27,995 research outputs found
Cloud Process Execution Engine - Evaluation of the Core Concepts
In this technical report we describe describe the Domain Specific Language
(DSL) of the Workflow Execution Execution (WEE). Instead of interpreting an XML
based workflow description language like BPEL, the WEE uses a minimized but
expressive set of statements that runs directly on to of a virtual machine that
supports the Ruby language.Frameworks/Virtual Machines supporting supporting
this language include Java, .NET and there exists also a standalone Virtual
Machine. Using a DSL gives us the advantage of maintaining a very compact code
base of under 400 lines of code, as the host programming language implements
all the concepts like parallelism, threads, checking for syntactic correctness.
The implementation just hooks into existing statements to keep track of the
workflow and deliver information about current existing context variables and
state to the environment that embeds WEE
Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud
With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00
Security, Performance and Energy Trade-offs of Hardware-assisted Memory Protection Mechanisms
The deployment of large-scale distributed systems, e.g., publish-subscribe
platforms, that operate over sensitive data using the infrastructure of public
cloud providers, is nowadays heavily hindered by the surging lack of trust
toward the cloud operators. Although purely software-based solutions exist to
protect the confidentiality of data and the processing itself, such as
homomorphic encryption schemes, their performance is far from being practical
under real-world workloads.
The performance trade-offs of two novel hardware-assisted memory protection
mechanisms, namely AMD SEV and Intel SGX - currently available on the market to
tackle this problem, are described in this practical experience.
Specifically, we implement and evaluate a publish/subscribe use-case and
evaluate the impact of the memory protection mechanisms and the resulting
performance. This paper reports on the experience gained while building this
system, in particular when having to cope with the technical limitations
imposed by SEV and SGX.
Several trade-offs that provide valuable insights in terms of latency,
throughput, processing time and energy requirements are exhibited by means of
micro- and macro-benchmarks.Comment: European Commission Project: LEGaTO - Low Energy Toolset for
Heterogeneous Computing (EC-H2020-780681
EAGLE—A Scalable Query Processing Engine for Linked Sensor Data
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
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