Energy-Efficient Computing using Agent-Based Multi-Objective Dynamic Optimization

in J. H. Kim and M. J. Lee (Ed.) Green IT: Technologies and Applications (2011)

Green Flexible Opportunistic Computing with Virtualization

in Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology, CIT'11 (2011)

Energy efficiency and High computing power are basic design considerations across modern-day computing solutions due to different concerns such as system performance, operational cost, and environmental ... [more ▼]

Energy efficiency and High computing power are basic design considerations across modern-day computing solutions due to different concerns such as system performance, operational cost, and environmental issues. Opportunistic grid infrastructures offer computational power at low cost focused on harvesting idle computing cycles of existing commodity computing resources. Other than allowing to customize the end user offer, virtualization is considered as one key tech- niques to reduce energy consumption in large-scale systems and contributes to the scalability of the system. This paper presents an energy efficient approach for opportunistic grids based on virtualization. The experimental results show that virtualization significantly improves the energy efficiency of opportunistic grids compared with dedicated computing sys- tems without disturbing the end-user. [less ▲]

Multi-objective Cooperative Coevolutionary Evolutionary Algorithms for Continuous and Combinatorial Optimization

in Intelligent Decision Systems in Large-Scale Distributed Environments (2011)

Energy-efficient scheduling on milliclusters with performance constraints

in Green Computing and Communications (GreenCom), 2011 IEEE/ACM International Conference on (2011)

Today’s datacenters and large scale enterprise com- puting are power hungry. A lot of research effort is devoted in industry and academy to address this challenging issue. In this context, a new type of ... [more ▼]

Today’s datacenters and large scale enterprise com- puting are power hungry. A lot of research effort is devoted in industry and academy to address this challenging issue. In this context, a new type of enterprise computing platform is being investigated. This computing platform is composed of hundred of millicomputers, each requiring orders of magnitude less power. However, this approach brings challenges that must be met in order to compete with the current practice. This paper addresses two such critical challenges. First, it suggests how to decompose large applications into smaller tasks, better suited to millicomputers. Then, it casts the performance oriented and energy efficient problem into a soft real-time scheduling problem, for which several algorithms are then proposed and evaluated. Sensitivity analysis is used to provide insights into the model, and plan the evaluation of the scheduling algorithms. The contention found in multi-core millicomputing processors is also accounted for. [less ▲]

Energy-Aware Fast Scheduling Heuristics in Heterogeneous Computing Systems

in High Performance Computing and Simulation (HPCS), 2011 International Conference on (2011)

In heterogeneous computing systems it is crucial to sched- ule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems perfor- mance. Moreover, the energy ... [more ▼]

In heterogeneous computing systems it is crucial to sched- ule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems perfor- mance. Moreover, the energy efficiency in these systems is of a great interest due to different concerns such as opera- tional costs and environmental issues associated to carbon emissions. In this paper, we present a series of original low complexity energy efficient algorithms for scheduling. The main idea is to map a task to the machine that executes it fastest while the energy consumption is minimum. On the practical side, the set of experimental results showed that the proposed heuristics perform as efficiently as related ap- proaches, demonstrating their applicability for the consid- ered problem and its good scalability. [less ▲]

Scheduling DAG applications on multi-core processor packages architectures

Scientific Conference (2010, October)

Low energy and high performance scheduling on scalable computing systems

in Proceedings of the Latin-American Conference on High Performance Computing (CLCAR 2010) (2010, August 28)

With the fast development of supercomputers, energy consumption by large scale computer systems has become a major concern. How to reduce energy consumption is now a critical issue in designing high ... [more ▼]

With the fast development of supercomputers, energy consumption by large scale computer systems has become a major concern. How to reduce energy consumption is now a critical issue in designing high-performance computing systems. Moreover, reducing energy consumption for high-performance computing can bring various benefits such as, reduce monetary operating costs, increase system reliability, and reduction of environmental impacts. Therefore, in this paper we address the problem of scheduling precedence-constrained parallel applications on heterogeneous scalable computing systems with the objectives of minimizing finish time and reduce energy consumption. We provide a scheduling algorithm based on the best-effort idea that adopts dynamic voltage scaling (DVS) to reduce energy consumption. That is, the algorithm firstly looks for nearoptimal solutions employing a list-based scheduling algorithm to find the minimum finish time (besteffort). Then, a fast random local search algorithm that exploits voltage scaling is used to reduce the energy consumption of the generated schedule without any performance degradation. Simulation results on structured graphs representing real-world applications emphasize the interest of the proposed approach. [less ▲]

Distributed Energy Self-Adaptation in ad hoc Networks

in IEEE Globecom (2010)

A Multi-Agent Organizational Framework for Coevolutionary Optimization

in Lecture Notes in Computer Science (2010), 4

This paper introduces DAFO, a Distributed Agent Framework for Optimization that helps in designing and applying Coevolutionary Genetic Algorithms (CGAs). CGAs have already proven to be efficient in ... [more ▼]

This paper introduces DAFO, a Distributed Agent Framework for Optimization that helps in designing and applying Coevolutionary Genetic Algorithms (CGAs). CGAs have already proven to be efficient in solving hard optimization problems, however they have not been considered in the existing agent-based metaheuristics frameworks that currently provide limited organization models. As a solution, DAFO includes a complete organization and reorganization model, Multi-Agent System for EVolutionary Optimization (MAS4EVO), that permits to formalize CGAs structure, interactions and adaptation. Examples of existing and original CGAs modeled using MAS4EVO are provided and an experimental proof of their efficiency is given on an emergent topology control problem in mobile hybrid ad hoc networks called the injection network problem. [less ▲]

A New Parallel Multi-objective Cooperative Coevolutionary Algorithm Based on SPEA2

in ALIO-INFORMS Joint International Meeting 2010 (2010)

We present in this work a new multi-objective cooperative coevolutionary algorithm based on SPEA2 (called CCSPEA2). In this algorithm, we split the solution chromosome into 4 different parts of the same ... [more ▼]

We present in this work a new multi-objective cooperative coevolutionary algorithm based on SPEA2 (called CCSPEA2). In this algorithm, we split the solution chromosome into 4 different parts of the same size, and 4 islands are optimizing every single partial solution by using SPEA2. For evaluating the solutions, the islands are sharing their best partial solutions. As a result, CCSPEA2 outperforms SPEA2 in most of tested problems. [less ▲]