arXiv subjects

Adam Barker

Publications and source records attributed to Adam Barker.

54 records · Page 3Linked to original sources

arXiv2014

Executing Bag of Distributed Tasks on the Cloud: Investigating the Trade-offs Between Performance and Cost

Bag of Distributed Tasks (BoDT) can benefit from decentralised execution on the Cloud. However, there is a trade-off between the performance that can be achieved by employing a large number of Cloud VMs for the tasks and the monetary constraints that are often placed by a user. The research reported in this paper is motivated towards investigating this trade-off so that an optimal plan for deploying BoDT applications on the cloud can be generated. A heuristic algorithm, which considers the user's preference of performance and cost is proposed and implemented. The feasibility of the algorithm is demonstrated by generating execution plans for a sample application. The key result is that the algorithm generates optimal execution plans for the application over 91\% of the time.

cs.DC↗

arXiv2014

Optimal Deployment of Geographically Distributed Workflow Engines on the Cloud

When orchestrating Web service workflows, the geographical placement of the orchestration engine(s) can greatly affect workflow performance. Data may have to be transferred across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper, we present a framework that, given a DAG-based workflow specification, computes the op- timal Amazon EC2 cloud regions to deploy the orchestration engines and execute a workflow. The framework incorporates a constraint model that solves the workflow deployment problem, which is generated using an automated constraint modelling system. The feasibility of the framework is evaluated by executing different sample workflows representative of sci- entific workloads. The experimental results indicate that the framework reduces the workflow execution time and provides a speed up of 1.3x-2.5x over centralised approaches.

cs.DC↗

arXiv2014

A Semantic Web of Know-How: Linked Data for Community-Centric Tasks

This paper proposes a novel framework for representing community know-how on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.

cs.AI↗

arXiv2014

Uncovering the Perfect Place: Optimising Workflow Engine Deployment in the Cloud

When orchestrating highly distributed and data-intensive Web service workflows the geographical placement of the orchestration engine can greatly affect the overall performance of a workflow. We present CloudForecast: a Web service framework and analysis tool which, given a workflow specification, computes the optimal Amazon EC2 Cloud region to automatically deploy the orchestration engine and execute the workflow. We use geographical distance of the workflow, network latency and HTTP round-trip time between Amazon Cloud regions and the workflow nodes to find a ranking of Cloud regions. This overall ranking predicts where the workflow orchestration engine should be deployed in order to reduce overall execution time. Our experimental results show that our proposed optimisation strategy, depending on the particular workflow, can speed up execution time on average by 82.25% compared to local execution.

cs.DC↗

arXiv2014

Workflow Partitioning and Deployment on the Cloud using Orchestra

Orchestrating service-oriented workflows is typically based on a design model that routes both data and control through a single point - the centralised workflow engine. This causes scalability problems that include the unnecessary consumption of the network bandwidth, high latency in transmitting data between the services, and performance bottlenecks. These problems are highly prominent when orchestrating workflows that are composed from services dispersed across distant geographical locations. This paper presents a novel workflow partitioning approach, which attempts to improve the scalability of orchestrating large-scale workflows. It permits the workflow computation to be moved towards the services providing the data in order to garner optimal performance results. This is achieved by decomposing the workflow into smaller sub workflows for parallel execution, and determining the most appropriate network locations to which these sub workflows are transmitted and subsequently executed. This paper demonstrates the efficiency of our approach using a set of experimental workflows that are orchestrated over Amazon EC2 and across several geographic network regions.

cs.DC↗

arXiv2014

Academic Cloud Computing Research: Five Pitfalls and Five Opportunities

This discussion paper argues that there are five fundamental pitfalls, which can restrict academics from conducting cloud computing research at the infrastructure level, which is currently where the vast majority of academic research lies. Instead academics should be conducting higher risk research, in order to gain understanding and open up entirely new areas. We call for a renewed mindset and argue that academic research should focus less upon physical infrastructure and embrace the abstractions provided by clouds through five opportunities: user driven research, new programming models, PaaS environments, and improved tools to support elasticity and large-scale debugging. The objective of this paper is to foster discussion, and to define a roadmap forward, which will allow academia to make longer-term impacts to the cloud computing community.

cs.DC↗

arXiv2014

Location, Location, Location: Data-Intensive Distributed Computing in the Cloud

When orchestrating highly distributed and data-intensive Web service workflows the geographical placement of the orchestration engine can greatly affect the overall performance of a workflow. Orchestration engines are typically run from within an organisations' network, and may have to transfer data across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper we present CloudForecast: a Web service framework and analysis tool which given a workflow specification, computes the optimal Amazon EC2 Cloud region to automatically deploy the orchestration engine and execute the workflow. We use geographical distance of the workflow, network latency and HTTP round-trip time between Amazon Cloud regions and the workflow nodes to find a ranking of Cloud regions. This combined set of simple metrics effectively predicts where the workflow orchestration engine should be deployed in order to reduce overall execution time. We evaluate our approach by executing randomly generated data-intensive workflows deployed on the PlanetLab platform in order to rank Amazon EC2 Cloud regions. Our experimental results show that our proposed optimisation strategy, depending on the particular workflow, can speed up execution time on average by 82.25% compared to local execution. We also show that the standard deviation of execution time is reduced by an average of almost 65% using the optimisation strategy.

cs.DC↗

arXiv2013

C2MS: Dynamic Monitoring and Management of Cloud Infrastructures

Server clustering is a common design principle employed by many organisations who require high availability, scalability and easier management of their infrastructure. Servers are typically clustered according to the service they provide whether it be the application(s) installed, the role of the server or server accessibility for example. In order to optimize performance, manage load and maintain availability, servers may migrate from one cluster group to another making it difficult for server monitoring tools to continuously monitor these dynamically changing groups. Server monitoring tools are usually statically configured and with any change of group membership requires manual reconfiguration; an unreasonable task to undertake on large-scale cloud infrastructures. In this paper we present the Cloudlet Control and Management System (C2MS); a system for monitoring and controlling dynamic groups of physical or virtual servers within cloud infrastructures. The C2MS extends Ganglia - an open source scalable system performance monitoring tool - by allowing system administrators to define, monitor and modify server groups without the need for server reconfiguration. In turn administrators can easily monitor group and individual server metrics on large-scale dynamic cloud infrastructures where roles of servers may change frequently. Furthermore, we complement group monitoring with a control element allowing administrator-specified actions to be performed over servers within service groups as well as introduce further customized monitoring metrics. This paper outlines the design, implementation and evaluation of the C2MS.

cs.DC↗

arXiv2013

Undefined By Data: A Survey of Big Data Definitions

The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.

cs.DB↗

arXiv2013

The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter

Analysis of information retrieved from microblogging services such as Twitter can provide valuable insight into public sentiment in a geographic region. This insight can be enriched by visualising information in its geographic context. Two underlying approaches for sentiment analysis are dictionary-based and machine learning. The former is popular for public sentiment analysis, and the latter has found limited use for aggregating public sentiment from Twitter data. The research presented in this paper aims to extend the machine learning approach for aggregating public sentiment. To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed. A dictionary-based approach and a machine learning approach are implemented within the framework and compared using one UK case study, namely the royal birth of 2013. The case study validates the feasibility of the framework for analysis and rapid visualisation. One observation is that there is good correlation between the results produced by the popular dictionary-based approach and the machine learning approach when large volumes of tweets are analysed. However, for rapid analysis to be possible faster methods need to be developed using big data techniques and parallel methods.

cs.CL↗

arXiv2013

RBioCloud: A Light-weight Framework for Bioconductor and R-based Jobs on the Cloud

Large-scale ad hoc analytics of genomic data is popular using the R-programming language supported by 671 software packages provided by Bioconductor. More recently, analytical jobs are benefitting from on-demand computing and storage, their scalability and their low maintenance cost, all of which are offered by the cloud. While Biologists and Bioinformaticists can take an analytical job and execute it on their personal workstations, it remains challenging to seamlessly execute the job on the cloud infrastructure without extensive knowledge of the cloud dashboard. How analytical jobs can not only with minimum effort be executed on the cloud, but also how both the resources and data required by the job can be managed is explored in this paper. An open-source light-weight framework for executing R-scripts using Bioconductor packages, referred to as `RBioCloud', is designed and developed. RBioCloud offers a set of simple command-line tools for managing the cloud resources, the data and the execution of the job. Three biological test cases validate the feasibility of RBioCloud. The framework is publicly available from http://www.rbiocloud.com.

cs.DC↗

arXiv2013

A Cloud Computing Survey: Developments and Future Trends in Infrastructure as a Service Computing

Cloud computing is a recent paradigm based around the notion of delivery of resources via a service model over the Internet. Despite being a new paradigm of computation, cloud computing owes its origins to a number of previous paradigms. The term cloud computing is well defined and no longer merits rigorous taxonomies to furnish a definition. Instead this survey paper considers the past, present and future of cloud computing. As an evolution of previous paradigms, we consider the predecessors to cloud computing and what significance they still hold to cloud services. Additionally we examine the technologies which comprise cloud computing and how the challenges and future developments of these technologies will influence the field. Finally we examine the challenges that limit the growth, application and development of cloud computing and suggest directions required to overcome these challenges in order to further the success of cloud computing.

cs.DC↗

arXiv2013

V-BOINC: The Virtualization of BOINC

The Berkeley Open Infrastructure for Network Computing (BOINC) is an open source client-server middleware system created to allow projects with large computational requirements, usually set in the scientific domain, to utilize a technically unlimited number of volunteer machines distributed over large physical distances. However various problems exist deploying applications over these heterogeneous machines using BOINC: applications must be ported to each machine architecture type, the project server must be trusted to supply authentic applications, applications that do not regularly checkpoint may lose execution progress upon volunteer machine termination and applications that have dependencies may find it difficult to run under BOINC. To solve such problems we introduce virtual BOINC, or V-BOINC, where virtual machines are used to run computations on volunteer machines. Application developers can then compile their applications on a single architecture, checkpointing issues are solved through virtualization API's and many security concerns are addressed via the virtual machine's sandbox environment. In this paper we focus on outlining a unique approach on how virtualization can be introduced into BOINC and demonstrate that V-BOINC offers acceptable computational performance when compared to regular BOINC. Finally we show that applications with dependencies can easily run under V-BOINC in turn increasing the computational potential volunteer computing offers to the general public and project developers.

cs.DC↗

arXiv2013

Monitoring Large-Scale Cloud Systems with Layered Gossip Protocols

Monitoring is an essential aspect of maintaining and developing computer systems that increases in difficulty proportional to the size of the system. The need for robust monitoring tools has become more evident with the advent of cloud computing. Infrastructure as a Service (IaaS) clouds allow end users to deploy vast numbers of virtual machines as part of dynamic and transient architectures. Current monitoring solutions, including many of those in the open-source domain rely on outdated concepts including manual deployment and configuration, centralised data collection and adapt poorly to membership churn. In this paper we propose the development of a cloud monitoring suite to provide scalable and robust lookup, data collection and analysis services for large-scale cloud systems. In lieu of centrally managed monitoring we propose a multi-tier architecture using a layered gossip protocol to aggregate monitoring information and facilitate lookup, information collection and the identification of redundant capacity. This allows for a resource aware data collection and storage architecture that operates over the system being monitored. This in turn enables monitoring to be done in-situ without the need for significant additional infrastructure to facilitate monitoring services. We evaluate this approach against alternative monitoring paradigms and demonstrate how our solution is well adapted to usage in a cloud-computing context.

cs.DC↗

arXiv2013

An Architecture for Decentralised Orchestration of Web Service Workflows

Service-oriented workflows are typically executed using a centralised orchestration approach that presents significant scalability challenges. These challenges include the consumption of network bandwidth, degradation of performance, and single-points of failure. We provide a decentralised orchestration architecture that attempts to address these challenges. Our architecture adopts a design model that permits the computation to be moved "closer" to services in a workflow. This is achieved by partitioning workflows specified using our simple dataflow language into smaller fragments, which may be sent to remote locations for execution.

cs.DC↗

arXiv2013

A Dataflow Language for Decentralised Orchestration of Web Service Workflows

Orchestrating centralised service-oriented workflows presents significant scalability challenges that include: the consumption of network bandwidth, degradation of performance, and single points of failure. This paper presents a high-level dataflow specification language that attempts to address these scalability challenges. This language provides simple abstractions for orchestrating large-scale web service workflows, and separates between the workflow logic and its execution. It is based on a data-driven model that permits parallelism to improve the workflow performance. We provide a decentralised architecture that allows the computation logic to be moved "closer" to services involved in the workflow. This is achieved through partitioning the workflow specification into smaller fragments that may be sent to remote orchestration services for execution. The orchestration services rely on proxies that exploit connectivity to services in the workflow. These proxies perform service invocations and compositions on behalf of the orchestration services, and carry out data collection, retrieval, and mediation tasks. The evaluation of our architecture implementation concludes that our decentralised approach reduces the execution time of workflows, and scales accordingly with the increasing size of data sets.

cs.DC↗

arXiv2009

Optimizing Service Orchestrations

As the number of services and the size of data involved in workflows increases, centralised orchestration techniques are reaching the limits of scalability. In the classic orchestration model, all data passes through a centralised engine, which results in unnecessary data transfer, wasted bandwidth and the engine to become a bottleneck to the execution of a workflow. This paper presents and evaluates the Circulate architecture which maintains the robustness and simplicity of centralised orchestration, but facilitates choreography by allowing services to exchange data directly with one another. Circulate could be realised within any existing workflow framework, in this paper, we focus on WS-Circulate, a Web services based implementation. Taking inspiration from the Montage workflow, a number of common workflow patterns (sequence, fan-in and fan-out), input to output data size relationships and network configurations are identified and evaluated. The performance analysis concludes that a substantial reduction in communication overhead results in a 2-4 fold performance benefit across all patterns. An end-to-end pattern through the Montage workflow results in an 8 fold performance benefit and demonstrates how the advantage of using the Circulate architecture increases as the complexity of a workflow grows.

cs.DC↗

arXiv2009

An e-Infrastructure for Collaborative Research in Human Embryo Development

Within the context of the EU Design Study Developmental Gene Expression Map, we identify a set of challenges when facilitating collaborative research on early human embryo development. These challenges bring forth requirements, for which we have identified solutions and technology. We summarise our solutions and demonstrate how they integrate to form an e-infrastructure to support collaborative research in this area of developmental biology.

cs.DC↗