183 research outputs found
Minimizing the Worst Slowdown: Off-Line and On-Line
Minimizing the slowdown (expected sojourn time divided by job size) is a key concern of fairness in scheduling and queuing problems where job sizes are very heterogeneous. We look for protocols (service disciplines) capping the worst slowdown (called here liability) a job may face no matter how large (or small) the other jobs are. In the scheduling problem (all jobs released at the same time), allowing the server to randomize the order of service cuts almost in half the liability profiles feasible under deterministic protocols. The same statement holds if cash transfers are feasible and users have linear waiting costs.
Heavy-traffic limits for Polling Models with Exhaustive Service and non-FCFS Service Order Policies
We study cyclic polling models with exhaustive service at each queue under a variety of non-FCFS local service orders, namely Last-Come-First-Served (LCFS) with and without preemption, Random-Order-of-Service (ROS), Processor Sharing (PS), the multi-class priority scheduling with and without preemption, Shortest-Job-First (SJF) and the Shortest Remaining Processing Time (SRPT) policy. For each of these policies, we rst express the waiting-time distributions in terms of intervisit-time distributions. Next, we use these expressions to derive the asymptotic waiting-time distributions under heavy-trac assumptions, i.e., when the system tends to saturate. The results show that in all cases the asymptotic wait
PSBS: Practical Size-Based Scheduling
Size-based schedulers have very desirable performance properties: optimal or
near-optimal response time can be coupled with strong fairness guarantees.
Despite this, such systems are very rarely implemented in practical settings,
because they require knowing a priori the amount of work needed to complete
jobs: this assumption is very difficult to satisfy in concrete systems. It is
definitely more likely to inform the system with an estimate of the job sizes,
but existing studies point to somewhat pessimistic results if existing
scheduler policies are used based on imprecise job size estimations. We take
the goal of designing scheduling policies that are explicitly designed to deal
with inexact job sizes: first, we show that existing size-based schedulers can
have bad performance with inexact job size information when job sizes are
heavily skewed; we show that this issue, and the pessimistic results shown in
the literature, are due to problematic behavior when large jobs are
underestimated. Once the problem is identified, it is possible to amend
existing size-based schedulers to solve the issue. We generalize FSP -- a fair
and efficient size-based scheduling policy -- in order to solve the problem
highlighted above; in addition, our solution deals with different job weights
(that can be assigned to a job independently from its size). We provide an
efficient implementation of the resulting protocol, which we call Practical
Size-Based Scheduler (PSBS). Through simulations evaluated on synthetic and
real workloads, we show that PSBS has near-optimal performance in a large
variety of cases with inaccurate size information, that it performs fairly and
it handles correctly job weights. We believe that this work shows that PSBS is
indeed pratical, and we maintain that it could inspire the design of schedulers
in a wide array of real-world use cases.Comment: arXiv admin note: substantial text overlap with arXiv:1403.599
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