Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Truthful Mechanisms For Resource Allocation And Pricing In Clouds

A major challenging problem for cloud providers is designing efficient mechanisms for Virtual Machine (VM) provisioning and allocation. Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. Recently, cloud providers have introduced auction-based models for VM provisioning and allocation which allow users to submit bids for their requested VMs. We formulate the dynamic VM provisioning and allocation problem for the auction-based model as an integer program considering multiple types of resources. We then design truthful greedy and optimal mechanisms for the problem such that the cloud provider provisions VMs based on the requests of the winning users and determines their payments. We show that the proposed mechanisms are truthful, that is, the users do not have incentives to manipulate the system by lying about their requested bundles of VM instances and their valuations. We perform extensive experiments using real workload traces in order to investigate the performance of the proposed mechanisms. Our proposed mechanisms achieve promising results in terms of revenue for the cloud provider

Towards a generic platform for developing CSCL applications using Grid infrastructure

The goal of this paper is to explore the possibility of using CSCL component-based software under a Grid infrastructure. The merge of these technologies represents an attractive, but probably quite laborious enterprise if we consider not only the benefits but also the barriers that we have to overcome. This work presents an attempt toward this direction by developing a generic platform of CSCL components and discussing the advantages that we could obtain if we adapted it to the Grid. We then propose a means that could make this adjustment possible due to the high degree of genericity that our library component is endowed with by being based on the generic programming paradigm. Finally, an application of our library is proposed both for validating the adequacy of the platform which it is based on and for indicating the possibilities gained by using it under the Grid.Peer ReviewedPostprint (published version

Grid-based semantic integration of heterogeneous data resources: Implementation on a HealthGrid

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The semantic integration of geographically distributed and heterogeneous data resources still remains a key challenge in Grid infrastructures. Today's mainstream Grid technologies hold the promise to meet this challenge in a systematic manner, making data applications more scalable and manageable. The thesis conducts a thorough investigation of the problem, the state of the art, and the related technologies, and proposes an Architecture for Semantic Integration of Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a simple mechanism for the interoperability of heterogeneous data sources in order to extract or discover information regardless of their different semantics. The constituent technologies of this architecture include Globus Toolkit (GT4) and OGSA-DAI (Open Grid Service Architecture Data Integration and Access) alongside other web services technologies such as XML (Extensive Markup Language). To show this, the ASIDS architecture was implemented and tested in a realistic setting by building an exemplar application prototype on a HealthGrid (pilot implementation). The study followed an empirical research methodology and was informed by extensive literature surveys and a critical analysis of the relevant technologies and their synergies. The two literature reviews, together with the analysis of the technology background, have provided a good overview of the current Grid and HealthGrid landscape, produced some valuable taxonomies, explored new paths by integrating technologies, and more importantly illuminated the problem and guided the research process towards a promising solution. Yet the primary contribution of this research is an approach that uses contemporary Grid technologies for integrating heterogeneous data resources that have semantically different. data fields (attributes). It has been practically demonstrated (using a prototype HealthGrid) that discovery in semantically integrated distributed data sources can be feasible by using mainstream Grid technologies, which have been shown to have some Significant advantages over non-Grid based approaches

Programming and parallelising applications for distributed infrastructures

The last decade has witnessed unprecedented changes in parallel and distributed infrastructures. Due to the diminished gains in processor performance from increasing clock frequency, manufacturers have moved from uniprocessor architectures to multicores; as a result, clusters of computers have incorporated such new CPU designs. Furthermore, the ever-growing need of scienti c applications for computing and storage capabilities has motivated the appearance of grids: geographically-distributed, multi-domain infrastructures based on sharing of resources to accomplish large and complex tasks. More recently, clouds have emerged by combining virtualisation technologies, service-orientation and business models to deliver IT resources on demand over the Internet. The size and complexity of these new infrastructures poses a challenge for programmers to exploit them. On the one hand, some of the di culties are inherent to concurrent and distributed programming themselves, e.g. dealing with thread creation and synchronisation, messaging, data partitioning and transfer, etc. On the other hand, other issues are related to the singularities of each scenario, like the heterogeneity of Grid middleware and resources or the risk of vendor lock-in when writing an application for a particular Cloud provider. In the face of such a challenge, programming productivity - understood as a tradeo between programmability and performance - has become crucial for software developers. There is a strong need for high-productivity programming models and languages, which should provide simple means for writing parallel and distributed applications that can run on current infrastructures without sacri cing performance. In that sense, this thesis contributes with Java StarSs, a programming model and runtime system for developing and parallelising Java applications on distributed infrastructures. The model has two key features: first, the user programs in a fully-sequential standard-Java fashion - no parallel construct, API call or pragma must be included in the application code; second, it is completely infrastructure-unaware, i.e. programs do not contain any details about deployment or resource management, so that the same application can run in di erent infrastructures with no changes. The only requirement for the user is to select the application tasks, which are the model's unit of parallelism. Tasks can be either regular Java methods or web service operations, and they can handle any data type supported by the Java language, namely les, objects, arrays and primitives. For the sake of simplicity of the model, Java StarSs shifts the burden of parallelisation from the programmer to the runtime system. The runtime is responsible from modifying the original application to make it create asynchronous tasks and synchronise data accesses from the main program. Moreover, the implicit inter-task concurrency is automatically found as the application executes, thanks to a data dependency detection mechanism that integrates all the Java data types. This thesis provides a fairly comprehensive evaluation of Java StarSs on three di erent distributed scenarios: Grid, Cluster and Cloud. For each of them, a runtime system was designed and implemented to exploit their particular characteristics as well as to address their issues, while keeping the infrastructure unawareness of the programming model. The evaluation compares Java StarSs against state-of-the-art solutions, both in terms of programmability and performance, and demonstrates how the model can bring remarkable productivity to programmers of parallel distributed applications