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Scalability of RAID systemsLi, Yan January 2010 (has links)
RAID systems (Redundant Arrays of Inexpensive Disks) have dominated backend storage systems for more than two decades and have grown continuously in size and complexity. Currently they face unprecedented challenges from data intensive applications such as image processing, transaction processing and data warehousing. As the size of RAID systems increases, designers are faced with both performance and reliability challenges. These challenges include limited back-end network bandwidth, physical interconnect failures, correlated disk failures and long disk reconstruction time. This thesis studies the scalability of RAID systems in terms of both performance and reliability through simulation, using a discrete event driven simulator for RAID systems (SIMRAID) developed as part of this project. SIMRAID incorporates two benchmark workload generators, based on the SPC-1 and Iometer benchmark specifications. Each component of SIMRAID is highly parameterised, enabling it to explore a large design space. To improve the simulation speed, SIMRAID develops a set of abstraction techniques to extract the behaviour of the interconnection protocol without losing accuracy. Finally, to meet the technology trend toward heterogeneous storage architectures, SIMRAID develops a framework that allows easy modelling of different types of device and interconnection technique.Simulation experiments were first carried out on performance aspects of scalability. They were designed to answer two questions: (1) given a number of disks, which factors affect back-end network bandwidth requirements; (2) given an interconnection network, how many disks can be connected to the system. The results show that the bandwidth requirement per disk is primarily determined by workload features and stripe unit size (a smaller stripe unit size has better scalability than a larger one), with cache size and RAID algorithm having very little effect on this value. The maximum number of disks is limited, as would be expected, by the back-end network bandwidth. Studies of reliability have led to three proposals to improve the reliability and scalability of RAID systems. Firstly, a novel data layout called PCDSDF is proposed. PCDSDF combines the advantages of orthogonal data layouts and parity declustering data layouts, so that it can not only survivemultiple disk failures caused by physical interconnect failures or correlated disk failures, but also has a good degraded and rebuild performance. The generating process of PCDSDF is deterministic and time-efficient. The number of stripes per rotation (namely the number of stripes to achieve rebuild workload balance) is small. Analysis shows that the PCDSDF data layout can significantly improve the system reliability. Simulations performed on SIMRAID confirm the good performance of PCDSDF, which is comparable to other parity declustering data layouts, such as RELPR. Secondly, a system architecture and rebuilding mechanism have been designed, aimed at fast disk reconstruction. This architecture is based on parity declustering data layouts and a disk-oriented reconstruction algorithm. It uses stripe groups instead of stripes as the basic distribution unit so that it can make use of the sequential nature of the rebuilding workload. The design space of system factors such as parity declustering ratio, chunk size, private buffer size of surviving disks and free buffer size are explored to provide guidelines for storage system design. Thirdly, an efficient distributed hot spare allocation and assignment algorithm for general parity declustering data layouts has been developed. This algorithm avoids conflict problems in the process of assigning distributed spare space for the units on the failed disk. Simulation results show that it effectively solves the write bottleneck problem and, at the same time, there is only a small increase in the average response time to user requests.
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A comparative study of open-source IoT middleware platforms. / En jämförande studie av open-source IoT middleware plattformar.Scott, Robert, Östberg, Daniel January 2018 (has links)
This is a comparative study of open-source IoT middleware platforms with the main focus on scalability and reliability. An initial evaluation of available open-source IoT platforms resulted in Kaa and Node-RED being the focus of this thesis. To further analyse the platforms, they were both subjected to testing with three real-world scenarios. The chosen scenarios were a remote-controlled LED, a chat application and a data transmitting sensor. Prototypes were developed for each scenario using a range of programming languages and devices like Raspberry Pi, Android and ESP8266.According to the tests Node-RED has better performance on a single server. It also scales better with the possibility to communicate with external APIs directly unlike Kaa which would require a gateway. Despite these factors, Kaa proved to have better overall scalability and reliability with its built-insecurity and device discovery, it also supports clustering and should prove better in larger environments. / Detta arbete är en komparativ studie av olika open-source IoT plattformarmed inriktning på skalbarhet och pålitlighet. I förstudien som utfördes undersöktes tillgängliga open-source plattformar varefter Kaa och Node-RED valdes som lämpliga studieobjekt. För att ytterligare analyseraplattformarna testades de med hjälp av tre olika verklighetsbaseradescenarion. De utvalda scenariona som användes var en fjärrstyrd LED, en chatapplikation och data som skickas trådlöst från en sensor. Prototyperna utvecklades för varje scenario med hjälp av flera olika programmeringsspråksamt olika hårdvaror som Raspberry Pi, Android och ESP8266.Enligt de tester som genomförts så har Node-RED bättre prestandaunderanvändning på en ensam server. Node-RED har även bättre skalbarhet motexterna API:er då detta endast är möjligt i Kaa via en gateway. Trots detta så tyder resultaten på att Kaa sammantaget har bättre skalbarhet och tillförlitlighet med plattformens inbyggda säkerhet och dess möjlighet att automatiskt upptäcka enheter. Kaa stödjer även klustring vilket förmodligen presterar bättre i utbredda miljöer.
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