Various control algorithms are used in autonomic control to maintain Quality of Service (QoS) and Service Level Agreements (SLAs). Controllers are all based to some extent on models of the relationship between resources, QoS measures, and the workload imposed by the environment. This work discusses the range of algorithms with an emphasis on richer and more powerful models to describe non-linear performance relationships, and strong interactions among the system resources. A hierarchical framework is described which accommodates different scopes and timescales of control actions, and different control algorithms. The control algorithms and architectures can be considered in three stages: tuning, load balancing and provisioning. Different situations warrant different solutions, so this work shows how different control algorithms and architectures at the three stages can be combined to fit into different autonomic environments to meet QoS and SLAs across a large variety of workloads.

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In this paper, we propose and implement a distributed autonomic manager that maintains service level agreements (SLA) for each application’ scenario. The proposed autonomic manager supports SLAs by configuring the bandwidth ratios for each application scenario and uses an overlay network as an infrastructure. The most important aspect of the proposed autonomic manager is its scalability which allows us to deal with geographically distributed cloud-based applications and a large volume of computation. This can be useful in look ahead optimization and in adaptations using complex models, such as machine learning. We formally prove the safety and liveness properties of the implemented distributed algorithms. Through experiments on Amazon AWS cloud, using two different use cases, we demonstrate the elasticity and flexibility of the autonomic manager as a measure of its applicability to different cloud applications with different types of workloads. Experiments also demonstrate that increasing the size of a look ahead window, up to a certain size, improves the accuracy of the adaptation decisions up to 50%.

The SHriMP (Simple Hierarchical Multi-Perspective) visualization technique was designed to enhance how people browse and explore complex information spaces. SHriMP uses a nested graph view to present information that is hierarchically structured. It introduces the concept of nested interchangeable views to allow a user to explore multiple perspectives of information at different levels of abstraction. SHriMP combines a hypertext following metaphor with animated panning and zooming motions over the nested graph to provide continuous orientation and contextual cues for the user. In this demo, we show how these ideas are proving useful in the areas of software visualization, knowledge management and flow diagram visualization.

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