scholarly journals ENHANCING JOB SCHEDULING IN CLOUD ENVIRONMENT: A REVIEW

2015 ◽  
Vol 14 (6) ◽  
pp. 5796-5802
Author(s):  
Mrs. Amita Rani ◽  
Dr. Mohita Garg
Keyword(s):  
Heterogeneous Computing ◽  
Job Scheduling ◽  
Heuristic Algorithms ◽  
Computing System ◽  
Technology Infrastructure ◽  
Computing Systems ◽  
Effective Utilization ◽  
Internet Users ◽  
Np Complete ◽  
Heterogeneous Computing Systems

Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. The problem of mapping meta-tasks to a machine is shown to be NP-complete. The NP-complete problem can be solved only using heuristic approach. There are a number of heuristic algorithms that were tailored to deal with scheduling of independent tasks. Different criteria can be used for evaluating the efficiency of scheduling algorithms. The most important of them are makespan, flowtime and resource utilization. In this paper, a new heuristic algorithm for scheduling meta-tasks in heterogeneous computing system is presented. The proposed algorithm improves the performance in both makespan and effective utilization of resources by reducing the waiting time. 

scholarly journals ENHANCED DYNAMIC RESOURCE ALLOCATION SCHEME BASED ON PACKAGE LEVEL ACCESS IN CLOUD COMPUTING : A REVIEW

2017 ◽  
Vol 16 (2) ◽  
pp. 6207-6212 ◽  
Author(s):  
Manpreet Kaur ◽  
Dr. Rajinder Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Heterogeneous Computing ◽  
Virtual Machines ◽  
Technology Infrastructure ◽  
Computing Systems ◽  
Computing Platform ◽  
Internet Users ◽  
Resource Allocation Scheme ◽  
Heterogeneous Computing Systems

Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. On cloud computing platform, load balancing of the entire system can be  dynamically handled  by  using  virtualization  technology through which it  becomes  possible  to  remap  virtual  machine  and physical resources  according  to  the  change  in  load. However, in order to improve performance, the virtual machines have to fully utilize its resources and services by adapting to computing environment dynamically.  The  load balancing  with  proper  allocation  of  resources  must  be guaranteed  in  order  to  improve  resource  utility.

scholarly journals A Review on Load Balancing In Cloud Environment

2018 ◽  
Vol 17 (1) ◽  
pp. 7120-7125 ◽  
Author(s):  
Amandeep Kaur ◽  
Mr. Pawan Luthra
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Heterogeneous Computing ◽  
Virtual Machines ◽  
Technology Infrastructure ◽  
Computing Systems ◽  
Computing Platform ◽  
Internet Users ◽  
Cloud Computing Platform ◽  
Heterogeneous Computing Systems

Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. On cloud computing platform, load balancing of the entire system can be  dynamically handled  by  using  virtualization  technology through which it  becomes  possible  to  remap  virtual  machine  and physical resources  according  to  the  change  in  load. However, in order to improve performance, the virtual machines have to fully utilize its resources and services by adapting to computing environment dynamically.  The  load balancing  with  proper  allocation  of  resources  must  be guaranteed  in  order  to  improve  resource  utility.

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

2020 ◽  
Vol 34 (12) ◽  
pp. 2051012
Author(s):  
Hui Xie ◽  
Li Wei ◽  
Dong Liu ◽  
Luda Wang
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Machine Learning Approach ◽  
Heterogeneous Computing Systems ◽  
Regression Theory

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2019 ◽  
pp. 115-127
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2021 ◽  
pp. 1303-1316
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

Redundant Computing Resources Management of Integrated Modular Avionics

2019 ◽  
Vol 20 (6) ◽  
pp. 376-384
Author(s):  
V. N. Bukov ◽  
V. A. Shurman ◽  
I. F. Gamayunov ◽  
A. M. Ageev
Keyword(s):  
Computer System ◽  
Heterogeneous Computing ◽  
Computing System ◽  
Computing Systems ◽  
Success Indicators ◽  
Hardware Failures ◽  
Processor Node ◽  
Integrated Modular Avionics ◽  
Heterogeneous Computing Systems ◽  
Hierarchical Scheme

In article the structure and the control algorithm are considered by diverse redundancy of the computing system of the perspective integrated modular avionics. Computing resources of the integrated modular avionics system are generally represented by heterogeneous computing systems used for information processing as part of the onboard integrated computing environment. The basis of heterogeneous computing systems are processor nodes, redundancy of computing systems is that the number of processor nodes is greater than one. The task is to synthesize such a computer system in which the automatic control of redundant computational resources would be carried out by using the own capabilities of the processor nodes and without the use of additional hardware resources. It is considered that the redundant computer system performs meaningful calculations of the problem solved by several processor nodes in parallel. All meaningful calculations for any signs initially divided into relatively short stages, providing an opportunity to assess the effectiveness of the completion of each of them. The computational system redundancy management is based on the periodic calculation and comparison of the success indicators of the stage. Pairwise arbitration of processor nodes is carried out according to a hierarchical scheme by comparing the values of the success indicators of the stages of the same name. Subsequent reconfiguration of the computer system allocates passive and leading processor nodes in pairs at all levels of the hierarchical scheme. The failure of the passive processor node does not affect the execution of the main cycle. The failure of the host processor node does not cause interruptions in the output of the results of calculations, but destroys the structure of reserves, which is restored after arbitration in the next cycle. Failure of the lead CPU top node leads to the failure output in the current cycle, the computational process is restored along with the new hierarchy of the computing system in the next cycle. The proposed solution is aimed at parrying both hardware failures and software malfunction. The methodical example based on the computer system of the modern onboard equipment complex of the transports category aircraft is resulted.

scholarly journals Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

2019 ◽  
Vol 8 (4) ◽  
pp. 11746-11759
Keyword(s):  
Heterogeneous Computing ◽  
Job Scheduling ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Np Hard ◽  
Computing Systems ◽  
High Resource ◽  
Parallel Jobs ◽  
Job Scheduling Problem ◽  
Heterogeneous Computing Systems

Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.

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