2,512 research outputs found
StackInsights: Cognitive Learning for Hybrid Cloud Readiness
Hybrid cloud is an integrated cloud computing environment utilizing a mix of
public cloud, private cloud, and on-premise traditional IT infrastructures.
Workload awareness, defined as a detailed full range understanding of each
individual workload, is essential in implementing the hybrid cloud. While it is
critical to perform an accurate analysis to determine which workloads are
appropriate for on-premise deployment versus which workloads can be migrated to
a cloud off-premise, the assessment is mainly performed by rule or policy based
approaches. In this paper, we introduce StackInsights, a novel cognitive system
to automatically analyze and predict the cloud readiness of workloads for an
enterprise. Our system harnesses the critical metrics across the entire stack:
1) infrastructure metrics, 2) data relevance metrics, and 3) application
taxonomy, to identify workloads that have characteristics of a) low sensitivity
with respect to business security, criticality and compliance, and b) low
response time requirements and access patterns. Since the capture of the data
relevance metrics involves an intrusive and in-depth scanning of the content of
storage objects, a machine learning model is applied to perform the business
relevance classification by learning from the meta level metrics harnessed
across stack. In contrast to traditional methods, StackInsights significantly
reduces the total time for hybrid cloud readiness assessment by orders of
magnitude
An Open Framework for Extensible Multi-Stage Bioinformatics Software
In research labs, there is often a need to customise software at every step
in a given bioinformatics workflow, but traditionally it has been difficult to
obtain both a high degree of customisability and good performance.
Performance-sensitive tools are often highly monolithic, which can make
research difficult. We present a novel set of software development principles
and a bioinformatics framework, Friedrich, which is currently in early
development. Friedrich applications support both early stage experimentation
and late stage batch processing, since they simultaneously allow for good
performance and a high degree of flexibility and customisability. These
benefits are obtained in large part by basing Friedrich on the multiparadigm
programming language Scala. We present a case study in the form of a basic
genome assembler and its extension with new functionality. Our architecture has
the potential to greatly increase the overall productivity of software
developers and researchers in bioinformatics.Comment: 12 pages, 1 figure, to appear in proceedings of PRIB 201
Rehearsal: A Configuration Verification Tool for Puppet
Large-scale data centers and cloud computing have turned system configuration
into a challenging problem. Several widely-publicized outages have been blamed
not on software bugs, but on configuration bugs. To cope, thousands of
organizations use system configuration languages to manage their computing
infrastructure. Of these, Puppet is the most widely used with thousands of
paying customers and many more open-source users. The heart of Puppet is a
domain-specific language that describes the state of a system. Puppet already
performs some basic static checks, but they only prevent a narrow range of
errors. Furthermore, testing is ineffective because many errors are only
triggered under specific machine states that are difficult to predict and
reproduce. With several examples, we show that a key problem with Puppet is
that configurations can be non-deterministic.
This paper presents Rehearsal, a verification tool for Puppet configurations.
Rehearsal implements a sound, complete, and scalable determinacy analysis for
Puppet. To develop it, we (1) present a formal semantics for Puppet, (2) use
several analyses to shrink our models to a tractable size, and (3) frame
determinism-checking as decidable formulas for an SMT solver. Rehearsal then
leverages the determinacy analysis to check other important properties, such as
idempotency. Finally, we apply Rehearsal to several real-world Puppet
configurations.Comment: In proceedings of ACM SIGPLAN Conference on Programming Language
Design and Implementation (PLDI) 201
An intelligent information forwarder for healthcare big data systems with distributed wearable sensors
© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed
Analysis and assessment of a knowledge based smart city architecture providing service APIs
Abstract The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented
Curracurrong: a stream processing system for distributed environments
Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay
- …