Cooperative Scheduling of Bag-of-Tasks Workflows on Hybrid Clouds

We address the problem of scheduling a class of large-scale applications inspired from real-world on hybrid Clouds, characterized by a large number of homogeneous and concurrent tasks that are the main sources of bottlenecks but open great potential for optimization. We formulate the scheduling problem as a new sequential cooperative game and propose a communication- and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, make span, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.(VLID)2217955Accepted versio

Cooperative Scheduling of Bag-of-Tasks Workflows on Hybrid Clouds

We address the problem of scheduling a class of large-scale applications inspired from real-world on hybrid Clouds, characterized by a large number of homogeneous and concurrent tasks that are the main sources of bottlenecks but open great potential for optimization. We formulate the scheduling problem as a new sequential cooperative game and propose a communication- and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, make span, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.(VLID)2217955Accepted versio

Decentralized Resource Scheduling in Grid/Cloud Computing

In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution

Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

Provendo robustez a escalonadores de workflows sensíveis às incertezas da largura de banda disponível

Orientadores: Edmundo Roberto Mauro Madeira, Luiz Fernando BittencourtTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Para que escalonadores de aplicações científicas modeladas como workflows derivem escalonamentos eficientes em nuvens híbridas, é necessário que se forneçam, além da descrição da demanda computacional desses aplicativos, as informações sobre o poder de computação dos recursos disponíveis, especialmente aqueles dados relacionados com a largura de banda disponível. Entretanto, a imprecisão das ferramentas de medição fazem com que as informações da largura de banda disponível fornecida aos escalonadores difiram dos valores reais que deveriam ser considerados para se obter escalonamentos quase ótimos. Escalonadores especialmente projetados para nuvens híbridas simplesmente ignoram a existência de tais imprecisões e terminam produzindo escalonamentos enganosos e de baixo desempenho, o que os tornam sensíveis às informações incertas. A presente Tese introduz um procedimento pró-ativo para fornecer um certo nível de robustez a escalonamentos derivados de escalonadores não projetados para serem robustos frente às incertezas decorrentes do uso de informações imprecisas dadas por ferramentas de medições de rede. Para tornar os escalonamentos sensíveis às incertezas em escalonamentos robustos às essas imprecisões, o procedimento propõe um refinamento (uma deflação) das estimativas da largura de banda antes de serem utilizadas pelo escalonador não robusto. Ao propor o uso de estimativas refinadas da largura de banda disponível, escalonadores inicialmente sensíveis às incertezas passaram a produzir escalonamentos com um certo nível de robustez às essas imprecisões. A eficácia e a eficiência do procedimento proposto são avaliadas através de simulação. Comparam-se, portanto, os escalonamentos gerados por escalonadores que passaram a usar o procedimento proposto com aqueles produzidos pelos mesmos escalonadores mas sem aplicar esse procedimento. Os resultados das simulações mostram que o procedimento proposto é capaz de prover robustez às incertezas da informação da largura de banda a escalonamentos derivados de escalonardes não robustos às tais incertezas. Adicionalmente, esta Tese também propõe um escalonador de aplicações científicas especialmente compostas por um conjunto de workflows. A novidade desse escalonador é que ele é flexível, ou seja, permite o uso de diferentes categorias de funções objetivos. Embora a flexibilidade proposta seja uma novidade no estado da arte, esse escalonador também é sensível às imprecisões da largura de banda. Entretanto, o procedimento mostrou-se capaz de provê-lo de robustez frente às tais incertezas. É mostrado nesta Tese que o procedimento proposto aumentou a eficácia e a eficiência de escalonadores de workflows não robustos projetados para nuvens híbridas, já que eles passaram a produzir escalonamentos com um certo nível de robustez na presença de estimativas incertas da largura de banda disponível. Dessa forma, o procedimento proposto nesta Tese é uma importante ferramenta para aprimorar os escalonadores sensíveis às estimativas incertas da banda disponível especialmente projetados para um ambiente computacional onde esses valores são imprecisos por natureza. Portanto, esta Tese propõe um procedimento que promove melhorias nas execuções de aplicações científicas em nuvens híbridasAbstract: To derive efficient schedules for the tasks of scientific applications modelled as workflows, schedulers need information on the application demands as well as on the resource availability, especially those regarding the available bandwidth. However, the lack of precision of bandwidth estimates provided by monitoring/measurement tools should be considered by the scheduler to achieve near-optimal schedules. Uncertainties of available bandwidth can be a result of imprecise measurement and monitoring network tools and/or their incapacity of estimating in advance the real value of the available bandwidth expected for the application during the scheduling step of the application. Schedulers specially designed for hybrid clouds simply ignore the inaccuracies of the given estimates and end up producing non-robust, low-performance schedules, which makes them sensitive to the uncertainties stemming from using these networking tools. This thesis introduces a proactive procedure to provide a certain level of robustness for schedules derived from schedulers that were not designed to be robust in the face of uncertainties of bandwidth estimates stemming from using unreliable networking tools. To make non-robust schedulers into robust schedulers, the procedure applies a deflation on imprecise bandwidth estimates before being used as input to non-robust schedulers. By proposing the use of refined (deflated) estimates of the available bandwidth, non-robust schedulers initially sensitive to these uncertainties started to produce robust schedules that are insensitive to these inaccuracies. The effectiveness and efficiency of the procedure in providing robustness to non-robust schedulers are evaluated through simulation. Schedules generated by induced-robustness schedulers through the use of the procedure is compared to that of produced by sensitive schedulers. In addition, this thesis also introduces a flexible scheduler for a special case of scientific applications modelled as a set of workflows grouped into ensembles. Although the novelty of this scheduler is the replacement of objective functions according to the user's needs, it is still a non-robust scheduler. However, the procedure was able to provide the necessary robustness for this flexible scheduler be able to produce robust schedules under uncertain bandwidth estimates. It is shown in this thesis that the proposed procedure enhanced the robustness of workflow schedulers designed especially for hybrid clouds as they started to produce robust schedules in the presence of uncertainties stemming from using networking tools. The proposed procedure is an important tool to furnish robustness to non-robust schedulers that are originally designed to work in a computational environment where bandwidth estimates are very likely to vary and cannot be estimated precisely in advance, bringing, therefore, improvements to the executions of scientific applications in hybrid cloudsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2012/02778-6FAPES

Strategic and operational services for workload management in the cloud

In hosting environments such as Infrastructure as a Service (IaaS) clouds, desirable application performance is typically guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated by a service provider for unencumbered use by customers to ensure proper operation of their workloads. Most IaaS offerings are presented to customers as fixed-size and fixed-price SLAs, that do not match well the needs of specific applications. Furthermore, arbitrary colocation of applications with different SLAs may result in inefficient utilization of hosts' resources, resulting in economically undesirable customer behavior. In this thesis, we propose the design and architecture of a Colocation as a Service (CaaS) framework: a set of strategic and operational services that allow the efficient colocation of customer workloads. CaaS strategic services provide customers the means to specify their application workload using an SLA language that provides them the opportunity and incentive to take advantage of any tolerances they may have regarding the scheduling of their workloads. CaaS operational services provide the information necessary for, and carry out the reconfigurations mandated by strategic services. We recognize that it could be the case that there are multiple, yet functionally equivalent ways to express an SLA. Thus, towards that end, we present a service that allows the provably-safe transformation of SLAs from one form to another for the purpose of achieving more efficient colocation. Our CaaS framework could be incorporated into an IaaS offering by providers or it could be implemented as a value added proposition by IaaS resellers. To establish the practicality of such offerings, we present a prototype implementation of our proposed CaaS framework

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

Broker-mediated Multiple-Cloud Orchestration Mechanisms for Cloud Computing

Ph.DDOCTOR OF PHILOSOPH

Performance-oriented service management in clouds

Cloud computing has provided the convenience for many IT-related and traditional industries to use feature-rich services to process complex requests. Various services are deployed in the cloud and they interact with each other to deliver the required results. How to effectively manage these services, the number of which is ever increasing, within the cloud has unavoidably become a critical issue for both tenants and service providers of the cloud. In this thesis, we develop the novel resource provision frameworks to determine resources provision for interactive services. Next, we propose the algorithms for mapping Virtual Machines (VMs) to Physical Machines (PMs) under different constraints, aiming to achieve the desired Quality-of-Services (QoS) while optimizing the provisions in both computing resources and communication bandwidth. Finally, job scheduling may become a performance bottleneck itself in such a large scale cloud. In order to address this issue, the distributed job scheduling framework has been proposed in the literature. However, such distributed job scheduling may cause resource conflict among distributed job schedulers due to the fact that individual job schedulers make their job scheduling decisions independently. In this thesis, we investigate the methods for reducing resource conflict. We apply the game theoretical methodology to capture the behaviour of the distributed schedulers in the cloud. The frameworks and methods developed in this thesis have been evaluated with a simulated workload, a large-scale workload trace and a real cloud testbed