Robust execution of service workflows using redundancy and advance reservations

In this paper, we develop a novel algorithm that allows service consumers to execute business processes (or workflows) of interdependent services in a dependable manner within tight time-constraints. In particular, we consider large inter-organisational service-oriented systems, where services are offered by external organisations that demand financial remuneration and where their use has to be negotiated in advance using explicit service-level agreements (as is common in Grids and cloud computing). Here, different providers often offer the same type of service at varying levels of quality and price. Furthermore, some providers may be less trustworthy than others, possibly failing to meet their agreements. To control this unreliability and ensure end-to-end dependability while maximising the profit obtained from completing a business process, our algorithm automatically selects the most suitable providers. Moreover, unlike existing work, it reasons about the dependability properties of a workflow, and it controls these by using service redundancy for critical tasks and by planning for contingencies. Finally, our algorithm reserves services for only parts of its workflow at any time, in order to retain flexibility when failures occur. We show empirically that our algorithm consistently outperforms existing approaches, achieving up to a 35-fold increase in profit and successfully completing most workflows, even when the majority of providers fail

Flexible Service Provisioning with Advance Agreements

In this paper, we develop a novel algorithm that allows service consumer agents to automatically select and provision service provider agents for their workflows in highly dynamic and uncertain computational service economies. In contrast to existing work, our algorithm reasons explicitly about the impact of failures on the overall feasibility of a workflow, and it mitigates them by proactively provisioning multiple providers in parallel for particularly critical tasks and by explicitly planning for contingencies. Furthermore, our algorithm provisions only part of its workflow at any given time, in order to retain flexibility and to decrease the potential for missing negotiated service time slots. We show empirically that current approaches are unable to achieve a high utility in such uncertain and dynamic environments; whereas our algorithm consistently outperforms them over a range of environments. Specifically, our approach can achieve up to a 27-fold increase in utility and successfully completes most workflows within a strict deadline, even when the majority of providers do not honour their contracts

Sensitivity Analysis of Flexible Provisioning

This technical report contains a sensitivity analysis to extend our previous work. We show that our flexible service provisioning strategy is robust to inaccurate performance information (when the available information is within 10% of the true value), and that it degrades gracefully as the information becomes less accurate. We also identify and discuss one particular case where inaccurate information may lead to undesirable losses in highly unreliable environments

An Effective Strategy for the Flexible Provisioning of Service Workflows

Recent advances in service-oriented frameworks and semantic Web technologies have enabled software agents to discover and invoke resources over large distributed systems, in order to meet their high-level objectives. However, most work has failed to acknowledge that such systems are complex and dynamic multi-agent systems, where service providers act autonomously and follow their own decision-making procedures. Hence, the behaviour of these providers is inherently uncertain - services may fail or take uncertain amounts of time to complete. In this work, we address this uncertainty and take an agent-oriented approach to the problem of provisioning service providers for the constituent tasks of abstract workflows. Specifically, we describe an algorithm that uses redundancy to deal with unreliable providers, and we demonstrate that it achieves an 8-14% improvement in average utility over previous work, while performing up to 6 times as well as approaches that do not consider service uncertainty. We also show that our algorithm performs well in the presence of inaccurate service performance information

Flexible QoS-Based Service Selection and Provisioning in Large-Scale Grids

As Grids become larger, more open and dynamic in nature, it will inevitably become necessary to deal with service failures and mitigate uncertainty in the execution of large service workflows. To this end, we propose a decision-theoretic approach to the provisioning problem (i.e., selecting service instances for the tasks of an abstract workflow). Our approach introduces redundancy into workflows to reduce the probability of failures, it dynamically re-provisions failed services and it negotiates advance agreements with providers when this is beneficial

Flexible Provisioning of Service Workflows

Service-oriented computing is a promising paradigm for highly distributed and complex computer systems. In such systems, services are offered by provider agents over a computer network and automatically discovered and provisioned by consumer agents that need particular resources or behaviours for their workflows. However, in open systems where there are significant degrees of uncertainty and dynamism and where the agents are self-interested, the provisioning of these services needs to be performed in a more flexible way than has hitherto been considered. To this end, we devise a number of heuristics that vary provisioning according to the predicted performance of provider agents. We then empirically benchmark our algorithms and show that they lead to a 350% improvement in average utility, while successfully completing 5-6 times as many workflows as current approaches

Gyrochronology: TESS Light Curve Analysis

Gyrochronology is the observed correlation between the age of a cool main-sequence star like the Sun and its rotational period. Various methods can be used to determine stellar rotation periods, however NASA’s Kepler mission and NASA’s Transiting Exoplanet Survey Satellite (TESS) mission provide complementary data for this type of project. Kepler focused on a very small observational field for almost four continuous years, whereas TESS continues to survey the entire night sky for intervals of about one month at a time. Due to this difference in cadence, it is important to compare the resulting rotation periods obtained from these surveys. We have constructed TESS light curves to compare to existing Kepler light curves of the same target stars observed at different epochs. Identifying the conditions under which TESS rotation periods may differ from those derived from the Kepler mission can help identify the random and systematic biases of each data set. This poster presents some preliminary results of this comparison. Support from NSF grants AST-1910396, AST-2108975 and NASA grants 80NSSC22K0622, 80NSSC21K0245, and NNX16AB76G is gratefully acknowledged. *Florida Gulf Coast Universit

Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures

Substructure in the Coma Cluster: Giants vs Dwarfs

The processes that form and shape galaxy clusters, such as infall, mergers and dynamical relaxation, tend to generate distinguishable differences between the distributions of a cluster's giant and dwarf galaxies. Thus the dynamics of dwarf galaxies in a cluster can provide valuable insights into its dynamical history. With this in mind, we look for differences between the spatial and velocity distributions of giant (b18) galaxies in the Coma cluster. Our redshift sample contains new measurements from the 2dF and WYFFOS spectrographs, making it more complete at faint magnitudes than any previously studied sample of Coma galaxies. It includes 745 cluster members - 452 giants and 293 dwarfs. We find that the line-of-sight velocity distribution of the giants is significantly non-Gaussian, but not that for the dwarfs. A battery of statistical tests of both the spatial and localised velocity distributions of the galaxies in our sample finds no strong evidence for differences between the giant and dwarf populations. These results rule out the cluster as a whole having moved significantly towards equipartition, and they are consistent with the cluster having formed via mergers between dynamically-relaxed subclusters.Comment: 23 pages, 6 figures, to appear in Ap