Energy-Aware Scheduling of Distributed Systems

Agrawal, Palak; Rao, Shrisha

doi:10.1109/tase.2014.2308955

Cited by 60 publications

(29 citation statements)

References 39 publications

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“…To incorporate these influencing factors, a multi-objective optimization method has been proposed with four performance metrics (makespan, cost, deadline violation rate, and resource utilization) [13]. As energy consumption becomes the key issue for the operation and maintenance of cloud datacenters [14], the authors of [15] introduced the concept of skewness to measure the unevenness of servers' resource utilization.…”

Section: Categorymentioning

confidence: 99%

A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

Moon

Gil

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.

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Section: Categorymentioning

confidence: 99%

A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

Moon

Gil

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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“…The algorithm is energy aware scheduling for distributed system. The purpose of this algorithm is to minimize the execution time that means minimize the makespan and most important increase the energy level [5].…”

Section: Related Workmentioning

confidence: 99%

Optimization of Task Scheduler in Cloud Computing

Geete¹,

Mandre²

2017

IJCA

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Cloud computing is a rising technology in distributed computing. Cloud computing is one of the latest technology. Cloud is developing day by day and focuses on many challenges one of the most important is scheduling. Main objective of scheduling algorithm is to make proper utilization of the resources. The Goal of this project is to apply the scheduling algorithm in public cloud to execute the given no of tasks within deadline and budget cost. Here PBACO algorithm is used to solve optimization problem and to achieve the global optimal path and avoid the local optima. The simulation result shows the algorithm reduces the execution time and user budget cost. The algorithm completes all task execution with Minimum makespan and minimum cost using the Cloudsim. General TermsCloud computing, scheduling algorithm, task Scheduling.

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“…Additionally, metaheuristic search algorithms have also been proposed in the literature over the last two decades for scheduling residential and commercial loads. Most of the existing metaheuristic such as Particle Swarm Optimization (PSO) [11,12], Ant Colony Optimization ACO [13], Simulated Annealing (SA) [14], Genetic Algorithm (GA) [15][16][17], etc., are inspired by natural phenomenon. These studies explore alternative means of scheduling and optimizing a power profile at any hour of the day since an optimal deterministic technique is unrealistic to most customers.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Heuristic Optimization of Consumer Electricity Costs Using a Generic Cost Model

2015

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Abstract:Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.

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Energy-Aware Scheduling of Distributed Systems

Cited by 60 publications

References 39 publications

A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

Optimization of Task Scheduler in Cloud Computing

Heuristic Optimization of Consumer Electricity Costs Using a Generic Cost Model

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