DECENTRALIZED RESOURCE ORCHESTRATION FOR HETEROGENEOUS GRIDS

Modern desktop machines now use multi-core CPUs to enable improved performance. However, achieving high performance on multi-core machines without optimized software support is still difficult even in a single machine, because contention for shared resources can make it hard to exploit multiple computing resources efficiently. Moreover, more diverse and heterogeneous hardware platforms (e.g. general-purpose GPU and Cell processors) have emerged and begun to impact grid computing. Given that heterogeneity and diversity are now a major trend going forward, grid computing must support these environmental changes. In this dissertation, I design and evaluate a decentralized resource management scheme to exploit heterogeneous multiple computing resources effectively. I suggest resource management algorithms that can efficiently utilize a diverse computational environment, including multiple symmetric computing entities and heterogeneous multi-computing entities, and achieve good load-balancing and high total system throughput. Moreover, I propose expressive resource description techniques to accommodate more heterogeneous environments, allowing incoming jobs with complex requirements to be matched to available resources. First, I develop decentralized resource management frameworks and job scheduling schemes to exploit multi-core nodes in peer-to-peer grids. I present two new load-balancing schemes that explicitly account for resource sharing and contention across multiple cores within a single machine, and propose a simple performance prediction model that can represent a continuum of resource sharing among cores of a CPU. Second, I provide scalable resource discovery and load balancing techniques to accommodate nodes with many types of computing elements, such as multi-core CPUs and GPUs, in a peer-to-peer grid architecture. My scheme takes into account diverse aspects of heterogeneous nodes to maximize overall system throughput as well as minimize messaging costs without sacrificing the failure resilience provided by an underlying peer-to-peer overlay network. Finally, I propose an expressive resource discovery method to support multi-attribute, range-based job constraints. The common approach of using simple attribute indexes does not suffice, as range-based constraints may be satisfied by more than a single value. I design a compact ID-based representation for resource characteristics, and integrate this representation into the decentralized resource discovery framework. By extensive experimental results via simulation, I show that my schemes can match heterogeneous jobs to heterogeneous resources both effectively (good matches are found, load is balanced), and efficiently (the new functionality imposes little overhead)

Why Is CryptoKitties (Not) Gambling?

CryptoKitties (Axiom Zen 2017) is a pioneering blockchain-based game that disrupts the ‘classic game model’ (Juul 2003; 2013) in a way that turns it into a gambling web application. As previous research has shown, its mechanics are almost exclusively based on chance (Scholten et al 2019), and the rest is mostly speculation with game assets (Lee, Yoo and Jang 2019). This raises the question whether this game requires any skill, such as strategic planning. In my case study, I revisit the game system and perform a practice of playing it to differentiate between unpredictable (or “aleatory”, as in Johnson 2018) and decision-making points in the game. I argue that mapping the journey of a player should complement analysis of the game system to assess the balance between skill and chance in a better-informed manner.© Alesja Serada. ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FDG '20 : International Conference on the Foundations of Digital Games, https://doi.org/10.1145/3402942.3402985.fi=vertaisarvioitu|en=peerReviewed

GPU-OSDDA: A Bit-Vector GPU-based Deadlock Detection Algorithm for Single-Unit Resource Systems

This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm ma- trices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents

Flexible ultraviolet and ambient light sensor based on nanomaterial network fabricated by using selective and localized wet-chemical reactions

We report ZnO nanowire- and TiO_2 nanotube-based light sensors on flexible polymer substrates fabricated by localized hydrothermal synthesis and liquid phase deposition (LPD). This method realized simple and cost-effective in situ synthesis and integration of one-dimensional ZnO and TiO_2 nanomaterials. The fabricated sensor devices with ZnO nanowires and TiO_2 nanotubes show very high sensitivity and quick response to the ultraviolet (UV) and ambient light, respectively. In addition, our direct synthesis and integration method result in mechanical robustness under external loading such as static and cyclic bending because of the strong bonding between the nanomaterial and the electrode. By controlling the reaction time of the LPD process, the Ti/Zn ratio could be simply modulated and the spectral sensitivity to the light in the UV to visible range could be controlled

Embedded workbench application of GPS sensor for agricultural tractor.

This paper presents a design of an embedded workbench application of Global Positioning System (GPS) for agricultural tractor. Electronic Control Unit (ECU) is Global Positioning System (GPS) sensor using IAR (IAR Embedded Workbench) and an open source library which follows the most important characteristics of International Organization for Standardization (ISO) 11783 communication protocol in the serial communication network of agricultural vehicles. These applications are written in C/C++ programming methods. We explain some test connection configuration between working Personal Computer (PC) and test board for studying the application program and GPS sensor working status. This research work mainly describes the system architecture and programming methodology of an application program which follows some standards for agricultural machinery

Adaptive-optics Optical Coherence Tomography Processing Using a Graphics Processing Unit

Graphics processing units are increasingly being used for scientific computing for their powerful parallel processing abilities, and moderate price compared to super computers and computing grids. In this paper we have used a general purpose graphics processing unit to process adaptive-optics optical coherence tomography (AOOCT) images in real time. Increasing the processing speed of AOOCT is an essential step in moving the super high resolution technology closer to clinical viability