32,205 research outputs found
Towards Autonomic Service Provisioning Systems
This paper discusses our experience in building SPIRE, an autonomic system
for service provision. The architecture consists of a set of hosted Web
Services subject to QoS constraints, and a certain number of servers used to
run session-based traffic. Customers pay for having their jobs run, but require
in turn certain quality guarantees: there are different SLAs specifying charges
for running jobs and penalties for failing to meet promised performance
metrics. The system is driven by an utility function, aiming at optimizing the
average earned revenue per unit time. Demand and performance statistics are
collected, while traffic parameters are estimated in order to make dynamic
decisions concerning server allocation and admission control. Different utility
functions are introduced and a number of experiments aiming at testing their
performance are discussed. Results show that revenues can be dramatically
improved by imposing suitable conditions for accepting incoming traffic; the
proposed system performs well under different traffic settings, and it
successfully adapts to changes in the operating environment.Comment: 11 pages, 9 Figures,
http://www.wipo.int/pctdb/en/wo.jsp?WO=201002636
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
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