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 Current monitoring solutions, including many of those in the open-source domain rely on outdated concepts including manual deployment and configuration, centralised data collection and adapt poorly to membership churn.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.Comment: Extended Abstract for the ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2013) Poster Trac

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.Comment: 6th International Workshop on Web Intelligence & Communities (WIC14), Proceedings of the companion publication of the 23rd International Conference on World Wide Web (WWW 2014

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

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.Comment: To appear in Proceedings of the IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC 2014

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