PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources

Sharifi, Mohsen; Shahrivari, Saeed; Salimi, Hadi

doi:10.1007/s00607-012-0212-1

Cited by 25 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used a dimensionality reduction method (KCCA) to relate the resource requirements to performance and power consumption, and developed a pSciMapper framework for task consolidation. PASTA [141] is an two-phase power aware solution to schedule workflows on heterogeneous computing resources. Their contributions are a resource selection mechanism that offers a fair trade-off between power efficiency and overall makespan, and a new list-based scheduling algorithm for task scheduling on selected resource.…”

Section: Energy-aware Workflow Schedulingmentioning

confidence: 99%

Workflow scheduling in cloud: a survey

2015

View full text Add to dashboard Cite

To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem-solution manner. Based on the analysis, we also highlight some research directions for future investigation.

show abstract

Section: Energy-aware Workflow Schedulingmentioning

confidence: 99%

Workflow scheduling in cloud: a survey

2015

View full text Add to dashboard Cite

show abstract

“…We consider that the discrete frequency or speed levels are in the range [ f min , f max ] where f min is 40 % of f max as mentioned in Table 5 (e.g., if f max is 2.0 GHz, then f min is 0.8 GHz). Further, the active power consumption of each processor at maximum voltage level is selected randomly by uniform distribution with mean being equal to twice the average power consumption (set as 200) [46]. The power consumption of processors at different supply voltages is then calculated using Eq.…”

Section: Simulation Modelmentioning

confidence: 99%

Energy-Aware Workflow Scheduling in Grid Under QoS Constraints

Garg

Singh

2015

Arab J Sci Eng

View full text Add to dashboard Cite

In this study, we have considered the problem of scheduling precedence-constraint parallel applications (workflows) in heterogeneous grid-computing environment. Recently many heuristics have been devoted to grid scheduling typically restricted to optimizing the execution time (makespan) only without paying much concentration on energy consumption. Reducing energy consumption can bring various advantages like reducing operating costs, environmental perspective and increase in system reliability. This paper aims to develop energy-aware task scheduling algorithm in grid based on the dynamic voltage and frequency scaling (DVFS) technique. The user negotiates with the service provider on their quality of service (QoS) requirements along with green computing specifications to reach the service level agreement. With the use of DVFS, the algorithm minimizes the energy consumption of task execution while satisfying the QoS constraints (deadline). The proposed static scheduling algorithm works in three phases: deadline distribution, tasks ordering and then assigning the best services to tasks along with selecting the appropriate voltage levels while meeting its sub-deadline. The simulation results using randomly generated task graphs and task graphs corresponding to real-world problems exhibit that the proposed algorithm achieves energy efficiency and reduces energy consumption up to 68 % with the increase in 30 % of the execution time.B Ritu Garg

show abstract

“…There are many energy efficient scheduling approaches [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] that have been studied from different scheduling perspectives. Energy efficient task scheduling is a scheduling algorithm that dynamically allocates jobs into processor to achieve better performance and to minimize energy consumption.…”

Section: Energy Efficient Schedulingmentioning

confidence: 99%

“…Heuristic is one of the most popular scheduling approaches to be chosen for focusing on green Cloud. The strategy performs effectively for scheduling jobs into processors in uncertain, dynamic and large-scale environments [11,16] [ [23][24][25]. The heuristic strategy is initiated by designing a small companion problem and is extended to complex issues to solve big problems.…”

Section: Heuristic Strategymentioning

confidence: 99%

See 1 more Smart Citation

Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy

Nor¹,

Hussin²,

Selamat³

2014

JCSCM

View full text Add to dashboard Cite

Cloud computing has been a champion in Information Technology (IT) world for supporting flexibility on-demand services. It is leveraged to instantly fulfill dynamic demands from heterogeneous environment. However, the massive demand for the Cloud computing services contributed to high energy consumption, thus affecting cost of service and processing components lifetime. Quite a portion of the energy consumed is dissipated for ensuring processing components (i.e., processor) running but idle. An effective task scheduling is the solution for better processing performance while reducing energy consumption. In this paper we compared several energy efficient scheduling from three (3) different perspectives (i.e., heuristic, economic and intelligent points of view). These different perspectives are chosen particularly for exemplifying essential principles and features of energy management in task scheduling. Novel knowledge on the energy management are identified through similarities, differences and energy models. We believed that better trade-off between energy consumption and system performance is the solution to green Cloud computing.

show abstract

PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources

Cited by 25 publications

References 27 publications

Workflow scheduling in cloud: a survey

Workflow scheduling in cloud: a survey

Energy-Aware Workflow Scheduling in Grid Under QoS Constraints

Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy

Contact Info

Product

Resources

About