79,818 research outputs found
Partial servicing of on-line jobs
We consider the problem of scheduling jobs online, where jobs may be served partially in order to optimize the overall use of the machines. Service requests arrive online to be executed immediately;Every job must start immediately or be refused. the scheduler must decide how long and if it will run a job (that is, it must fix the Quality of Service level of the job) at the time of arrival of the job: preemption is not allowed. We give lower bounds on the competitive ratio and present algorithmsanalyze the competitive performance of algorithms for jobs with varying sizes and for jobs with uniform size, and for jobs that can be run for an arbitrary time or only for some fixed fraction of their full execution time
Bounded Delay Scheduling with Packet Dependencies
A common situation occurring when dealing with multimedia traffic is having
large data frames fragmented into smaller IP packets, and having these packets
sent independently through the network. For real-time multimedia traffic,
dropping even few packets of a frame may render the entire frame useless. Such
traffic is usually modeled as having {\em inter-packet dependencies}. We study
the problem of scheduling traffic with such dependencies, where each packet has
a deadline by which it should arrive at its destination. Such deadlines are
common for real-time multimedia applications, and are derived from stringent
delay constraints posed by the application. The figure of merit in such
environments is maximizing the system's {\em goodput}, namely, the number of
frames successfully delivered.
We study online algorithms for the problem of maximizing goodput of
delay-bounded traffic with inter-packet dependencies, and use competitive
analysis to evaluate their performance. We present competitive algorithms for
the problem, as well as matching lower bounds that are tight up to a constant
factor. We further present the results of a simulation study which further
validates our algorithmic approach and shows that insights arising from our
analysis are indeed manifested in practice
Truthful Online Scheduling with Commitments
We study online mechanisms for preemptive scheduling with deadlines, with the
goal of maximizing the total value of completed jobs. This problem is
fundamental to deadline-aware cloud scheduling, but there are strong lower
bounds even for the algorithmic problem without incentive constraints. However,
these lower bounds can be circumvented under the natural assumption of deadline
slackness, i.e., that there is a guaranteed lower bound on the ratio
between a job's size and the time window in which it can be executed.
In this paper, we construct a truthful scheduling mechanism with a constant
competitive ratio, given slackness . Furthermore, we show that if is
large enough then we can construct a mechanism that also satisfies a commitment
property: it can be determined whether or not a job will finish, and the
requisite payment if so, well in advance of each job's deadline. This is
notable because, in practice, users with strict deadlines may find it
unacceptable to discover only very close to their deadline that their job has
been rejected
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