11,003 research outputs found
Models and algorithms for energy-efficient scheduling with immediate start of jobs
We study a scheduling model with speed scaling for machines and the immediate start requirement for jobs. Speed scaling improves the system performance, but incurs the energy cost. The immediate start condition implies that each job should be started exactly at its release time. Such a condition is typical for modern Cloud computing systems with abundant resources. We consider two cost functions, one that represents the quality of service and the other that corresponds to the cost of running. We demonstrate that the basic scheduling model to minimize the aggregated cost function with n jobs is solvable in O(nlogn) time in the single-machine case and in O(n²m) time in the case of m parallel machines. We also address additional features, e.g., the cost of job rejection or the cost of initiating a machine. In the case of a single machine, we present algorithms for minimizing one of the cost functions subject to an upper bound on the value of the other, as well as for finding a Pareto-optimal solution
Energy-Aware Lease Scheduling in Virtualized Data Centers
Energy efficiency has become an important measurement of scheduling
algorithms in virtualized data centers. One of the challenges of
energy-efficient scheduling algorithms, however, is the trade-off between
minimizing energy consumption and satisfying quality of service (e.g.
performance, resource availability on time for reservation requests). We
consider resource needs in the context of virtualized data centers of a private
cloud system, which provides resource leases in terms of virtual machines (VMs)
for user applications. In this paper, we propose heuristics for scheduling VMs
that address the above challenge. On performance evaluation, simulated results
have shown a significant reduction on total energy consumption of our proposed
algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling
algorithm with the same fulfillment of performance requirements. We also
discuss the improvement of energy saving when additionally using migration
policies to the above mentioned algorithms.Comment: 10 pages, 2 figures, Proceedings of the Fifth International
Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi,
Vietna
Minimisation of energy consumption variance for multi-process manufacturing lines through genetic algorithm manipulation of production schedule
Typical manufacturing scheduling algorithms do not consider the energy consumption of each job, or its variance, when they generate a production schedule. This can become problematic for manufacturers when local infrastructure has limited energy distribution capabilities. In this paper, a genetic algorithm based schedule modification algorithm is presented. By referencing energy consumption models for each job, adjustments are made to the original schedule so that it produces a minimal variance in the total energy consumption in a multi-process manufacturing production line, all while operating within the constraints of the manufacturing line and individual processes. Empirical results show a significant reduction in energy consumption variance can be achieved on schedules containing multiple concurrent jobs
A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud
Energy efficiency has become an important measurement of scheduling algorithm
for private cloud. The challenge is trade-off between minimizing of energy
consumption and satisfying Quality of Service (QoS) (e.g. performance or
resource availability on time for reservation request). We consider resource
needs in context of a private cloud system to provide resources for
applications in teaching and researching. In which users request computing
resources for laboratory classes at start times and non-interrupted duration in
some hours in prior. Many previous works are based on migrating techniques to
move online virtual machines (VMs) from low utilization hosts and turn these
hosts off to reduce energy consumption. However, the techniques for migration
of VMs could not use in our case. In this paper, a genetic algorithm for
power-aware in scheduling of resource allocation (GAPA) has been proposed to
solve the static virtual machine allocation problem (SVMAP). Due to limited
resources (i.e. memory) for executing simulation, we created a workload that
contains a sample of one-day timetable of lab hours in our university. We
evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list
of virtual machines in start time (i.e. earliest start time first) and using
best-fit decreasing (i.e. least increased power consumption) algorithm, for
solving the same SVMAP. As a result, the GAPA algorithm obtains total energy
consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page
On Idle Energy Consumption Minimization in Production: Industrial Example and Mathematical Model
This paper, inspired by a real production process of steel hardening,
investigates a scheduling problem to minimize the idle energy consumption of
machines. The energy minimization is achieved by switching a machine to some
power-saving mode when it is idle. For the steel hardening process, the mode of
the machine (i.e., furnace) can be associated with its inner temperature.
Contrary to the recent methods, which consider only a small number of machine
modes, the temperature in the furnace can be changed continuously, and so an
infinite number of the power-saving modes must be considered to achieve the
highest possible savings. To model the machine modes efficiently, we use the
concept of the energy function, which was originally introduced in the domain
of embedded systems but has yet to take roots in the domain of production
research. The energy function is illustrated with several application examples
from the literature. Afterward, it is integrated into a mathematical model of a
scheduling problem with parallel identical machines and jobs characterized by
release times, deadlines, and processing times. Numerical experiments show that
the proposed model outperforms a reference model adapted from the literature.Comment: Accepted to 9th International Conference on Operations Research and
Enterprise Systems (ICORES 2020
3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems
Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity
Modeling and Algorithmic Development for Selected Real-World Optimization Problems with Hard-to-Model Features
Mathematical optimization is a common tool for numerous real-world optimization problems.
However, in some application domains there is a scope for improvement of currently used optimization techniques.
For example, this is typically the case for applications that contain features which are difficult to model, and applications of interdisciplinary nature where no strong optimization knowledge is available.
The goal of this thesis is to demonstrate how to overcome these challenges by considering five problems from two application domains.
The first domain that we address is scheduling in Cloud computing systems, in which we investigate three selected problems.
First, we study scheduling problems where jobs are required to start immediately when they are submitted to the system.
This requirement is ubiquitous in Cloud computing but has not yet been addressed in mathematical scheduling.
Our main contributions are (a) providing the formal model, (b) the development of exact and efficient solution algorithms, and (c) proofs of correctness of the algorithms.
Second, we investigate the problem of energy-aware scheduling in Cloud data centers.
The objective is to assign computing tasks to machines such that the energy required to operate the data center, i.e., the energy required to operate computing devices plus the energy required to cool computing devices, is minimized.
Our main contributions are (a) the mathematical model, and (b) the development of efficient heuristics.
Third, we address the problem of evaluating scheduling algorithms in a realistic environment.
To this end we develop an approach that supports mathematicians to evaluate scheduling algorithms through simulation with realistic instances.
Our main contributions are the development of (a) a formal model, and (b) efficient heuristics.
The second application domain considered is powerline routing.
We are given two points on a geographic area and respective terrain characteristics.
The objective is to find a ``good'' route (which depends on the terrain), connecting both points along which a powerline should be built.
Within this application domain, we study two selected problems.
First, we study a geometric shortest path problem, an abstract and simplified version of the powerline routing problem.
We introduce the concept of the k-neighborhood and contribute various analytical results.
Second, we investigate the actual powerline routing problem.
To this end, we develop algorithms that are built upon the theoretical insights obtained in the previous study.
Our main contributions are (a) the development of exact algorithms and efficient heuristics, and (b) a comprehensive evaluation through two real-world case studies.
Some parts of the research presented in this thesis have been published in refereed publications [119], [110], [109]
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