In a large-scale computing cluster, the job completions can be substantially delayed due to two sources of variability, namely, variability in the job size and that in the machine service capacity. To tackle this issue, existing works have proposed various scheduling algorithms which exploit redundancy wherein a job runs on multiple servers until the first completes. In this paper, we explore t...

We investigate the following two semi-on-line scheduling problems with machine activation cost. We are given two 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 al...

We consider a variety of preemptive scheduling problems with controllable processing times on a single machine and on identical/uniform parallel machines, where the objective is to minimize the total compression cost. In this paper, we propose fast divide-and-conquer algorithms for these scheduling problems. Our approach is based on the observation that each scheduling problem we discuss can be...

Today’s communication networks provide a wide range of algorithmic challenges for managing network resources, such as memory buffers, computation power, bandwidth, as well as other resources. Challenges include developing algorithms, which are efficiently computable on one hand, and achieve optimal or near optimal optimization on the other hand. Additional aspects of algorithmic design in netwo...

    Hub covering location problem, Network design,   Single machine scheduling, Genetic algorithm,   Shuffled frog leaping algorithm   Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc....

<p style='text-indent:20px;'>In this research, a parallel machine sequence-dependent group scheduling problem with the goal of minimizing total weighted earliness and tardiness is investigated. First, mathematical model developed for research which can be used solving small-sized instances. Since shown to NP-hard, focuses on proposing meta-heuristic algorithms finding near-optimal solutio...

In this paper I investigate several offline and online data transfer scheduling problems and propose efficient algorithms and techniques for addressing them. In the offline case, I present a novel, heuristic, algorithm for scheduling files with divisible sizes on multiple disjoint paths, in order to maximize the total profit (the problem is equivalent to the multiple knapsack problem with divis...

A competitive analysis method for online algorithms is developed based on the idea of instance transformation. The method is applied on a single machine online scheduling problem where an assumption is made that the longest processing time among the jobs in any instance is not longer than a constant, say γ, times the shortest processing time. An online algorithm is designed and its competitive ...

This paper considers the problem of online job scheduling on M identical machines with hard deadlines. The processing time and delay time of a job are known only on its arrival. Jobs are discarded if they do not meet delay time requirement. The objective of the present work is to minimize the number of tardy jobs. A lower bound of competitive ratio when the processing time and delay time are bo...

In this paper, we present a methodology for automatically generating online scheduling strategies for a complex scheduling objective with the help of real life workload data. The scheduling problem includes independent parallel jobs and multiple identical machines. The objective is defined by the machine provider and considers different priorities of user groups. In order to allow a wide range ...