Hafiz Fahad Sheikh

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This paper addresses the joint optimization of performance, energy, and temperature, termed as PET - optimization. This multi-objective PET-optimization is achieved in scheduling DAGs on multi-core systems. Our technique is based on multi-objective evolutionary algorithm (MOEA) for finding Pareto optimal solutions using scheduling and voltage selection.(More)
This paper presents heuristic algorithms for solving the three-way joint optimization of Performance, Energy and temperature (PET) in scheduling tasks to multi-core processors. The problem, called as PET optimized scheduling (PETOS) problem is a high-complexity problem due to conflicting objectives. While solutions to the PETOS problem can be obtained by(More)
Given an initial schedule of a parallel program represented by a directed acyclic graph (DAG) and an energy constraint, the question arises how to effectively determine what nodes (tasks) can be penalized (slowed down) through the use of dynamic voltage scaling. The resulting re-schedule length with a strict energy budget should have a minimum amount of(More)
Enabled by high-speed networking in commercial, scientific, and government settings, the realm of high performance is burgeoning with greater amounts of computational and storage resources. Large-scale systems such as computational grids consume a significant amount of energy due to their massive sizes. The energy and cooling costs of such systems are often(More)
There is a lack of generally applicable methods for reducing energy consumption while ensuring good quality of service in distributed computational grids. We study the energy-aware task allocation problem for assigning a set of tasks onto the machines in a grid environment where the conflicting goals of ensuring quality of service and reducing energy(More)
This paper proposes a multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperature (T). Our algorithm includes problem-specific solution encoding, determining the initial population of the solution space, and the genetic(More)
Despite significant advancements in multicore processor technology for reducing the chip-level energy consumption, higher levels of power dissipation resulting in thermal implications and cooling costs remain as unsolved problems. Although several scheduling methods of controlling and managing the power dissipation and temperature exist, most schemes are(More)
We have proposed two algorithms for simultaneously optimizing performance, energy, and temperature while scheduling a set of tasks on a multi-core system (PET-Scheduling). The proposed algorithms differ in the way they use task allocation and voltage selection decisions to obtain multiple schedules (trade-off solutions) with a wide range of values along(More)
Thermal management is highly crucial for efficient exploitation of the potentially enormous computational power offered by advanced multi-core processors. Higher temperatures can adversely affect these processors. Without any thermal constraint, a task graph may be scheduled to run on the cores at their maximum voltage. Very often, multiple factors lead to(More)
Multi-objective evolutionary algorithms (MOEAs) are effective techniques for solving the DVFS-enabled performance (P), energy (E), and temperature (T) optimized scheduling (PETOS) problem. There are several MOEA techniques proposed in the literature for general multi-objective optimization. For example, SPEA-II is efficient for solving the PETOS problem.(More)