically applicable, as the search space is too large to come to reasonable results for realistic instances. For small instances however, the DSS is able to improve the decisions of the grower, demonstrating the potential of using CP for economic greenhouse decision making.

In recent years, researchers proposed several universal caching policies. These universal caching policies aim to work well with any request sequence. However, with this universal well-working property, these caching policies sometimes do not work as well as conventional caching policies such as Least-Recently-Used and Least-Frequently-Used. This thesis introduces an online learning approach to dynamically switch between caching policies. The dynamical switching selects the most suitable caching policy for the request sequence and thus ensures the best of both worlds, working well under any request sequence and having comparable performance to the conventional caching policies. The Dynamic Expert Caching (DEC) policy designed in this thesis uses algorithms from the expert selection problem to achieve the dynamic caching policy selection. The DEC policy is evaluated using various datasets where it is shown to have comparable performance to the best caching policies in the simulations. The DEC policy is also adapted to work in a network of caches, named MultiDEC. In this setting, MultiDEC has significant advantages over other universal caching policies where each caching node can have a different caching policy that is optimal.

Binary Neural Networks (BNNs) are receiving an upsurge of attention for bringing power-hungry deep learning towards edge devices. The traditional wisdom in this space is to employ sign(.) for binarizing featuremaps. We argue and illustrate that sign(.) is a uniqueness bottleneck, limiting information propagation throughout the network. To alleviate this, we propose to dispense sign(.), replacing it with a learnable activation binarizer (LAB), allowing the network to learn a fine-grained binarization kernel per layer - as opposed to global thresholding. LAB is a novel universal module that can seamlessly be integrated into existing architectures. To confirm this, we plug it into four seminal BNNs and show a considerable performance boost at the cost of tolerable increase in delay and complexity. Finally, we build an end-to-end BNN (coined as LAB-BNN) around LAB, and demonstrate that it achieves competitive performance on par with the state-of-the-art on ImageNet

Web-based interaction logging is an important concept for understanding user behavior on web-pages. LogUI is a powerful modern framework for logging a user's interactions. Integrating such a framework in web-pages requires the construction of configuration objects to define selectors that indicate which elements on a web-page should be under observation. Writing such configuration object can be a tedious task for researchers who are less experienced in programming. Therefore, this paper addresses the potential of a graphical user interface (GUI) to simplify the creation of configuration objects. An experimental GUI-based tool was devised to aid a user in the process of producing configuration objects in an interactive fashion. Results from conducting a small scale user study show that users are capable of creating configurations utilizing the GUI-based tool with an average accuracy of 70% measured in terms of selector equivalence. User experience evaluation shows that the tool is perceived as efficient.

Cross Browser Inconsistencies (XBI) were created when different browser vendors implemented their products without deciding upon common protocols for interoperability. It is hard to pinpoint these inconsistencies with precision because of a lack of a good tool. Here we show how to use a web-based event logger (LogUI) to find XBI at a granular level. LogUI attaches itself to the DOM of a webpage and makes a log file based on how the browser re- sponds to user interactions. A test suite is made by simulating user interactions (Selenium WebDriver) to test different browsers and interaction events (individually and in sequences) to generate log files which are then analyzed to spot differences in the actions performed and entries logged. It is found that XBI are few and hard to find, the two XBI found are that browsers load differently and sometimes change the order of actions being performed.