scholarly journals InterRC: An Inter-Resources Collaboration Heuristic for Scheduling Independent Tasks on Heterogeneous Distributed Environments

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 179-188
Author(s):  
Abdelhamid Khiat ◽  
Abdelkamel Tari
Keyword(s):  
Task Scheduling ◽  
Initial Solution ◽  
Distributed Environments ◽  
Scheduling Problem ◽  
Reasonable Time ◽  
Independent Task ◽  
Np Hard Problem ◽  
Computing Environments ◽  
Scheduling Heuristic ◽  
Independent Tasks

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms.

scholarly journals Teaching-Learning based Task Scheduling Optimization in Cloud Computing Environments

2019 ◽  
Vol 8 (2) ◽  
pp. 2952-2958
Keyword(s):  
Task Scheduling ◽  
Research Work ◽  
Search Space ◽  
Scheduling Problem ◽  
Optimal Resource ◽  
Np Hard Problem ◽  
Cloud Environments ◽  
Computing Environments ◽  
Teaching Learning ◽  
First Time

Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.

scholarly journals Static vs. Dynamic List-Scheduling Performance Comparison

10.14311/490 ◽  
2003 ◽  
Vol 43 (6) ◽  
Author(s):  
T. Hagras ◽  
J. Janeček
Keyword(s):  
Task Scheduling ◽  
Optimal Solution ◽  
Scheduling Algorithms ◽  
Low Complexity ◽  
Performance Comparison ◽  
List Scheduling ◽  
Scheduling Problem ◽  
Computing Environments ◽  
Np Complete ◽  
List Scheduling Algorithms

The problem of efficient task scheduling is one of the most important and most difficult issues in homogeneous computing environments. Finding an optimal solution for a scheduling problem is NP-complete. Therefore, it is necessary to have heuristics to find a reasonably good schedule rather than evaluate all possible schedules. List-scheduling is generally accepted as an attractive approach, since it pairs low complexity with good results. List-scheduling algorithms schedule tasks in order of priority. This priority can be computed either statically (before scheduling) or dynamically (during scheduling). This paper presents the characteristics of the two main static and the two main dynamic list-scheduling algorithms. It also compares their performance in dealing with random generated graphs with various characteristics.

scholarly journals Bio-inspired negotiation approach for smart-grid colocation datacenter operation

2022 ◽  
Vol 19 (3) ◽  
pp. 2403-2423
Author(s):  
Santiago Iturriaga ◽  
◽  
Jonathan Muraña ◽  
Sergio Nesmachnow
Keyword(s):  
Energy Consumption ◽  
Smart Grid ◽  
Task Scheduling ◽  
Demand Response ◽  
Planning Problem ◽  
Scheduling Problem ◽  
Upper Level ◽  
Scheduling Heuristic ◽  
Demand Response Programs ◽  
Business As Usual

<abstract><p>Demand response programs allow consumers to participate in the operation of a smart electric grid by reducing or shifting their energy consumption, helping to match energy consumption with power supply. This article presents a bio-inspired approach for addressing the problem of colocation datacenters participating in demand response programs in a smart grid. The proposed approach allows the datacenter to negotiate with its tenants by offering monetary rewards in order to meet a demand response event on short notice. The objective of the underlying optimization problem is twofold. The goal of the datacenter is to minimize its offered rewards while the goal of the tenants is to maximize their profit. A two-level hierarchy is proposed for modeling the problem. The upper-level hierarchy models the datacenter planning problem, and the lower-level hierarchy models the task scheduling problem of the tenants. To address these problems, two bio-inspired algorithms are designed and compared for the datacenter planning problem, and an efficient greedy scheduling heuristic is proposed for task scheduling problem of the tenants. Results show the proposed approach reports average improvements between $ 72.9\% $ and $ 82.2\% $ when compared to the business as usual approach.</p></abstract>

A Fuzzy Enabled Genetic Algorithm for Task Scheduling Problem in Cloud Computing

2020 ◽  
Vol 10 (3) ◽  
pp. 334-344
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fundamental Problem ◽  
Data Sets ◽  
Scheduling Problem ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Roulette Wheel Selection ◽  
Np Hard Problem

Background & Objective: Cloud computing emerges out as a new way of computing which enables the users to fulfill their computation need using the underlying computing resources like software, memory, computing nodes or machines without owning them purely on the basis of pay-per-use that too round the clock and from anywhere. People defined this as the extension of the existing technologies like parallel computing, distributed computing or grid computing. Lots of research have been conducted in the field of cloud computing but the task scheduling is considered to be the most fundamental problem which is still in infancy and requires a lot of attention and a proper mechanism for the optimal utilization of the underlying computing resources. Task scheduling in cloud computing environment lies into the category of NP-hard problem and many heuristics and Meta heuristics strategies have been applied to solve the problem. Methods: In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem of task scheduling in cloud computing environment as classical roulette wheel selection method has certain limitations to solve complex optimization problem. Results & Discussion: In this work, an efficient fuzzy enabled genetic algorithm based task scheduling mechanism has been designed, implemented and investigated. The efficiency of the proposed FEGA algorithm is tested using various randomly generated data sets in different situations and compared with the other meta-heuristics. Conclusion: The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to solve the task scheduling problem helps in minimizing the total execution time or makespan and on comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that FEGA outperforms the both in different set of experiments.

Towards Efficient Task Scheduling on 2D Reconfigurable FPGAs

2013 ◽  
Vol 850-851 ◽  
pp. 961-964
Author(s):  
Peng Wang ◽  
Jun Feng Zhang ◽  
Xue Chen
Keyword(s):  
Task Scheduling ◽  
Field Programmable Gate Array ◽  
Rapid Development ◽  
Scheduling Problem ◽  
Task Sequencing ◽  
Sequencing Method ◽  
Field Programmable ◽  
And Performance ◽  
Task Sets ◽  
Independent Tasks

With the rapid development of dynamical partial reconfiguration technology, FPGA (Field Programmable Gate Array) is able to allow independent tasks to be executed concurrently without interfering with each other, which increases its flexibility and performance, but on the other hand leads to multi-task scheduling problem. The task scheduling in this paper is the problem of task sequencing, which adjusts the entering sequence of the incoming tasks with the consideration of the attributes of tasks and the utilization of FPGA. A conditional preemption based task sequencing method is proposed that allows tasks that arrive later to be executed in advance as long as the previous tasks can still be guaranteed to enter the FPGA on time. Simulations show that these methods can effectively decrease the waiting ratio of task sets, thus improving the flexibility and utilization of FPGA.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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