Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques

In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they...
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Autor*in:	Sheikh, Hafiz Fahad [verfasserIn] Ahmad, Ishfaq [verfasserIn] Arshad, Sheheryar Ali [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling

Übergeordnetes Werk:	Enthalten in: Sustainable Computing - Amsterdam [u.a.] : Elsevier, 2011, 22, Seite 272-286
Übergeordnetes Werk:	volume:22 ; pages:272-286

DOI / URN:	10.1016/j.suscom.2017.10.002

Katalog-ID:	ELV002481685

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650		4	\|a Dynamic thermal management
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650		4	\|a DAG scheduling
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allfields	10.1016/j.suscom.2017.10.002 doi (DE-627)ELV002481685 (ELSEVIER)S2210-5379(17)30093-8 DE-627 ger DE-627 rda eng 004 DE-600 Sheikh, Hafiz Fahad verfasserin aut Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front. Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling Ahmad, Ishfaq verfasserin aut Arshad, Sheheryar Ali verfasserin aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 22, Seite 272-286 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:22 pages:272-286 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 22 272-286
spelling	10.1016/j.suscom.2017.10.002 doi (DE-627)ELV002481685 (ELSEVIER)S2210-5379(17)30093-8 DE-627 ger DE-627 rda eng 004 DE-600 Sheikh, Hafiz Fahad verfasserin aut Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front. Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling Ahmad, Ishfaq verfasserin aut Arshad, Sheheryar Ali verfasserin aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 22, Seite 272-286 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:22 pages:272-286 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 22 272-286
allfields_unstemmed	10.1016/j.suscom.2017.10.002 doi (DE-627)ELV002481685 (ELSEVIER)S2210-5379(17)30093-8 DE-627 ger DE-627 rda eng 004 DE-600 Sheikh, Hafiz Fahad verfasserin aut Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front. Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling Ahmad, Ishfaq verfasserin aut Arshad, Sheheryar Ali verfasserin aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 22, Seite 272-286 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:22 pages:272-286 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 22 272-286
allfieldsGer	10.1016/j.suscom.2017.10.002 doi (DE-627)ELV002481685 (ELSEVIER)S2210-5379(17)30093-8 DE-627 ger DE-627 rda eng 004 DE-600 Sheikh, Hafiz Fahad verfasserin aut Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front. Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling Ahmad, Ishfaq verfasserin aut Arshad, Sheheryar Ali verfasserin aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 22, Seite 272-286 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:22 pages:272-286 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 22 272-286
allfieldsSound	10.1016/j.suscom.2017.10.002 doi (DE-627)ELV002481685 (ELSEVIER)S2210-5379(17)30093-8 DE-627 ger DE-627 rda eng 004 DE-600 Sheikh, Hafiz Fahad verfasserin aut Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front. Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling Ahmad, Ishfaq verfasserin aut Arshad, Sheheryar Ali verfasserin aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 22, Seite 272-286 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:22 pages:272-286 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 22 272-286
language	English
source	Enthalten in Sustainable Computing 22, Seite 272-286 volume:22 pages:272-286
sourceStr	Enthalten in Sustainable Computing 22, Seite 272-286 volume:22 pages:272-286
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling
dewey-raw	004
isfreeaccess_bool	false
container_title	Sustainable Computing
authorswithroles_txt_mv	Sheikh, Hafiz Fahad @@aut@@ Ahmad, Ishfaq @@aut@@ Arshad, Sheheryar Ali @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	647657384
dewey-sort	14
id	ELV002481685
language_de	englisch
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author	Sheikh, Hafiz Fahad
spellingShingle	Sheikh, Hafiz Fahad ddc 004 misc Task scheduling misc Dynamic thermal management misc Multi-objective evolutionary algorithms misc Multi-core systems misc DAG scheduling Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques
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topic_title	004 DE-600 Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques Task scheduling Dynamic thermal management Multi-objective evolutionary algorithms Multi-core systems DAG scheduling
topic	ddc 004 misc Task scheduling misc Dynamic thermal management misc Multi-objective evolutionary algorithms misc Multi-core systems misc DAG scheduling
topic_unstemmed	ddc 004 misc Task scheduling misc Dynamic thermal management misc Multi-objective evolutionary algorithms misc Multi-core systems misc DAG scheduling
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title	Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques
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title_full	Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques
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title_sort	performance, energy, and temperature enabled task scheduling using evolutionary techniques
title_auth	Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques
abstract	In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front.
abstractGer	In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front.
abstract_unstemmed	In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front.
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title_short	Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques
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author2	Ahmad, Ishfaq Arshad, Sheheryar Ali
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up_date	2024-07-07T00:52:22.011Z
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Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques

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