9,196 research outputs found
Simpler and Better Algorithms for Minimum-Norm Load Balancing
Recently, Chakrabarty and Swamy (STOC 2019) introduced the minimum-norm load-balancing problem on unrelated machines, wherein we are given a set J of jobs that need to be scheduled on a set of m unrelated machines, and a monotone, symmetric norm; We seek an assignment sigma: J -> [m] that minimizes the norm of the resulting load vector load_{sigma} in R_+^m, where load_{sigma}(i) is the load on machine i under the assignment sigma. Besides capturing all l_p norms, symmetric norms also capture other norms of interest including top-l norms, and ordered norms. Chakrabarty and Swamy (STOC 2019) give a (38+epsilon)-approximation algorithm for this problem via a general framework they develop for minimum-norm optimization that proceeds by first carefully reducing this problem (in a series of steps) to a problem called min-max ordered load balancing, and then devising a so-called deterministic oblivious LP-rounding algorithm for ordered load balancing.
We give a direct, and simple 4+epsilon-approximation algorithm for the minimum-norm load balancing based on rounding a (near-optimal) solution to a novel convex-programming relaxation for the problem. Whereas the natural convex program encoding minimum-norm load balancing problem has a large non-constant integrality gap, we show that this issue can be remedied by including a key constraint that bounds the "norm of the job-cost vector." Our techniques also yield a (essentially) 4-approximation for: (a) multi-norm load balancing, wherein we are given multiple monotone symmetric norms, and we seek an assignment respecting a given budget for each norm; (b) the best simultaneous approximation factor achievable for all symmetric norms for a given instance
How the Experts Algorithm Can Help Solve LPs Online
We consider the problem of solving packing/covering LPs online, when the
columns of the constraint matrix are presented in random order. This problem
has received much attention and the main focus is to figure out how large the
right-hand sides of the LPs have to be (compared to the entries on the
left-hand side of the constraints) to allow -approximations
online. It is known that the right-hand sides have to be times the left-hand sides, where is the number of constraints.
In this paper we give a primal-dual algorithm that achieve this bound for
mixed packing/covering LPs. Our algorithms construct dual solutions using a
regret-minimizing online learning algorithm in a black-box fashion, and use
them to construct primal solutions. The adversarial guarantee that holds for
the constructed duals helps us to take care of most of the correlations that
arise in the algorithm; the remaining correlations are handled via martingale
concentration and maximal inequalities. These ideas lead to conceptually simple
and modular algorithms, which we hope will be useful in other contexts.Comment: An extended abstract appears in the 22nd European Symposium on
Algorithms (ESA 2014
Load-Sharing Policies in Parallel Simulation of Agent-Based Demographic Models
Execution parallelism in agent-Based Simulation (ABS) allows to deal with complex/large-scale models. This raises the need for runtime environments able to fully exploit hardware parallelism, while jointly offering ABS-suited programming abstractions. In this paper, we target last-generation Parallel Discrete Event Simulation (PDES) platforms for multicore systems. We discuss a programming model to support both implicit (in-place access) and explicit (message passing) interactions across concurrent Logical Processes (LPs). We discuss different load-sharing policies combining event rate and implicit/explicit LPs’ interactions.
We present a performance study conducted on a synthetic test case, representative of a class of agent-based models
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