This document proposes a new Ranking Chaos Optimization (RCO) algorithm to solve the dual scheduling problem of cloud services and computing resources (DS-CSCR) in private clouds. It introduces the DS-CSCR concept and models the characteristics of cloud services and computing resources. The RCO algorithm uses ranking selection, individual chaos, and dynamic heuristic operators. Experimental results show RCO has better searching ability, time complexity, and stability compared to other algorithms for solving DS-CSCR. Future work is needed to study additional quality of service properties and improve RCO for other optimization problems.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
TASK SCHEDULING USING AMALGAMATION OF MET HEURISTICS SWARM OPTIMIZATION ALGOR...Journal For Research
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
task scheduling in cloud datacentre using genetic algorithmSwathi Rampur
Task scheduling and resource provisioning is the core and challenging issues in cloud environment. Processes running in the cloud environment will race for available resources in order to complete their tasks with the minimum execution time; it is clear that we need an efficient scheduling technique for mapping between processes running and available resources. In this research paper, we are presented a non-traditional optimization technique, which mimics the process of evolution and based on the mechanics of natural selection and natural genetics called Genetic algorithm (GA), which minimizes the execution time and in turn reduces computation cost. We had done comparison with Round Robin algorithm and used CloudSim toolkit for our tests, results shows that Meta heuristic GA gives better performance than other scheduling algorithm.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
LOAD BALANCING ALGORITHM ON CLOUD COMPUTING FOR OPTIMIZE RESPONE TIMEijccsa
To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource. The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.
The users can easily figure out the response time of their requests, and it becomes one of the important QoSs. When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency. This is one of the most promising
research directions for improving the cloud technology. Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value. The experiment
result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
TASK SCHEDULING USING AMALGAMATION OF MET HEURISTICS SWARM OPTIMIZATION ALGOR...Journal For Research
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
task scheduling in cloud datacentre using genetic algorithmSwathi Rampur
Task scheduling and resource provisioning is the core and challenging issues in cloud environment. Processes running in the cloud environment will race for available resources in order to complete their tasks with the minimum execution time; it is clear that we need an efficient scheduling technique for mapping between processes running and available resources. In this research paper, we are presented a non-traditional optimization technique, which mimics the process of evolution and based on the mechanics of natural selection and natural genetics called Genetic algorithm (GA), which minimizes the execution time and in turn reduces computation cost. We had done comparison with Round Robin algorithm and used CloudSim toolkit for our tests, results shows that Meta heuristic GA gives better performance than other scheduling algorithm.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
LOAD BALANCING ALGORITHM ON CLOUD COMPUTING FOR OPTIMIZE RESPONE TIMEijccsa
To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource. The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.
The users can easily figure out the response time of their requests, and it becomes one of the important QoSs. When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency. This is one of the most promising
research directions for improving the cloud technology. Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value. The experiment
result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Towards a Lightweight Multi-Cloud DSL for Elastic and Transferable Cloud-nati...Nane Kratzke
Cloud-native applications are intentionally designed for the cloud in order to leverage cloud platform features like horizontal scaling and elasticity – benefits coming along with cloud platforms. In addition to classical (and very often static) multi-tier deployment scenarios, cloud-native applications are typically operated on much more complex but elastic infrastructures. Furthermore, there is a trend to use elastic container platforms like Kubernetes, Docker Swarm or Apache Mesos. However, especially multi-cloud use cases are astonishingly complex to handle. In consequence, cloud-native applications are prone to vendor lock-in. Very often TOSCA-based approaches are used to tackle this aspect. But, these application topology defining approaches are limited in supporting multi-cloud adaption of a cloud-native application at runtime. In this paper, we analyzed several approaches to define cloud-native applications being multi-cloud transferable at runtime. We have not found an approach that fully satisfies all of our requirements. Therefore we introduce a solution proposal that separates elastic platform definition from cloud application definition. We present first considerations for a domain specific language for application definition and demonstrate evaluation results on the platform level showing that a cloud-native application can be transfered between different cloud service providers like Azure and Google within minutes and without downtime. The evaluation covers public and private cloud service infrastructures provided by Amazon Web Services, Microsoft Azure, Google Compute Engine and OpenStack.
Reconfigurable CORDIC Low-Power Implementation of Complex Signal Processing f...Editor IJMTER
In recent years, CORDIC algorithms has been used extensively for various image processing
system& biomedical applications. By using CORDIC algorithm we can able to reducing the number of
iteration to process the image in the system. Low power design is to be challenging process during system
operations. Previous approaches scope to minimize the power consumption without image quality
consideration. In this paper CORDIC Based Low Power DCT iterations process equally based upon their
image quality. An hardware implementation of ROM & control logic circuit to require large hardware
space in this system. Look-ahead CORDIC Approach is used to finish the iteration at one time. When
reducing hardware area & reducing number of iterations for maximize battery lifetime. This idea used to
achieve the low power design of image and video compression application
PERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [(M/G/1) : (∞/GDM O...ijgca
The ever-increasing status of the cloud computing h
ypothesis and the budding concept of federated clou
d
computing have enthused research efforts towards in
tellectual cloud service selection aimed at develop
ing
techniques for enabling the cloud users to gain max
imum benefit from cloud computing by selecting
services which provide optimal performance at lowes
t possible cost. Cloud computing is a novel paradig
m
for the provision of computing infrastructure, whic
h aims to shift the location of the computing
infrastructure to the network in order to reduce th
e maintenance costs of hardware and software resour
ces.
Cloud computing systems vitally provide access to l
arge pools of resources. Resources provided by clou
d
computing systems hide a great deal of services fro
m the user through virtualization. In this paper, t
he
cloud data center is modelled as
queuing system with a single task arrivals
and a task request buffer of infinite capacity.
COST-EFFECTIVE LOW-DELAY DESIGN FOR MULTI-PARTY CLOUD VIDEO CONFERENCINGnexgentechnology
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NEURO-FUZZY SYSTEM BASED DYNAMIC RESOURCE ALLOCATION IN COLLABORATIVE CLOUD C...ijccsa
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
Neuro-Fuzzy System Based Dynamic Resource Allocation in Collaborative Cloud C...neirew J
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
Score based deadline constrained workflow scheduling algorithm for cloud systemsijccsa
Cloud Computing is the latest and emerging trend in information technology domain. It offers utility- based
IT services to user over the Internet. Workflow scheduling is one of the major problems in cloud systems. A
good scheduling algorithm must minimize the execution time and cost of workflow application along with
QoS requirements of the user. In this paper we consider deadline as the major constraint and propose a
score based deadline constrained workflow scheduling algorithm that executes workflow within
manageable cost while meeting user defined deadline constraint. The algorithm uses the concept of score
which represents the capabilities of hardware resources. This score value is used while allocating
resources to various tasks of workflow application. The algorithm allocates those resources to workflow
application which are reliable and reduce the execution cost and complete the workflow application within
user specified deadline. The experimental results show that score based algorithm exhibits less execution
time and also reduces the failure rate of workflow application within manageable cost. All the simulations
have been done using CloudSim toolkit.
Quality of Service Control Mechanisms in Cloud Computing EnvironmentsSoodeh Farokhi
The growth in popularity of the Internet, along with the rapid development of processing and storage technologies, has brought a paradigm shift in the way computing resources are provisioned. The technological trend today is to offer computing resources as services, leased and exposed via the Internet in a pay-as-you-go and on-demand fashion, called cloud computing...
Cloud infrastructure providers are trying to reduce their operating costs while offering their services with higher quality; something they strive to do to stand out among other providers. However, this is becoming challenging as providing such services needs operating large-scale and geographically distributed data centers. On the other hand, the main purpose of customers in using clouds is to achieve a high quality of service (QoS) while reducing their overall costs. Given the variety of offered services in terms of quality and cost, customers are encouraged to simultaneously use services from multiple cloud providers, known as multi-cloud. However, utilizing multi-cloud brings a new set of open challenges, such as selecting and composing the most appropriate services. Furthermore, despite the critical need of customers in having predictable service performance, in general cloud providers do not yet offer any performance guarantees. This gap is due to the complexity of practically addressing this issue in a cost-effective way. Such a complexity mainly comes from the dynamic nature of the cloud, unpredictable workloads, and non-linearity of mapping performance measurements into required cloud resources. Hence, controlling the trade-off between QoS and cost is a challenging goal for both cloud infrastructure providers and customers.
This thesis investigates models, algorithms, and mechanisms to tackle this trade-off from both perspectives. More specifically, in the scope of this thesis, we first take the cloud provider viewpoint by proposing an approach for virtual machine placement across geographically distributed infrastructures. In this approach, a Bayesian network model is used to address decision making under uncertainty. Then, we address the trade-off between QoS and cost from the cloud customer point of view by facilitating the utilization of the multi-cloud paradigm. We propose a service selection approach using prospect theory to rank the comparable service offerings. Furthermore, to guarantee the performance objectives of customers, we propose autonomic resource provisioning techniques. To this aim, control theory is used to design resource provisioning controllers, and fuzzy control is utilized to coordinate multiple controllers toward meeting the service performance objectives in a cost-effective manner. Finally, the evaluations of these contributions
2. A Ranking Chaos Algorithm for dual scheduling of cloud service and
computing resource in private cloud
A R T I C L E I N F O
Article history:
Received 20 September 2012
Received in revised form 22 January
2013
Accepted 15 February 2013
Available online 16 March 2013
2
3. Abstract
Service Composition Optimal Selection (SCOS)
Optimal Allocation of Computing Resources (OACR)
Dual Scheduling of Cloud Services and Computing Resources (DS-CSCR)
Ranking Chaos Optimization (RCO)
Private cloud
3
4. Introduction
Addressing SCOS and OACR step by step with two different algorithms
independently becomes very cumbersome and inefficient.
We propose the idea of combining two stages decision-making into one
and put forward the concept, Dual Scheduling of Cloud Service and
Computing Resource (DS-CSCR), in private cloud.
To achieve high efficient one-time decision in DS-CSCR, a new Ranking
Chaos Optimization (RCO) is designed.
4
6. The modeling of DS-CSCR in private cloud
The characteristics and QoS indexes of cloud services
The characteristics and QoS indexes of software cloud services
The characteristics and QoS indexes of hardware cloud services
The characteristics and QoS indexes of VMs
Problem formulation of DS-CSCR in private cloud
6
7. Flowchart RCO
begin
initialization
Ranking section
Individual Chaos
Dynamic Heuristic
evaluation
Global updating
Stoppin
g
criteria
Return best
strategy
yes
no
7
8. The complexity of the operators in GA and RCO.
GA Roulette
wheel
selection
O(n2)
Crossover
O(nm)
Mutation
O(nm)
O(n2) O(m) O(1) O(1)
RCO Ranking
selection
O(n log n)
Individual
chaos
O(n)
Dynamic
heuristic
O(n*(max(p, s)))
O(n log n) O(1) O(s) O(p)
8
Algorithm | time complexities of operators | n→∞ | m→∞ | s→∞ | p→∞
n:population
m : size
P :computing resource
S: availableCloudService
9. Searching capability of RCO for solving DS-CSCR
from bad to good is: GA < CGA < CO < RCO2 < RCO1 < RCO
Simple chaos optimization can get much better solution than the traditional
GA and improved CGA.
9
11. sort of stability of the six algorithms
Sort of stability of the six algorithms from bad to good is:
CGA < CO < RCO2 < RCO1 < RCO < GA
11
12. Disadvantage of DS-CSCR
quite cumbersome
mutual relations between cloud services and underlying computing
resources are always ignored.
12
13. Advantages of DS-CSCR
With the new DS-CSCR and RCO, cloud services and computing
infrastructures can then be quickly combined and shared with high
efficient decision.
13
14. Conclusions
New DS-CSCR model was presented in private cloud for high efficient one
time decision. Properties of software/hardware cloud services, VMs and
computing resources are deeply analyzed.
For addressing the complex dual scheduling problem (DS-CSCR),a new
intelligent algorithm – RCO was presented. Individual chaos operator was
designed as the backbone operator of the algorithm.
RCO with these three operators showed remarkable performances in
terms of searching ability, time complexity and stability in solving the DS-CSCR
problem in such private cloud compared with other algorithms.
14
15. Future work
The QoS properties for manufacturing capabilities and human resources
are needed to be studied.
RCO presented in this paper still has some disadvantages.
As a new improved intelligent algorithm, its effectiveness in various other
complex combinatorial optimization problems remains to be further
explored and validated.
15
