2,930 research outputs found
Virtual Cluster Management for Analysis of Geographically Distributed and Immovable Data
Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Scenarios exist in the era of Big Data where computational analysis needs to utilize widely distributed and remote compute clusters, especially when the data sources are sensitive or extremely large, and thus unable to move. A large dataset in Malaysia could be ecologically sensitive, for instance, and unable to be moved outside the country boundaries. Controlling an analysis experiment in this virtual cluster setting can be difficult on multiple levels: with setup and control, with managing behavior of the virtual cluster, and with interoperability issues across the compute clusters. Further, datasets can be distributed among clusters, or even across data centers, so that it becomes critical to utilize data locality information to optimize the performance of data-intensive jobs. Finally, datasets are increasingly sensitive and tied to certain administrative boundaries, though once the data has been processed, the aggregated or statistical result can be shared across the boundaries. This dissertation addresses management and control of a widely distributed virtual cluster having sensitive or otherwise immovable data sets through a controller. The Virtual Cluster Controller (VCC) gives control back to the researcher. It creates virtual clusters across multiple cloud platforms. In recognition of sensitive data, it can establish a single network overlay over widely distributed clusters. We define a novel class of data, notably immovable data that we call "pinned data", where the data is treated as a first-class citizen instead of being moved to where needed. We draw from our earlier work with a hierarchical data processing model, Hierarchical MapReduce (HMR), to process geographically distributed data, some of which are pinned data. The applications implemented in HMR use extended MapReduce model where computations are expressed as three functions: Map, Reduce, and GlobalReduce. Further, by facilitating information sharing among resources, applications, and data, the overall performance is improved. Experimental results show that the overhead of VCC is minimum. The HMR outperforms traditional MapReduce model while processing a particular class of applications. The evaluations also show that information sharing between resources and application through the VCC shortens the hierarchical data processing time, as well satisfying the constraints on the pinned data
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure
Recent technology advancements in the areas of compute, storage and
networking, along with the increased demand for organizations to cut costs
while remaining responsive to increasing service demands have led to the growth
in the adoption of cloud computing services. Cloud services provide the promise
of improved agility, resiliency, scalability and a lowered Total Cost of
Ownership (TCO). This research introduces a framework for minimizing cost and
maximizing resource utilization by using an Integer Linear Programming (ILP)
approach to optimize the assignment of workloads to servers on Amazon Web
Services (AWS) cloud infrastructure. The model is based on the classical
minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin
Resource management in a containerized cloud : status and challenges
Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Cloud computing is offering utility-oriented IT services to users worldwide.
Based on a pay-as-you-go model, it enables hosting of pervasive applications
from consumer, scientific, and business domains. However, data centers hosting
Cloud applications consume huge amounts of energy, contributing to high
operational costs and carbon footprints to the environment. Therefore, we need
Green Cloud computing solutions that can not only save energy for the
environment but also reduce operational costs. This paper presents vision,
challenges, and architectural elements for energy-efficient management of Cloud
computing environments. We focus on the development of dynamic resource
provisioning and allocation algorithms that consider the synergy between
various data center infrastructures (i.e., the hardware, power units, cooling
and software), and holistically work to boost data center energy efficiency and
performance. In particular, this paper proposes (a) architectural principles
for energy-efficient management of Clouds; (b) energy-efficient resource
allocation policies and scheduling algorithms considering quality-of-service
expectations, and devices power usage characteristics; and (c) a novel software
technology for energy-efficient management of Clouds. We have validated our
approach by conducting a set of rigorous performance evaluation study using the
CloudSim toolkit. The results demonstrate that Cloud computing model has
immense potential as it offers significant performance gains as regards to
response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference
on Parallel and Distributed Processing Techniques and Applications (PDPTA
2010), Las Vegas, USA, July 12-15, 201
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