489 research outputs found
Online Revenue Maximization for Server Pricing
Efficient and truthful mechanisms to price resources on remote
servers/machines has been the subject of much work in recent years due to the
importance of the cloud market. This paper considers revenue maximization in
the online stochastic setting with non-preemptive jobs and a unit capacity
server. One agent/job arrives at every time step, with parameters drawn from an
underlying unknown distribution.
We design a posted-price mechanism which can be efficiently computed, and is
revenue-optimal in expectation and in retrospect, up to additive error. The
prices are posted prior to learning the agent's type, and the computed pricing
scheme is deterministic, depending only on the length of the allotted time
interval and on the earliest time the server is available. If the distribution
of agent's type is only learned from observing the jobs that are executed, we
prove that a polynomial number of samples is sufficient to obtain a
near-optimal truthful pricing strategy
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
Integrating Consumer Flexibility in Smart Grid and Mobility Systems - An Online Optimization and Online Mechanism Design Approach
Consumer flexibility may provide an important lever to align supply and demand in service systems. However, harnessing dispersed flexibility endowments in the presence of self-interested agents requires appropriate incentive structures. This thesis quantifies the potential value of consumers\u27 flexibility in smart grid and mobility systems. In order to include incentives, online optimization approaches are augmented with methods from online mechanism design
Delay and price differentiation in cloud computing: a service model, supporting architectures, and performance
Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures
Online Job Scheduling in Distributed Machine Learning Clusters
Nowadays large-scale distributed machine learning systems have been deployed
to support various analytics and intelligence services in IT firms. To train a
large dataset and derive the prediction/inference model, e.g., a deep neural
network, multiple workers are run in parallel to train partitions of the input
dataset, and update shared model parameters. In a shared cluster handling
multiple training jobs, a fundamental issue is how to efficiently schedule jobs
and set the number of concurrent workers to run for each job, such that server
resources are maximally utilized and model training can be completed in time.
Targeting a distributed machine learning system using the parameter server
framework, we design an online algorithm for scheduling the arriving jobs and
deciding the adjusted numbers of concurrent workers and parameter servers for
each job over its course, to maximize overall utility of all jobs, contingent
on their completion times. Our online algorithm design utilizes a primal-dual
framework coupled with efficient dual subroutines, achieving good long-term
performance guarantees with polynomial time complexity. Practical effectiveness
of the online algorithm is evaluated using trace-driven simulation and testbed
experiments, which demonstrate its outperformance as compared to commonly
adopted scheduling algorithms in today's cloud systems
A general framework for handling commitment in online throughput maximization
We study a fundamental online job admission problem where jobs with deadlines
arrive online over time at their release dates, and the task is to determine a
preemptive single-server schedule which maximizes the number of jobs that
complete on time. To circumvent known impossibility results, we make a standard
slackness assumption by which the feasible time window for scheduling a job is
at least times its processing time, for some .
We quantify the impact that different provider commitment requirements have on
the performance of online algorithms. Our main contribution is one universal
algorithmic framework for online job admission both with and without
commitments. Without commitment, our algorithm with a competitive ratio of
is the best possible (deterministic) for this problem. For
commitment models, we give the first non-trivial performance bounds. If the
commitment decisions must be made before a job's slack becomes less than a
-fraction of its size, we prove a competitive ratio of
, for .
When a provider must commit upon starting a job, our bound is
. Finally, we observe that for scheduling with commitment
the restriction to the `unweighted' throughput model is essential; if jobs have
individual weights, we rule out competitive deterministic algorithms
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 s > 1 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 s > 1. Furthermore, we show that if s 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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