ONLINE SCHEDULING OF DYNAMIC TREES

1995 ◽  
Vol 05 (04) ◽  
pp. 635-646 ◽  
Author(s):  
MICHAEL A. PALIS ◽  
JING-CHIOU LIOU ◽  
SANGUTHEVAR RAJASEKARAN ◽  
SUNIL SHENDE ◽  
DAVID S.L. WEI
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Online Scheduling ◽  
The Other ◽  
Static Case ◽  
Scheduling Problem ◽  
Dynamic Trees ◽  
Dynamic Tree

The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

An Optimal Preemptive Algorithm for Online MapReduce Scheduling on Two Parallel Machines

2018 ◽  
Vol 35 (03) ◽  
pp. 1850013 ◽  
Author(s):  
Yiwei Jiang ◽  
Wei Zhou ◽  
Ping Zhou
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Online Algorithm ◽  
Parallel Machines ◽  
Online Scheduling ◽  
Scheduling Problem ◽  
Mapreduce Scheduling

In this paper, we study an online scheduling on two parallel machines in MapReduce-like system where each job contains two kinds of tasks: map tasks and reduce tasks. A job’s reduce tasks can only be processed after all its map tasks are finished. We assume that the map tasks are fractional and the reduce tasks are preemptive. Our objective is to minimize makespan. We show that the lower bound for this MapReduce scheduling problem is [Formula: see text]. We then present an online algorithm with competitive ratio of [Formula: see text] and thus it is optimal.

scholarly journals Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method

2019 ◽  
Vol 11 (7) ◽  
pp. 1826 ◽  
Author(s):  
Yuxia Cheng ◽  
Zhiwei Wu ◽  
Kui Liu ◽  
Qing Wu ◽  
Yu Wang
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Scheduling Problem ◽  
Scheduling Policies ◽  
Monte Carlo Tree Search ◽  
Reward Function ◽  
Tasks Scheduling

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

Ground Antenna Scheduling Algorithm for Multi-Satellite Tracking

2011 ◽  
Author(s):  
Sang-Hyuk Yun ◽  
Hyo-Sung Ahn ◽  
Sun-Ju Park ◽  
Ok-Chul Jung ◽  
Dae-Won Chung
Keyword(s):  
Weight Function ◽  
Greedy Algorithm ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Satellite Tracking ◽  
Scheduling Problem ◽  
Depth First Search ◽  
Ground Station ◽  
A Priority

In this paper, we address the optimal ground antenna scheduling problem for multiple satellites when multiple satellites have visibility conflicts at a ground station. Visibility conflict occurs when multiple satellites have either overlapping visibilities at a ground station or difference with time of loss of signal (LOS) of a satellite and time of acquisition of signal (AOS) of another satellite is less than reconfiguration time of ground station. Each satellite has a priority value that is a weight function with various factors. Multi-antenna scheduling (MAS) algorithm 1 and Multi-antenna scheduling (MAS) algorithm 2 are proposed to find the optimal schedule of multi-antenna at a ground station using pre-assigned priority values of satellites. We use the depth first search (DFS) method to search the optimal schedule in MAS algorithm 1 and MAS algorithm 2. Through the simulations, we confirm the efficiency of these algorithms by comparing with greedy algorithm.

A SEMI-ON-LINE SCHEDULING PROBLEM OF TWO PARALLEL MACHINES WITH COMMON MAINTENANCE TIME

2012 ◽  
Vol 29 (04) ◽  
pp. 1250020 ◽  
Author(s):  
YUHUA CAI ◽  
QI FENG ◽  
WENJIE LI
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Parallel Machines ◽  
Time Interval ◽  
Scheduling Problem ◽  
Maintenance Time ◽  
Identical Machines ◽  
On Line

In this paper, we consider a semi-on-line scheduling problem of two identical machines with common maintenance time interval and nonresumable availability. We prove a lower bound of 2.79129 on the competitive ratio and give an on-line algorithm with competitive ratio 2.79633 for this problem.

scholarly journals Semi-Equalizing Load in Multi-hop Wireless Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 3047-3051 ◽  
Keyword(s):  
Distributed Algorithms ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Local Information ◽  
The Other ◽  
Delivery Time ◽  
Average Delay ◽  
Node Scheduling ◽  
Maximum Delay ◽  
Simulation Results

Scheduling transmissions in a well-organized and fair manner in multi hop wireless network [MWN] is very crucial and challenging .For semi equalizing the load a distributed node scheduling algorithm is used through slot reallocation based on local information swap . The algorithm helps to find the delay or shortest delivery time is achieved when the load is semi-equalized throughout the network. We have simulated the Local voting algorithm and found that the system converges asymptotically toward the optimal schedule. In this paper we propose a congestion free scheme to schedule the node transmissions conflict free. The proposed algorithm achieves better performance than the other distributed algorithms in terms of fairness, average delay, and maximum delay in simulation results.

ONLINE SCHEDULING OF UNIT JOBS WITH BOUNDED IMPORTANCE RATIO

2005 ◽  
Vol 16 (03) ◽  
pp. 581-598 ◽  
Author(s):  
STANLEY P. Y. FUNG ◽  
FRANCIS Y. L. CHIN ◽  
HONG SHEN
Keyword(s):  
Quality Of Service ◽  
Competitive Ratio ◽  
Online Scheduling ◽  
Release Time ◽  
Scheduling Problem ◽  
Network Switches ◽  
Unit Processing

We consider the following online scheduling problem. We are given a set of jobs, each having an integral release time and deadline, unit processing length, and a nonnegative real weight. In each time unit one job is to be scheduled, and the objective is to maximize the total value (weight) obtained by scheduling the jobs. This problem arises in the scheduling of packets in network switches supporting quality-of-service (QoS). Previous algorithms for this problem are 2-competitive. In this paper we propose a new algorithm that achieves an improved competitive ratio when the importance ratio is bounded. Specifically, for job weights within the range [1..B], our algorithm is 2 - 1/(⌈ lg B⌉ + 2)-competitive, and the bound is tight.

ONLINE SCHEDULING OF MIXED CPU-GPU JOBS

2014 ◽  
Vol 25 (06) ◽  
pp. 745-761 ◽  
Author(s):  
LIN CHEN ◽  
DESHI YE ◽  
GUOCHUAN ZHANG
Keyword(s):  
Competitive Ratio ◽  
Completion Time ◽  
Online Algorithm ◽  
Online Scheduling ◽  
Scheduling Problem ◽  
Competitive Algorithm ◽  
Processing Times ◽  
Special Cases ◽  
Identical Machines ◽  
The One

We consider the online scheduling problem in a CPU-GPU cluster. In this problem there are two sets of processors, the CPU processors and the GPU processors. Each job has two distinct processing times, one for the CPU processor and the other for the GPU processor. Once a job is released, a decision should be made immediately about which processor it should be assigned to. The goal is to minimize the makespan, i.e., the largest completion time among all the processors. Such a problem could be seen as an intermediate model between the scheduling problem on identical machines and unrelated machines. We provide a 3.85-competitive online algorithm for this problem and show that no online algorithm exists with competitive ratio strictly less than 2. We also consider two special cases of this problem, the balanced case where the number of CPU processors equals to that of GPU processors, and the one-sided case where there is only one CPU or GPU processor. For the balanced case, we first provide a simple 3-competitive algorithm, and then a better algorithm with competitive ratio of 2.732 is derived. For the one-sided case, a 3-competitive algorithm is given.

ONLINE ALGORITHMS FOR SCHEDULING WITH MACHINE ACTIVATION COST

2007 ◽  
Vol 24 (02) ◽  
pp. 263-277 ◽  
Author(s):  
YONG HE ◽  
SHUGUANG HAN ◽  
YIWEI JIANG
Keyword(s):  
Lower Bound ◽  
Online Algorithms ◽  
Competitive Ratio ◽  
Online Algorithm ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problem ◽  
Identical Machines ◽  
Parallel Machine Scheduling Problem

In this paper, we consider a variant of the classical parallel machine scheduling problem. For this problem, we are given m potential identical machines to non-preemptively process a sequence of independent jobs. Machines need to be activated before starting to process, and each machine activated incurs a fixed machine activation cost. No machines are initially activated, and when a job is revealed the algorithm has the option to activate new machines. The objective is to minimize the sum of the makespan and activation cost of machines. We first present two optimal online algorithms with competitive ratios of 3/2 and 5/3 for m = 2, 3 cases, respectively. Then we present an online algorithm with a competitive ratio of at most 2 for general m ≥ 4, while the lower bound is 1.88.

A NEW ALGORITHM FOR MULTI-SKILL RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON CUCKOO SEARCH STRATEGY

2020 ◽  
Vol 65 (6) ◽  
pp. 98-109
Author(s):  
Huu Dang Quoc ◽  
Loc Nguyen The ◽  
Cuong Nguyen Doan ◽  
Toan Phan Thanh
Keyword(s):  
Project Scheduling ◽  
Search Strategy ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Cuckoo Search ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Start Time ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

The purpose of this paper is to consider the project scheduling problem under such limited constraint, called Multi-Skill Resource-Constrained Project Scheduling Problem or MS-RCPSP. The algorithm proposed in this paper is to find the optimal schedule, determine the start time for each task so that the execution time (also called makespan) taken is minimal. At the same time, our scheduling algorithm ensures that the given priority relationships and constraints are not violated. Our scheduling algorithm is built based on the Cuckoo Search strategy. In order to evaluate the proposed algorithm, experiments were conducted by using the iMOPSE dataset. The experimental results proved that the proposed algorithm found better solutions than the previous algorithm.

Online Single Machine Scheduling to Minimize the Maximum Starting Time

2017 ◽  
Vol 34 (05) ◽  
pp. 1750022
Author(s):  
Lingfa Lu ◽  
Liqi Zhang
Keyword(s):  
Single Machine ◽  
Competitive Ratio ◽  
Online Algorithm ◽  
Optimal Schedule ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Scheduling Problem ◽  
Starting Time

In this paper, we consider the online single machine scheduling problem to minimize the maximum starting time of the jobs. For the non-preemptive model, we show that there is no determined or randomized online algorithm with a bounded competitive ratio. For the preemption-resume model, we show that the well-known SRPT rule yields an optimal schedule. For the preemption-restart model, we show that any determined online algorithm has a competitive ratio of at least 2 and present an online algorithm with the best-possible competitive ratio of 2.

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