15 research outputs found
Design and evaluation of a tabu search method for job scheduling in distributed enviorments
The efficient allocation of jobs to grid resources is indispensable for high performance grid-based applications. The scheduling problem is computationally hard even when there are no dependencies among jobs. Thus, we present in this paper a new tabu search (TS) algorithm for the problem of batch job scheduling on computational grids. We consider the job scheduling as a bi-objective optimization problem consisting of the minimization of the makespan and flowtime. The bi-objectivity is tackled through a hierarchic approach in which makespan is considered a primary objective and flowtime a secondary one. An extensive experimental study has been first conducted in order to fine-tune the parameters of our TS algorithm. Then, our tuned TS is compared versus two well known TS algorithms in the literature (one of them is hybridized with an ant colony optimization algorithm) for the problem. The computational results show that our TS implementation clearly outperforms the compared algorithms. Finally, we evaluated the performance of our TS algorithm on a new set of instances that better fits with the concept of computational grid. These instances are composed of a higher number of -heterogeneous- machines (up to 256) and emulate the dynamic behavior of these systems.Peer ReviewedPostprint (published version
A parallel hybrid evolutionary algorithm for the optimization of broker virtual machines subletting in cloud systems
This article presents a new parallel hybrid evolutionary
algorithm to solve the problem of virtual machines
subletting in cloud systems. The problem deals with the efficient
allocation of a set of virtual machine requests from customers
into available pre-booked resources from a cloud broker, in
order to maximize the broker profit. The proposed parallel
algorithm uses a distributed subpopulations model, and a
Simulated Annealing operator. The experimental evaluation
analyzes the profit and makespan results of the proposed
methods over a set of problem instances that account for
realistic workloads and scenarios using real data from cloud
providers. A comparison with greedy heuristics indicates that
the proposed method is able to compute solutions with up to
133.8% improvement in the profit values, while accounting for
accurate makespan results
The sandpile scheduler: How self-organized criticality may lead to dynamic load-balancing
This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is designed as a decentralized agent system characterizing a cellular automaton, which works in a critical state at the edge of chaos. Depending on the state of the cellular automaton, different responses may occur when a new task is assigned to a resource: it may
change nothing or generate avalanches that reconfigure the state of the system. The abundance of such avalanches is in power-law relation with their sizes, a scale-invariant behavior that emerges without requiring tuning or control parameters. That means that large—catastrophic—avalanches are very rare but small ones occur very often. Such emergent pattern can be efficiently adapted for non-clairvoyant scheduling, where tasks are load balanced in computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. The algorithm design is experimentally validated showing that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures
VoIP Service Model for Multi-objective Scheduling in Cloud Infrastructure
Voice over IP (VoIP) is very fast growing technology for the
delivery of voice communications and multimedia data over internet with lower
cost. Early technical solutions mirrored the architecture of the legacy telephone
network. Now, they have adopted the concept of distributed cloud VoIP. These
solutions typically allow dynamic interconnection between users on any
domains. However, providers face challenges to use infrastructure in the best
efficient and cost-effective ways. Hence, efficient scheduling and load
balancing algorithms are a fundamental part of this approach, especially in
presence of the uncertainty of a very dynamic and unpredictable environment.
In this paper, we formulate the problem of dynamic scheduling of VoIP
services in distributed cloud environments and propose a model for bi-objective
optimisation. We consider it as the special case of the bin packing problem, and discuss solutions for provider cost optimisation while ensuring quality of
service
Evolutionary algorithms based on game theory and cellular automata with coalitions
Cellular genetic algorithms (cGAs) are a kind of genetic algorithms
(GAs) with decentralized population in which interactions among individuals are
restricted to the closest ones. The use of decentralized populations in GAs allows to
keep the population diversity for longer, usually resulting in a better exploration of
the search space and, therefore in a better performance of the algorithm. However,
the use of decentralized populations supposes the need of several new parameters
that have a major impact on the behavior of the algorithm. In the case of cGAs,
these parameters are the population and neighborhood shapes. Hence, in this work
we propose a new adaptive technique based in Cellular Automata, Game Theory
and Coalitions that allow to manage dynamic neighborhoods. As a result, the new
adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed
neighborhood for the selected benchmark of combinatorial optimization problems
Energy Efficient Scheduling in Heterogeneous Systems with a Parallel Multiobjective Local Search
This article introduces ME-MLS, an e cient multithreading local search
algorithm for solving the multiobjective scheduling problem in heterogeneous com-
puting systems. We consider the minimization of both the makespan and energy
consumption objectives. The proposed method follows a fully multiobjective ap-
proach, applying a Pareto-based dominance search that is executed in parallel by
using several threads. The experimental analysis demonstrates that the new multi-
threading algorithm outperforms a set of fast and accurate two-phases deterministic
heuristics based on the traditional MinMin. The new ME-MLS method is able to
achieve signi cant improvements in both makespan and energy consumption objec-
tives in reduced execution times for a large set of testbed instances, while exhibiting
a near linear speedup behavior when using up to 24 threads
Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems
We address a multicriteria nonpreemptive
energy-aware scheduling problem for
computationalGrid systems. This work introduces
a new formulation of the scheduling problem for
multicore heterogeneous computational Grid
systems in which the minimization of the energy
consumption, along with the makespan metric,
is considered. We adopt a two-level model, in
which a meta-broker agent (level 1) receives all
user tasks and schedules them on the available
resources, belonging to different local providers
(level 2). The computing capacity and energy consumption of resources are taken from real
multi-core processors from the main current
vendors. Twenty novel list scheduling methods
for the problem are proposed, and a comparative
analysis of all of them over a large set of problem
instances is presented. Additionally, a scalability
study is performed in order to analyze the contribution
of the best new bi-objective list scheduling
heuristics when the problem dimension grows.
We conclude after the experimental analysis that
accurate trade-off schedules are computed by
using the new proposed methods
A Parallel Multi-objective Local Search for AEDB Protocol Tuning
International audienceMobile ad hoc networks are infrastructure less communication networks that are spontaneously created by a number of mobile devices. Due to the highly fluctuating topology of such networks, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. Small changes in the values of the parameters directly affects the performance of the protocol, promoting one objective while reducing another. Therefore, multi-objective optimisation is needed for fine tuning the protocol. In this work, we propose a novel parallel multi-objective local search that optimises an energy efficient broadcasting algorithm in terms of coverage, energy used, broadcasting time, and network resources. The proposed method looks for appropriate values for a set of 5 variables that markedly influence the behavior of the protocol to provide accurate tradeoff configurations in a reasonable short execution time. The new proposed algorithm is validated versus two efficient multi-objective evolutionary algorithms from the state of the art, offering comparable quality results in much shorter times