11,593 research outputs found
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
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
Performance Measurements of Supercomputing and Cloud Storage Solutions
Increasing amounts of data from varied sources, particularly in the fields of
machine learning and graph analytics, are causing storage requirements to grow
rapidly. A variety of technologies exist for storing and sharing these data,
ranging from parallel file systems used by supercomputers to distributed block
storage systems found in clouds. Relatively few comparative measurements exist
to inform decisions about which storage systems are best suited for particular
tasks. This work provides these measurements for two of the most popular
storage technologies: Lustre and Amazon S3. Lustre is an open-source, high
performance, parallel file system used by many of the largest supercomputers in
the world. Amazon's Simple Storage Service, or S3, is part of the Amazon Web
Services offering, and offers a scalable, distributed option to store and
retrieve data from anywhere on the Internet. Parallel processing is essential
for achieving high performance on modern storage systems. The performance tests
used span the gamut of parallel I/O scenarios, ranging from single-client,
single-node Amazon S3 and Lustre performance to a large-scale, multi-client
test designed to demonstrate the capabilities of a modern storage appliance
under heavy load. These results show that, when parallel I/O is used correctly
(i.e., many simultaneous read or write processes), full network bandwidth
performance is achievable and ranged from 10 gigabits/s over a 10 GigE S3
connection to 0.35 terabits/s using Lustre on a 1200 port 10 GigE switch. These
results demonstrate that S3 is well-suited to sharing vast quantities of data
over the Internet, while Lustre is well-suited to processing large quantities
of data locally.Comment: 5 pages, 4 figures, to appear in IEEE HPEC 201
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