Thread pools, integrated in programming languages, packages and dependencies are widely used by developers. Thread pools assume they are running alone on the system, which is not always the case. Previous research has shown that adapting thread pool size has been effective under specific conditions. In this research, scaling the thread pool with respect to CPU and network usage is examined. However, simpler metrics can achieve better results. Two solutions are provided for algorithmically scaling the thread pool, both with a different use-case in mind. Next to that an improved way to collect CPU and network metrics is provided, allowing for real-time thread-based measurements. The result of the scaling solution is improved performance, while also offering reduced energy usage. This research shows that when multiple thread pools are running on the same machine, performance and efficiency is improved.

Cloud gaming is a new paradigm that allows users to play games in the cloud and stream them to a thin client. While there is little research about cloud gaming, containerization technologies such as Docker could provide a virtualization alternative to Virtual Machines, as these suffer from overhead and GPU pass-through constraints. With Rapture, we provide an efficient cloud gaming platform based on containerization. Stable GPU multi-tenancy is achieved by resource restrictions such as frame rate and resolution limiting.
A resource-aware best-fit scheduling algorithm accomplishes workload placement in the cloud gaming cluster. The best-fit algorithm outperforms other scheduling algorithms in node utilization and preservation of Quality of Experience. Furthermore, Checkpoint and Restore technologies enable migration, maintaining a high node utilization when down-scaling services. While migration improves efficiency, Quality of Experience is affected.

Generative Adversarial Networks (GANs) can create artwork images and we need effective ways of rating their aesthetic values. This could help us determine the most aesthetic artwork images (and identify the GANs that created them) and train GANs to produce more aesthetic artwork images in the future. In this research, we analyzed the effectiveness of using two different survey formats (binary-choice and four-choice) for displaying GAN-produced artwork images to crowd- sourced workers and gathering their ratings. The artwork images were of different landscapes like the desert, arctic, coastal regions, etc. Additionally, we investigated how the choice of showing different images together (image groupings) per question affects the final rating results. Results demonstrate that the four-choice format is superior to the binary-choice format in producing more consistent, reliable, and accurate results. The effects of the different image groupings were insignif- icant for the results of the four-choice format. In contrast, different image groupings displayed statistically significant changes in the results for the binary-choice format. However, it was found that crowdsourced workers preferred the binary-choice format more as they found it to be less strenuous and more effective in allowing them to express their rating choices.

This paper aims to evaluate which automated measures of aesthetic beauty are the best predictors for human ratings of aesthetics and proposes that typicality and novelty may increase the correlation between the two. To study the correlation between these metrics, a literature study was performed to find a select amount of potentially good predictors, a pipeline was created to extract these values from each image within our dataset, a survey was conducted to vote for which images were considered most aesthetic, and finally regression analysis was performed to see which metrics offered highest correlation with the human rating data. From this we could see there were indeed a number of automated metrics that consistently scored high as predictors for the human aesthetic ratings and there was a slight improvement in the fit of the prediction model upon including novelty as a feature. However, at this moment, the improvement is not significant to conclude these features are better at predicting human ratings.

Despite the fact that climate change is becoming increasingly dangerous and prevalent, there is still a lack of public engagement. This can be explained by the fact that the media portrays climate change as an abstract concept. The message can be more effectively communicated through visual art because it is more likely to invoke emotional responses in individuals. By including human perception and rating data, the generative adversarial neural network (GAN) produces better image output. Therefore, this paper explores methods for using the human perception of beauty in order to improve StyleGAN2 outputs. In GANAesthetic, UI sliders allow users to explore satellite images interactively, that is, visually appealing satellite images generated from StyleGAN2. The GANAesthetic was determined to be the most appropriate methodology for the study. The choice of GANAesthetic over other approaches will be explained in this paper, as well as its implementation. The paper will also describe an experiment to discover aesthetic latent dimensions.

Generative Adversarial Networks (GANs) brought rapid developments in generating synthetic images by mimicking structures in the training data. With the list of application of GANs growing drastically, it has lately become an exciting technology to explore for designers to communicate their ideas and arts through technology and create engaging experiences for humans. Nevertheless, translating human experiences to artificial intelligence and creating visually pleasant imagery is a challenging task due to complex semantics of human perception. To address this issue, we introduce an iterative training approach in which the generated images are curated by humans and the most pleasing ones are fed back into the network to retrain. Additionally, we do a factorial analysis to investigate how the aesthetic quality and the diversity are affected by the size of training data and training length. In experiments, we validate that this method can significantly improve the aesthetic quality of generated images regardless of the dataset size and training length, however the use of smaller datasets comes with a cost of reduction in the image diversity and novelty in the output images. The aesthetic bias towards certain contexts can also deteriorate the diversity and affect the model evaluations. On the other hand, no significant relationship has been found regarding the training length, however this could possibly be due to instabilities that happen during the model convergence progress.

The democratization of data science, and in particular of the machine learning pipeline, has focused on the automation of model selection, feature processing, and hyperparameter tuning. Nevertheless, the need for high-quality data for increased performance has sparked interest in the inclusion of data augmentation in these automatic machine learning techniques. This research approaches this topic by examining different feature selection techniques that will ultimately allow devising what makes a feature desirable. We introduce an automatic data augmentation process, tailored for support vector machines, that employs sample joins. This approach is evaluated through different setups, datasets, and other machine learning models: CART, random forests, and XGBoost. The results are mixed: the algorithm identifies the features containing the signal, resulting in accuracy scores close to the models trained with all the data. However, the computational time is higher. A theoretical analysis suggest that the methodology might be helpful in particular cases where data is structured in specific ways.

Machine learning models require rich, quality data sets to achieve high accuracy. With current exponential growth of data being generated it is becoming increasingly hard to prepare high-quality tables within reasonable time frame. To combat this issue automated data augmentation methods has emerged in recent years. However, existing solution do not focus on specific ML algorithm used for training the data.

In this paper we propose data augmentation framework designed specifically for the random forest classifier. The algorithm uses sample joins to estimate partial correlation between features in the neighbouring tables and the target column, while controlling for all other features.

Moreover, we show that partial correlation is the most optimal characteristic for determining features’ importance for random forest classifier. Apart from it, we demonstrate hat PCADA can improve accuracy and run-time in comparison with other baseline data augmentation approaches.

Finally, we show that the framework can also be used for other decision trees classifiers (CART, XGBoost) and linear classifier (Support Vector Machine).

Automatic machine learning is a subfield of machine learning that automates the common procedures faced in predictive tasks. The problem of one such procedure is automatic data augmentation, where one desires to enrich the existing data to increase model performance. In relational data repositories, the data is stored in normal form. This causes problems, since joining all tables and subsequently performing feature selection is highly inefficient. This paper provides AFAR, an approach to efficiently and effectively perform automated feature augmentation by ranking candidate joins in a data repository. Additionally, an experimental evaluation that validates the approach’s capabilities, is presented.

Cloud datacenters underpin our increasingly digital society, serving stakeholders across industry, government, and academia. These stakeholders have come to expect reliable operation and high quality of service, yet demand low cost, high scalability, and corporate (environmental) responsibility. Datacenter operators are confronted frequently with highly complex decisions that involve numerous aspects of risk. The consequence of bad decisions can be financial penalties or even loss of customers on the one hand, or a competitive disadvantage or unsustainable environmental impact on the other hand. Despite risk analysis being an integral part of the design and operation of cloud infrastructure, relatively few comprehensive approaches and tools exist, leaving many datacenter operators ill-equipped to make informed decisions with confidence.

We propose Radice, an instrument for data-driven analysis of IT-related operational risks in sustainable cloud datacenters. Unlike most state-of-the-art approaches used by the industry, Radice automates the process of risk analysis in datacenters and utilizes the large and diverse volume of data reported by the monitoring systems in datacenters, including environmental data. Underpinning this system is the trace-based, discrete-event simulator OpenDC, which enables the exploration of many risk scenarios through its support for diverse workloads, datacenter topologies, and operational phenomena. Radice’s interactive and explorative user interface assists datacenter operators in addressing complex decisions involving risks, providing them with actionable insights, automated visualizations, and suggestions to reduce risk.

We implement Radice and conduct a comprehensive evaluation of the system to demonstrate how it can aid datacenter operators when confronted with fundamental risk trade-offs. Although Radice is designed to work across many kinds of datacenters, in this work, we focus on private-cloud, business-critical workloads, and on public-cloud operations, representing the majority of workloads in Dutch datacenters. Our experiments show many interesting findings, supporting our claim for a need for data-driven risk analysis in datacenters. We highlight the increasing risk faced by datacenter operators due to price surges in the electricity and CO2 bond markets, and demonstrate how Radice can be used to control such risks. We further show that Radice can automatically optimize topology and operational settings in datacenters for risk, revealing configurations that reduce the overall risk by 10%–30%. Following extensive performance engineering, Radice is able to evaluate risk scenarios by a factor 70x–330x faster than others, opening possibilities for interactive risk exploration. We release Radice as free and open-source software for the community to inspect and re-use.

With the advent of the cloud-native paradigm, the software development and deployment style has significantly reformed. An increasing number of enterprises are migrating their microservice applications onto Kubernetes, a production-grade container orchestration platform, to fully use the advantages of Kubernetes in self-healing, service discovery, auto-scaling, etc. Kubernetes introduces a host of novel software design ideas but also poses challenges to newcomers. In this thesis, we focus on the cold start problem in microservice auto-scaling on Kubernetes. A cold start refers to a situation where services receive overwhelming requests from the outside but has no sufficient backend containers to serve. As the auto-scaling process is launching slow, the cold start problem may elongate response latency or even cause service unavailable in a more severe case. To study and mitigate the adverse effect of cold start, we pose three research questions.

The first research question is to identify what factors contribute to the cold start. We first learn lessons from research on cold start in Serverless and examine the current service auto-scaling process powered by Horizontal Pod Autoscaler (HPA), then suggest five hypothetical factors that may affect cold-start performance. Our findings indicate that reactive autoscalers like HPA are dilatory in determining scale events and lack a coordination mechanism to scale correlated workloads in sync. Besides, different programming languages differ in code loading and execution time. The major factors lie in slow scale decision making and application code loading. In contrast, other factors such as cluster size, the choice of container runtimes and CNI network solutions are negligible.

In the next two research questions, we investigate how to mitigate cold start and how effective the proposed solutions are. Based on our study from the first research question, we conclude that extra efforts must be made to speed up scaling decision-making and reduce application startup time. We compare a few technical solutions to the hypothetical factors. According to the results, we encourage microservice application developers, especially Java developers, to try and test different programming language stacks, preferably Go, for rapid auto-scaling. In addition, we propose a coordinated horizontal pod autoscaler, named CHPA, a supplement to HPA, allowing a group of identified, correlated scale targets to scale out in sync, rather than each making independent scaling decisions. As a result, no laggard pod in the auto-scaling process, thereby mitigating the cold start problem holistically.

Serverless computing is an emerging paradigm for structuring applications in such a way that they can benefit from on-demand computing resources and achieve horizontal scalability. As such, it is an ideal substrate for the resource-intensive and often ad-hoc task of training deep learning models. However, the design and stateless nature of serverless platforms make it difficult to translate distributed machine learning systems directly to this new world. With KubeML, we present a purpose-built serverless machine learning system that runs atop Kubernetes and seamlessly embeds into the popular PyTorch framework. Unlike alternative systems, KubeML fully embraces GPU acceleration and is able to outperform TensorFlow, especially with smaller local batches, while allowing for higher resource density. KubeML reaches a 3.98x faster time-to-accuracy with small batch sizes, and maintains a 2.02x speedup between the top results of both platforms for commonly benchmarked machine learning models like ResNet34.

Thread pools are a pervasive building block for concurrent applications, but their optimal size is often tedious to determine or it changes during execution. Many modern systems use dedicated thread pools for operations that are restricted to a specific resource (e.g IO-bound), their performance can be correlated to OS metrics such as disk throughput. We propose an OS-level adaptive scheme for disk-IO workloads that can act as drop-in replacement for such use cases. Our approach often performs close to optimally tuned fixed-size pools, while being prone to fluctuations in the chosen target metric. The results warrant further exploration of more sophisticated OS metrics and the development of more tightly integrated global in-kernel implementations.

Cloud datacenters provide a backbone to our digital society. Crucial to meeting increasing demand while maintaining efficient operation is the activity of capacity planning. Inaccurate capacity planning for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist, leaving many planners with merely rule-of-thumb judgement.

We propose Capelin, a data-driven, scenario-based capacity planning system for cloud datacenters. We design Capelin to address requirements we have derived from a unique survey of experts in charge of diverse datacenters in several countries. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. The approach centers around a notion of portfolios of scenarios as a framework for probing alternative decisions and courses of events. Capelin gives detailed quantitative operational information for each scenario, which could facilitate human decisions in capacity planning.

We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. Although Capelin is designed to work across many kinds of datacenters, in this work we focus on private-cloud, business-critical workloads, and on public-cloud operations. The results give evidence that choices that seem reasonable and common in practice could be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption. We also show evidence of Capelin identifying meaningful choices that are different from the baseline proposed by a team of professional datacenter engineers. We open-source Capelin and release data artifacts for public inspection and reuse.

Edge computing extends the cloud computing capabilities to the edge of the network to facilitate processing of the data in the close proximity of its generation. It augments the deployment of several applications in the Internet of Things (IoT) domain which demand low latency and near real-time response for their reliable operation. However, the existing approaches that help accomplish this are inherently static and suffice only for scenarios offering a fixed service at the edge. Thus, rendering them infeasible for a large scale IoT scenario with several heterogeneous services and end users such as a Smart City. Among the several challenges in realizing edge computing solutions for such a scenario, the impact of incorporating key aspects of the interactions of the IoT devices and the clients with the edge, remains to be explored. These interactions translate to the ease of use which is vital to determining the degree of adoption of an edge computing infrastructure by the end users and subsequently its profitability for the service provider. This thesis presents EDIRO, which is an edge-driven distributed orchestration framework for edge computing that enables the edge to drive the workload orchestration through the collaboration of multiple constituent edge nodes. It takes into account the existence of on-demand and ephemeral nature of workloads that require an input, i.e., an IoT resource such as an image or sensor data for their execution. These IoT resources are sourced from the end users and the IoT devices in the vicinity. Recent studies that highlight the idea of collaborative processing and the simultaneous presence of data producers and consumers in an IoT ecosystem, support this vision. The underlying concept in EDIRO is the utilization of such IoT resources that are contributed by the end users in the vicinity, to carry out service orchestration for the client requests. To the best of author’s knowledge, this is the first work in the edge computing domain to conceptualize the idea of edge-driven distributed orchestration and implement a proof of concept followed by its evaluation and practical feasibility analysis. The main contribution of this thesis is the edge computing orchestration framework EDIRO, which is developed in Golang and released as open source to encourage collaboration with the community. Experiments are conducted on three different types of computing devices emulating edge nodes in a field scenario to determine the practical feasibility of EDIRO. The measurements include the time to serve a client request, the overhead due to the distributed orchestration approach and the computing resource utilization under bursty and normal traffic scenarios. The evaluation suggests that EDIRO is feasible for practical IoT use cases and provides a reasonable trade-off in terms of the benefits offered by this edge-driven approach and the overhead incurred. This thesis shares valuable insights into the ways in which this work opens up the scope of further research in this domain along with the key findings from the system development and experimentation phase.

Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices,especially for image-based analysis, e.g., identifying objects, faces, and genders. While very successful in resource rich environments like the cloud of powerful computers, utilizing Deep Learning on edge devices for inference is not used to great extend. The resource constraints of edge devices present bottlenecks that prevent all but small networks from on-device inference. Existing solutions like compression or off-loading to the cloud sacrifice either model accuracy or inference latency. To counter the aforementioned shortcomings, Masa and EdgeCaffe are proposed as a solution. The process of DNN inference by existing frameworks is to loaded and execute a model in its entirety. As this ignores the resource limitations that are inherent to edge devices, a new framework EdgeCaffe is proposed. EdgeCaffe is able to execute models partially and thereby reducing the memory footprint of the model. Additional, EdgeCaffe allows for coordination between models when a multi-model DNN inference is executed. Masa , a memory-aware multi-DNN scheduling algorithm, featuring on modeling inter-and intra-network dependency and leveraging complimentary memory usage of each layer. Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analysis. With extensive evaluations, Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. We extensively evaluate Masa on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different arrival patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 8× and 16× for deterministic and stochastic arrivals respectively on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.

Given the increasing popularity of Machine Learning, and the ever increasing need to solve larger and more complex learning challenges, it is unsurprising that numerous distributed learning strategies have been brought forward in recent years, along with many large scale Machine Learning frameworks. It is however unclear how well these strategies perform across different cluster and batch sizes, or what their hardware demands are, as there is little research in the public domain on this matter. Identifying the weaknesses and limitations of the parameter update strategies is, however, essential towards increasing the efficiency of large scale Machine Learning and making it commonplace. This thesis seeks to find the answers to these aforementioned issues, and provide evidence of the strategies’ limitations and the root causes behind them. To make the study possible, the thesis looks into particular implementations of the strategies within the TensorFlow and Caffe2 frameworks.

A network of devices capable of transmitting quantum information called a quantum network has promising applications with vast benefits. One of the most near term achievable application is the quantum key distribution. It could be used for sharing a secret key between two end-users through any insecure authenticated communication channel. Other than sharing a secret key, a quantum network can be used for connecting quantum computers. Quantum computers have the potential to solve computational problems that their classical counterparts would require significantly more time to do so. By connecting such devices, distributed computation problems such as leader election or distributed consensus between nodes can be solved securely.

For such applications to be put into practice on a large scale, there are open practical questions still to be answered. In a quantum network, a data qubit containing quantum information is transmitted by an operation called quantum teleportation. For it, two nodes need to share entanglement. Quantum teleportation then ensures the safe transmission of a qubit by using the shared entanglement and the exchange of two classical bits. This operation, however, consumes entanglement between the nodes, changing the network topology with each served request.

In quantum networks, certain nodes share entanglement between each other. Routing in quantum networks entails determining which of these shared entanglements are used to transmit quantum information. As this proves to be a difficult task, we study quantum networks and in particular consider the problem of routing entanglement in quantum networks.

The average latency for routing entanglement in a quantum network has been studied so far using a distributed routing approach. Thus, we present numerical simulation results of centralised routing for the average latency of demands in two existing entanglement generation models. In the first, shared entanglement between nodes is created on-demand. In the second, certain nodes pre-share entanglement before the demand comes. Our results show the intuition that using a centralised routing approach in the on-demand model results in drastically more average latency than in the model with pre-shared entanglement.

Since some nodes might not be informed about the change in topology, we study the effect of the propagation of information about the network topology to nodes in the network. Our observations based on simulation results show that the average latency is significantly higher if the information about the topology is not propagated well in the network. As propagating information to all nodes in a network would be a significant load for the network, we consider information propagation within a radius and still observe a considerable decrease in average latency.

At last, we present a technique called link prediction which can be performed by any node without the need for information propagation and still achieve a considerable decrease in average latency. It can be effectively used to predict the change of topology in a quantum network by using knowledge about the network topology for a certain future point in time and previous knowledge about the network traffic.

Simultaneous Localization And Mapping (SLAM) algorithms provide accurate localization for autonomous vehicles and provide essential information for the path planning module. However, SLAM algorithms as- sume a static environment in order to estimate a location. This assumption influences the pose estimation in dynamic urban environments. The impact of this assumption on day-to-day scenarios of an intelligent vehicle is unknown. A deeper understanding on the effect of dynamic scenarios in an urban environment could lead to simple and robust solutions for SLAM algorithms in intelligent vehicles. The objective of this research is to develop a methodology that isolates the effect of an urban dynamic environment on the per- formance of a SLAM algorithm. This requires constant environment conditions including constant weather conditions, lighting conditions and identical trajectories over time. The methodology is tested with a stereo feature based V-SLAM algorithm called ORB SLAM [19], which illustrates the in-depth analysis that is possi- ble with this experiment. The main research question is: How does a dynamic urban environment influence the pose estimation accuracy of stereo ORB SLAM? Two specific dynamic scenarios are designed to represent a dynamic urban environment: driving behind another vehicle and vehicles approaching on the other side of the road. On these scenarios, an in-depth anal- ysis of ORB SLAM is performed to observe how the algorithm’s design influences the robustness to a dynamic environment. Functions within the algorithm are bypassed to analyze the effect on the performance. Specifi- cally, the place recognition function and map point filtering function are bypassed. The analysis proofs which functions assist in the overall robustness to a dynamic environment. Moreover, an analysis is performed of the algorithm in localization mode to research the effect of utilizing maps that were created under different conditions. The knowledge gained from the full analysis can be utilized to improve other V-SLAM algorithms. The experiment is performed in CARLA [6], an open source simulator. CARLA provides an elaborate sen- sor suite which support multiple camera setups and LIDAR sensors. Furthermore, the simulator provides free maps which represent realistic urban environments and allows for easy and accurate access to the ground truth position. A setup is designed with the simulator that allows complete isolation of the effect of a dy- namic environment. The setup allows full control of lighting conditions, weather conditions and allows iden- tical trajectories over time in different dynamic scenarios. Each scenario is simulated over several different trajectories in which the camera images are converted to rosbags. Each variation of the ORB SLAM algorithm is tested on the produced rosbags. The resulting pose estimations in dynamic conditions are compared to the pose estimations made during static conditions to analyze the effect of dynamic scenarios on the perfor- mance of the algorithm. The method successfully isolated the effect of a dynamic environment on the performance of stereo ORB SLAM. It allows for a detailed analysis which aids in finding the source of performance differences. In general, stereo ORB SLAM displays robust behavior to a dynamic environment. The experiment shows that the algo- rithm is sensitive to false relocalization when the stereo camera setup is driving 10 meters behind another vehicle for a long period of time. During these conditions, ORB SLAM cannot provide accurate pose esti- mations even when the place recognition module is deactivated. Furthermore, the map point filtering does increase the robustness in certain dynamic scenarios. Finally, the data suggests that utilizing maps created in different conditions does influence the pose estimation in localization mode. However, more data is needed to confirm these results. The methodology has proven its value for in depth analysis of robustness to an urban dynamic environ- ment for a SLAM algorithm. This experiment is not limited to ORB SLAM but could be utilized for other monocular and stereo V-SLAM methods, as well as LIDAR based methods. New solutions can be developed to increase robustness to a dynamic environment and tested on the same rosbags. This methodology could be an important tool for the development SLAM algorithms for intelligent vehicles.

Payment Channel Networks(PCN) utilize payment channels with an established link capacity between two nodes to route transactions over multiple links to carry out transactions. Such transactions can support a blockchain due to the transactions happening off-chain, i.e., not requiring any information to be published to a ledger. PCNs can help aid in the scalability of blockchains, by moving transactions off-chain not all transactions need to be stored on the blockchain, reducing the amount of data that needs to be stored on the blockchain. Lightning is the PCN implementation that makes use of Bitcoins blockchain. As transactions occur over the network the capacity of the link may vary over time between two nodes. This change may lead to the link being only available from one side. If enough links become unavailable then processing transactions may take longer or in the worst-case scenario transactions may no longer be feasible in the network. To help avoid these short-comings in PCN strategies can be designed in path-based transaction algorithms to help keep links capable of handling transactions bidirectionally. This work presents two such algorithms, the Passive Merchant and Active Merchant. Additionally, two synthetic data-set models are proposed to help evaluate the effectiveness of the Merchant algorithms, due to a lack of data-sets in this field. In the evaluation two different topologies are examined to evaluate the impact a graph has on the success rate of transactions within a PCN. The evaluation of the The Merchant algorithms is simulation based, experiments evaluated how different algorithms effected the success rate of transactions. The simulation did indicate that the algorithms were able to help increase the success rate of transactions, up to 8%. As these algorithms are embedded in the transaction process of a payment the algorithms are a first of there kind, other solutions have been proposed for rebalancing as a separate protocol. In addition to being the first to propose transaction embedded rebalancing algorithms, no other synthetic data-set models for PCN have been proposed. The synthetic data-set models may allow this area of research to be have constant data-sets that are used to evaluate the effectiveness of path-based transactions.