Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management

As users of big data applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the pay-as-you-go model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs - systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results. Our key idea is to expose internal operator state explicitly to the SPS through a set of state management primitives. Based on them, we describe an integrated approach for dynamic scale out and recovery of stateful operators. Externalised operator state is checkpointed periodically by the SPS and backed up to upstream VMs. The SPS identifies individual operator bottlenecks and automatically scales them out by allocating new VMs and partitioning the check-pointed state. At any point, failed operators are recovered by restoring checkpointed state on a new VM and replaying unprocessed tuples. We evaluate this approach with the Linear Road Benchmark on the Amazon EC2 cloud platform and show that it can scale automatically to a load factor of L=350 with 50 VMs, while recovering quickly from failures. Copyright © 2013 ACM

Hemodynamics of Stent Implantation Procedures in Coronary Bifurcations: an in vitro study

Stent implantation in coronary bifurcations presents unique challenges and currently there is no universally accepted stent deployment approach. Despite clinical and computational studies, to date, the effect of each stent implantation method on the coronary artery hemodynamics is not well understood. In this study the hemodynamics of stented coronary bifurcations under pulsatile flow conditions were investigated experimentally. Three implantation methods, provisional side branch (PSB), culotte (CUL), and crush (CRU), were investigated using time-resolved particle image velocimetry (PIV) to measure the velocity fields. Subsequently, hemodynamic parameters including wall shear stress (WSS), oscillatory shear index (OSI), and relative residence time (RRT) were calculated and the pressure field through the vessel was non-invasively quantified. The effects of each stented case were evaluated and compared against an un-stented case. CRU provided the lowest compliance mismatch, but demonstrated detrimental stent interactions. PSB, the clinically preferred method, and CUL maintained many normal flow conditions. However, PSB provided about a 300% increase in both OSI and RRT. CUL yielded a 10% and 85% increase in OSI and RRT, respectively. The results of this study support the concept that different bifurcation stenting techniques result in hemodynamic environments that deviate from that of un-stented bifurcations, to varying degrees.Comment: 33 pages, 8 figures, 3 table

A short introduction to historical earthquakes in Libya

As a result of the relative motion of the African and European plates, Libya, located at the north central margin of the African continent, has experienced a considerable intraplate tectonism, particularly in its northern coastal regions. If the seismic activity of the last fifty years, at most, is known from instrumental recording, macroseismic effects of those earthquakes which affected Libya in the past centuries are still imperfectly known. To try and partly overcome this lack of information, in this contribution we present a short introduction to historical earthquakes in Libya, focusing on the period up to 1935. According to the studies published in the last twenty years, the earliest records of earthquakes in Libya are documented in the Roman period (3rd and 4th century A.D.). There is a gap in information along the Middle and Modern Ages, while the 19th and early 20th century evidence is concentrated on effects in Tripoli, in the western part of nowadays Libya. The Hun Graben area (western part of the Gulf of Sirt) has been identified as the location of many earthquakes affecting Libya, and it is in this area that the 19 April 1935 earthquake (Mw = 7.1) struck, followed by many aftershocks. Further investigations are needed, and some hints are here given at historical sources potentially reporting on earthquake effects in Libya. Their investigation could result in the needed improvement to lay the foundations of a database and a catalogue of the historical seismicity of Libya

Resistance to Change Processes and Strategies for the Implementation of Harmonization Reforms: The Separation of the Health Care Expenditure in the Regional Financial Statements

The Legislative Decree n. 118/2011, in setting the rules for the harmonization of the financial accounting of the local governments, represents a further progress for the accounting process also for the health care. In the specific case the article 20 defines a precise identification perimeter of revenue and expenditure related with National Health Service (NHS) by the regulations in the regional financial statements, in a way to make possible an immediate comparability between the Health Care incomes and expenditures in the Regional financial statement. The aim of this paper, always referred to the Rational Management based on financial statement, focuses the attention on the possible correlation between organizational responses to institutional pressure and the theoretical roles of accounting, tracing lines of best practices compliance or not on the sample above explained

Scalable and Fault-tolerant Stateful Stream Processing.

As users of "big data" applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the "pay-as-you-go" model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs—systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results. Our key idea is to expose internal operator state explicitly to the SPS through a set of state management primitives. Based on them, we describe an integrated approach for dynamic scale out and recovery of stateful operators. Externalised operator state is checkpointed periodically by the SPS and backed up to upstream VMs. The SPS identifies individual operator bottlenecks and automatically scales them out by allocating new VMs and partitioning the checkpointed state. At any point, failed operators are recovered by restoring checkpointed state on a new VM and replaying unprocessed tuples. We evaluate this approach with the Linear Road Benchmark on the Amazon EC2 cloud platform and show that it can scale automatically to a load factor of L=350 with 50 VMs, while recovering quickly from failures

Making State Explicit for Imperative Big Data Processing

Data scientists often implement machine learning algorithms in imperative languages such as Java, Matlab and R. Yet such implementations fail to achieve the performance and scalability of specialised data-parallel processing frameworks. Our goal is to execute imperative Java programs in a data-parallel fashion with high throughput and low latency. This raises two challenges: how to support the arbitrary mutable state of Java programs without compromising scalability, and how to recover that state after failure with low overhead. Our idea is to infer the dataflow and the types of state accesses from a Java program and use this information to generate a stateful dataflow graph (SDG). By explicitly separating data from mutable state, SDGs have specific features to enable this translation: to ensure scalability, distributed state can be partitioned across nodes if computation can occur entirely in parallel; if this is not possible, partial state gives nodes local instances for independent computation, which are reconciled according to application semantics. For fault tolerance, large inmemory state is checkpointed asynchronously without global coordination. We show that the performance of SDGs for several imperative online applications matches that of existing data-parallel processing frameworks

Solvent nature effect in preparation of perovskites by flame pyrolysis: 1: carboxylic acids

The effect of a series of carboxylic acids (C(2)-C(8)), as solvents for the preparation by flame spray pyrolysis of LaCoO(3) catalyst for the flameless combustion of methane, has been investigated. Acetic acid showed to be unsatisfactory from several points of view: low phase purity of the catalyst, higher amount of unburnt carbonaceous residua, lower catalytic activity and low thermal stability. By increasing the carbon chain length of the solvent, the consequent increase of flame temperature led to an increase of crystal phase purity and of particle size and to a decrease of specific surface area of the catalyst. Catalytic activity showed only marginally affected by the last parameter, phase purity seeming more important. Thermal resistance showed directly related to flame temperature, i.e. to the combustion enthalpy of the solvent, but a relatively high amount of residual organic matter can negatively affect this property

Solvent nature effect in preparation of perovskites by flame pyrolysis: 2 : Alcohols and alcohols plus propionic acid mixtures

The effect of either pure alcohols or alcohols + propionic acid mixtures as solvents for the preparation by flame pyrolysis of a standard LaCoO3 catalyst, to be employed for the catalytic flameless combustion of methane, has been investigated. All the catalysts proved very active for the mentioned reaction. Low-MW pure alcohols showed however less suitable than alcohols-propionic acid mixtures, leading to lower perovskite phase purity, less particle size homogeneity and lower specific surface area. The high volatility of the solvent seems to be the major cause, together with the improper behaviour of nitrates (forced by solubility reasons) as perovskite metals precursors. However, the addition of propionic acid to the alcohols allowed to use the acetates as precursors and hence to obtain high perovskitic phase purity, high SSA and Uniform particle size. Moreover, the increase of combustion enthalpy of the solvent, through the addition of higher-MW alcohols, leading to progressively higher flame temperature, strongly improved the thermal resistance of the catalyst, without lowering catalytic performance

DEFCON: high-performance event processing with information security

In finance and healthcare, event processing systems handle sensitive data on behalf of many clients. Guaranteeing information security in such systems is challenging because of their strict performance requirements in terms of high event throughput and low processing latency. We describe DEFCON, an event processing system that enforces constraints on event flows between event processing units. DEFCON uses a combination of static and runtime techniques for achieving light-weight isolation of event flows, while supporting efficient sharing of events. Our experimental evaluation in a financial data processing scenario shows that DEFCON can provide information security with significantly lower processing latency compared to a traditional approach