4,439 research outputs found
Asymptotically Optimal Size-Interval Task Assignments
International audienceSize-based routing provides robust strategies to improve the performance of computer and communication systems with highly variable workloads because it is able to isolate small jobs from large ones in a static manner. The basic idea is that each server is assigned all jobs whose sizes belong to a distinct and continuous interval. In the literature, dispatching rules of this type are referred to as SITA (Size Interval Task Assignment) policies. Though their evident benefits, the problem of finding a SITA policy that minimizes the overall mean (steady-state) waiting time is known to be intractable. In particular it is not clear when it is preferable to balance or unbalance server loads and, in the latter case, how. In this paper, we provide an answer to these questions in the celebrated limiting regime where the system capacity grows linearly with the system demand to infinity. Within this framework, we prove that the minimum mean waiting time achievable by a SITA policy necessarily converges to the mean waiting time achieved by SITA-E, the SITA policy that equalizes server loads, provided that servers are homogeneous. However, within the set of SITA policies we also show that SITA-E can perform arbitrarily bad if servers are heterogeneous. In this case we prove that there exist exactly C! asymptotically optimal policies, where C denotes the number of server types, and all of them are linked to the solution of a single strictly convex optimization problem. It turns out that the mean waiting time achieved by any of such asymptotically optimal policies does not depend on how job-size intervals are mapped to servers. Our theoretical results are validated by numerical simulations with respect to realistic parameters and suggest that the above insights are also accurate in small systems composed of a few servers, i.e., ten
Asymptotically optimal load balancing in large-scale heterogeneous systems with multiple dispatchers
We consider the load balancing problem in large-scale heterogeneous systems with multiple dispatchers. We introduce a general framework called Local-Estimation-Driven (LED). Under this framework, each dispatcher keeps local (possibly outdated) estimates of the queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates. The local estimates are updated via infrequent communications between dispatchers and servers. We derive sufficient conditions for LED policies to achieve throughput optimality and delay optimality in heavy-traffic, respectively. These conditions directly imply delay optimality for many previous local-memory based policies in heavy traffic. Moreover, the results enable us to design new delay optimal policies for heterogeneous systems with multiple dispatchers. Finally, the heavy-traffic delay optimality of the LED framework also sheds light on a recent open question on how to design optimal load balancing schemes using delayed information
Analysis of randomized join-the-shortest-queue (JSQ) schemes in large heterogeneous processor-sharing systems
In this paper, we investigate the stability and performance
of randomized dynamic routing schemes for jobs based on
the Join-the-Shortest Queue (JSQ) criterion in a heterogeneous
system of many parallel servers. In particular, we consider servers
that use processor sharing but with different server rates, and
jobs are routed to the server with the smallest occupancy among
a finite number of randomly sampled servers. We focus on the
case of two servers that is often referred to as a Power-of-Two
scheme. We first show that in the heterogeneous setting, uniform
sampling of servers can cause a loss in the stability region and thus
such randomized dynamic schemes need not outperform static
randomized schemes in terms of mean delay in opposition to
the homogeneous case of equal server speeds where the stability
region is maximal and coincides with that of the static randomized
routing. We explicitly characterize the stationary distributions
of the server occupancies and show that the tail distribution
of the server occupancy has a super-exponential behavior as in
the homogeneous case as the number of servers goes to infinity.
To overcome the stability issue, we show that it is possible to
combine the static state-independent scheme with a randomized
JSQ scheme that allows us to recover the maximal stability region
combined with the benefits of JSQ, and such a scheme is preferable
in terms of average delay. The techniques are based on a mean field
analysis where we show that the stationary distributions coincide
with those obtained under asymptotic independence of the servers
and, moreover, the stationary distributions are insensitive to the
job-size distribution
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