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“One video fit for all”
(2023)
Online learning in mathematics has always been challenging, especially for mathematics in STEM education. This paper presents how to make “one fit for all” lecture videos for mathematics in STEM education. In general, we do believe that there is no such thing as “one fit for all” video. The curriculum requires a high level of prior knowledge in mathematics from high school to get a good understanding, and the variation of prior knowledge levels among STEM education students is often high. This creates challenges for both online teaching and on-campus teaching. This article presents experimenting and researching on a video format where students can get a real-time feeling, and which fits their needs regarding their existing prior knowledge. They have the possibility to ask and receive answers during the video without having to feel that they must jump into different sources, which helps to reduce unnecessary distractions. The fundamental video format presented here is that of dynamic branching videos, which has to little degree been researched in education related studies. The reason might be that this field is quite new for higher education, and there is relatively high requirement on the video editing skills from the teachers’ side considering the platforms that are available so far. The videos are implemented for engineering students who take the Linear Algebra course at the Norwegian University of Science and Technology in spring 2023. Feedback from the students gathered via anonymous surveys so far (N = 21) is very positive. With the high suitability for online teaching, this video format might lead the trend of online learning in the future. The design and implementation of dynamic videos in mathematics in higher education was presented for the first time at the EMOOCs conference 2023.
We present fully polynomial time approximation schemes for a broad class of Holant problems with complex edge weights, which we call Holant polynomials. We transform these problems into partition functions of abstract combinatorial structures known as polymers in statistical physics. Our method involves establishing zero-free regions for the partition functions of polymer models and using the most significant terms of the cluster expansion to approximate them. Results of our technique include new approximation and sampling algorithms for a diverse class of Holant polynomials in the low-temperature regime (i.e. small external field) and approximation algorithms for general Holant problems with small signature weights. Additionally, we give randomised approximation and sampling algorithms with faster running times for more restrictive classes. Finally, we improve the known zero-free regions for a perfect matching polynomial.
xMOOCs
(2023)
The World Health Organization designed OpenWHO.org to provide an inclusive and accessible online environment to equip learners across the globe with critical up-to-date information and to be able to effectively protect themselves in health emergencies. The platform thus focuses on the eXtended Massive Open Online Course (xMOOC) modality – contentfocused and expert-driven, one-to-many modelled, and self-paced for scalable learning. In this paper, we describe how OpenWHO utilized xMOOCs to reach mass audiences during the COVID-19 pandemic; the paper specifically examines the accessibility, language inclusivity and adaptability of hosted xMOOCs. As of February 2023, OpenWHO had 7.5 million enrolments across 200 xMOOCs on health emergency, epidemic, pandemic and other public health topics available across 65 languages, including 46 courses targeted for the COVID-19 pandemic. Our results suggest that the xMOOC modality allowed OpenWHO to expand learning during the pandemic to previously underrepresented groups, including women, participants ages 70 and older, and learners younger than age 20. The OpenWHO use case shows that xMOOCs should be considered when there is a need for massive knowledge transfer in health emergency situations, yet the approach should be context-specific according to the type of health emergency, targeted population and region. Our evidence also supports previous calls to put intervention elements that contribute to removing barriers to access at the core of learning and health information dissemination. Equity must be the fundamental principle and organizing criteria for public health work.
Modern server systems with large NUMA architectures necessitate (i) data being distributed over the available computing nodes and (ii) NUMA-aware query processing to enable effective parallel processing in database systems. As these architectures incur significant latency and throughout penalties for accessing non-local data, queries should be executed as close as possible to the data. To further increase both performance and efficiency, data that is not relevant for the query result should be skipped as early as possible. One way to achieve this goal is horizontal partitioning to improve static partition pruning. As part of our ongoing work on workload-driven partitioning, we have implemented a recent approach called aggressive data skipping and extended it to handle both analytical as well as transactional access patterns. In this paper, we evaluate this approach with the workload and data of a production enterprise system of a Global 2000 company. The results show that over 80% of all tuples can be skipped in average while the resulting partitioning schemata are surprisingly stable over time.
Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming
(2019)
In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark.
With the recent growth of sensors, cloud computing handles the data processing of many applications. Processing some of this data on the cloud raises, however, many concerns regarding, e.g., privacy, latency, or single points of failure. Alternatively, thanks to the development of embedded systems, smart wireless devices can share their computation capacity, creating a local wireless cloud for in-network processing. In this context, the processing of an application is divided into smaller jobs so that a device can run one or more jobs.
The contribution of this thesis to this scenario is divided into three parts. In part one, I focus on wireless aspects, such as power control and interference management, for deciding which jobs to run on which node and how to route data between nodes. Hence, I formulate optimization problems and develop heuristic and meta-heuristic algorithms to allocate wireless and computation resources. Additionally, to deal with multiple applications competing for these resources, I develop a reinforcement learning (RL) admission controller to decide which application should be admitted. Next, I look into acoustic applications to improve wireless throughput by using microphone clock synchronization to synchronize wireless transmissions.
In the second part, I jointly work with colleagues from the acoustic processing field to optimize both network and application (i.e., acoustic) qualities. My contribution focuses on the network part, where I study the relation between acoustic and network qualities when selecting a subset of microphones for collecting audio data or selecting a subset of optional jobs for processing these data; too many microphones or too many jobs can lessen quality by unnecessary delays. Hence, I develop RL solutions to select the subset of microphones under network constraints when the speaker is moving while still providing good acoustic quality. Furthermore, I show that autonomous vehicles carrying microphones improve the acoustic qualities of different applications. Accordingly, I develop RL solutions (single and multi-agent ones) for controlling these vehicles.
In the third part, I close the gap between theory and practice. I describe the features of my open-source framework used as a proof of concept for wireless in-network processing. Next, I demonstrate how to run some algorithms developed by colleagues from acoustic processing using my framework. I also use the framework for studying in-network delays (wireless and processing) using different distributions of jobs and network topologies.
Clustering in education is important in identifying groups of objects in order to find linked patterns of correlations in educational datasets. As such, MOOCs provide a rich source of educational datasets which enable a wide selection of options to carry out clustering and an opportunity for cohort analyses. In this experience paper, five research studies on clustering in MOOCs are reviewed, drawing out several reasonings, methods, and students’ clusters that reflect certain kinds of learning behaviours. The collection of the varied clusters shows that each study identifies and defines clusters according to distinctive engagement patterns. Implications and a summary are provided at the end of the paper.
First come, first served: Critical choices between alternative actions are often made based on events external to an organization, and reacting promptly to their occurrence can be a major advantage over the competition. In Business Process Management (BPM), such deferred choices can be expressed in process models, and they are an important aspect of process engines. Blockchain-based process execution approaches are no exception to this, but are severely limited by the inherent properties of the platform: The isolated environment prevents direct access to external entities and data, and the non-continual runtime based entirely on atomic transactions impedes the monitoring and detection of events. In this paper we provide an in-depth examination of the semantics of deferred choice, and transfer them to environments such as the blockchain. We introduce and compare several oracle architectures able to satisfy certain requirements, and show that they can be implemented using state-of-the-art blockchain technology.
Which event happened first?
(2021)
First come, first served: Critical choices between alternative actions are often made based on events external to an organization, and reacting promptly to their occurrence can be a major advantage over the competition. In Business Process Management (BPM), such deferred choices can be expressed in process models, and they are an important aspect of process engines. Blockchain-based process execution approaches are no exception to this, but are severely limited by the inherent properties of the platform: The isolated environment prevents direct access to external entities and data, and the non-continual runtime based entirely on atomic transactions impedes the monitoring and detection of events. In this paper we provide an in-depth examination of the semantics of deferred choice, and transfer them to environments such as the blockchain. We introduce and compare several oracle architectures able to satisfy certain requirements, and show that they can be implemented using state-of-the-art blockchain technology.
What Stays in Mind?
(2018)
In an effort to describe and produce different formats for video instruction, the research community in technology-enhanced learning, and MOOC scholars in particular, have focused on the general style of video production: whether it is a digitally scripted “talk-and-chalk” or a “talking head” version of a learning unit. Since these production styles include various sub-elements, this paper deconstructs the inherited elements of video production in the context of educational live-streams. Using over 700 videos – both from synchronous and asynchronous modalities of large video-based platforms (YouTube and Twitch), 92 features were found in eight categories of video production. These include commonly analyzed features such as the use of green screen and a visible instructor, but also less studied features such as social media connections and changing camera perspective depending on the topic being covered. Overall, the research results enable an analysis of common video production styles and a toolbox for categorizing new formats – independent of their final (a)synchronous use in MOOCs. Keywords: video production, MOOC video styles, live-streaming.
The goal of this paper is to study the demand factors driving enrollment in massive open online courses. Using course level data from a French MOOC platform, we study the course, teacher and institution related characteristics that influence the enrollment decision of students, in a setting where enrollment is open to all students without administrative barriers. Coverage from social and traditional media done around the course is a key driver. In addition, the language of instruction and the (estimated) amount of work needed to complete the course also have a significant impact. The data also suggests that the presence of same-side externalities is limited. Finally, preferences of national and of international students tend to differ on several dimensions.
Virtual 3D city models represent and integrate a variety of spatial data and georeferenced data related to urban areas. With the help of improved remote-sensing technology, official 3D cadastral data, open data or geodata crowdsourcing, the quantity and availability of such data are constantly expanding and its quality is ever improving for many major cities and metropolitan regions. There are numerous fields of applications for such data, including city planning and development, environmental analysis and simulation, disaster and risk management, navigation systems, and interactive city maps.
The dissemination and the interactive use of virtual 3D city models represent key technical functionality required by nearly all corresponding systems, services, and applications. The size and complexity of virtual 3D city models, their management, their handling, and especially their visualization represent challenging tasks. For example, mobile applications can hardly handle these models due to their massive data volume and data heterogeneity. Therefore, the efficient usage of all computational resources (e.g., storage, processing power, main memory, and graphics hardware, etc.) is a key requirement for software engineering in this field. Common approaches are based on complex clients that require the 3D model data (e.g., 3D meshes and 2D textures) to be transferred to them and that then render those received 3D models. However, these applications have to implement most stages of the visualization pipeline on client side. Thus, as high-quality 3D rendering processes strongly depend on locally available computer graphics resources, software engineering faces the challenge of building robust cross-platform client implementations.
Web-based provisioning aims at providing a service-oriented software architecture that consists of tailored functional components for building web-based and mobile applications that manage and visualize virtual 3D city models. This thesis presents corresponding concepts and techniques for web-based provisioning of virtual 3D city models. In particular, it introduces services that allow us to efficiently build applications for virtual 3D city models based on a fine-grained service concept. The thesis covers five main areas:
1. A Service-Based Concept for Image-Based Provisioning of
Virtual 3D City Models It creates a frame for a broad range of services related to the rendering and image-based dissemination of virtual 3D city models.
2. 3D Rendering Service for Virtual 3D City Models This service provides efficient, high-quality 3D rendering functionality for virtual 3D city models. In particular, it copes with requirements such as standardized data formats, massive model texturing, detailed 3D geometry, access to associated feature data, and non-assumed frame-to-frame coherence for parallel service requests. In addition, it supports thematic and artistic styling based on an expandable graphics effects library.
3. Layered Map Service for Virtual 3D City Models It generates a map-like representation of virtual 3D city models using an oblique view. It provides high visual quality, fast initial loading times, simple map-based interaction and feature data access. Based on a configurable client framework, mobile and web-based applications for virtual 3D city models can be created easily.
4. Video Service for Virtual 3D City Models It creates and synthesizes videos from virtual 3D city models. Without requiring client-side 3D rendering capabilities, users can create camera paths by a map-based user interface, configure scene contents, styling, image overlays, text overlays, and their transitions. The service significantly reduces the manual effort typically required to produce such videos. The videos can automatically be updated when the underlying data changes.
5. Service-Based Camera Interaction It supports task-based 3D camera interactions, which can be integrated seamlessly into service-based visualization applications. It is demonstrated how to build such web-based interactive applications for virtual 3D city models using this camera service.
These contributions provide a framework for design, implementation, and deployment of future web-based applications, systems, and services for virtual 3D city models. The approach shows how to decompose the complex, monolithic functionality of current 3D geovisualization systems into independently designed, implemented, and operated service- oriented units. In that sense, this thesis also contributes to microservice architectures for 3D geovisualization systems—a key challenge of today’s IT systems engineering to build scalable IT solutions.
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
Founded in 2013, OpenClassrooms is a French online learning company that offers both paid courses and free MOOCs on a wide range of topics, including computer science and education. In 2021, in partnership with the EDA research unit, OpenClassrooms shared a database to solve the problem of how to increase persistence in their paid courses, which consist of a series of MOOCs and human mentoring. Our statistical analysis aims to identify reasons for dropouts that are due to the course design rather than demographic predictors or external factors.We aim to identify at-risk students, i.e. those who are on the verge of dropping out at a specific moment. To achieve this, we use learning analytics to characterize student behavior. We conducted data analysis on a sample of data related to the “Web Designers” and “Instructional Design” courses. By visualizing the student flow and constructing speed and acceleration predictors, we can identify which parts of the course need to be calibrated and when particular attention should be paid to these at-risk students.
Virtualizing physical space
(2021)
The true cost for virtual reality is not the hardware, but the physical space it requires, as a one-to-one mapping of physical space to virtual space allows for the most immersive way of navigating in virtual reality. Such “real-walking” requires physical space to be of the same size and the same shape of the virtual world represented. This generally prevents real-walking applications from running on any space that they were not designed for.
To reduce virtual reality’s demand for physical space, creators of such applications let users navigate virtual space by means of a treadmill, altered mappings of physical to virtual space, hand-held controllers, or gesture-based techniques. While all of these solutions succeed at reducing virtual reality’s demand for physical space, none of them reach the same level of immersion that real-walking provides.
Our approach is to virtualize physical space: instead of accessing physical space directly, we allow applications to express their need for space in an abstract way, which our software systems then map to the physical space available. We allow real-walking applications to run in spaces of different size, different shape, and in spaces containing different physical objects. We also allow users immersed in different virtual environments to share the same space.
Our systems achieve this by using a tracking volume-independent representation of real-walking experiences — a graph structure that expresses the spatial and logical relationships between virtual locations, virtual elements contained within those locations, and user interactions with those elements. When run in a specific physical space, this graph representation is used to define a custom mapping of the elements of the virtual reality application and the physical space by parsing the graph using a constraint solver. To re-use space, our system splits virtual scenes and overlap virtual geometry. The system derives this split by means of hierarchically clustering of our virtual objects as nodes of our bi-partite directed graph that represents the logical ordering of events of the experience. We let applications express their demands for physical space and use pre-emptive scheduling between applications to have them share space. We present several application examples enabled by our system. They all enable real-walking, despite being mapped to physical spaces of different size and shape, containing different physical objects or other users.
We see substantial real-world impact in our systems. Today’s commercial virtual reality applications are generally designing to be navigated using less immersive solutions, as this allows them to be operated on any tracking volume. While this is a commercial necessity for the developers, it misses out on the higher immersion offered by real-walking. We let developers overcome this hurdle by allowing experiences to bring real-walking to any tracking volume, thus potentially bringing real-walking to consumers.
Die eigentlichen Kosten für Virtual Reality Anwendungen entstehen nicht primär durch die erforderliche Hardware, sondern durch die Nutzung von physischem Raum, da die eins-zu-eins Abbildung von physischem auf virtuellem Raum die immersivste Art von Navigation ermöglicht. Dieses als „Real-Walking“ bezeichnete Erlebnis erfordert hinsichtlich Größe und Form eine Entsprechung von physischem Raum und virtueller Welt. Resultierend daraus können Real-Walking-Anwendungen nicht an Orten angewandt werden, für die sie nicht entwickelt wurden.
Um den Bedarf an physischem Raum zu reduzieren, lassen Entwickler von Virtual Reality-Anwendungen ihre Nutzer auf verschiedene Arten navigieren, etwa mit Hilfe eines Laufbandes, verfälschten Abbildungen von physischem zu virtuellem Raum, Handheld-Controllern oder gestenbasierten Techniken. All diese Lösungen reduzieren zwar den Bedarf an physischem Raum, erreichen jedoch nicht denselben Grad an Immersion, den Real-Walking bietet.
Unser Ansatz zielt darauf, physischen Raum zu virtualisieren: Anstatt auf den physischen Raum direkt zuzugreifen, lassen wir Anwendungen ihren Raumbedarf auf abstrakte Weise formulieren, den unsere Softwaresysteme anschließend auf den verfügbaren physischen Raum abbilden. Dadurch ermöglichen wir Real-Walking-Anwendungen Räume mit unterschiedlichen Größen und Formen und Räume, die unterschiedliche physische Objekte enthalten, zu nutzen. Wir ermöglichen auch die zeitgleiche Nutzung desselben Raums durch mehrere Nutzer verschiedener Real-Walking-Anwendungen.
Unsere Systeme erreichen dieses Resultat durch eine Repräsentation von Real-Walking-Erfahrungen, die unabhängig sind vom gegebenen Trackingvolumen – eine Graphenstruktur, die die räumlichen und logischen Beziehungen zwischen virtuellen Orten, den virtuellen Elementen innerhalb dieser Orte, und Benutzerinteraktionen mit diesen Elementen, ausdrückt. Bei der Instanziierung der Anwendung in einem bestimmten physischen Raum wird diese Graphenstruktur und ein Constraint Solver verwendet, um eine individuelle Abbildung der virtuellen Elemente auf den physischen Raum zu erreichen. Zur mehrmaligen Verwendung des Raumes teilt unser System virtuelle Szenen und überlagert virtuelle Geometrie. Das System leitet diese Aufteilung anhand eines hierarchischen Clusterings unserer virtuellen Objekte ab, die als Knoten unseres bi-partiten, gerichteten Graphen die logische Reihenfolge aller Ereignisse repräsentieren. Wir verwenden präemptives Scheduling zwischen den Anwendungen für die zeitgleiche Nutzung von physischem Raum. Wir stellen mehrere Anwendungsbeispiele vor, die Real-Walking ermöglichen – in physischen Räumen mit unterschiedlicher Größe und Form, die verschiedene physische Objekte oder weitere Nutzer enthalten.
Wir sehen in unseren Systemen substantielles Potential. Heutige Virtual Reality-Anwendungen sind bisher zwar so konzipiert, dass sie auf einem beliebigen Trackingvolumen betrieben werden können, aber aus kommerzieller Notwendigkeit kein Real-Walking beinhalten. Damit entgeht Entwicklern die Gelegenheit eine höhere Immersion herzustellen. Indem wir es ermöglichen, Real-Walking auf jedes Trackingvolumen zu bringen, geben wir Entwicklern die Möglichkeit Real-Walking zu ihren Nutzern zu bringen.
Dynamic resource management is an essential requirement for private and public cloud computing environments. With dynamic resource management, the physical resources assignment to the cloud virtual resources depends on the actual need of the applications or the running services, which enhances the cloud physical resources utilization and reduces the offered services cost. In addition, the virtual resources can be moved across different physical resources in the cloud environment without an obvious impact on the running applications or services production. This means that the availability of the running services and applications in the cloud is independent on the hardware resources including the servers, switches and storage failures. This increases the reliability of using cloud services compared to the classical data-centers environments.
In this thesis we briefly discuss the dynamic resource management topic and then deeply focus on live migration as the definition of the compute resource dynamic management. Live migration is a commonly used and an essential feature in cloud and virtual data-centers environments. Cloud computing load balance, power saving and fault tolerance features are all dependent on live migration to optimize the virtual and physical resources usage. As we will discuss in this thesis, live migration shows many benefits to cloud and virtual data-centers environments, however the cost of live migration can not be ignored. Live migration cost includes the migration time, downtime, network overhead, power consumption increases and CPU overhead.
IT admins run virtual machines live migrations without an idea about the migration cost. So, resources bottlenecks, higher migration cost and migration failures might happen. The first problem that we discuss in this thesis is how to model the cost of the virtual machines live migration. Secondly, we investigate how to make use of machine learning techniques to help the cloud admins getting an estimation of this cost before initiating the migration for one of multiple virtual machines. Also, we discuss the optimal timing for a specific virtual machine before live migration to another server. Finally, we propose practical solutions that can be used by the cloud admins to be integrated with the cloud administration portals to answer the raised research questions above.
Our research methodology to achieve the project objectives is to propose empirical models based on using VMware test-beds with different benchmarks tools. Then we make use of the machine learning techniques to propose a prediction approach for virtual machines live migration cost. Timing optimization for live migration is also proposed in this thesis based on using the cost prediction and data-centers network utilization prediction. Live migration with persistent memory clusters is also discussed at the end of the thesis. The cost prediction and timing optimization techniques proposed in this thesis could be practically integrated with VMware vSphere cluster portal such that the IT admins can now use the cost prediction feature and timing optimization option before proceeding with a virtual machine live migration.
Testing results show that our proposed approach for VMs live migration cost prediction shows acceptable results with less than 20% prediction error and can be easily implemented and integrated with VMware vSphere as an example of a commonly used resource management portal for virtual data-centers and private cloud environments. The results show that using our proposed VMs migration timing optimization technique also could save up to 51% of migration time of the VMs migration time for memory intensive workloads and up to 27% of the migration time for network intensive workloads. This timing optimization technique can be useful for network admins to save migration time with utilizing higher network rate and higher probability of success.
At the end of this thesis, we discuss the persistent memory technology as a new trend in servers memory technology. Persistent memory modes of operation and configurations are discussed in detail to explain how live migration works between servers with different memory configuration set up. Then, we build a VMware cluster with persistent memory inside server and also with DRAM only servers to show the live migration cost difference between the VMs with DRAM only versus the VMs with persistent memory inside.
Viper
(2021)
Key-value stores (KVSs) have found wide application in modern software systems. For persistence, their data resides in slow secondary storage, which requires KVSs to employ various techniques to increase their read and write performance from and to the underlying medium. Emerging persistent memory (PMem) technologies offer data persistence at close-to-DRAM speed, making them a promising alternative to classical disk-based storage. However, simply drop-in replacing existing storage with PMem does not yield good results, as block-based access behaves differently in PMem than on disk and ignores PMem's byte addressability, layout, and unique performance characteristics. In this paper, we propose three PMem-specific access patterns and implement them in a hybrid PMem-DRAM KVS called Viper. We employ a DRAM-based hash index and a PMem-aware storage layout to utilize the random-write speed of DRAM and efficient sequential-write performance PMem. Our evaluation shows that Viper significantly outperforms existing KVSs for core KVS operations while providing full data persistence. Moreover, Viper outperforms existing PMem-only, hybrid, and disk-based KVSs by 4-18x for write workloads, while matching or surpassing their get performance.