ABC: Adaptive Brokerage for the Cloud

The answer to both these questions is usually 'No' even by those familiar with cloud computing. Such uncertainty is caused by 3 main reasons:

1) A bewildering choice of service offerings by the many service providers (e.g. Amazon, Google, Microsoft, etc.).
2) Difficulty in comparison between offered services due to non-uniform description of specifications.
3) High time and monetary costs associated with continuous monitoring of the services on offer from various providers.

This project will introduce more certainty into the selection of cloud services through the introduction of a smart and continuously adaptive cloud broker. The broker will act as an intermediary between end users and cloud service providers in order to enhance service delivery and service value. This brokerage service will be designed to manage heterogeneous cloud offerings, including public, private or hybrid environments. Such brokerage will open up an entirely new multi-cloud marketplace, allowing applications to be simply deployed to the optimal provider and resource type, reducing complexity, vendor lock-in and computational running costs.

Our research will result in a number of fundamental contributions to the cloud computing field. First we will address the problem of how to define, schedule and enforce user-defined Service Level Objectives (SLOs): high-level intentions, which specify the desired end goal of a deployment for applications that span multiple cloud providers with complex inter-dependencies. This will allow users to focus on what (e.g., failure tolerance) needs to be achieved, rather than low-level specifics about how (e.g., deploy to Amazon compute optimised VM) applications are deployed. This automation will in turn help abstract many of the complexities associated with low-level configuration from the user.

Second, we will develop novel lightweight container-based benchmarking techniques, which can gather cloud-level performance metrics in near real-time in a multi-cloud environment. These techniques will be general in scope and allow users to obtain a near real time perspective of the 'weather', or current state across a range of cloud providers.

Third, we will develop adaptive machine learning strategies for the autonomic and pro-active management of cloud-based applications. The application of machine learning will aid decisions about which providers and resource configuration meet the requirements specified in the SLO, how these trade off against cost, and when to redeploy to different providers, or instance types based on active management, etc.

We are confident that this project will address an urgent and fundamental question: how to leverage cloud infrastructure to quickly, cheaply and efficiently perform vital computational workloads. Solving this problem is crucial to the UK digital economy, which is increasingly reliant on the cloud. The developed smart brokerage framework will enable digital economy stakeholders to optimise their use of cloud resources. This is beneficial to all areas of business, including start-ups and micro-businesses who can benefit greatly from the flexibility created by platform independence and adaptive management strategies.