The Messaging Mechanism Based on Multi-Level Feedback Queue Scheduling Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 1763-1765
Author(s):  
Ting Shun Li ◽  
Jiao Hui Xu ◽  
Hui Yu
Keyword(s):  
Wireless Communication ◽  
Communication Technology ◽  
Scheduling Algorithm ◽  
Communication Tools ◽  
Queue Scheduling ◽  
Multi Level ◽  
Feedback Queue

With the development of wireless communication technology, SMS , as a kind of flexible communication tools, is widely used in the various units. Aimed at large quantities of SMS processing, this paper proposes a new scheduling algorithm based on multi-level feedback queue. Multi-level feedback queue scheduling algorithm can not only make the high priority jobs response, but also make the short operations (process) done quickly.

scholarly journals Load Balancing Using SJF-MMBF and SJF-ELM Approach

2021 ◽  
pp. 74-86
Author(s):  
S. Rekha ◽  
C. Kalaiselvi
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Dynamic Environment ◽  
Scheduling Algorithms ◽  
Computing System ◽  
Priority Queue ◽  
Lower Priority ◽  
Cloud Computing System ◽  
Queue Scheduling ◽  
Multi Level

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.

Design and Performance Evaluation of Smart Job First Multilevel Feedback Queue (SJFMLFQ) Scheduling Algorithm With Dynamic Smart Time Quantum

2021 ◽  
pp. 111-126
Author(s):  
Amit Kumar Gupta ◽  
Narendra Singh Yadav ◽  
Dinesh Goyal
Keyword(s):  
Scheduling Algorithm ◽  
Turnaround Time ◽  
Queue Scheduling ◽  
Time Quantum ◽  
First Come First Serve ◽  
Overall Performance ◽  
And Performance ◽  
Ready Queue ◽  
Priority Factor ◽  
Feedback Queue

Multilevel feedback queue scheduling (MLFQ) algorithm is based on the concept of several queues in which a process moves. In earlier scenarios there are three queues defined for scheduling. The two higher level queues are running on Round Robin scheduling and last level queue is running on FCFS (First Come First Serve). A fix time quantum is defined for RR scheduling and scheduling of process depends upon the arrival time in ready queue. Previously a lot of work has been done in MLFQ. In our propose algorithm Smart Job First Multilevel feedback queue (SJFMLFQ) with smart time quantum (STQ), the processes are arranged in ascending order of their CPU execution time and calculate a Smart Priority Factor SPF on which processes are scheduled in queue. The process which has lowest SPF value will schedule first and the process which has highest SF value will schedule last in queue. Then a smart time quantum (STQ) is calculated for each queue. As a result, we found decreasing in turnaround time, average waiting time and increasing throughput as compared to the previous approaches and hence increase in the overall performance.

Design and Performance Evaluation of Smart Job First Multilevel Feedback Queue (SJFMLFQ) Scheduling Algorithm with Dynamic Smart Time Quantum

2017 ◽  
Vol 8 (2) ◽  
pp. 50-64
Author(s):  
Amit Kumar Gupta ◽  
Narendra Singh Yadav ◽  
Dinesh Goyal
Keyword(s):  
Scheduling Algorithm ◽  
Turnaround Time ◽  
Queue Scheduling ◽  
Time Quantum ◽  
First Come First Serve ◽  
Overall Performance ◽  
And Performance ◽  
Ready Queue ◽  
Priority Factor ◽  
Feedback Queue

Multilevel feedback queue scheduling (MLFQ) algorithm is based on the concept of several queues in which a process moves. In earlier scenarios there are three queues defined for scheduling. The two higher level queues are running on Round Robin scheduling and last level queue is running on FCFS (First Come First Serve). A fix time quantum is defined for RR scheduling and scheduling of process depends upon the arrival time in ready queue. Previously a lot of work has been done in MLFQ. In our propose algorithm Smart Job First Multilevel feedback queue (SJFMLFQ) with smart time quantum (STQ), the processes are arranged in ascending order of their CPU execution time and calculate a Smart Priority Factor SPF on which processes are scheduled in queue. The process which has lowest SPF value will schedule first and the process which has highest SF value will schedule last in queue. Then a smart time quantum (STQ) is calculated for each queue. As a result, we found decreasing in turnaround time, average waiting time and increasing throughput as compared to the previous approaches and hence increase in the overall performance.

scholarly journals Multi Level Queue Scheduling with Particle Swarm Optimization (Mlqs-Pso) of Vms in Queueing Heterogeneous Cloud Computing Systems

2020 ◽  
Vol 8 (5) ◽  
pp. 864-872
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Priority Queue ◽  
Computing Systems ◽  
Swarm Optimization ◽  
Queue Scheduling ◽  
Multi Level ◽  
Learning Machine

This article investigates in cloud computing systems about problem of delay optimal Virtual Machine (VM) scheduling holds constant resources with full infrastructure like CPU, memory and storage in the resource pool. Cloud computing offers users with VMs as utilities. Cloud consumers randomly demand different VM types over time, and the usual length of the VM hosting differs greatly. A scheduling algorithm for a multilevel queue divides the prepared queue towards lengthy and various queues. System is allocated with single queue in to several longer queues. The systems are allocated to one queue indefinitely, usually on any basis of process property, like memory size, process priority, or process sort. Every queue will have its self-algorithm for scheduling. Likewise, a system that’s taking in a less preference queue is so lengthy, a high-priority queue can be transferred. Using Particle Swarm Optimization Algorithm (MQPSO), Multi-level queue scheduling has been done. To evaluate the solutions, it explores both Shortest-JobFirst (SJF) buffering and Min-Min Best Fit (MMBF) programming algorithms, i.e., SJF-MMBF. The scheme incorporating the SJF-ELM-specific scheduling algorithms depending SJF buffering and Extreme Learning Machine (ELM) is also being proposed to prevent work hunger in an SJF-MMBF system. Furthermore, the queues must be planned, which is usually used as a preventive fixed priority schedule. The results of the simulation show that the SJF-ELM is ideal inside strong duty as well as maximum is environment dynamically, with an efficient average employment hosting rate.

scholarly journals Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing

2021 ◽  
Vol 21 (3) ◽  
pp. 1-33
Author(s):  
Qianmu Li ◽  
Shunmei Meng ◽  
Xiaonan Sang ◽  
Hanrui Zhang ◽  
Shoujin Wang ◽  
...  
Keyword(s):  
Dynamic Scheduling ◽  
Scheduling Algorithm ◽  
Dynamic Change ◽  
High Energy ◽  
Transformation Rule ◽  
Volunteer Computing ◽  
Attack Behavior ◽  
Time Threshold ◽  
Security And Reliability ◽  
Multi Level

Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.

scholarly journals Applications of Smart Helmet in Applied Sciences: A Systematic Review

2021 ◽  
Vol 11 (11) ◽  
pp. 5039
Author(s):  
Yosoon Choi ◽  
Yeanjae Kim
Keyword(s):  
Applied Sciences ◽  
Wireless Communication ◽  
Communication Technology ◽  
Academic Research ◽  
Current Status ◽  
Smart Devices ◽  
The Past ◽  
Modular Concept ◽  
Research Questions ◽  
Cloud Servers

A smart helmet is a wearable device that has attracted attention in various fields, especially in applied sciences, where extensive studies have been conducted in the past decade. In this study, the current status and trends of smart helmet research were systematically reviewed. Five research questions were set to investigate the research status of smart helmets according to the year and application field, as well as the trend of smart helmet development in terms of types of sensors, microcontrollers, and wireless communication technology. A total of 103 academic research articles published in the past 11 years (2009–2020) were analyzed to address the research questions. The results showed that the number of smart helmet applications reported in literature has been increasing rapidly since 2018. The applications have focused mostly on ensuring the safety of motorcyclists. A single-board-based modular concept unit, such as the Arduino board, and sensor for monitoring human health have been used the most for developing smart helmets. Approximately 85% of smart helmets have been developed to date using wireless communication technology to transmit data obtained from smart helmets to other smart devices or cloud servers.

Measuring Dynamic Sales Impacts of LBA Using Wireless Communication Technology

2013 ◽  
Vol 662 ◽  
pp. 896-901
Author(s):  
Zong Jin Liu ◽  
Yang Yang ◽  
Zheng Fang ◽  
Yan Yan Xu
Keyword(s):  
Wireless Communication ◽  
Error Correction ◽  
Communication Technology ◽  
Dynamic Error ◽  
Rapid Development ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Vector Error ◽  
Equilibrium Relationship

Because of rapid development of wireless communication technology, there is an increasing adoption of mobile advertising, such as location based advertising (LBA). To what extent can LBA improve advertising effectiveness is an important topic in the field of wireless communication technology research. Most researches quantify long term impacts of advertisings by VAR (Vector Autoregressive) model. However, compared to VAR model, VECM (Vector Error Correction Model) is a better method in that it allows one to estimate both a long-term equilibrium relationship and a short-term dynamic error correction process. In this study, we employ VECM to explore LBA’s (Location Based Advertising) and PUA’s (Pop-up Advertising) sales impact in both short and long terms. The developed VECM reveals that LBA’s sales impact is about more than2 times as big as PUA’s in short dynamic term and nearly 6 times bigger than PUA’s in long equilibrium term. These findings add to advertising and VECM literatures. These results can give managers more confident to apply wireless communication technology to advertising.

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