107,775 research outputs found
Prior-Independent Mechanisms for Scheduling
We study the makespan minimization problem with unrelated selfish machines
under the assumption that job sizes are stochastic. We design simple truthful
mechanisms that under various distributional assumptions provide constant and
sublogarithmic approximations to expected makespan. Our mechanisms are
prior-independent in that they do not rely on knowledge of the job size
distributions. Prior-independent approximation mechanisms have been previously
studied for the objective of revenue maximization [Dhangwatnotai, Roughgarden
and Yan'10, Devanur, Hartline, Karlin and Nguyen'11, Roughgarden, Talgam-Cohen
and Yan'12]. In contrast to our results, in prior-free settings no truthful
anonymous deterministic mechanism for the makespan objective can provide a
sublinear approximation [Ashlagi, Dobzinski and Lavi'09].Comment: This paper will appear in Proceedings of the ACM Symposium on Theory
of Computing 2013 (STOC'13
The VCG Mechanism for Bayesian Scheduling
We study the problem of scheduling m tasks to n selfish, unrelated machines in order to minimize the makespan, in which the execution times are independent random variables, identical across machines. We show that the VCG mechanism, which myopically allocates each task to its best machine, achieves an approximation ratio of O(ln n&frac; ln ln n). This improves significantly on the previously best known bound of O(m/n) for prior-independent mechanisms, given by Chawla et al. [7] under the additional assumption of Monotone Hazard Rate (MHR) distributions. Although we demonstrate that this is tight in general, if we do maintain the MHR assumption, then we get improved, (small) constant bounds for m ≥ n ln n i.i.d. tasks. We also identify a sufficient condition on the distribution that yields a constant approximation ratio regardless of the number of tasks
Abstract Timers and their Implementation onto the ARM Cor tex-M family of MCUs
Presented at Embed with Linux Workshop (EWiLi 2015). 4 to 9, Oct, 2015. Amsterdam, Netherlands.Real-Time For the Masses (RTFM) is a set of languages andto ols b eing develop ed to facilitate emb edded
software development and provide highly efficient implementations gearedto static verification. The RTFM-kernel is an architecturedesigned to provide highly efficient and predicable Stack Resource Policy based scheduling, targeting bare metal (singlecore) platforms.We contribute b eyond prior work by intro ducing a platform independent timer abstraction that relies on existingRTFM-kernel primitives. We develop two alternative implementations for the ARM Cortex-M family of MCUs: ageneric implementation, using the ARM defined SysTick-/DWT hardware; and a target sp ecific implementation, using the match compare/free running timers. While sacrificing generality, the latter is more flexible and may reduceoverall overhead. Invariants for correctness are presented,and metho ds to static and run-time verification are discussed. Overhead is b ound and characterized.
In b oth casesthe critical section from release time to dispatch is less than2us on a 100MHz MCU. Queue and timer mechanisms aredirectly implemented in the RTFM-core language and canb e included in system-wide scheduling analysis
Truth and Regret in Online Scheduling
We consider a scheduling problem where a cloud service provider has multiple
units of a resource available over time. Selfish clients submit jobs, each with
an arrival time, deadline, length, and value. The service provider's goal is to
implement a truthful online mechanism for scheduling jobs so as to maximize the
social welfare of the schedule. Recent work shows that under a stochastic
assumption on job arrivals, there is a single-parameter family of mechanisms
that achieves near-optimal social welfare. We show that given any such family
of near-optimal online mechanisms, there exists an online mechanism that in the
worst case performs nearly as well as the best of the given mechanisms. Our
mechanism is truthful whenever the mechanisms in the given family are truthful
and prompt, and achieves optimal (within constant factors) regret.
We model the problem of competing against a family of online scheduling
mechanisms as one of learning from expert advice. A primary challenge is that
any scheduling decisions we make affect not only the payoff at the current
step, but also the resource availability and payoffs in future steps.
Furthermore, switching from one algorithm (a.k.a. expert) to another in an
online fashion is challenging both because it requires synchronization with the
state of the latter algorithm as well as because it affects the incentive
structure of the algorithms. We further show how to adapt our algorithm to a
non-clairvoyant setting where job lengths are unknown until jobs are run to
completion. Once again, in this setting, we obtain truthfulness along with
asymptotically optimal regret (within poly-logarithmic factors)
Average-case Approximation Ratio of Scheduling without Payments
Apart from the principles and methodologies inherited from Economics and Game
Theory, the studies in Algorithmic Mechanism Design typically employ the
worst-case analysis and approximation schemes of Theoretical Computer Science.
For instance, the approximation ratio, which is the canonical measure of
evaluating how well an incentive-compatible mechanism approximately optimizes
the objective, is defined in the worst-case sense. It compares the performance
of the optimal mechanism against the performance of a truthful mechanism, for
all possible inputs.
In this paper, we take the average-case analysis approach, and tackle one of
the primary motivating problems in Algorithmic Mechanism Design -- the
scheduling problem [Nisan and Ronen 1999]. One version of this problem which
includes a verification component is studied by [Koutsoupias 2014]. It was
shown that the problem has a tight approximation ratio bound of (n+1)/2 for the
single-task setting, where n is the number of machines. We show, however, when
the costs of the machines to executing the task follow any independent and
identical distribution, the average-case approximation ratio of the mechanism
given in [Koutsoupias 2014] is upper bounded by a constant. This positive
result asymptotically separates the average-case ratio from the worst-case
ratio, and indicates that the optimal mechanism for the problem actually works
well on average, although in the worst-case the expected cost of the mechanism
is Theta(n) times that of the optimal cost
Collision Helps - Algebraic Collision Recovery for Wireless Erasure Networks
Current medium access control mechanisms are based on collision avoidance and
collided packets are discarded. The recent work on ZigZag decoding departs from
this approach by recovering the original packets from multiple collisions. In
this paper, we present an algebraic representation of collisions which allows
us to view each collision as a linear combination of the original packets. The
transmitted, colliding packets may themselves be a coded version of the
original packets.
We propose a new acknowledgment (ACK) mechanism for collisions based on the
idea that if a set of packets collide, the receiver can afford to ACK exactly
one of them and still decode all the packets eventually. We analytically
compare delay and throughput performance of such collision recovery schemes
with other collision avoidance approaches in the context of a single hop
wireless erasure network. In the multiple receiver case, the broadcast
constraint calls for combining collision recovery methods with network coding
across packets at the sender. From the delay perspective, our scheme, without
any coordination, outperforms not only a ALOHA-type random access mechanisms,
but also centralized scheduling. For the case of streaming arrivals, we propose
a priority-based ACK mechanism and show that its stability region coincides
with the cut-set bound of the packet erasure network
Improving DRAM Performance by Parallelizing Refreshes with Accesses
Modern DRAM cells are periodically refreshed to prevent data loss due to
leakage. Commodity DDR DRAM refreshes cells at the rank level. This degrades
performance significantly because it prevents an entire rank from serving
memory requests while being refreshed. DRAM designed for mobile platforms,
LPDDR DRAM, supports an enhanced mode, called per-bank refresh, that refreshes
cells at the bank level. This enables a bank to be accessed while another in
the same rank is being refreshed, alleviating part of the negative performance
impact of refreshes. However, there are two shortcomings of per-bank refresh.
First, the per-bank refresh scheduling scheme does not exploit the full
potential of overlapping refreshes with accesses across banks because it
restricts the banks to be refreshed in a sequential round-robin order. Second,
accesses to a bank that is being refreshed have to wait.
To mitigate the negative performance impact of DRAM refresh, we propose two
complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and
SARP (Subarray Access Refresh Parallelization). The goal is to address the
drawbacks of per-bank refresh by building more efficient techniques to
parallelize refreshes and accesses within DRAM. First, instead of issuing
per-bank refreshes in a round-robin order, DARP issues per-bank refreshes to
idle banks in an out-of-order manner. Furthermore, DARP schedules refreshes
during intervals when a batch of writes are draining to DRAM. Second, SARP
exploits the existence of mostly-independent subarrays within a bank. With
minor modifications to DRAM organization, it allows a bank to serve memory
accesses to an idle subarray while another subarray is being refreshed.
Extensive evaluations show that our mechanisms improve system performance and
energy efficiency compared to state-of-the-art refresh policies and the benefit
increases as DRAM density increases.Comment: The original paper published in the International Symposium on
High-Performance Computer Architecture (HPCA) contains an error. The arxiv
version has an erratum that describes the error and the fix for i
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