arXiv subjects

Adam Barker

Publications and source records attributed to Adam Barker.

At least 37 records · Page 2Linked to original sources

arXiv2017

MAMoC: Multisite Adaptive Offloading Framework for Mobile Cloud Applications

This paper presents MAMoC, a framework which brings together a diverse range of infrastructure types including mobile devices, cloudlets, and remote cloud resources under one unified API. MAMoC allows mobile applications to leverage the power of multiple offloading destinations. MAMoC's intelligent offloading decision engine adapts to the contextual changes in this heterogeneous environment, in order to reduce the overall runtime for both single-site and multi-site offloading scenarios. MAMoC is evaluated through a set of offloading experiments, which evaluate the performance of our offloading decision engine. The results show that offloading computation using our framework can reduce the overall task completion time for both single-site and multi-site offloading scenarios.

cs.DC↗

arXiv2017

Towards a Comprehensive Framework for Telemetry Data in HPC Environments

Current HPC platforms do not provide the infrastructure, interfaces and conceptual models to collect, store, analyze, and access such data. Today, applications depend on application and platform specific techniques for collecting telemetry data; introducing significant development overheads that inhibit portability and mobility. The development and adoption of adaptive, context-aware strategies is thereby impaired. To facilitate 2nd generation applications, more efficient application development, and swift adoption of adaptive applications in production, a comprehensive framework for telemetry data management must be provided by future HPC systems and services. We introduce a conceptual model and a software framework to collect, store, analyze, and exploit streams of telemetry data generated by HPC systems and their applications. We show how this framework can be integrated with HPC platform architectures and how it enables common application execution strategies.

cs.DC↗

arXiv2017

Rethinking High Performance Computing Platforms: Challenges, Opportunities and Recommendations

A new class of Second generation high-performance computing applications with heterogeneous, dynamic and data-intensive properties have an extended set of requirements, which cover application deployment, resource allocation, -control, and I/O scheduling. These requirements are not met by the current production HPC platform models and policies. This results in a loss of opportunity, productivity and innovation for new computational methods and tools. It also decreases effective system utilization for platform providers due to unsupervised workarounds and rogue resource management strategies implemented in application space. In this paper we critically discuss the dominant HPC platform model and describe the challenges it creates for second generation applications because of its asymmetric resource view, interfaces and software deployment policies. We present an extended, more symmetric and application-centric platform model that adds decentralized deployment, introspection, bidirectional control and information flow and more comprehensive resource scheduling. We describe cHPC: an early prototype of a non-disruptive implementation based on Linux Containers (LXC). It can operate alongside existing batch queuing systems and exposes a symmetric platform API without interfering with existing applications and usage modes. We see our approach as a viable, incremental next step in HPC platform evolution that benefits applications and platform providers alike. To demonstrate this further, we layout out a roadmap for future research and experimental evaluation.

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arXiv2017

Quasiparticle energy in a strongly interacting homogeneous Bose-Einstein condensate

Using two-photon Bragg spectroscopy, we study the energy of particle-like excitations in a strongly interacting homogeneous Bose-Einstein condensate, and observe dramatic deviations from Bogoliubov theory. In particular, at large scattering length $a$ the shift of the excitation resonance from the free-particle energy changes sign from positive to negative. For an excitation with wavenumber $q$, this sign change occurs at $a \approx 4/(πq)$, in agreement with the Feynman energy relation and the static structure factor expressed in terms of the two-body contact. For $a \gtrsim 3/q$ we also see a breakdown of this theory, and better agreement with calculations based on the Wilson operator product expansion. Neither theory explains our observations across all interaction regimes, inviting further theoretical efforts.

cond-mat.quant-gas↗

arXiv2016

Cloud Benchmarking For Maximising Performance of Scientific Applications

How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machines (VMs) with different performance capabilities. The research reported in this paper addresses the above question by proposing a six step benchmarking methodology in which a user provides a set of weights that indicate how important memory, local communication, computation and storage related operations are to an application. The user can either provide a set of four abstract weights or eight fine grain weights based on the knowledge of the application. The weights along with benchmarking data collected from the cloud are used to generate a set of two rankings - one based only on the performance of the VMs and the other takes both performance and costs into account. The rankings are validated on three case study applications using two validation techniques. The case studies on a set of experimental VMs highlight that maximum performance can be achieved by the three top ranked VMs and maximum performance in a cost-effective manner is achieved by at least one of the top three ranked VMs produced by the methodology.

cs.DC↗

arXiv2016

Integrating Know-How into the Linked Data Cloud

This paper presents the first framework for integrating procedural knowledge, or "know-how", into the Linked Data Cloud. Know-how available on the Web, such as step-by-step instructions, is largely unstructured and isolated from other sources of online knowledge. To overcome these limitations, we propose extending to procedural knowledge the benefits that Linked Data has already brought to representing, retrieving and reusing declarative knowledge. We describe a framework for representing generic know-how as Linked Data and for automatically acquiring this representation from existing resources on the Web. This system also allows the automatic generation of links between different know-how resources, and between those resources and other online knowledge bases, such as DBpedia. We discuss the results of applying this framework to a real-world scenario and we show how it outperforms existing manual community-driven integration efforts.

cs.AI↗

arXiv2016

DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool

Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.

cs.DC↗

arXiv2016

A Linked Data Scalability Challenge: Concept Reuse Leads to Semantic Decay

The increasing amount of available Linked Data resources is laying the foundations for more advanced Semantic Web applications. One of their main limitations, however, remains the general low level of data quality. In this paper we focus on a measure of quality which is negatively affected by the increase of the available resources. We propose a measure of semantic richness of Linked Data concepts and we demonstrate our hypothesis that the more a concept is reused, the less semantically rich it becomes. This is a significant scalability issue, as one of the core aspects of Linked Data is the propagation of semantic information on the Web by reusing common terms. We prove our hypothesis with respect to our measure of semantic richness and we validate our model empirically. Finally, we suggest possible future directions to address this scalability problem.

cs.AI↗

arXiv2016

Container-Based Cloud Virtual Machine Benchmarking

With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight - time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed. In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near real-time on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite - Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.

cs.DC↗

arXiv2015

Budget Constrained Execution of Multiple Bag-of-Tasks Applications on the Cloud

Optimising the execution of Bag-of-Tasks (BoT) applications on the cloud is a hard problem due to the trade- offs between performance and monetary cost. The problem can be further complicated when multiple BoT applications need to be executed. In this paper, we propose and implement a heuristic algorithm that schedules tasks of multiple applications onto different cloud virtual machines in order to maximise performance while satisfying a given budget constraint. Current approaches are limited in task scheduling since they place a limit on the number of cloud resources that can be employed by the applications. However, in the proposed algorithm there are no such limits, and in comparison with other approaches, the algorithm on average achieves an improved performance of 10%. The experimental results also highlight that the algorithm yields consistent performance even with low budget constraints which cannot be achieved by competing approaches.

cs.DC↗

arXiv2015

Task Scheduling on the Cloud with Hard Constraints

Scheduling Bag-of-Tasks (BoT) applications on the cloud can be more challenging than grid and cluster environ- ments. This is because a user may have a budgetary constraint or a deadline for executing the BoT application in order to keep the overall execution costs low. The research in this paper is motivated to investigate task scheduling on the cloud, given two hard constraints based on a user-defined budget and a deadline. A heuristic algorithm is proposed and implemented to satisfy the hard constraints for executing the BoT application in a cost effective manner. The proposed algorithm is evaluated using four scenarios that are based on the trade-off between performance and the cost of using different cloud resource types. The experimental evaluation confirms the feasibility of the algorithm in satisfying the constraints. The key observation is that multiple resource types can be a better alternative to using a single type of resource.

cs.DC↗

arXiv2015

Ad hoc Cloud Computing: From Concept to Realization

This paper presents the first complete, integrated and end-to-end solution for ad hoc cloud computing environments. Ad hoc clouds harvest resources from existing sporadically available, non-exclusive (i.e. primarily used for some other purpose) and unreliable infrastructures. In this paper we discuss the problems ad hoc cloud computing solves and outline our architecture which is based on BOINC.

cs.DC↗

arXiv2015

Executing Bag of Distributed Tasks on Virtually Unlimited Cloud Resources

Bag-of-Distributed-Tasks (BoDT) application is the collection of identical and independent tasks each of which requires a piece of input data located around the world. As a result, Cloud computing offers an ef- fective way to execute BoT application as it not only consists of multiple geographically distributed data centres but also allows a user to pay for what she actually uses only. In this paper, BoDT on the Cloud using virtually unlimited cloud resources. A heuristic algorithm is proposed to find an execution plan that takes budget constraints into account. Compared with other approaches, with the same given budget, our algorithm is able to reduce the overall execution time up to 50%.

cs.DC↗

arXiv2015

Cloud Services Brokerage: A Survey and Research Roadmap

A Cloud Services Brokerage (CSB) acts as an intermediary between cloud service providers (e.g., Amazon and Google) and cloud service end users, providing a number of value adding services. CSBs as a research topic are in there infancy. The goal of this paper is to provide a concise survey of existing CSB technologies in a variety of areas and highlight a roadmap, which details five future opportunities for research.

cs.DC↗

arXiv2015

Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines

Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating investigation leads that help identify systems faults, and extends our previous work in this area by leveraging Restricted Boltzmann Machines (RBMs) and contrastive divergence learning to analyse changes in historical feature data. This allows us to heuristically identify the root cause of a fault, and demonstrate an improvement to the state of the art by showing feature data can be predicted heuristically beyond a single instance to include entire sequences of information.

cs.AI↗

arXiv2014

Are Clouds Ready to Accelerate Ad hoc Financial Simulations?

Applications employed in the financial services industry to capture and estimate a variety of risk metrics are underpinned by stochastic simulations which are data, memory and computationally intensive. Many of these simulations are routinely performed on production-based computing systems. Ad hoc simulations in addition to routine simulations are required to obtain up-to-date views of risk metrics. Such simulations are currently not performed as they cannot be accommodated on production clusters, which are typically over committed resources. Scalable, on-demand and pay-as-you go Virtual Machines (VMs) offered by the cloud are a potential platform to satisfy the data, memory and computational constraints of the simulation. However, "Are clouds ready to accelerate ad hoc financial simulations?" The research reported in this paper aims to experimentally verify this question by developing and deploying an important financial simulation, referred to as 'Aggregate Risk Analysis' on the cloud. Parallel techniques to improve efficiency and performance of the simulations are explored. Challenges such as accommodating large input data on limited memory VMs and rapidly processing data for real-time use are surmounted. The key result of this investigation is that Aggregate Risk Analysis can be accommodated on cloud VMs. Acceleration of up to 24x using multiple hardware accelerators over the implementation on a single accelerator, 6x over a multiple core implementation and approximately 60x over a baseline implementation was achieved on the cloud. However, computational time is wasted for every dollar spent on the cloud due to poor acceleration over multiple virtual cores. Interestingly, private VMs can offer better performance than public VMs on comparable underlying hardware.

cs.DC↗

arXiv2014

BigExcel: A Web-Based Framework for Exploring Big Data in Social Sciences

This paper argues that there are three fundamental challenges that need to be overcome in order to foster the adoption of big data technologies in non-computer science related disciplines: addressing issues of accessibility of such technologies for non-computer scientists, supporting the ad hoc exploration of large data sets with minimal effort and the availability of lightweight web-based frameworks for quick and easy analytics. In this paper, we address the above three challenges through the development of 'BigExcel', a three tier web-based framework for exploring big data to facilitate the management of user interactions with large data sets, the construction of queries to explore the data set and the management of the infrastructure. The feasibility of BigExcel is demonstrated through two Yahoo Sandbox datasets. The first dataset is the Yahoo Buzz Score data set we use for quantitatively predicting trending technologies and the second is the Yahoo n-gram corpus we use for qualitatively inferring the coverage of important events. A demonstration of the BigExcel framework and source code is available at http://bigdata.cs.st-andrews.ac.uk/projects/bigexcel-exploring-big-data-for-social-sciences/.

cs.DC↗

arXiv2014

Cloud Benchmarking for Performance

How can applications be deployed on the cloud to achieve maximum performance? This question has become significant and challenging with the availability of a wide variety of Virtual Machines (VMs) with different performance capabilities in the cloud. The above question is addressed by proposing a six step benchmarking methodology in which a user provides a set of four weights that indicate how important each of the following groups: memory, processor, computation and storage are to the application that needs to be executed on the cloud. The weights along with cloud benchmarking data are used to generate a ranking of VMs that can maximise performance of the application. The rankings are validated through an empirical analysis using two case study applications; the first is a financial risk application and the second is a molecular dynamics simulation, which are both representative of workloads that can benefit from execution on the cloud. Both case studies validate the feasibility of the methodology and highlight that maximum performance can be achieved on the cloud by selecting the top ranked VMs produced by the methodology.

cs.DC↗