Flavio Frattini

Scalable Analytics for IaaS Cloud Availability

In a large Infrastructure-as-a-Service (IaaS) cloud, component failures are quite common. Such failures may lead to occasional system downtime and eventual violation of Service Level Agreements (SLAs) on the cloud service availability. The availability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a defined SLA, as well as to evaluate the capabilities of an existing one. This paper presents a scalable, stochastic model-driven approach to quantify the availability of a large-scale IaaS cloud, where failures are typically dealt with through migration of physical machines among three pools: hot (running), warm (turned on, but not ready), and cold (turned off). Since monolithic models do not scale for large systems, we use an interacting Markov chain based approach to demonstrate the reduction in the complexity of analysis and the solution time. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-point iteration, for which existence of a solution is proved. The analytic-numeric solutions obtained from the proposed approach and from the monolithic model are compared. We show that the errors introduced by interacting sub-models are insignificant and that our approach can handle very large size IaaS clouds. The simulative solution is also considered for the proposed model, and solution time of the methods are compared.

Cost-Benefit Analysis of Virtualizing Batch Systems: Performance-Energy-Dependability Trade-Offs

Performance, energy efficiency, and dependability are key characteristics of batch systems, which can be differently affected when adopting virtualization. Scientific literature usually analyzes the variation with respect to different configurations of one characteristic, or the trade-off between two. In this paper, instead, we assess the impact of virtualization encompassing all of them. Results show that the joint analysis helps in finding the proper tuning of the system for balancing costs and benefits due to virtualization and related techniques.

GAMESH: A grid architecture for scalable monitoring and enhanced dependable job scheduling

Abstract Grid computing is a largely adopted paradigm to federate geographically distributed data centers. Due to their size and complexity, grid systems are often affected by failures that may hinder the correct and timely execution of jobs, thus causing a non-negligible waste of computing resources. Despite the relevance of the problem, state-of-the-art management solutions for grid systems usually neglect the identification and handling of failures at runtime. Among the primary goals to be considered, we claim the need for novel approaches capable to achieve the objectives of scalable integration with efficient monitoring solutions and of fitting large and geographically distributed systems, where dynamic and configurable tradeoffs between overhead and targeted granularity are necessary. This paper proposes GAMESH, a Grid Architecture for scalable Monitoring and Enhanced dependable job ScHeduling. GAMESH is conceived as a completely distributed and highly efficient management infrastructure, concentrating on two crucial aspects for large-scale and multi-domain grid environments: (i) the scalable dissemination of monitoring data and (ii) the troubleshooting of job execution failures. GAMESH has been implemented and tested in a real deployment encompassing geographically distributed data centers across Europe. Experimental results show that GAMESH (i) enables the collection of measurements of both computing resources and conditions of task scheduling at geographically sparse sites, while imposing a limited overhead on the entire infrastructure, and (ii) provides a failure-aware scheduler able to improve the overall system performance, even in the presence of failures, by coordinating local job schedulers at multiple domains.

Scalable monitoring and dependable job scheduling support for multi-domain grid infrastructures

The management of Grid systems commonly lacks information for identifying the failures that may hinder the timely completion of jobs, and cause the wasting of computing resources. Monitoring can certainly help, but novel approaches need to be conceived for such large and geographically distributed systems. We propose a Grid Architecture for scalable Monitoring and Enhanced dependable job ScHeduling (GAMESH). GAMESH is a completely distributed and highly efficient management infrastructure for the dissemination of monitoring data and troubleshooting of job execution failures in large-scale and multi-domain Grid environments. Challenged in a real deployment and compared to other Grid management systems, GAMESH demonstrates to (i) ensure measurements of both computing resources and conditions of task scheduling at geographically sparse sites, while inducing a low overhead on the entire infrastructure, and (ii) enable failure-aware scheduling and improve overall system performance, even in the presence of failures, by coordinating local job schedulers at multiple domains.

Performance degradation analysis of a supercomputer

We analyze performance degradation phenomena due to software aging on a real supercomputer deployed at the Federico II University of Naples, by considering a dataset of ten months of operational usage. We adopted a statistical approach for identifying when and where the supercomputer experienced a performance degradation trend. The analysis pinpointed performance degradation trends that were actually caused by the gradual error accumulation within basic software of the supercomputer.

Automatic Invariant Selection for Online Anomaly Detection

Invariants are stable relationships among system metrics expected to hold during normal operating conditions. The violation of such relationships can be used to detect anomalies at runtime. However, this approach does not scale to large systems, as the number of invariants quickly grows with the number of considered metrics. The resulting “background noise” for the invariant-based detection system hinders its effectiveness. In this paper we propose a general and automatic approach for identifying a subset of mined invariants that properly model system runtime behavior with a reduced amount of background noise. This translates into better overall performance (i.e., less false positives).

Using Invariants for Anomaly Detection: The Case Study of a SaaS Application

Invariants represent properties of a system that are expected to hold when everything goes well. Thus, the violation of an invariant most likely corresponds to the occurrence of an anomaly in the system. In this paper, we discuss the accuracy and the completeness of an anomaly detection system based on invariants. The case study we have taken is a back-end operation of a SaaS platform. Results show the rationality of the approach and discuss the impact of the invariant mining strategy on the detection capabilities, both in terms of accuracy and of time to reveal violations.

Mining Invariants from SaaS Application Logs (Practical Experience Report)

The increasing popularity of Software as a Service (SaaS) stresses the need of solutions to predict failures and avoid service interruptions, which invariably result in SLA violations and severe loss of revenue. A promising approach to continuously monitor the correct functioning of the system is to check the execution conformance to a set of invariants, i.e., properties that must hold when the system is deemed to run correctly. In this paper we propose a framework and a tool to automatically discover invariants from application logs and to online detect their violation. The framework has been applied on 9 months of log events from a real-world SaaS application. Results show that the proposed tool is able to automatically select 12 invariants with a stringent goodness of fit criteria out of more than 500 potential relationships. We also show the usefulness of our approach to detect runtime issues from logs in the form of violations of selected invariants, corresponding to silent errors that usually go unnoticed by the system maintenance personnel, even if they could represent symptoms of upcoming service failures.

ROCRSSI++: An Efficient Localization Algorithm for Wireless Sensor Networks

Localization within a Wireless Sensor Network consists of defining the position of a given set of sensors by satisfying some non-functional requirements such as (1) efficient energy consumption, (2) low communication or computation overhead, (3) no, or limited, use of particular hardware components, (4) fast localization, (5) robustness, and (6) low localization error. Although there are several algorithms and techniques available in literature, localization is viewed as an open issue because none of the current solutions are able to jointly satisfy all the previous requirements. An algorithm called ROCRSSI appears to be a suitable solution; however, it is affected by several inefficiencies that limit its effectiveness in real case scenarios. This paper proposes a refined version of this algorithm, called ROCRSSI++, which resolves such inefficiencies using and storing information gathered by the sensors in a more efficient manner. Several experiments on actual devices have been performed. The results show a reduction of the localization error with respect to the original algorithm. This paper investigates energy consumption and localization time required by the proposed approach.

Reproducibility of Software Bugs

Understanding software bugs and their effects is important in several engineering activities, including testing, debugging, and design of fault containment or tolerance methods. Dealing with hard-to-reproduce failures requires a deep comprehension of the mechanisms leading from bug activation to software failure. This chapter surveys taxonomies and recent studies about bugs from the perspective of their reproducibility, providing insights into the process of bug manifestation and the factors influencing it. These insights are based on the analysis of thousands of bug reports of a widely used open-source software, namely MySQL Server. Bug reports are automatically classified according to reproducibility characteristics, providing figures about the proportion of hard to reproduce bug their features, and evolution over releases.