4,625 research outputs found
Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters
To improve customer experience, datacenter operators offer support for
simplifying application and resource management. For example, running workloads
of workflows on behalf of customers is desirable, but requires increasingly
more sophisticated autoscaling policies, that is, policies that dynamically
provision resources for the customer. Although selecting and tuning autoscaling
policies is a challenging task for datacenter operators, so far relatively few
studies investigate the performance of autoscaling for workloads of workflows.
Complementing previous knowledge, in this work we propose the first
comprehensive performance study in the field. Using trace-based simulation, we
compare state-of-the-art autoscaling policies across multiple application
domains, workload arrival patterns (e.g., burstiness), and system utilization
levels. We further investigate the interplay between autoscaling and regular
allocation policies, and the complexity cost of autoscaling. Our quantitative
study focuses not only on traditional performance metrics and on
state-of-the-art elasticity metrics, but also on time- and memory-related
autoscaling-complexity metrics. Our main results give strong and quantitative
evidence about previously unreported operational behavior, for example, that
autoscaling policies perform differently across application domains and by how
much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based
Performance Study of Autoscaling Workloads of Workflows in Datacenters
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Cloud computing systems promise to offer subscription-oriented,
enterprise-quality computing services to users worldwide. With the increased
demand for delivering services to a large number of users, they need to offer
differentiated services to users and meet their quality expectations. Existing
resource management systems in data centers are yet to support Service Level
Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to
realize cloud computing and utility computing. In addition, no work has been
done to collectively incorporate customer-driven service management,
computational risk management, and autonomic resource management into a
market-based resource management system to target the rapidly changing
enterprise requirements of Cloud computing. This paper presents vision,
challenges, and architectural elements of SLA-oriented resource management. The
proposed architecture supports integration of marketbased provisioning policies
and virtualisation technologies for flexible allocation of resources to
applications. The performance results obtained from our working prototype
system shows the feasibility and effectiveness of SLA-based resource
provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE
International Conference on Cloud and Service Computing (CSC 2011, IEEE
Press, USA), Hong Kong, China, December 12-14, 201
Service Quality and Profit Control in Utility Computing Service Life Cycles
Utility Computing is one of the most discussed business models in the context of Cloud Computing. Service providers are more and more pushed into the role of utilities by their customer's expectations. Subsequently, the demand for predictable service availability and pay-per-use pricing models increases. Furthermore, for providers, a new opportunity to optimise resource usage offers arises, resulting from new virtualisation techniques. In this context, the control of service quality and profit depends on a deep understanding of the representation of the relationship between business and technique.
This research analyses the relationship between the business model of Utility Computing and Service-oriented Computing architectures hosted in Cloud environments. The relations are clarified in detail for the entire service life cycle and throughout all architectural layers. Based on the elaborated relations, an approach to a delivery framework is evolved, in order to enable the optimisation of the relation attributes, while the service implementation passes through business planning, development, and operations.
Related work from academic literature does not cover the collected requirements on service offers in this context. This finding is revealed by a critical review of approaches in the fields of Cloud Computing, Grid Computing, and Application Clusters. The related work is analysed regarding appropriate provision architectures and quality assurance approaches.
The main concepts of the delivery framework are evaluated based on a simulation model. To demonstrate the ability of the framework to model complex pay-per-use service cascades in Cloud environments, several experiments have been conducted. First outcomes proof that the contributions of this research undoubtedly enable the optimisation of service quality and profit in Cloud-based Service-oriented Computing architectures
Grid-job scheduling with reservations and preemption
Computational grids make it possible to exploit grid resources across multiple clusters when grid jobs are deconstructed into tasks and allocated across clusters. Grid-job tasks are often scheduled in the form of workflows which require synchronization, and advance reservation makes it easy to guarantee predictable resource provisioning for these jobs. However, advance reservation for grid jobs creates roadblocks and fragmentation which adversely affects the system utilization and response times for local jobs. We provide a solution which incorporates relaxed reservations and uses a modified version of the standard grid-scheduling algorithm, HEFT, to obtain flexibility in placing reservations for workflow grid jobs. Furthermore, we deploy the relaxed reservation with modified HEFT as an extension of the preemption based job scheduling framework, SCOJO-PECT job scheduler. In SCOJO-PECT, relaxed reservations serve the additional purpose of permitting scheduler optimizations which shift the overall schedule forward. Furthermore, a propagation heuristics algorithm is used to alleviate the workflow job makespan extension caused by the slack of relaxed reservation. Our solution aims at decreasing the fragmentation caused by grid jobs, so that local jobs and system utilization are not compromised, and at the same time grid jobs also have reasonable response times
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