Online Scheduling on Identical Machines using SRPT

Due to its optimality on a single machine for the problem of minimizing average flow time, Shortest-Remaining-Processing-Time (\srpt) appears to be the most natural algorithm to consider for the problem of minimizing average flow time on multiple identical machines. It is known that \srpt achieves the best possible competitive ratio on multiple machines up to a constant factor. Using resource augmentation, \srpt is known to achieve total flow time at most that of the optimal solution when given machines of speed $2- \frac{1}{m}$. Further, it is known that \srpt's competitive ratio improves as the speed increases; \srpt is $s$-speed $\frac{1}{s}$-competitive when $s \geq 2- \frac{1}{m}$. However, a gap has persisted in our understanding of \srpt. Before this work, the performance of \srpt was not known when \srpt is given (1+\eps)-speed when 0 < \eps < 1-\frac{1}{m}, even though it has been thought that \srpt is (1+\eps)-speed $O(1)$-competitive for over a decade. Resolving this question was suggested in Open Problem 2.9 from the survey "Online Scheduling" by Pruhs, Sgall, and Torng \cite{PruhsST}, and we answer the question in this paper. We show that \srpt is \emph{scalable} on $m$ identical machines. That is, we show \srpt is (1+\eps)-speed O(\frac{1}{\eps})-competitive for \eps >0. We complement this by showing that \srpt is (1+\eps)-speed O(\frac{1}{\eps^2})-competitive for the objective of minimizing the $\ell_k$-norms of flow time on $m$ identical machines. Both of our results rely on new potential functions that capture the structure of \srpt. Our results, combined with previous work, show that \srpt is the best possible online algorithm in essentially every aspect when migration is permissible.Comment: Accepted for publication at SODA. This version fixes an error in a preliminary versio

Online Scheduling on Identical Machines Using SRPT

Due to its optimality on a single machine for the problem of minimizing average flow time, Shortest-Remaining-Processing-Time (SRPT) appears to be the most natural algorithm to consider for the problem of minimizing average flow time on multiple identical machines. It is known that SRPT achieves the best possible competitive ratio on multiple machines up to a constant factor. Using resource augmentation, SRPT is known to achieve total flow time at most that of the optimal solution when given machines of speed $2- 1/m$. Further, it is known that SRPT's competitive ratio improves as the speed increases; SRPT is $s$-speed $1/s$-competitive when $s \geq 2 - 1/m$. However, a gap has persisted in our understanding of SRPT. Before this work, we did not know the performance of SRPT when given machines of speed 1+\eps for any 0 < \eps < 1 - 1/m. We answer the question in this thesis. We show that SRPT is scalable on $m$ identical machines. That is, we show SRPT is (1+\eps)-speed O(1/\eps)-competitive for any \eps > 0. We also show that SRPT is (1+\eps)-speed O(1/\eps^2)-competitive for the objective of minimizing the $l_k$ norms of flow time on $m$ identical machines. Both of our results rely on new potential functions that capture the structure of SRPT. Our results, combined with previous work, show that SRPT is the best possible online algorithm in essentially every aspect when migration is permissible

Existence Theorems for Scheduling to Meet Two Objectives

We will look at the existence of schedules which are simultaneously near-optimal for two criteria. First,we will present some techniques for proving existence theorems,in a very general setting,for bicriterion scheduling problems. We will then use these techniques to prove existence theorems for a large class of problems. We will consider the relationship between objective functions based on completion time,flow time,lateness and the number of on-time jobs. We will also present negative results first for the problem of simultaneously minimizing the maximum flow time and average weighted flow time and second for minimizing the maximum flow time and simultaneously maximizing the number of on-time jobs. In some cases we will also present lower bounds and algorithms that approach our bicriterion existence theorems. Finally we will improve upon our general existence results in one more specific environment

Balancing SRPT and FCFS via Starvation Mitigation

In this paper, we balance two fundamental yet seemingly contradicting job scheduling objectives, namely the average flow time and the maximum flow time. Specifically, Shortest Remaining Processing Time (SRPT) minimizes the average flow time but may lead to job starvation. In contrast, First-Come-First-Served (FCFS) minimizes the maximum flow time but may result in poor average flow time. A natural way to balance these two objectives is to minimize the $\ell_2$ norm of flow time. For this problem, no online algorithm is known to achieve a better competitive ratio than SRPT and FCFS. It can be argued that SRPT and FCFS complement each other. To exploit this complementary relationship, we mitigate the starvation caused by SRPT with the help of FCFS. Specifically, when there are starving jobs, we process the job that becomes starving first. The main question is: when should a job be viewed as starving? If the timing is too early or too late, then the algorithm still behaves like FCFS or SRPT, respectively. In this paper, we answer the above question by estimating the number of jobs. Our algorithm significantly improves upon SRPT and FCFS in terms of the competitive ratio for minimizing the $\ell_2$ norm of flow time, even if the estimate is loose.Comment: 1. Introduction is rewritten. 2. Add Theorem 1.4 and numerical study. 3. The proposed algorithm and the proof of Theorem 1.5 are simplifie

Asymptotically Optimal Approximation Algorithms for Coflow Scheduling

Many modern datacenter applications involve large-scale computations composed of multiple data flows that need to be completed over a shared set of distributed resources. Such a computation completes when all of its flows complete. A useful abstraction for modeling such scenarios is a {\em coflow}, which is a collection of flows (e.g., tasks, packets, data transmissions) that all share the same performance goal. In this paper, we present the first approximation algorithms for scheduling coflows over general network topologies with the objective of minimizing total weighted completion time. We consider two different models for coflows based on the nature of individual flows: circuits, and packets. We design constant-factor polynomial-time approximation algorithms for scheduling packet-based coflows with or without given flow paths, and circuit-based coflows with given flow paths. Furthermore, we give an $O(\log n/\log \log n)$-approximation polynomial time algorithm for scheduling circuit-based coflows where flow paths are not given (here $n$ is the number of network edges). We obtain our results by developing a general framework for coflow schedules, based on interval-indexed linear programs, which may extend to other coflow models and objective functions and may also yield improved approximation bounds for specific network scenarios. We also present an experimental evaluation of our approach for circuit-based coflows that show a performance improvement of at least 22% on average over competing heuristics.Comment: Fixed minor typo

Separable Concave Optimization Approximately Equals Piecewise-Linear Optimization

We study the problem of minimizing a nonnegative separable concave function over a compact feasible set. We approximate this problem to within a factor of 1+epsilon by a piecewise-linear minimization problem over the same feasible set. Our main result is that when the feasible set is a polyhedron, the number of resulting pieces is polynomial in the input size of the polyhedron and linear in 1/epsilon. For many practical concave cost problems, the resulting piecewise-linear cost problem can be formulated as a well-studied discrete optimization problem. As a result, a variety of polynomial-time exact algorithms, approximation algorithms, and polynomial-time heuristics for discrete optimization problems immediately yield fully polynomial-time approximation schemes, approximation algorithms, and polynomial-time heuristics for the corresponding concave cost problems. We illustrate our approach on two problems. For the concave cost multicommodity flow problem, we devise a new heuristic and study its performance using computational experiments. We are able to approximately solve significantly larger test instances than previously possible, and obtain solutions on average within 4.27% of optimality. For the concave cost facility location problem, we obtain a new 1.4991+epsilon approximation algorithm.Comment: Full pape