Project Ideas
These are queueing research ideas that I’m interested in, but haven’t gotten around to yet. If you’re interested in any of them as a potential collaborator or advisee, let me know!
I’m particularly interested in working with either students at the school I am at (Georgia Tech, UIUC, or Northwestern), or people who already have a background in queueing theory research.
Last updated: March 6th, 2024.
Table of contents
I’ve separated my ideas into five categories:

Ideas that I think are quite promising, where I have promising directions for a result.

Ideas that are just starting out, or where I don’t quite know how I’d prove a result.

Ideas that I’m actively pursuing.

Ideas I pursued to some kind of completion, and am not currently pursuing. I’m keeping these around for archival purposes.

Ideas I’m no longer interested in. I’m keeping these around for archival purposes.
I’m most interested in collaborating on ideas in the “quite promising” category, but all of them are worth looking at and discussing.
This page is for ideas that look promising but I’m not pursuing yet, or were in that stage earlier but I am now pursuing. I typically have several more projects that I’m actively pursuing, that never went through the “look promising but not pursuing” stage, and so aren’t listed here.
The order within a category is roughly chronological.
Quite promising
Starting out/Not sure how to proceed
Active projects
Archive: Completed
Archive: No longer interested
Quite promising
Scheduling to minimize E[T^2]
See Section 8.3.5 of my thesis.
Setting: M/G/1 scheduling for tail, e.g. minimize E[T^2].
Policy: Priority is t/s, where t is a job’s time in system and s is the job’s size. Higher is better. Without preemption to start, for simplicity of analysis. Note that this is an “Accumulating Priority Queue”, but with infinite continuous classes, not 2 classes.
Waiting time distributions in the accumulating priority queue, David A. Stanford, Peter Taylor & Ilze Ziedins
First step: Implement this policy. Compare it against FCFS, SRPT. Poisson arrivals, medium variance, medium load. Does it do well empirically for E[T^2]?
Future steps: Use APQ methods to characterize steady state. Poisson point process of (size, time in system). Characterize for arbitrary joint (Size, Accumulation rate) distribution, specialize to above setting. Characterize transform of response time, moments of response time.
Beating SRPTk
See Section 8.3.1 of my thesis.
Setting: SRPTk (M/G/k/SRPT) is heavytraffic optimal for mean response time, as I proved in SRPT for Multiserver Systems, but it can be beaten outside of heavy traffic.
Idea: Consider a 2server system with 3 jobs in it: Two are small, one is large. There are two scheduling options: Run both small jobs first (SRPT), or one small and one large first (New concept). Once a small job finishes, start running the third job. If no new jobs arrive before the long job finishes, both options have the same total response time. If new jobs arrive after the small jobs finish but before the large job finishes, starting the large job sooner (New concept) is better. If new jobs arrive before both small jobs are done, SRPT is preferable.
Policy: Flip3. In an M/G/2, if there are at least 4 jobs, just run SRPT. If there are 3 jobs, and 2 have remaining size below a, and the third has size above b, run the smallest and largest jobs. Otherwise, SRPT. Set a at roughly 20% of the mean job size, and b at roughly the mean job size.
First step: Implement this policy. Compare it against SRPTk. Fiddle around with job size distributions, loads, and a and b thresholds to find a relatively large separation (0.1% is normal, 1% is good).
Future steps: Use a Nudgestyle argument to prove that if a is small enough and b is large enough, the Flip3 policy has lower mean response time than SRPT2.
Optimal Relative Completions in the Multiserverjob system
As I showed in my preliminary RESET paper, the mean response time in the FirstCome FirstServed Multiserverjob system is controlled by the throughput and relative completions of the corresponding saturated system. It is therefore natural to find the optimal scheduling policy, minimizing throughput and relative completions, under some scheduling restriction, such as the restriction that only the k oldest jobs in arrival order can be served.
First step: Implement a way to specify a such a policy and compute its throughput and relative completions, perhaps in a 3 server system.
Future steps: Search over all possible policies with an MDP solver. Find the Paretooptimal tradeoff of relative completions vs. throughput, perhaps corresponding to best policies at a variety of loads. Solve symbolically for throughput and relative completions.
Meanvariance tradeoff: Follow up with Shubhada.
Hybrid ServerFilling and MSJ FCFS to avoid starvation
I think I now understand what practitioners mean when they talk about “starvation”. Consider a job that encounters a system where there are relatively few jobs present, but the arrival rate is high, around the critical load. The response time of that job should be relatively low: Proportionate to the number of jobs that were present on arrival, ideally. Practical systems often have feedback mechanisms on the arrival rate, resulting in this pattern of high load but relatively short queue lengths.
MSJ FCFS satisfies this “no starvation” goal, as do many backfilling policies. In contrast, ServerFilling does not: A smallserverneed job can be delayed until the system empties.
To overcome this, consider a policy which serves a 95%/5% mixture of ServerFilling and FCFS, or ServerFilling and a backfilling policy. We could then give a samplepath bound on job’s response time in terms of the number of jobs seen on arrival, and the size of the job. Load doesn’t enter into it. We could define this as “no starvation”. We could analyze this policy with our finiteskip analysis.
Scheduling with epsilon prediction errors
Setting: M/G/1 scheduling for the mean. Predictions are given, and there’s a small (epsilonsized) error in the predictions.
Goal: Schedule to achieve a mean response time performance of the form E[T^SRPT](1+f(epsilon)), for some function f that goes to zero as epsilon goes to zero. This is called “Consistency”.
Twist: There are many kinds of epsilonerror to consider. In our SRPTBounce paper, we show that our SRPTBounce policy can handle the situation where all predictions may be off by a multiplicative error of epsilon. Adam Wierman and Misja Nuyens have a paper, “Scheduling despite inexact jobsize information”, looking at predictions being off by an additive error  consistency is not possible there.
I’d like to instead consider the situation where an epsilon fraction of jobs may have seriously poor predictions, but all other predictions are accurate. There are two natural scenarios:

Epsilonfraction of jobs have null predictions. In this case, we know that we’re getting no prediction information.

Epsilonfraction of jobs have predictions that are incorrect by an arbitrary magnitude. In this case, we don’t know that we’re getting no prediction information.
We could also look at loadfractions rather than job fractions.
Even in scenario 1, which is much simpler, things aren’t trivial by any means. If we did something simple like put the nullprediction jobs at the end of the queue, their response time would be something horrible like 1/(1rho)^2 in the epsilon>0 limit, which would dominate mean response time and not remotely achieve consistency.
Initial question: What’s the mean response time impact of a misprediction from true size x to predicted size x’, under a policy like SRPTCheckmark or SRPTBounce?
Longerterm question: Can we achieve consistency against rare large errors? Can we define a useful mispredictiondistance metric such that if that metric is small, we have consistent performance?
Tails for ServerFilling
Setting: MSJ Scheduling, ServerFilling policy (or generally WCFS scheduling). Want to analyze tail of response time.
In our WCFS paper, we analyze the ServerFilling policy’s mean response time, and more generally any WCFS policy’s mean response time, showing that it is near that of resourcepooled FCFS. Because these policies are nearFCFS, we would like to analyze their tail of response time, which is likely quite good in the lighttailsizes/bounded expected remaining size setting of the paper.
Our analysis separates response time into two pieces: Time in the “front” and time in the “back” (the back is called the queue in the paper, but we’ve since changed terminology to clarify that some of the jobs in the front are not in service).
Time in the back dominates mean response time in heavy traffic, and our analysis could likely be generalized to tightly bound the transform of time in the front to be near that of resourcepooled FCFS. We would just have to change out the W^2 test function in the paper for an exponential test function.
For time in the front, things are trickier. In the paper, we used Little’s law, which allows us to bound mean time in front but does not say anything about the distribution of time in the front. Because ServerFilling prioritizes the largest serverneed jobs in the front, we have to worry about the smallest serverneed jobs and the tail of their time in the front.
In the worstcase, a 1server job can only be guaranteed to run once there are k 1server jobs in the front. Thus, the timeinfront of 1server jobs could be about k times the interarrival time of 1server jobs. The ktimes is fine, but the issue is the interarrival time of 1server jobs.
If 1server jobs are very rare, then their interarrival time will be very large. However, because these jobs are so rare, that won’t have a big impact on metrics like the transform or the tail probability. If 1server jobs are exceptionally rare, then we can bound their response time by the excess of a busy period, at which point the system will run low on jobs and all jobs remaining in the system will be in service.
Initial question: Let’s bound the transform or tail probability of time in front, either for a specific policy or uniformly over all policies.
Starting out/Not sure how to proceed
Scheduling in the lowload limit
Setting: The knownsize M/G/k, under low load.
Intuition: The dominant term comes from jobs arriving alone, then twojob interactions, etc. We find which policy is optimal, for which number of jobs. It’s similar to the noarrivals setting, for which SRPTk is optimal, but more stochastic. SRPTk was proven to be optimal in the noarrivals setting by Robert McNaughton in “Scheduling with deadlines and loss functions.”
Basics: For at most k jobs, there are no nontrivial decisions. For k+1 jobs, just be sure to serve the smallest job. For k+2, it becomes nontrivial.
First step: If k=2, and we consider 4job sequences, I believe we find that we must serve the smallest pair of any 3 jobs. Confirm?
Future steps: Is SRPT2 uniquely optimal at low load? Is SRPTk? Expand to dispatching, MSJ, unknown sizes?
General constrainedservice queue
The Multiserverjob system and the switch can both be thought of as special cases of the “Constrained service queue”: Jobs have classes, and a certain multisets of classes can be served at once. In the 2x2 switch, the service options are (ad, bc), while in the 2server MSJ setting, the service options are (aa, b).
What policies and analysis make sense in the general constrainedservice queue? MaxWeight, used e.g. in “Stochastic models of load balancing and scheduling in cloud computing clusters”, seems to be always throughputoptimal. When does a ServerFilling equivalent exist? My RESET paper seems like it always applies to FCFStype service.
Value function service and dispatching
Can define a SRPT value function, which quantifies the total future response time impact of a set of jobs. If we started two systems, one from empty and one from this set of jobs, and then ran both forever, in expectation by how much would the total response time go up? Relatively simple function, e.g. using WINE.
Using this value function, in systems with constrained service such as MSJ or the switch, serve the subset of jobs that most rapidly decreases the value function. Or dispatch to minimize the value function impact.
First step: Derive the value function.
Future steps: Implement the policy. Compare against ServerFillingSRPT, Guardrails, etc.
Multiserver Nudge
Nudge was defined for the singleserver setting. However, much of the analysis of Nudge relative to FCFS only relied on the arrival process, not the departure process. Does Nudge have better asymptotic tail than FCFS in the M/G/k? Stochastic dominance?
First step: Simulate Nudge in the M/G/k.
Future step: Port the analysis to the M/G/k. How much transfers?
Optimal dispatching to Gittins queues
See Section 8.3.3 of my thesis.
In my guardrails paper, I studied optimal dispatching with full size information. But what if we just have estimates? Or no info? A good candidate for the scheduling policy is the Gittins index policy, and we are trying to match resourcepooled Gittins, which intuitively requires that we always spread out the jobs of each rank across all of the servers.
If estimates are relatively good, a combination that makes sense is estimatedGittins + PSJF with estimates.
If we have no information, we might just use the greedy policy. For each server, calculate how long the arriving job will have to wait behind all other jobs at that server, in expectation. Also calculate how long other jobs will have to wait behind the arriving job, in expectation. Send to the server where the total expected added waiting time is minimized. We can use SOAP to do this analysis.
First step: Choose a size distribution for which Gittins is simple. Try the above greedy policy. Compare against e.g. Join the Shortest Queue (JSQ).
Future steps: Can we prove that unbalancing isn’t worth it, if the dispatcher and the server have the same information? Can we prove any convergence to resourcepooled Gittins, if the distribution is simple enough?
Restless MDPs for tail scheduling
In our Gittinsk paper and in Ziv Scully’s Gittins paper, “The Gittins Policy in the M/G/1 Queue”, we relate the Gittins policy to that of the Gittins Game, a corresponding MDP whose optimal solution describes the Gittins scheduling policy, and gives rise to the optimality of the scheduling policy. This relationship is at the heart of Gittins’ original paper, “Bandit Processes and Dynamic Allocation Indices”, which introduces both the MDP policy and the scheduling policy.
For the mean response time objective, the corresponding MDP is a restful MDP, giving the optimal solution strong enough properties to carry over to the scheduling setting. In contrast, for a tail response time objective such as T^2, the corresponding MDP is a restless MDP. Recently, there have been advances in the theory of multiarmed restless MDPs, such as the FollowtheVirtualLeader (FTVL) policy of Yige Hong and Weina Wang, in their paper “Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption”.
Question: Can we formulate a restless Gittins game, solve the singlearm version, and use the FTVL policy or something similar to design a scheduling policy?
Active projects
Product form steadystate distributions from graph structure
The 2class MSJ saturated system has a product form steadysteady distribution, as a consequence of the graph structure of the Markov chain. This is in contrast to the singleexponential saturated system, for which the transition rates are also important to the productform argument.
In general, a directed graph has a product form if there is an “directed elimination ordering” to its vertices, defined as follows:

For each vertex i, define its neighborhood to be all vertices j for which there exists an edge j>i, as well as i itself.

Start with a source vertex, and place it in the “eliminated” set.

Repeatedly select vertex neighborhoods that contains exactly one uneliminated vertex. Each time such a neighborhood is selected, eliminate the new vertex.

If this process can be continued until all vertices are eliminated, the directed graph has a “directed elimination ordering”.
All Markov chains with such an underlying graph have product form steadystate distributions. Moreover, such chains have summationform relative completions, a new concept which allows relative completions to be characterized in closedform.
First step: What are some classes of graphs that have elimination orderings? I know undirected trees and the ladder graphs are examples. What others?
Future steps: Have these graphs been studied already, likely under a different name? Can we give a closedform characterization of this family of graphs? Are they closed under any operations, such as taking minors?
Update: Elimination ordering seems better for summationform relative arrivals/relative completions. Instead, for productform you need something slightly stronger: A sequence of cuts such that on each side of the cut, there’s exactly one vertex with transitions across the cut.
Optimal scheduling in the general MSJ model
See Section 8.3.4 of my thesis.
Outside of the divisible server need setting behind the DivisorFillingSRPT policy, we can’t guarantee that all of the servers can be filled by an arbitrary set of k jobs. This can cause problems in two ways:

The smallest jobs might not pack well.

If we prioritize the smallest jobs, the jobs that are left over might not be able to fill the servers.
For example, consider a system with k=3 servers and jobs of server need 1 and 2. If the 2server jobs have smaller size, we can’t fill the servers with just 2server jobs. If the 1server jobs have smaller size, and we prioritize them, we’ll run out of 1server jobs and have just 2server jobs left, which can’t fill the servers.
To fix problem 1, we should just find the set of jobs with smallest sizes that can fill the servers, and serve those jobs. Proving that this is optimal will be challenging.
To fix problem 2, we should set a floor on the number of 1server jobs that we want to keep in the system, in the style of CRAB, and when we reach the floor, use the least 1serverintensive strategy. Proving this is optimal will also be hard.
First step: Find a prospective policy for the k=3 setting that “feels” optimal.
Relative arrivals/completions with infinite state spaces
Setting: Markovian arrivals/markovian service systems.
In my RESET and MARC paper, the MARC technique allows us to characterize the mean response time of systems with markovian service rates, if those service rate process is finite. See also my SNAPP talk, which is a cleaner presentation of the idea and focuses on markovian arrivals.
The “finite modulation chain” assumption isn’t really necessary  the actual assumptions needed are much more minor. In particular, we should be able to analyze systems like the Nsystem or Martin’s system by thinking of the nonheavilyloaded server as a modulation process on the service rate of the main server.
A good starting point would an Nsystem where the recipient server is critically loaded, but the donor server is not.
Starting point: Compute relative completions in the aforementioned Nsystem, compare against simulation. Perhaps pursue with Hayriye?
M/G/k response time lower bounds (known size)
See Section 8.3.2 of my thesis.
There are two straightforward lower bounds on mean response time for the M/G/k: kE[S], the mean service duration, and E[T^SRPT1], response time in an M/G/1/SRPT. Empirically, as ρ>1, SRPTk achieves a mean response time around E[T^SRPT1] + kE[S]. Can we prove a lower bound that’s asymptotically additively larger than E[T^SRPT1]?
Idea: Use WINE (see my thesis), with M/G/1 and M/G/infinity work bounds at different sizes. Mainly only improves things at lower loads.
Idea: Look at the “Increasing Speed Queue”, which starts at speed 1/k at the beginning of a busy period, then 2/k, etc., capping at speed 1 until the end of the busy period. Provides a lower bound on work. A higher lower bound than the M/G/1. Incorporate into the WINE bound.
First step: Derive the WINE bound.
Future step: Quantity expected work in the increasingspeed queue, perhaps with renewalreward.
Update: We can analyze the increasingspeed queue via the constantdrift/affinedrift method, akin to the MARC method from my RESET and MARC paper and my SNAPP talk.
See my photonotes on the subject. For the 2server setting, the constantdrift test function is:
f(w, 1) = w, f(0, 0) = 0,
f(w, 1/2) = w + (1e^(2lw))/2l
The affinedrift test function is:
f(w, 1) = w^2, f(0, 0) = 0,
f(w, 1/2) = w^2 + w/l + (1e^(2lw))/2l^2
These should be sufficient to compute mean work!
If we make the state space consist of work, time until next arrival, and speed, we can simplify this considerably. The constantdrift test function is now:
f(w, 1) = w, f(0, 0) = 0,
f(w, a, 1/2) = w + min(w, a/2)
If we plug in a = Exp(l)
and take expectations, we get the first expression above.
Archived: Completed
Known size dispatching to FCFS queues
This paper has been accepted to SIGMETRICS 2024: HeavyTraffic Optimal Sizeand StateAware Dispatching!
Starting point: CRAB by Runhan Xie and Ziv Scully, initial work presented at MAMA 2023.
Setup: Imagine web requests are arriving to a server farm. Jobs arrive, are dispatched to servers, and are served. Let’s optimize this.
When a job arrives, it must be dispatched to one of several servers. At dispatch time, the size of the job is known (or estimated), and that size is used for the dispatching decision. Once at a server, jobs are served in FCFS order.
What’s a good dispatching policy to minimize mean response time? What’s optimal? I’m especially interested in heavy traffic (arrival rate near capacity).
Idea: There’s an unavoidable amount of work in the system, M/G/1 lower bound. However, if we concentrate almost all of the work onto one server, and only dispatch large jobs to that server, then almost all of the jobs will avoid that long delay. Of course, we need to keep the other servers busy to avoid wasting capacity, but we’ll keep their queue lengths short.
Concrete policy: “Many Short Servers” (MASS). Based on size, divide jobs into classes small, medium, and large. Set these cutoffs so that the small jobs make up (k1)/k  ε fraction of the load, where k is the number of servers, the large jobs are 1/k  ε of the load, and the medium jobs are the other 2ε of the load. ε is a small constant to be determined.
Designate k1 servers as the short servers (low workload), and one as the long server (high workload). Small jobs go to the short server, large jobs go the long server, and for medium jobs it depends.
Designate a target amount of work for the short servers. This should be o(1/(1ρ)), to be smaller than the long server, and it should be omega(log(1/(1ρ))), so it doesn’t run empty due to bad luck. sqrt(1/(1ρ)), for instance.
Whenever a small job arrives, send it to the short queue with least work. Whenever a large job arrives, send it to the long server. When a medium job arrives, if the short server with the least work is below the target amount of work, send the medium job there. If all short servers are above the target, send the medium job to the long server.
First step: Implement this policy. Start with k=2, for simplicity. Compare it against JSQ, LWL, SITA. Poisson arrivals, high variance sizes, high load. Does it do well empirically, for appropriate settings of ε and the target work?
Refinement: Dynamic relabeling. Whenever a job arrives, the long server is whichever has the most work at that moment, not static.
Future steps: Prove state space collapse. The system is almost always close to having all short servers at the target, or all servers below the target.
Use SSC to bound response time/waiting time.
Lower bound waiting time. Argument: All servers must have 1/k of the load going through them. The work has to be somewhere, and there’s theta (1/(1ρ)) of it in total. Best case scenario is that the largest jobs are the only jobs delayed by the work. This should dominate waiting time. This should match the waiting time of MASS, up to ratio 1, if the distribution is not too crazy.
Archived: No longer interested
These are projects that I was once interested in, but I’m not interested in any more. Maybe the approaches that I wanted to pursue didn’t pan out, maybe others took it in more interesting directions. Either way, you can see my old ideas here.
The Time Index scheduling policy
Setting: M/G/1 scheduling for the tail, especially the asymptotic tail, especially in comparison to FCFS.
Policy: Time Index. Priority is s  t, where s is a job’s size and t is the job’s time in system.Lower is better. Relatively simple proof that waiting time dominates FCFS waiting time.
First step: Implement this policy. Compare against FCFS, Nudge.
Future steps: By how much does it dominate FCFS? Characterize leading constant of asymptotic?
Optimal Transform
My Nudge paper works very hard to do even the most basic analysis of the tail probability P(T>t). But maybe the reason this is hard is because we’re effectively comparing the response time random variable against a constant, and the constant random variable is obnoxious to work with – it has a sharp cutoff.
The smoothest random variable is the exponential random variable. If we use that as our cutoff, we get P(T>Exp(s)), which is the LaplaceStieltjes Transform of response time (Technically, it’s P(T<Exp(s)), not P(T>Exp(s)). This still captures similar information, if we set s=1/t. It is also much easier to analyze: All SOAP policies and Nudge have transform analysis. So let’s try to optimize the transform.
Intuition: Effectively, jobs abandon at rate s, and we want to maximize the fraction that we complete before they abandon. If jobs told us when they abandoned, the optimal policy is straightforward: run the small job that hasn’t abandoned yet. But we don’t know which jobs have abandoned. We need to use time in system as a proxy.
First step: Compute the transform for some common policies, like FCFS, SRPT, Nudge, via simulation and/or formula. Compare against simulated P(T>t), which we can call the “hard tail”.
Future steps: Is the transform a good proxy for the hard tail?
Is the inverse transform a good proxy for the inverse hard tail, e.g. a percentile?
For a given pair of (time in system, remaining size), what’s the optimal 2job policy? Is it that index policy I came up with a while back? What’s a semantic understanding of that policy? Can we analyze it? Is it empirically optimal in the full M/G/1? Does it perform well for the hard tail/percentiles?
Update: The optimal 2job strategy is to serve the job that maximizes e^st e^sr/(1e^sr).
Semantically, this is the probability of not abandoning prior to the time of the decision, times the probability of not abandoning while run, divided by the probability of abandoning while being run.
Note that this is a “conveyor belt” policy: jobs never interchange priority. This is a class containing SOAP and Nudge.
Further steps: Implement this policy in simulation. What’s its empirical transform? Is it empirically optimal?
Is the policy the optimal 3job policy? Optimal without arrivals?
Important update: This is a pretty bad tail metric, and hence a pretty bad policy. This metric gives jobs diminishing importance as they age, while a good tail metric should give jobs increasing importance as they age. This issue is reflected in the policy, which rates jobs as less important the larger their time in system.
Instead, one should consider the metric E[T^(st)], in contrast to the above discussion of E[e^(st)]. The optimal 2job strategy is then to maximize e^st e^sr / (1e^sr). This is a better metric and a better policy. It’s equivalent to using negative inputs to the transform, so it’s still extractable from the transform. One must be careful to only consider values of s for which the metric is finite.