Autonomic Management And Performance Optimization For Cloud Computing Services

Cloud computing has become an increasingly important computing paradigm. It offers three levels of on-demand services to cloud users: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) . The success of cloud services heavily depends on the effectiveness of cloud management strategies. In this dissertation work, we aim to design and implement an automatic cloud management system to improve application performance, increase platform efficiency and optimize resource allocation. For large-scale multi-component applications, especially web-based cloud applica- tions, parameter setting is crucial to the service availability and quality. The increas- ing system complexity requires an automatic and efficient application configuration strategy. To improve the quality of application services, we propose a reinforcement learning(RL)-based autonomic configuration framework. It is able to adapt appli- cation parameter settings not only to the variations in workload, but also to the change of virtual resource allocation. The RL approach is enhanced with an efficient initialization policy to reduce the learning time for online decision. Experiments on Xen-based virtual cluster with TPC-W benchmarks show that the framework can drive applications into a optimal configuration in less than 25 iterations. For cloud platform service, one of the key challenges is to efficiently adapt the offered platforms to the virtualized environment, meanwhile maintaining their service features. MapReduce has become an important distributed parallel programming paradigm. Offering MapReduce cloud service presents an attractive usage model for enterprises. In a virtual MapReduce cluster, the interference between virtual machines (VMs) causes performance degradation of map and reduce tasks and renders existing data locality-aware task scheduling policy, like delay scheduling, no longer effective. On the other hand, virtualization offers an extra opportunity of data locality for co-hosted VMs. To address these issues, we present a task scheduling strategy to mitigate interference and meanwhile preserving task data locality for MapReduce applications. The strategy includes an interference-aware scheduling policy, based on a task performance prediction model, and an adaptive delay scheduling algorithm for data locality improvement. Experimental results on a 72-node Xen-based virtual cluster show that the scheduler is able to achieve a speedup of 1.5 to 6.5 times for individual jobs and yield an improvement of up to 1.9 times in system throughput in comparison with four other MapReduce schedulers. Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both virtual machines (VMs) and hosted applications need to be configured on-the fly to adapt to system dynamics. The in- terplay between the layers of VMs and applications further complicates the problem of cloud configuration. Independent tuning of each aspect may not lead to optimal system wide performance. In this work, we propose a framework for coordinated configuration of VMs and resident applications. At the heart of the framework is a model-free hybrid reinforcement learning (RL) approach, which combines the advan- tages of Simplex method and RL method and is further enhanced by the use of system knowledge guided exploration policies. Experimental results on Xen based virtualized environments with TPC-W and TPC-C benchmarks demonstrate that the framework is able to drive a virtual server cluster into an optimal or near-optimal configuration state on the fly, in response to the change of workload. It improves the systems throughput by more than 30% over independent tuning strategies. In comparison with the coordinated tuning strategies based on basic RL or Simplex algorithm, the hybrid RL algorithm gains 25% to 40% throughput improvement

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

Bidding strategy for a virtual power plant for trading energy in the wholesale electricity market

Virtual power plants (VPPs) are an effective way to increase renewable integration. In this PhD research, the concept design and the detailed costs and benefits of implementing a realistic VPP in Western Australia (WA), comprising 67 dwellings, are developed. The VPP is designed to integrate and coordinate an 810kW rooftop solar PV farm, 350kW/700kWh vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and smart appliances through demand management mechanisms. This research develops a robust bidding strategy for the VPP to participate in both load following ancillary service (LFAS) and energy market in the wholesale electricity market in WA considering the uncertainties associated with PV generation and electricity market prices. Using this strategy, the payback period can be improved by 3 years (to a payback period of 6 years) and the internal rate of return (IRR) by 7.5% (to an IRR of 18%) by participating in both markets. The daily average error of the proposed robust method is 2.7% over one year when compared with a robust mathematical method. The computational effort is 0.66 sec for 365 runs for the proposed method compared to 947.10 sec for the robust mathematical method. To engage customers in the demand management schemes by the VPP owner, the gamified approach is adopted to make the exercise enjoyable while not compromising their comfort levels. Seven gamified applications are examined using a developed methodology based on Kim’s model and Fogg’s model, and the most suitable one is determined. The simulation results show that gamification can improve the payback period by 1 to 2 months for the VPP owner. Furthermore, an efficient and fog-based monitoring and control platform is proposed for the VPP to be flexible, scalable, secure, and cost-effective to realise the full capabilities and profitability of the VPP

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling

Self-configured Elastic Database with Deep Q-Learning Approach

Elastic databases have grown in popularity over conventional databases in recent years due to their ability to be allocated with sufficient capacity for peak load. Especially with the support of the cloud platform, which provides flexible resources and low cost, elastic databases on the cloud show their excellent potential in scalability, flexibility, and accessibility. However, the interaction between the cloud layers of virtual machines (VMs) and databases further complicates the issue of cloud configuration to adapt to dynamic workloads. In this paper, I explore a framework for a self-configured elastic database that can optimize the cloud configuration and adaptively allocate resources under the constraints of databases\u27 Service Level Agreement (SLA). At the core of the framework is a Deep Q learning approach, which combines the advantages of Reinforcement Learning (RL) and Deep Learning (DL). The framework is built on Amazon Web Service (AWS)\u27s cloud environment and uses MySQL database for its high availability replication mechanism. Experimental results on the TPC-W benchmark demonstrate that with the implementation of Deep Q learning, the elastic database reduces SLA violation by more than 90\%, in the response to the steep slope of workload change

Continuous-action reinforcement learning for memory allocation in virtualized servers

In a virtualized computing server (node) with multiple Virtual Machines (VMs), it is necessary to dynamically allocate memory among the VMs. In many cases, this is done only considering the memory demand of each VM without having a node-wide view. There are many solutions for the dynamic memory allocation problem, some of which use machine learning in some form. This paper introduces CAVMem (Continuous-Action Algorithm for Virtualized Memory Management), a proof-of-concept mechanism for a decentralized dynamic memory allocation solution in virtualized nodes that applies a continuous-action reinforcement learning (RL) algorithm called Deep Deterministic Policy Gradient (DDPG). CAVMem with DDPG is compared with other RL algorithms such as Q-Learning (QL) and Deep Q-Learning (DQL) in an environment that models a virtualized node. In order to obtain linear scaling and be able to dynamically add and remove VMs, CAVMem has one agent per VM connected via a lightweight coordination mechanism. The agents learn how much memory to bid for or return, in a given state, so that each VM obtains a fair level of performance subject to the available memory resources. Our results show that CAVMem with DDPG performs better than QL and a static allocation case, but it is competitive with DQL. However, CAVMem incurs significant less training overheads than DQL, making the continuous-action approach a more cost-effective solution.This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 754337 (EuroEXA) and the European Union’s 7th Framework Programme under grant agreement number 610456 (Euroserver). It also received funding from the Spanish Ministry of Science and Technology (project TIN2015-65316-P), Generalitat de Catalunya (contract 2014-SGR-1272), and the Severo Ochoa Programme (SEV-2015-0493) of the Spanish Government.Peer ReviewedPostprint (author's final draft