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%

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.Comment: Paper published in the 8th IEEE International Conference on Cloud Computing (CLOUD 2015

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.Comment: 8th IEEE International Conference on Cloud Computing (CLOUD 2015

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.Comment: Visionary Track of the IEEE 11th World Congress on Services (IEEE SERVICES 2015

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

Capital structure study: Accounting and statistical issues

Capital structure is one of the most important topics in Corporate Finance, and still attracting many famous scholars to explain factors that affect firms’ choice of capital structure. In this paper, we first discuss the importance of capital structure to a company itself, and to its investors. Secondly, I will introduce the concept of operating leases as an important component of debt. Then, we review the most famous capital structure theories to see what factors affects theoretically optimal capital structure and CFO decisions on capital structure. Finally, I point out some statistical issues in studying capital structure and suggest some remedies to these problems

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.Comment: 6 pages, 6th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom) 2014, Singapor

Glassy carbon electrode modified with ex-in-situ gold film – A simple and effective working electrode for As(III) determination by using differential pulse anodic stripping voltammetry

An easy-to-make new working electrode, an ex-in-situ AuF/GCE, was developed for trace As(III) detection. A gold film electrode prepared ex-situ was re-plated in-situ during each arsenic deposition step by adding Au(III) into the analyte solution. The factors affecting arsenic stripping response, namely, gold film preparation conditions, electrolyte concentration, electrode cleaning potential, cleaning time, deposition potential, and deposition time, were investigated. Compared with the traditional gold film electrodes prepared ex-situ, the new electrode has better precision and linearity of arsenic differential pulse anodic stripping voltammetry responses. For a deposition time of 90 s at –200 mV, the new electrode exhibits a sensitivity, a limit of detection (3-Sigma), a limit of quantitation of 0.103 μA·L·μg–1, 0.4 μg·L–1, and 1.3 μg·L–1, respectively

Algorithms for optimising heterogeneous Cloud virtual machine clusters

This research was supported by an Amazon Web Services Education Research grant.It is challenging to execute an application in a heterogeneous cloud cluster, which consists of multiple types of virtual machines with different performance capabilities and prices. This paper aims to mitigate this challenge by proposing a scheduling mechanism to optimise the execution of Bag-of-Task jobs on a heterogeneous cloud cluster. The proposed scheduler considers two approaches to select suitable cloud resources for executing a user application while satisfying pre-defined Service Level Objectives (SLOs) both in terms of execution deadline and minimising monetary cost. Additionally, a mechanism for dynamic re-assignment of jobs during execution is presented to resolve potential violation of SLOs. Experimental studies are performed both in simulation and on a public cloud using real-world applications. The results highlight that our scheduling approaches result in cost saving of up to 31% in comparison to naive approaches that only employ a single type of virtual machine in a homogeneous cluster. Dynamic reassignment completely prevents deadline violation in the best-case and reduces deadline violations by 95% in the worst-case scenario.Postprin

Quantitative results for the Fleming-Viot particle system in discrete space

We show, for a class of discrete Fleming-Viot type particle systems, that the convergence to the equilibrium is exponential for a suitable Wassertein coupling distance. The approach provides an explicit quantitative estimate on the rate of convergence. As a consequence, we show that the conditioned process converges exponentially fast to a unique quasi-stationary distribution. Moreover, by estimating the two-particle correlations, we prove that the Fleming-Viot process converges, uniformly in time, to the conditioned process with an explicit rate of convergence. We illustrate our results on the examples of the complete graph and of the two point space