Self-adaptive Grid Resource Monitoring and discovery

The Grid provides a novel platform where the scientific and engineering communities can share data and computation across multiple administrative domains. There are several key services that must be offered by Grid middleware; one of them being the Grid Information Service( GIS). A GIS is a Grid middleware component which maintains information about hardware, software, services and people participating in a virtual organisation( VO). There is an inherent need in these systems for the delivery of reliable performance. This thesis describes a number of approaches which detail the development and application of a suite of benchmarks for the prediction of the process of resource discovery and monitoring on the Grid. A series of experimental studies of the characterisation of performance using benchmarking, are carried out. Several novel predictive algorithms are presented and evaluated in terms of their predictive error. Furthermore, predictive methods are developed which describe the behaviour of MDS2 for a variable number of user requests. The MDS is also extended to include job information from a local scheduler; this information is queried using requests of greatly varying complexity. The response of the MDS to these queries is then assessed in terms of several performance metrics. The benchmarking of the dynamic nature of information within MDS3 which is based on the Open Grid Services Architecture (OGSA), and also the successor to MDS2, is also carried out. The performance of both the pull and push query mechanisms is analysed. GridAdapt (Self-adaptive Grid Resource Monitoring) is a new system that is proposed, built upon the Globus MDS3 benchmarking. It offers self-adaptation, autonomy and admission control at the Index Service, whilst ensuring that the MIDS is not overloaded and can meet its quality-of-service,f or example,i n terms of its average response time for servicing synchronous queries and the total number of queries returned per unit time

Checkpoint and run-time adaptation with pluggable parallelisation

Enabling applications for computational Grids requires new approaches to develop applications that can effectively cope with resource volatility. Applications must be resilient to resource faults, adapting the behaviour to available resources. This paper describes an approach to application-level adaptation that efficiently supports application-level checkpointing. The key of this work is the concept of pluggable parallelisation, which localises parallelisation issues into multiple modules that can be (un)plugged to match resource availability. This paper shows how pluggable parallelisation can be extended to effectively support checkpointing and run-time adaptation. We present the developed pluggable mechanism that helps the programmer to include checkpointing in the base (sequential). Based on these mechanisms and on previous work on pluggable parallelisation, our approach is able to automatically add support for checkpointing in parallel execution environments. Moreover, applications can adapt from a sequential execution to a multi-cluster configuration. Adaptation can be performed by checkpointing the application and restarting on a different mode or can be performed during run-time. Pluggable parallelisation intrinsically promotes the separation of software functionality from fault-tolerance and adaptation issues facilitating their analysis and evolution. The work presented in this paper reinforces this idea by showing the feasibility of the approach and performance benefits that can be achieved.(undefined

Autonomic Computing

Autonomic computing (AC) has as its vision the creation of self-managing systems to address today’s con-cerns of complexity and total cost of ownership while meeting tomorrow’s needs for pervasive and ubiquitous computation and communication. This paper reports on the latest auto-nomic systems research and technologies to influence the industry; it looks behind AC, summarising what it is, the current state-of-the-art research, related work and initiatives, highlights research and technology transfer issues and concludes with further and recommended reading

PaaS-IaaS Inter-Layer Adaptation in an Energy-Aware Cloud Environment

Cloud computing providers resort to a variety of techniques to improve energy consumption at each level of the cloud computing stack. Most of these techniques consider resource-level energy optimization at IaaS layer. This paper argues energy gains can be obtained by creating a cooperation between the PaaS layer (in charge of hosting the application/service) and the IaaS layer (in charge of handling the computing resources). It presents a novel method based on steering information and decision taking to trigger the PaaS and IaaS layers to adapt their energy mode in service operation, therefore enabling the Cloud stack to actively adapt to changing situations. Experimental results demonstrate such adaptation achieves dynamic energy management in each of the PaaS and IaaS cloud layers

Developing and operating time critical applications in clouds: the state of the art and the SWITCH approach

Cloud environments can provide virtualized, elastic, controllable and high quality on-demand services for supporting complex distributed applications. However, the engineering methods and software tools used for developing, deploying and executing classical time critical applications do not, as yet, account for the programmability and controllability provided by clouds, and so time critical applications cannot yet benefit from the full potential of cloud technology. This paper reviews the state of the art of technologies involved in developing time critical cloud applications, and presents the approach of a recently funded EU H2020 project: the Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications (SWITCH). SWITCH aims to improve the existing development and execution model of time critical applications by introducing a novel conceptual model—the application-infrastructure co-programming and control model—in which application QoS and QoE, together with the programmability and controllability of cloud environments, is included in the complete application lifecycle

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/