scholarly journals Predictive Job Scheduling under Uncertain Constraints in Cloud Computing

2021 ◽  
Author(s):  
Hang Dong ◽  
Boshi Wang ◽  
Bo Qiao ◽  
Wenqian Xing ◽  
Chuan Luo ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Optimization Problem ◽  
Job Scheduling ◽  
High Reliability ◽  
Capacity Management ◽  
Bayesian Optimization ◽  
Scheduling Problem ◽  
Effective Algorithm ◽  
Scheduling Problems ◽  
On Demand

Capacity management has always been a great challenge for cloud platforms due to massive, heterogeneous on-demand instances running at different times. To better plan the capacity for the whole platform, a class of cloud computing instances have been released to collect computing demands beforehand. To use such instances, users are allowed to submit jobs to run for a pre-specified uninterrupted duration in a flexible range of time in the future with a discount compared to the normal on-demand instances. Proactively scheduling those pre-collected job requests considering the capacity status over the platform can greatly help balance the computing workloads along time. In this work, we formulate the scheduling problem for these pre-collected job requests under uncertain available capacity as a Prediction + Optimization problem with uncertainty in constraints, and propose an effective algorithm called Controlling under Uncertain Constraints (CUC), where the predicted capacity guides the optimization of job scheduling and job scheduling results are leveraged to improve the prediction of capacity through Bayesian optimization. The proposed formulation and solution are commonly applicable for proactively scheduling problems in cloud computing. Our extensive experiments on three public, industrial datasets shows that CUC has great potential for supporting high reliability in cloud platforms.

scholarly journals Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ibrahim Attiya ◽  
Mohamed Abd Elaziz ◽  
Shengwu Xiong
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithms ◽  
Scheduling Problems ◽  
It Services ◽  
On Demand

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

scholarly journals 3/2-approximation algorithm for a single machine scheduling problem

2021 ◽  
Vol 17 (3) ◽  
pp. 240-253
Author(s):  
Natalia S. Grigoreva ◽  
Keyword(s):  
Approximation Algorithm ◽  
Optimization Problem ◽  
Single Machine Scheduling ◽  
Release Time ◽  
Scheduling Problem ◽  
Delivery Time ◽  
Scheduling Problems ◽  
Delivery Times ◽  
Jobshop Scheduling ◽  
Single Processor

The problem of minimizing the maximum delivery times while scheduling tasks on a single processor is a classical combinatorial optimization problem. Each task ui must be processed without interruption for t(ui) time units on the machine, which can process at most one task at time. Each task uw; has a release time r(ui), when the task is ready for processing, and a delivery time g(ui). Its delivery begins immediately after processing has been completed. The objective is to minimize the time, by which all jobs are delivered. In the Graham notation this problem is denoted by 1|rj,qi|Cmax, it has many applications and it is NP-hard in a strong sense. The problem is useful in solving owshop and jobshop scheduling problems. The goal of this article is to propose a new 3/2-approximation algorithm, which runs in O(n log n) times for scheduling problem 1|rj.qi|Cmax. An example is provided which shows that the bound of 3/2 is accurate. To compare the effectiveness of proposed algorithms, random generated problems of up to 5000 tasks were tested.

WBAT Job Scheduler: A Multi-Objective Approach for Job Scheduling Problem on Cloud Computing

2019 ◽  
Vol 29 (06) ◽  
pp. 2050089
Author(s):  
B. Hariharan ◽  
D. Paul Raj
Keyword(s):  
Job Scheduling ◽  
Fitness Function ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Multi Objective ◽  
Job Scheduling Problem ◽  
Job Scheduler ◽  
New Good ◽  
Better Than

The main objective of the proposed methodology is multi-objective job scheduling using hybridization of whale and BAT optimization algorithm (WBAT) which is used to change existing solution and to adopt a new good solution based on the objective function. The scheduling function in the proposed job scheduling strategy first creates a set of jobs and cloud node to generate the population by assigning jobs to cloud node randomly and evaluate the fitness function which minimizes the makespan and maximizes the quality of jobs. Second, the function uses iterations to regenerate populations based on WBAT behavior to produce the best job schedule that gives minimum makespan and good quality of jobs. The experimental results show that the performance of the proposed methods is better than the other methods of job scheduling problems.

Job Scheduling in Cloud Using Seagull Optimization Algorithm

2021 ◽  
pp. 27-40
Author(s):  
Meenakshi Garg ◽  
Gaurav Dhiman
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Job Scheduling ◽  
Scheduling Algorithms ◽  
It Services ◽  
On Demand ◽  
Resource Dynamics ◽  
Np Complete ◽  
Application Requirements ◽  
The Web

In recent years, cloud computing technology has gained a great deal of interest from both academia and industry. Cloud computing's success benefited from its ability to offer global IT services such as core infrastructure, platforms, and applications to cloud customers around the web. It also promises on-demand offerings and new ways of pricing packages. However, cloud job scheduling is still NP-complete and has become more difficult due to certain factors such as resource dynamics and on-demand customer application requirements. To fill this void, this chapter presents the seagull optimization algorithm (SOA) for scheduling work in the cloud world. The efficiency of the SOA approach is compared to that of state-of-the-art job scheduling algorithms by having them all implemented in the CloudSim toolkit.

Load Balancing in Cloud Computing

2020 ◽  
pp. 294-316
Author(s):  
Pradeep Kumar Tiwari ◽  
Geeta Rani ◽  
Tarun Jain ◽  
Ankit Mundra ◽  
Rohit Kumar Gupta
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
High Reliability ◽  
Large Data ◽  
Resource Provisioning ◽  
Data Handling ◽  
On Demand ◽  
Load Imbalance ◽  
Physical Resources ◽  
And Storage

Cloud computing is an effective alternative information technology paradigm with its on-demand resource provisioning and high reliability. This technology has the potential to offer virtualized, distributed, and elastic resources as utilities to users. Cloud computing offers numerous types of computing and storage means by connecting to a vast pool of systems. However, because of its large data handling property, the major issue the technology facing is the load balancing problem. Load balancing is the maximum resource utilization with effective management of load imbalance. This chapter shares information about logical and physical resources, load balancing metrics, challenges and techniques, and also gives some suggestions that could be helpful for future studies.

scholarly journals Analysis of flow shop problems and their algorithms

2009 ◽  
Vol 50 ◽  
Author(s):  
Lina Rajeckaitė ◽  
Narimantas Listopadskis
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Flow Shop ◽  
Exact Algorithm ◽  
Combinatorial Optimization Problem ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

The combinatorial optimization problem considered in this paper is flow shop scheduling problem arising in logistics, management, business, manufacture and etc. A set of machines and a set of jobs are given. Each job consists of a set of operations. Machines are working with unavailability intervals. The task is to minimize makespan, i.e. the overall length of the schedule. There is overview of combinatorial optimization, scheduling problems and methods used to solve them. There is also presented and realized one exact algorithm – Branch and Bound, and two meta-heuristics: Simulated Annealing and Tabu Search. Analysis of these three algorithms is made.

Experimental Evaluation of Energy Saving Task Scheduling (ESTS) in Cloud Computing

2018 ◽  
Vol 7 (3.34) ◽  
pp. 107
Author(s):  
N Kalyana Sundaram ◽  
Dr S.P.Rajagopalan
Keyword(s):  
Cloud Computing ◽  
Energy Saving ◽  
Computer System ◽  
Task Scheduling ◽  
Experimental Evaluation ◽  
Job Scheduling ◽  
Performance Comparison ◽  
On Demand ◽  
First Come First Serve ◽  
Made In

Cloud Computing provides services, on-demand access, infrastructure, storage of data and application.  It possesses the reliability, availability and the scalability. One of the issues in cloud computing is Energy Saving. In this paper, the proposed work is Energy Saving Task Scheduling (ESTS) methodology. The aim of this methodology is to show the performance comparison of all the task scheduling types. Task scheduling or Job scheduling is referred to as policies that control the work order to be performed by a computer system. Types of Task Scheduling are Shortest Job First (SJF), First Come First Serve (FCFS), Round Robin (RR) and Priority Scheduling. In each type of schedule, the processes used by the parameters were calculated. Finally, the performance comparison is made in scheduling algorithms and shows better results. This method is implemented in net beans toolkit.  

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