A MapReduce architecture for web site user behaviour monitoring in real time

Monitoring the behaviour of large numbers of web site users in real time poses significant performance challenges, due to the decentralised location and volume of generated data. This paper proposes a MapReduce-style architecture where the processing of event series from the Web users is performed by a number of cascading mappers, reducers and rereducers, local to the event origin. With the use of static analysis and a prototype implementation, we show how this architecture is capable to carry out time series analysis in real time for very large web data sets, based on the actual events, instead of resorting to sampling or other extrapolation techniques

Run Time Approximation of Non-blocking Service Rates for Streaming Systems

Stream processing is a compute paradigm that promises safe and efficient parallelism. Modern big-data problems are often well suited for stream processing's throughput-oriented nature. Realization of efficient stream processing requires monitoring and optimization of multiple communications links. Most techniques to optimize these links use queueing network models or network flow models, which require some idea of the actual execution rate of each independent compute kernel within the system. What we want to know is how fast can each kernel process data independent of other communicating kernels. This is known as the "service rate" of the kernel within the queueing literature. Current approaches to divining service rates are static. Modern workloads, however, are often dynamic. Shared cloud systems also present applications with highly dynamic execution environments (multiple users, hardware migration, etc.). It is therefore desirable to continuously re-tune an application during run time (online) in response to changing conditions. Our approach enables online service rate monitoring under most conditions, obviating the need for reliance on steady state predictions for what are probably non-steady state phenomena. First, some of the difficulties associated with online service rate determination are examined. Second, the algorithm to approximate the online non-blocking service rate is described. Lastly, the algorithm is implemented within the open source RaftLib framework for validation using a simple microbenchmark as well as two full streaming applications.Comment: technical repor

Analysing B2B electronic procurement benefits – Information systems perspective

This paper presents electronic procurement benefits identified in four case companies. The benefits achieved in the case companies were classified according to taxonomies from the Information Systems discipline. Existing taxonomies were combined into a new taxonomy which allows evaluation of the complex e-procurement impact. Traditional financial-based methods failed to capture the nature of e-procurement benefits. In the new taxonomy, eprocurement benefits are classified using scorecard dimensions (strategic, tactical and operational), which allows the identification of areas of e-procurement impact, in addition the benefits characteristic is captured (tangible, intangible, financial and non-financial)

The Collective Action as Potential Driver of Bottom-up Reconfiguration from Captive to Relational Value Chain : the Case Study of the Northern District in Sierra Leone

In recent decades, the increasing growth rate of the African cashew business has involved a large number of corporate actors such as global retailers, processors and exporters in cashew supply networks. The increasing role of agro-food supply chains enables African countries to enhance their position in global markets and to sustain local development and growth, by encouraging a higher market-orientation in the governance of global value chains. In this paper, an exploratory analysis based on a questionnaire involving 319 smallholder farmers in the North of Sierra Leone is conducted in order to explore the role of collective action in driving the potential bottom-up reconfiguration of cashew value chain. A change from captive to relational governance is expected to positively support the local industry upgrading, to reduce transaction costs and information asymmetries, and to increase the local development and growth by enhancing employment creation and poverty alleviation

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter

Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft