1,108 research outputs found
Bid-Centric Cloud Service Provisioning
Bid-centric service descriptions have the potential to offer a new cloud
service provisioning model that promotes portability, diversity of choice and
differentiation between providers. A bid matching model based on requirements
and capabilities is presented that provides the basis for such an approach. In
order to facilitate the bidding process, tenders should be specified as
abstractly as possible so that the solution space is not needlessly restricted.
To this end, we describe how partial TOSCA service descriptions allow for a
range of diverse solutions to be proposed by multiple providers in response to
tenders. Rather than adopting a lowest common denominator approach, true
portability should allow for the relative strengths and differentiating
features of cloud service providers to be applied to bids. With this in mind,
we describe how TOSCA service descriptions could be augmented with additional
information in order to facilitate heterogeneity in proposed solutions, such as
the use of coprocessors and provider-specific services
Application-centric Resource Provisioning for Amazon EC2 Spot Instances
In late 2009, Amazon introduced spot instances to offer their unused
resources at lower cost with reduced reliability. Amazon's spot instances allow
customers to bid on unused Amazon EC2 capacity and run those instances for as
long as their bid exceeds the current spot price. The spot price changes
periodically based on supply and demand, and customers whose bids exceed it
gain access to the available spot instances. Customers may expect their
services at lower cost with spot instances compared to on-demand or reserved.
However the reliability is compromised since the instances(IaaS) providing the
service(SaaS) may become unavailable at any time without any notice to the
customer. Checkpointing and migration schemes are of great use to cope with
such situation. In this paper we study various checkpointing schemes that can
be used with spot instances. Also we device some algorithms for checkpointing
scheme on top of application-centric resource provisioning framework that
increase the reliability while reducing the cost significantly
Reliable Provisioning of Spot Instances for Compute-intensive Applications
Cloud computing providers are now offering their unused resources for leasing
in the spot market, which has been considered the first step towards a
full-fledged market economy for computational resources. Spot instances are
virtual machines (VMs) available at lower prices than their standard on-demand
counterparts. These VMs will run for as long as the current price is lower than
the maximum bid price users are willing to pay per hour. Spot instances have
been increasingly used for executing compute-intensive applications. In spite
of an apparent economical advantage, due to an intermittent nature of biddable
resources, application execution times may be prolonged or they may not finish
at all. This paper proposes a resource allocation strategy that addresses the
problem of running compute-intensive jobs on a pool of intermittent virtual
machines, while also aiming to run applications in a fast and economical way.
To mitigate potential unavailability periods, a multifaceted fault-aware
resource provisioning policy is proposed. Our solution employs price and
runtime estimation mechanisms, as well as three fault tolerance techniques,
namely checkpointing, task duplication and migration. We evaluate our
strategies using trace-driven simulations, which take as input real price
variation traces, as well as an application trace from the Parallel Workload
Archive. Our results demonstrate the effectiveness of executing applications on
spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure
HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation
Historically, high energy physics computing has been performed on large
purpose-built computing systems. These began as single-site compute facilities,
but have evolved into the distributed computing grids used today. Recently,
there has been an exponential increase in the capacity and capability of
commercial clouds. Cloud resources are highly virtualized and intended to be
able to be flexibly deployed for a variety of computing tasks. There is a
growing nterest among the cloud providers to demonstrate the capability to
perform large-scale scientific computing. In this paper, we discuss results
from the CMS experiment using the Fermilab HEPCloud facility, which utilized
both local Fermilab resources and virtual machines in the Amazon Web Services
Elastic Compute Cloud. We discuss the planning, technical challenges, and
lessons learned involved in performing physics workflows on a large-scale set
of virtualized resources. In addition, we will discuss the economics and
operational efficiencies when executing workflows both in the cloud and on
dedicated resources.Comment: 15 pages, 9 figure
Notes on Cloud computing principles
This letter provides a review of fundamental distributed systems and economic
Cloud computing principles. These principles are frequently deployed in their
respective fields, but their inter-dependencies are often neglected. Given that
Cloud Computing first and foremost is a new business model, a new model to sell
computational resources, the understanding of these concepts is facilitated by
treating them in unison. Here, we review some of the most important concepts
and how they relate to each other
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
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
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