scholarly journals Static Memory Deduplication for Performance Optimization in Cloud Computing

Sensors ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 968 ◽  
Author(s):  
Gangyong Jia ◽  
Guangjie Han ◽  
Hao Wang ◽  
Xuan Yang
Keyword(s):  
Cloud Computing ◽  
Performance Optimization ◽  
Memory Deduplication

scholarly journals A Fast Optimization Method for Reliability and Performance of Cloud Services Composition Application

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhao Wu ◽  
Naixue Xiong ◽  
Yannong Huang ◽  
Qiong Gu ◽  
Chunyang Hu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
State Space ◽  
Generating Function ◽  
Performance Optimization ◽  
Optimization Method ◽  
Cloud Services ◽  
Services Composition ◽  
Fast Optimization ◽  
And Performance ◽  
Universal Generating Function

At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.

scholarly journals Aerodock (a smart, autonomous charging and docking station for unmanned aerial vehicles)

2022 ◽  
Vol 2161 (1) ◽  
pp. 012058
Author(s):  
Laaboni Mukerjee ◽  
Mukul Yadav ◽  
Amit Choraria ◽  
Atharv Tendolkar ◽  
Arjun Hariharan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Unmanned Aerial Vehicles ◽  
Performance Optimization ◽  
Wireless Power Transfer ◽  
External Factors ◽  
Battery Management ◽  
Human Intervention ◽  
Wireless Power ◽  
Aerial Vehicles ◽  
And Performance

Abstract The COVID-19 pandemic has laid bare the need for contactless operations. While unmanned aerial vehicles (UAVs) are being developed to aid humans in countless domains, the need for effective battery management and performance optimization remains a huge task. The proposed solution, the “AeroDock”, aims to tackle these challenges by using wireless power transfer (WPT) technology coupled with smart monitoring of the drone’s health. The performance and hardware checks are assessed at the user end via cloud computing and IoT technology. This system is contact-less, safe, reliable and its usage is not affected by external factors. Thus, the AeroDock is a smart docking station for UAVs which eliminates the need for human intervention in effective charging and maintenance.

scholarly journals SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing

2021 ◽  
Vol 11 (20) ◽  
pp. 9360
Author(s):  
Kaibin Li ◽  
Zhiping Peng ◽  
Delong Cui ◽  
Qirui Li
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Performance Optimization ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Q Learning ◽  
Reward Function ◽  
Task Scheduling Algorithm ◽  
Comparison Algorithms

Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in the cloud environment. After analyzing the process, we propose a server level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS) to reduce the makespan, cost, and average overdue time under the constraints of virtual machine (VM) resources and deadlines. In the algorithm, we prevent the change of the model input dimension with the number of VMs by taking the Gaussian distribution of related parameters as a part of the state space. Through the design of the reward function, the model can be optimized for different goals and task loads. We evaluate the performance of the algorithm by comparing it with three heuristic algorithms (Min-Min, random, and round robin) under different loads. The results show that the algorithm in this paper can achieve similar or better results than the comparison algorithms at a lower cost.

scholarly journals Evaluating Load Balancing Algorithms for Performance Optimization in Cloud Computing

2019 ◽  
Vol 8 (12) ◽  
pp. 2217-2221
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Optimization ◽  
Memory Capacity ◽  
Load Management ◽  
Performance Parameters ◽  
Network Load ◽  
Important Concern

Cloud computing is a new sort of computing over internet. It has many advantages along with several issues. These issues are related to load management, security of data in cloud. In this paper, the most important concern is to prevent bottleneck in cloud computing. The load can be (CPU load, memory capacity, delay or network-load). Load balancing is the process of distributing the load among various servers so that none of the servers is underloaded. Load-balancing also prevents the situation where some servers are heavily loaded while others are idle. This process of Load balancing ensures that load is distributed equally among the servers. In this paper, some algorithms of load balancing is discussed along with its benefits and drawbacks and also tested these algorithms on some performance parameters.

Coordinate Memory Deduplication and Partition for Improving Performance in Cloud Computing

2019 ◽  
Vol 7 (2) ◽  
pp. 357-368 ◽  
Author(s):  
Gangyong Jia ◽  
Guangjie Han ◽  
Joel J.P.C. Rodrigues ◽  
Jaime Lloret ◽  
Wei Li
Keyword(s):  
Cloud Computing ◽  
Memory Deduplication

scholarly journals Dynamic Performance Optimization for Cloud Computing Using M/M/m Queueing System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Lizheng Guo ◽  
Tao Yan ◽  
Shuguang Zhao ◽  
Changyuan Jiang
Keyword(s):  
Cloud Computing ◽  
Performance Optimization ◽  
Queueing System ◽  
Dynamic Performance ◽  
Wait Time ◽  
Optimization Methods ◽  
Proposed Model ◽  
Set Up ◽  
Qos Performance ◽  
The Cost

Successful development of cloud computing has attracted more and more people and enterprises to use it. On one hand, using cloud computing reduces the cost; on the other hand, using cloud computing improves the efficiency. As the users are largely concerned about the Quality of Services (QoS), performance optimization of the cloud computing has become critical to its successful application. In order to optimize the performance of multiple requesters and services in cloud computing, by means of queueing theory, we analyze and conduct the equation of each parameter of the services in the data center. Then, through analyzing the performance parameters of the queueing system, we propose the synthesis optimization mode, function, and strategy. Lastly, we set up the simulation based on the synthesis optimization mode; we also compare and analyze the simulation results to the classical optimization methods (short service time first and first in, first out method), which show that the proposed model can optimize the average wait time, average queue length, and the number of customer.

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