3,139 research outputs found
Coalition Formation and Combinatorial Auctions; Applications to Self-organization and Self-management in Utility Computing
In this paper we propose a two-stage protocol for resource management in a
hierarchically organized cloud. The first stage exploits spatial locality for
the formation of coalitions of supply agents; the second stage, a combinatorial
auction, is based on a modified proxy-based clock algorithm and has two phases,
a clock phase and a proxy phase. The clock phase supports price discovery; in
the second phase a proxy conducts multiple rounds of a combinatorial auction
for the package of services requested by each client. The protocol strikes a
balance between low-cost services for cloud clients and a decent profit for the
service providers. We also report the results of an empirical investigation of
the combinatorial auction stage of the protocol.Comment: 14 page
ERA: A Framework for Economic Resource Allocation for the Cloud
Cloud computing has reached significant maturity from a systems perspective,
but currently deployed solutions rely on rather basic economics mechanisms that
yield suboptimal allocation of the costly hardware resources. In this paper we
present Economic Resource Allocation (ERA), a complete framework for scheduling
and pricing cloud resources, aimed at increasing the efficiency of cloud
resources usage by allocating resources according to economic principles. The
ERA architecture carefully abstracts the underlying cloud infrastructure,
enabling the development of scheduling and pricing algorithms independently of
the concrete lower-level cloud infrastructure and independently of its
concerns. Specifically, ERA is designed as a flexible layer that can sit on top
of any cloud system and interfaces with both the cloud resource manager and
with the users who reserve resources to run their jobs. The jobs are scheduled
based on prices that are dynamically calculated according to the predicted
demand. Additionally, ERA provides a key internal API to pluggable algorithmic
modules that include scheduling, pricing and demand prediction. We provide a
proof-of-concept software and demonstrate the effectiveness of the architecture
by testing ERA over both public and private cloud systems -- Azure Batch of
Microsoft and Hadoop/YARN. A broader intent of our work is to foster
collaborations between economics and system communities. To that end, we have
developed a simulation platform via which economics and system experts can test
their algorithmic implementations
Cloud Market Maker: An automated dynamic pricing marketplace for cloud users
© 2015 Elsevier B.V. Abstract Cloud providers commonly incur heavy upfront set up costs which remain almost constant whether they serve a single or many customers. In order to generate a return on this investment, a suitable pricing strategy is required by providers. Established industries such as the airlines employ dynamic pricing to maximize their revenues. In order to increase their resource utilization rates, cloud providers could also use dynamic pricing for their services. At present however most providers use static schemes for pricing their resources. This work presents a new dynamic pricing mechanism for cloud providers. Furthermore, at present no platform exists that provides a dynamic unified view of the different cloud offerings in real-time. Due to a rapidly changing landscape and a limited knowledge of the cloud marketplace, consumers can often end up choosing a cloud provider that is more expensive or does not give them what they really need. This is because some providers spend significantly on advertising their services online. In order to assist cloud customers in the selection of a suitable resource and cloud providers in implementing dynamic pricing, this work describes an automated dynamic pricing marketplace and a decision support system for cloud users. We present a multi-agent multi-auction based system through which such services are delivered. An evaluation has been carried out to determine how effectively the Cloud Market Maker selects the resource, dynamically adjusts the price for the cloud users and the suitability of dynamic pricing for the cloud environment
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Cloud computing systems promise to offer subscription-oriented,
enterprise-quality computing services to users worldwide. With the increased
demand for delivering services to a large number of users, they need to offer
differentiated services to users and meet their quality expectations. Existing
resource management systems in data centers are yet to support Service Level
Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to
realize cloud computing and utility computing. In addition, no work has been
done to collectively incorporate customer-driven service management,
computational risk management, and autonomic resource management into a
market-based resource management system to target the rapidly changing
enterprise requirements of Cloud computing. This paper presents vision,
challenges, and architectural elements of SLA-oriented resource management. The
proposed architecture supports integration of marketbased provisioning policies
and virtualisation technologies for flexible allocation of resources to
applications. The performance results obtained from our working prototype
system shows the feasibility and effectiveness of SLA-based resource
provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE
International Conference on Cloud and Service Computing (CSC 2011, IEEE
Press, USA), Hong Kong, China, December 12-14, 201
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Analyzing Spark Performance on Spot Instances
Amazon Spot Instances provide inexpensive service for high-performance computing. With spot instances, it is possible to get at most 90% off as discount in costs by bidding spare Amazon Elastic Computer Cloud (Amazon EC2) instances. In exchange for low cost, spot instances bring the reduced reliability onto the computing environment, because this kind of instance could be revoked abruptly by the providers due to supply and demand, and higher-priority customers are first served.
To achieve high performance on instances with compromised reliability, Spark is applied to run jobs. In this thesis, a wide set of spark experiments are conducted to study its performance on spot instances. Without stateful replicating, Spark suffers from cascad- ing rollback and is forced to regenerate these states for ad hoc practices repeatedly. Such downside leads to discussion on trade-off between compatible slow checkpointing and regenerating on rollback and inspires us to apply multiple fault tolerance schemes. And Spark is proven to finish a job only with proper revocation rate. To validate and evaluate our work, prototype and simulator are designed and implemented. And based on real history price records, we studied how various checkpoint write frequencies and bid level affect performance. In case study, experiments show that our presented techniques can lead to ~20% shorter completion time and ~25% lower costs than those cases without such techniques. And compared with running jobs on full-price instance, the absolute saving in costs can be ~70%
Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets
Cloud spot markets rent VMs for a variable price that is typically much lower
than the price of on-demand VMs, which makes them attractive for a wide range
of large-scale applications. However, applications that run on spot VMs suffer
from cost uncertainty, since spot prices fluctuate, in part, based on supply,
demand, or both. The difficulty in predicting spot prices affects users and
applications: the former cannot effectively plan their IT expenditures, while
the latter cannot infer the availability and performance of spot VMs, which are
a function of their variable price. To address the problem, we use properties
of cloud infrastructure and workloads to show that prices become more stable
and predictable as they are aggregated together. We leverage this observation
to define an aggregate index price for spot VMs that serves as a reference for
what users should expect to pay. We show that, even when the spot prices for
individual VMs are volatile, the index price remains stable and predictable. We
then introduce cloud index tracking: a migration policy that tracks the index
price to ensure applications running on spot VMs incur a predictable cost by
migrating to a new spot VM if the current VM's price significantly deviates
from the index price.Comment: ACM Symposium on Cloud Computing 201
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