Passive NFS Tracing of Email and Research Workloads

We present an analysis of a pair of NFS traces of contemporary email and research workloads. We show that although the research workload resembles previously studied workloads, the email workload is quite different. We also perform several new analyses that demonstrate the periodic nature of file system activity, the effect of out-of-order NFS calls, and the strong relationship between the name of a file and its size, lifetime, and access pattern.Engineering and Applied Science

Thermal Benchmark and Power Benchmark Software

Power consumption and heat dissipation become key elements in the field of high-end integrated circuits, especially those used in mobile and high-speed applications, due to their increase of transistor count and clock frequencies. Dynamic thermal management strategies have been proposed and implemented in order to mitigate heat dissipation. However, there is a lack of a tool that can be used to evaluate DTM strategies and thermal response of real life systems. Therefore, in this paper we introduce and define the concepts of thermal benchmark software and power benchmark software as a software application for run-time system level thermal and power characterizationComment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

Catch Me If You Can: Using Power Analysis to Identify HPC Activity

Monitoring users on large computing platforms such as high performance computing (HPC) and cloud computing systems is non-trivial. Utilities such as process viewers provide limited insight into what users are running, due to granularity limitation, and other sources of data, such as system call tracing, can impose significant operational overhead. However, despite technical and procedural measures, instances of users abusing valuable HPC resources for personal gains have been documented in the past \cite{hpcbitmine}, and systems that are open to large numbers of loosely-verified users from around the world are at risk of abuse. In this paper, we show how electrical power consumption data from an HPC platform can be used to identify what programs are executed. The intuition is that during execution, programs exhibit various patterns of CPU and memory activity. These patterns are reflected in the power consumption of the system and can be used to identify programs running. We test our approach on an HPC rack at Lawrence Berkeley National Laboratory using a variety of scientific benchmarks. Among other interesting observations, our results show that by monitoring the power consumption of an HPC rack, it is possible to identify if particular programs are running with precision up to and recall of 95\% even in noisy scenarios

Capture and analysis of the NFS workload of an ISP email service

Tese de mestrado Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Os objectivos desta tese são capturar a carga de comandos NFS de um serviço de email de um provedor de Internet, converter a captura para um formato mais flexível, e analisar as características do mesmo. Até ao momento, nenhum outro trabalho publicado, analisou a carga de comandos de um serviço de email de um provedor de Internet. Um novo estudo, irá ajudar a compreender qual o impacto das diferenças na carga de comandos de um sistema de ficheiros de rede, e o que caracteriza a carga de comandos de um sistema de email real. A captura será analisada, de forma a encontrar novas propriedades que futuros sistemas de ficheiros poderão suportar ou explorar. Nesta tese, fazemos uma análise exaustiva de como capturar altos débitos de tráfego, que envolve vários desafios. Identificamos os problemas encontrados e explicamos como contornar esses problemas. Devido ao elevado tamanho da captura e devido ao espaço limitado de armazenamento disponível, precisámos de converter a captura para um formato mais compacto e flexível, de forma a podermos fazer uma análise de forma eficiente. Descrevemos os desafios para analisar grandes volumes de dados e quais as técnicas utilizadas. Visto que a captura contém dados sensíveis das caixas de correio dos utilizadores, tivemos que anonimizar a captura. Descrevemos que dados têm de ser anonimizados de forma a disponibilizarmos a captura gratuitamente. Também analisamos a captura e demonstramos as características únicas da captura estudada, tais como a natureza periódica da actividade do sistema de ficheiros, a distribuição de tamanhos de todos os ficheiros acedidos, a sequencialidade dos dados acedidos e os tipos de anexos mais comuns numa típica caixa de correio.The aims of this thesis are to capture a real-world NFS workload of an ISP email service, convert the traces to a more useful and flexible format and analyze the characteristics of the workload. No published work has ever analyzed a large-scale, real-world ISP email workload. A new study will help to understand how these changes impact network file system workloads and what characterizes a real-world email workload. Storage traces are analyzed to find properties that future systems should support or exploit. In this thesis, we provide an in-depth explanation of how we were able to capture high data rates, which involves several challenges. We identify the bottlenecks faced and explain how we circumvented them. Due to the large size of the captured workload and limited available storage, we needed to convert the traces to a more compact and flexible format so we could further analyze the workload in an efficient manner. We describe the challenges of analyzing large datasets and the techniques that were used. Since the workload contains sensitive information about the mailboxes, we had to anonymize the workload. We will describe what needed to be anonymized and how it was done. This was an important step to get permission from the ISP to publish the anonymized traces, which will be available for free download. We also performed several analyses that demonstrate unique characteristics of the studied workload, such as the periodic nature of file system activity, the file size distribution for all accessed files, the sequentiality of accessed data, and the most common type of attachments found in a typical mailbox

Analyzing Storage System Workloads

Analysis of storage system workloads is important for a number of reasons. The analysis might be performed to understand the usage patterns of existing storage systems. It is very important for the architects to understand the usage patterns when designing and developing a new, or improving upon the existing design of a storage system. It is also important for a system administrator to understand the usage patterns when configuring and tuning a storage system. The analysis might also be performed to determine the relationship between any two given workloads. Before a decision is taken to pool storage resources to increase the throughput, there is need to establish whether the different workloads involved are correlated or not. Furthermore, the analysis of storage system workloads can be done to monitor the usage and to understand the storage requirements and behavior of system and application software. Another very important reason for analyzing storage system workloads, is the need to come up with correct workload models for storage system evaluation. For the evaluation, based on simulations or otherwise, to be reliable, one has to analyze, understand and correctly model the workloads. In our work we have developed a general tool, called ESSWA (Enterprize Storage System Workload Analyzer) for analyzing storage system workloads, which has a number of advantages over other storage system workload analyzers described in literature. Given a storage system workload in the form of an I/O trace file containing data for the workload parameters, ESSWA gives statistics of the data. From the statistics one can derive mathematical models in the form of probability distribution functions for the workload parameters. The statistics and mathematical models describe only the particular workload for which they are produced. This is because storage system workload characteristics are sensitive to the file system and buffer pool design and implementation, so that the results of any analysis are less broadly applicable. We experimented with ESSWA by analyzing storage system workloads represented by three sets of I/O traces at our disposal. Our results, among other things show that: I/O request sizes are influenced by the operating system in use; the start addresses of I/O requests are somewhat influenced by the application; and the exponential probability density function, which is often used in simulation of storage systems to generate inter-arrival times of I/O requests, is not the best model for that purpose in the workloads that we analyzed. We found the Weibull, lognormal and beta probability density functions to be better models