Current generation of Internet-based services are typically hosted on large data centers that take the form of warehouse-size structures housing tens of thousands of servers. Continued availability of a modern data center is the result of a complex orchestration among many internal and external actors including computing hardware, multiple layers of intricate software, networking and storage devices, electrical power and cooling plants. During the course of their operation, many of these components produce large amounts of data in the form of event and error logs that are essential not only for identifying and resolving problems but also for improving data center efficiency and management. Most of these activities would benefit significantly from data analytics techniques to exploit hidden statistical patterns and correlations that may be present in the data. The sheer volume of data to be analyzed makes uncovering these correlations and patterns a challenging task. This paper presents Big Data analyzer (BiDAl), a prototype Java tool for log-data analysis that incorporates several Big Data technologies in order to simplify the task of extracting information from data traces produced by large clusters and server farms. BiDAl provides the user with several analysis languages (SQL, R and Hadoop MapReduce) and storage backends (HDFS and SQLite) that can be freely mixed and matched so that a custom tool for a specific task can be easily constructed. BiDAl has a modular architecture so that it can be extended with other backends and analysis languages in the future. In this paper we present the design of BiDAl and describe our experience using it to analyze publicly-available traces from Google data clusters, with the goal of building a realistic model of a complex data center.
Recently, the Failure Trace Archive (FTA) has published a toolkit for analysis of failure trace data @cite_2 . This toolkit is implemented in Matlab and enables analysis of traces from the FTA repository, which consists of about 20 public traces. It is, to our knowledge, the only other tool for large scale trace data analysis. However, the analysis is only possible if traces are stored in the FTA format in a relational database, and is only available for traces containing failure information. on the other hand provides two different storage options, including HDFS, with transfer among them transparent to the user, and is available for any trace data, regardless of what process it describes. Additionally, usage of FTA on new data requires publication of the data in their repository, while can be used also for sensitive data that cannot be made public.
@cite_2
2158197021 2138198492 2963684489 2150620836
With the increasing presence, scale, and complexity of distributed systems, resource failures are becoming an important and practical topic of computer science research. While numerous failure models and failure-aware algorithms exist, their comparison has been hampered by the lack of public failure data sets and data processing tools. To facilitate the design, validation, and comparison of fault-tolerant models and algorithms, we have created the Failure Trace Archive (FTA)-an online, public repository of failure traces collected from diverse parallel and distributed systems. In this work, we first describe the design of the archive, in particular of the standard FTA data format, and the design of a toolbox that facilitates automated analysis of trace data sets. We also discuss the use of the FTA for various current and future purposes. Second, after applying the toolbox to nine failure traces collected from distributed systems used in various application domains (e.g., HPC, Internet operation, and various online applications), we present a comparative analysis of failures in various distributed systems. Our analysis presents various statistical insights and typical statistical modeling results for the availability of individual resources in various distributed systems. The analysis results underline the need for public availability of trace data from different distributed systems. Last, we show how different interpretations of the meaning of failure data can result in different conclusions for failure modeling and job scheduling in distributed systems. Our results for different interpretations show evidence that there may be a need for further revisiting existing failure-aware algorithms, when applied for general rather than for domain-specific distributed systems. The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors. However, extracting green knowledge from location traces is not a trivial task. Conventional data analysis tools are usually not customized for handling the massive quantity, complex, dynamic, and distributed nature of location traces. To that end, in this paper, we provide a focused study of extracting energy-efficient transportation patterns from location traces. Specifically, we have the initial focus on a sequence of mobile recommendations. As a case study, we develop a mobile recommender system which has the ability in recommending a sequence of pick-up points for taxi drivers or a sequence of potential parking positions. The goal of this mobile recommendation system is to maximize the probability of business success. Along this line, we provide a Potential Travel Distance (PTD) function for evaluating each candidate sequence. This PTD function possesses a monotone property which can be used to effectively prune the search space. Based on this PTD function, we develop two algorithms, LCP and SkyRoute, for finding the recommended routes. Finally, experimental results show that the proposed system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and leading to the efficient use of energy. Fault Tree Analysis FTA is a dependability analysis technique that has been widely used to predict reliability, availability and safety of many complex engineering systems. Traditionally, these FTA-based analyses are done using paper-and-pencil proof methods or computer simulations, which cannot ascertain absolute correctness due to their inherent limitations. As a complementary approach, we propose to use the higher-order-logic theorem prover HOL4 to conduct the FTA-based analysis of safety-critical systems where accuracy of failure analysis is a dire need. In particular, the paper presents a higher-order-logic formalization of generic Fault Tree gates, i.e., AND, OR, NAND, NOR, XOR and NOT and the formal verification of their failure probability expressions. Moreover, we have formally verified the generic probabilistic inclusion-exclusion principle, which is one of the foremost requirements for conducting the FTA-based failure analysis of any given system. For illustration purposes, we conduct the FTA-based failure analysis of a solar array that is used as the main source of power for the Dong Fang Hong-3 DFH-3 satellite. Erasure codes such as Reed-Solomon (RS) codes are being extensively deployed in data centers since they offer significantly higher reliability than data replication methods at much lower storage overheads. These codes however mandate much higher resources with respect to network bandwidth and disk IO during reconstruction of data that is missing or otherwise unavailable. Existing solutions to this problem either demand additional storage space or severely limit the choice of the system parameters. In this paper, we present "Hitchhiker", a new erasure-coded storage system that reduces both network traffic and disk IO by around 25 to 45 during reconstruction of missing or otherwise unavailable data, with no additional storage, the same fault tolerance, and arbitrary flexibility in the choice of parameters, as compared to RS-based systems. Hitchhiker 'rides' on top of RS codes, and is based on novel encoding and decoding techniques that will be presented in this paper. We have implemented Hitchhiker in the Hadoop Distributed File System (HDFS). When evaluating various metrics on the data-warehouse cluster in production at Facebook with real-time traffic and workloads, during reconstruction, we observe a 36 reduction in the computation time and a 32 reduction in the data read time, in addition to the 35 reduction in network traffic and disk IO. Hitchhiker can thus reduce the latency of degraded reads and perform faster recovery from failed or decommissioned machines.