Validating the INTERPRETOR Software Architecture for the Interpretation of Large and Noisy Data Sets

In this chapter, the authors validate INTERPRETOR software architecture as a dataflow model of com- putation for filtering, abstracting, and interpreting large and noisy datasets with two detailed empirical studies from the authors’ former research endeavours. Also discussed are five further recent and distinct systems that can be tailored or adapted to use the software architecture. The detailed case studies pre- sented are from two disparate domains that include intensive care unit data and building sensor data. By performing pattern mining on five further systems in the way the authors have suggested herein, they argue that INTERPRETOR software architecture has been validated

Design of competitive paging algorithms with good behaviour in practice

Paging is one of the most prominent problems in the field of online algorithms. We have to serve a sequence of page requests using a cache that can hold up to k pages. If the currently requested page is in cache we have a cache hit, otherwise we say that a cache miss occurs, and the requested page needs to be loaded into the cache. The goal is to minimize the number of cache misses by providing a good page-replacement strategy. This problem is part of memory-management when data is stored in a two-level memory hierarchy, more precisely a small and fast memory (cache) and a slow but large memory (disk). The most important application area is the virtual memory management of operating systems. Accessed pages are either already in the RAM or need to be loaded from the hard disk into the RAM using expensive I/O. The time needed to access the RAM is insignificant compared to an I/O operation which takes several milliseconds. The traditional evaluation framework for online algorithms is competitive analysis where the online algorithm is compared to the optimal offline solution. A shortcoming of competitive analysis consists of its too pessimistic worst-case guarantees. For example LRU has a theoretical competitive ratio of k but in practice this ratio rarely exceeds the value 4. Reducing the gap between theory and practice has been a hot research issue during the last years. More recent evaluation models have been used to prove that LRU is an optimal online algorithm or part of a class of optimal algorithms respectively, which was motivated by the assumption that LRU is one of the best algorithms in practice. Most of the newer models make LRU-friendly assumptions regarding the input, thus not leaving much room for new algorithms. Only few works in the field of online paging have introduced new algorithms which can compete with LRU as regards the small number of cache misses. In the first part of this thesis we study strongly competitive randomized paging algorithms, i.e. algorithms with optimal competitive guarantees. Although the tight bound for the competitive ratio has been known for decades, current algorithms matching this bound are complex and have high running times and memory requirements. We propose the algorithm OnlineMin which processes a page request in O(log k/log log k) time in the worst case. The best previously known solution requires O(k^2) time. Usually the memory requirement of a paging algorithm is measured by the maximum number of pages that the algorithm keeps track of. Any algorithm stores information about the k pages in the cache. In addition it can also store information about pages not in cache, denoted bookmarks. We answer the open question of Bein et al. '07 whether strongly competitive randomized paging algorithms using only o(k) bookmarks exist or not. To do so we modify the Partition algorithm of McGeoch and Sleator '85 which has an unbounded bookmark complexity, and obtain Partition2 which uses O(k/log k) bookmarks. In the second part we extract ideas from theoretical analysis of randomized paging algorithms in order to design deterministic algorithms that perform well in practice. We refine competitive analysis by introducing the attack rate parameter r, which ranges between 1 and k. We show that r is a tight bound on the competitive ratio of deterministic algorithms. We give empirical evidence that r is usually much smaller than k and thus r-competitive algorithms have a reasonable performance on real-world traces. By introducing the r-competitive priority-based algorithm class OnOPT we obtain a collection of promising algorithms to beat the LRU-standard. We single out the new algorithm RDM and show that it outperforms LRU and some of its variants on a wide range of real-world traces. Since RDM is more complex than LRU one may think at first sight that the gain in terms of lowering the number of cache misses is ruined by high runtime for processing pages. We engineer a fast implementation of RDM, and compare it to LRU and the very fast FIFO algorithm in an overall evaluation scheme, where we measure the runtime of the algorithms and add penalties for each cache miss. Experimental results show that for realistic penalties RDM still outperforms these two algorithms even if we grant the competitors an idealistic runtime of 0

Efficient caching algorithms for memory management in computer systems

As disk performance continues to lag behind that of memory systems and processors, fully utilizing memory to reduce disk accesses is a highly effective effort to improve the entire system performance. Furthermore, to serve the applications running on a computer in distributed systems, not only the local memory but also the memory on remote servers must be effectively managed to minimize I/O operations. The critical challenges in an effective memory cache management include: (1) Insightfully understanding and quantifying the locality inherent in the memory access requests; (2) Effectively utilizing the locality information in replacement algorithms; (3) Intelligently placing and replacing data in the multi-level caches of a distributed system; (4) Ensuring that the overheads of the proposed schemes are acceptable.;This dissertation provides solutions and makes unique and novel contributions in application locality quantification, general replacement algorithms, low-cost replacement policy, thrashing protection, as well as multi-level cache management in a distributed system. First, the dissertation proposes a new method to quantify locality strength, and accurately to identify the data with strong locality. It also provides a new replacement algorithm, which significantly outperforms existing algorithms. Second, considering the extremely low-cost requirements on replacement policies in virtual memory management, the dissertation proposes a policy meeting the requirements, and considerably exceeding the performance existing policies. Third, the dissertation provides an effective scheme to protect the system from thrashing for running memory-intensive applications. Finally, the dissertation provides a multi-level block placement and replacement protocol in a distributed client-server environment, exploiting non-uniform locality strengths in the I/O access requests.;The methodology used in this study include careful application behavior characterization, system requirement analysis, algorithm designs, trace-driven simulation, and system implementations. A main conclusion of the work is that there is still much room for innovation and significant performance improvement for the seemingly mature and stable policies that have been broadly used in the current operating system design

A cache memory system based on a dynamic/adaptive replacement approach

En este trabajo, nosotros proponemos un sistema de memoria cache basado en un esquema de reemplazo adaptativo, el cual formaría parte del Sistema Manejador de la Memoria Virtual de un Sistema Operativo. Nosotros usamos un simulador de eventos discretos para comparar nuestro enfoque con trabajos previos. Nuestro esquema de reemplazo adaptativo esta basado en varias propiedades del sistema y de las aplicaciones, para estimar/escoger la mejor política de reemplazo. Nosotros definidos un valor de prioridad de reemplazo a cada bloque de la memoria cache, según el conjunto de propiedades del sistema y de las aplicaciones, para seleccionar cual bloque eliminar. El objetivo es proveer un uso efectivo de la memoria cache y un buen rendimiento para las aplicaciones.Palabras Claves: Sistema de Manejo de Memoria, Memoria Cache, Evaluación de Rendimiento

Cache Hit Rate Optimization for Network Flow Measurement

Tato bakalářská práce se zabývá analýzou, návrhem a implementací vhodných algoritmů pro zvýšení efektivnosti vyrovnávací paměti v architektuře běžného počítače. Hlavním cílem bylo navrhnout a implementovat aplikaci, která by efektivně pracovala se síťovými daty. Práce obsahuje popis problematiky optimalizace a použitých algoritmů.This bachelor's thesis is focused on analyse, design and implementation algorithms for increase the effectiveness of buffers on standard PC architecture. Work objective is to design and implement an application, that would work effectively with network data. Work includes description of optimization and used algorithms.