Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

Towards achieving execution time predictability in web services middleware

Web services middleware are typically designed optimised for throughput. Requests are accepted unconditionally and no differentiation is made in processing. Many use the thread-pool pattern to execute requests in parallel using processor sharing. Clusters hosting web services dispatch requests only to balance out the load among the executors. Such optimisations for throughput work out negatively on the predictability of execution. Processor sharing results in the increase of execution time with the number of concurrent requests, making it impossible to predict or control the execution of a request. Existing works fail to address the need for predictability in web service execution. Some achieve a level of differentiated processing, but fail to consider predictability as their main quality attribute. Some give a probabilistic guarantee on service levels. However, from a predictability perspective they are inconsistent. A few achieve predictable execution times, though only in closed systems where request properties are known at system design time. Web services operate on the Internet, where request properties are relatively unknown. This thesis investigates the problem of achieving predictable times in web service executions. We introduce the notion of a processing deadline for service execution, which the web services engine must adhere to in completing the request in a repeatable and a consistent manner. Reaching such execution deadlines by the services engine is made possible by three main features. Firstly a deadline based scheduling algorithm introduced, ensures the processing deadlines are followed. A laxity based analytical model and an admission control algorithm it is based on, selects requests for execution, resulting in a wider range of laxities to enable more requests with overlapping executions to be scheduled together. Finally, a real-time scheduler component introduced in to the server uses a priority model to schedule the execution of requests by controlling the execution of individual worker threads in custom-made thread pools. Predictability of execution in cluster based deployments is further facilitated by four dispatching algorithms that consider the request deadlines and laxity property in the dispatching process. A performance model derived for a similar system approximates the waiting time where requests with smaller deadlines (having higher priority) experience smaller waiting times than requests with longer deadlines. These techniques are implemented in web services middleware in standalone and cluster-based configurations. They are evaluated against their unmodified versions and techniques such as round-robin and class based dispatching, to measure their predictability gain. Empirical evidence indicate the enhancements enable the middleware to achieve more than 90% of the deadlines, while accepting at least 20% of the requests in high traffic conditions. The enhancements additionally prevent the middleware from reaching overloaded conditions in heavy traffic, while maintaining comparable throughput rates to the unmodified versions of the middleware. Analytical and simulation results for the performance model confirms that deadline based preemptive scheduling results in a better balance of waiting times where high priority requests experience lower waiting times while lower priority requests are not over-starved compared to other techniques such as static priority ordering, First-Come-First-Served, Round-Robin and non-preemptive deadline based scheduling

MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

Integrating multiple clusters for compute-intensive applications

Multicluster grids provide one promising solution to satisfying the growing computational demands of compute-intensive applications. However, it is challenging to seamlessly integrate all participating clusters in different domains into a single virtual computational platform. In order to fully utilize the capabilities of multicluster grids, computer scientists need to deal with the issue of joining together participating autonomic systems practically and efficiently to execute grid-enabled applications. Driven by several compute-intensive applications, this theses develops a multicluster grid management toolkit called Pelecanus to bridge the gap between user\u27s needs and the system\u27s heterogeneity. Application scientists will be able to conduct very large-scale execution across multiclusters with transparent QoS assurance. A novel model called DA-TC (Dynamic Assignment with Task Containers) is developed and is integrated into Pelecanus. This model uses the concept of a task container that allows one to decouple resource allocation from resource binding. It employs static load balancing for task container distribution and dynamic load balancing for task assignment. The slowest resources become useful rather than be bottlenecks in this manner. A cluster abstraction is implemented, which not only provides various cluster information for the DA-TC execution model, but also can be used as a standalone toolkit to monitor and evaluate the clusters\u27 functionality and performance. The performance of the proposed DA-TC model is evaluated both theoretically and experimentally. Results demonstrate the importance of reducing queuing time in decreasing the total turnaround time for an application. Experiments were conducted to understand the performance of various aspects of the DA-TC model. Experiments showed that our model could significantly reduce turnaround time and increase resource utilization for our targeted application scenarios. Four applications are implemented as case studies to determine the applicability of the DA-TC model. In each case the turnaround time is greatly reduced, which demonstrates that the DA-TC model is efficient for assisting application scientists in conducting their research. In addition, virtual resources were integrated into the DA-TC model for application execution. Experiments show that the execution model proposed in this thesis can work seamlessly with multiple hybrid grid/cloud resources to achieve reduced turnaround time

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future