12,946 research outputs found
Reducing Electricity Demand Charge for Data Centers with Partial Execution
Data centers consume a large amount of energy and incur substantial
electricity cost. In this paper, we study the familiar problem of reducing data
center energy cost with two new perspectives. First, we find, through an
empirical study of contracts from electric utilities powering Google data
centers, that demand charge per kW for the maximum power used is a major
component of the total cost. Second, many services such as Web search tolerate
partial execution of the requests because the response quality is a concave
function of processing time. Data from Microsoft Bing search engine confirms
this observation.
We propose a simple idea of using partial execution to reduce the peak power
demand and energy cost of data centers. We systematically study the problem of
scheduling partial execution with stringent SLAs on response quality. For a
single data center, we derive an optimal algorithm to solve the workload
scheduling problem. In the case of multiple geo-distributed data centers, the
demand of each data center is controlled by the request routing algorithm,
which makes the problem much more involved. We decouple the two aspects, and
develop a distributed optimization algorithm to solve the large-scale request
routing problem. Trace-driven simulations show that partial execution reduces
cost by for one data center, and by for geo-distributed
data centers together with request routing.Comment: 12 page
DCCast: Efficient Point to Multipoint Transfers Across Datacenters
Using multiple datacenters allows for higher availability, load balancing and
reduced latency to customers of cloud services. To distribute multiple copies
of data, cloud providers depend on inter-datacenter WANs that ought to be used
efficiently considering their limited capacity and the ever-increasing data
demands. In this paper, we focus on applications that transfer objects from one
datacenter to several datacenters over dedicated inter-datacenter networks. We
present DCCast, a centralized Point to Multi-Point (P2MP) algorithm that uses
forwarding trees to efficiently deliver an object from a source datacenter to
required destination datacenters. With low computational overhead, DCCast
selects forwarding trees that minimize bandwidth usage and balance load across
all links. With simulation experiments on Google's GScale network, we show that
DCCast can reduce total bandwidth usage and tail Transfer Completion Times
(TCT) by up to compared to delivering the same objects via independent
point-to-point (P2P) transfers.Comment: 9th USENIX Workshop on Hot Topics in Cloud Computing,
https://www.usenix.org/conference/hotcloud17/program/presentation/noormohammadpou
Hydrological Models as Web Services: An Implementation using OGC Standards
<p>Presentation for the HIC 2012 - 10th International Conference on Hydroinformatics. "Understanding Changing Climate and Environment and Finding Solutions" Hamburg, Germany July 14-18, 2012</p>
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