6,146 research outputs found
A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud
Energy efficiency has become an important measurement of scheduling algorithm
for private cloud. The challenge is trade-off between minimizing of energy
consumption and satisfying Quality of Service (QoS) (e.g. performance or
resource availability on time for reservation request). We consider resource
needs in context of a private cloud system to provide resources for
applications in teaching and researching. In which users request computing
resources for laboratory classes at start times and non-interrupted duration in
some hours in prior. Many previous works are based on migrating techniques to
move online virtual machines (VMs) from low utilization hosts and turn these
hosts off to reduce energy consumption. However, the techniques for migration
of VMs could not use in our case. In this paper, a genetic algorithm for
power-aware in scheduling of resource allocation (GAPA) has been proposed to
solve the static virtual machine allocation problem (SVMAP). Due to limited
resources (i.e. memory) for executing simulation, we created a workload that
contains a sample of one-day timetable of lab hours in our university. We
evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list
of virtual machines in start time (i.e. earliest start time first) and using
best-fit decreasing (i.e. least increased power consumption) algorithm, for
solving the same SVMAP. As a result, the GAPA algorithm obtains total energy
consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated 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: 20 pages, 4 figures, 3 tables, conference pape
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