24,129 research outputs found
Performance-oriented Cloud Provisioning: Taxonomy and Survey
Cloud computing is being viewed as the technology of today and the future.
Through this paradigm, the customers gain access to shared computing resources
located in remote data centers that are hosted by cloud providers (CP). This
technology allows for provisioning of various resources such as virtual
machines (VM), physical machines, processors, memory, network, storage and
software as per the needs of customers. Application providers (AP), who are
customers of the CP, deploy applications on the cloud infrastructure and then
these applications are used by the end-users. To meet the fluctuating
application workload demands, dynamic provisioning is essential and this
article provides a detailed literature survey of dynamic provisioning within
cloud systems with focus on application performance. The well-known types of
provisioning and the associated problems are clearly and pictorially explained
and the provisioning terminology is clarified. A very detailed and general
cloud provisioning classification is presented, which views provisioning from
different perspectives, aiding in understanding the process inside-out. Cloud
dynamic provisioning is explained by considering resources, stakeholders,
techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table
Predictive Analysis for Cloud Infrastructure Metrics
In a cloud computing environment, enterprises have the flexibility to request resources according to their application demands. This elastic feature of cloud computing makes it an attractive option for enterprises to host their applications on the cloud. Cloud providers usually exploit this elasticity by auto-scaling the application resources for quality assurance. However, there is a setup-time delay that may take minutes between the demand for a new resource and it being prepared for utilization. This causes the static resource provisioning techniques, which request allocation of a new resource only when the application breaches a specific threshold, to be slow and inefficient for the resource allocation task. To overcome this limitation, it is important to foresee the upcoming resource demand for an application before it becomes overloaded and trigger resource allocation in advance to allow setup time for the newly allocated resource. Machine learning techniques like time-series forecasting can be leveraged to provide promising results for dynamic resource allocation.
In this research project, I developed a predictive analysis model for dynamic resource provisioning for cloud infrastructure. The researched solution demonstrates that it can predict the upcoming workload for various cloud infrastructure metrics upto 4 hours in future to allow allocation of virtual machines in advance
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
Predictive dynamic resource allocation for web hosting environments
E-Business applications are subject to significant variations in workload and this can
cause exceptionally long response times for users, the timing out of client requests
and/or the dropping of connections. One solution is to host these applications in virtualised
server pools, and to dynamically reassign compute servers between pools to
meet the demands on the hosted applications. Switching servers between pools is not
without cost, and this must therefore be weighed against possible system gain.
This work is concerned with dynamic resource allocation for multi-tiered, clusterbased
web hosting environments. Dynamic resource allocation is reactive, that is, when
overloading occurs in one resource pool, servers are moved from another (quieter) pool
to meet this demand. Switching servers comes with some overhead, so it is important
to weigh up the costs of the switch against possible system gains. In this thesis we
combine the reactive behaviour of two server switching policies – the Proportional
Switching Policy (PSP) and the Bottleneck Aware Switching Policy (BSP) – with the
proactive properties of several workload forecasting models.
We evaluate the behaviour of the two switching policies and compare them against
static resource allocation under a range of reallocation intervals (the time it takes to
switch a server from one resource pool to another) and observe that larger reallocation
intervals have a negative impact on revenue. We also construct model- and simulation-based environments in which the combination of workload prediction and dynamic
server switching can be explored. Several different (but common) predictors – Last
Observation (LO), Simple Average (SA), Sample Moving Average (SMA) and Exponential
Moving Average (EMA), Low Pass Filter (LPF), and an AutoRegressive Integrated
Moving Average (ARIMA) – have been applied alongside the switching policies.
As each of the forecasting schemes has its own bias, we also develop a number of
meta-forecasting algorithms – the Active Window Model (AWM), the Voting Model
(VM), the Selective Model (SM), the Dynamic Active Window Model (DAWM), and
a method based on Workload Pattern Analysis (WPA). The schemes are tested with
real-world workload traces from several sources to ensure consistent and improved results.
We also investigate the effectiveness of these schemes on workloads containing
extreme events (e.g. flash crowds). The results show that workload forecasting can be
very effective when applied alongside dynamic resource allocation strategies
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