AU2021102711A4 - System and method for cloud management for provisioning multiple services through smart virtual green cloud - Google Patents
System and method for cloud management for provisioning multiple services through smart virtual green cloud Download PDFInfo
- Publication number
- AU2021102711A4 AU2021102711A4 AU2021102711A AU2021102711A AU2021102711A4 AU 2021102711 A4 AU2021102711 A4 AU 2021102711A4 AU 2021102711 A AU2021102711 A AU 2021102711A AU 2021102711 A AU2021102711 A AU 2021102711A AU 2021102711 A4 AU2021102711 A4 AU 2021102711A4
- Authority
- AU
- Australia
- Prior art keywords
- cloud
- resources
- user
- services
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Ceased
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000013459 approach Methods 0.000 claims abstract description 7
- 238000007726 management method Methods 0.000 claims description 31
- 230000000737 periodic effect Effects 0.000 claims description 10
- 238000013468 resource allocation Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 4
- 238000005352 clarification Methods 0.000 claims description 2
- 238000002507 cathodic stripping potentiometry Methods 0.000 claims 8
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 abstract description 3
- 229910052799 carbon Inorganic materials 0.000 abstract description 3
- 238000011084 recovery Methods 0.000 abstract description 2
- 230000008901 benefit Effects 0.000 description 9
- 238000005457 optimization Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 241000254158 Lampyridae Species 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 208000025721 COVID-19 Diseases 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000012856 packing Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 238000002922 simulated annealing Methods 0.000 description 2
- 241000711573 Coronaviridae Species 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007596 consolidation process Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000004801 process automation Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000000714 time series forecasting Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45562—Creating, deleting, cloning virtual machine instances
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer And Data Communications (AREA)
Abstract
The present disclosure relates to a system and a method for cloud management for
provisioning multiple services through smarty virtual green cloud. The proposed model
SVGCaaS is designed to incorporate multiple service approaches under an umbrella for
maximum resource utilization and to manage the resources, data backup and recovery, power
management, control carbon emission, security measures, billing standards, and overall the
quality of service should be maintained for accessing the e-Applications. The system consists of
a user interface, a VCSP (Virtual Cloud Service Provider), a CSP (Cloud Service Provider), a
Smart Virtual Green Cloud architecture (SVGC), and a service management layer. The proposed
model allows the services to be consolidated to a smaller number of physical servers than was
initially required, considering all the cases.
17
Yes
No
Yes
P&ame Mlowd gelaess e ben-desu b . PaimeMft i
SM-Wh~~~ ~~sed Wds Osseo" faoso@ yf mid eu
"*""Modes'
99
Figure 4
Description
Yes
No
Yes
P&ame Mlowd gelaess eben-desu b . PaimeMft i
SM-Wh~~~~~sed Wds Osseo" faoso@ yf mid eu
"*""Modes'
99
Figure 4
The present disclosure relates to a system and method for cloud management for provisioning multiple services through smart virtual green cloud.
Because of the coronavirus outbreak remote work increased at a much higher rate. Most of the work is now shifted to work from home. This change has rushed organizations into adopting cloud computing more quickly and at a wider scale than ever. This puts a strain not only on physical resources (such as servers) as more people adopt cloud services but also on network infrastructure (and traffic, implicitly). And let's not forget about the security procedures that must be put in place by organizations to make remote working possible. Yet cloud computing is just the starting point for IT automation and companies are having a taste of scalability, agility, versatility, productivity and cost savings right now. All in all, COVID-19 is accelerating decisions already under way.
The Flexera 2020 State of the Cloud Report points out that cloud use is accelerating, with respondents expecting that next year's cloud spending will rise by an average of 47 percent. In fact, the confidence of cloud companies in their ability to scale much more in order to help even more users forced to function and learn from home is so high that some provide free collaboration and conference services to those affected by COVID-19, including some promising free services for the rest of 2020. New edge computing technologies often require cloud access; luckily, edgeware research from Syracuse University has identified, demonstrated, and tested promising approaches to trusted cloud access and immediate connectivity in the field, even where reliable wired and wireless broadband is unavailable
Software that enables multiple operating systems to share a single host is called Hypervisor or Virtual Machine Manager (VMM), which in turn manages the host processor and allocates the necessary resources for each operating system and ensures that the guest operating systems (called virtual machines) can not interrupt each other. In this case, each guest operating system will appear as the host processor, memory, and other resources on its own.
In an existing solution the number of physical machines needed to deploy the requested virtual machine instances is reduced by combining time series forecasting techniques and heuristic bin packing, but multiple resource relationships such as Processor and I / 0 have not been implemented in the model. The VM placement algorithms typically use the VMs behavior to have some properties in general. In another existing solution a two-level control scheme is used to position virtual machines on physical devices, using combination and multi-phase efficiency to overcome potentially incoherent scheduling constraints. In another existing solution multidimensional Knapsack dilemma declares VM scheduling constraints to be a single dimension. In another exiting solution the Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Cloud Computing load balancing aims to optimally position virtual machines which are critical for improving power efficiency and resource utilization in the cloud environment. Its goal is to obtain efficiently a set of non-dominated solutions (the Pareto set), which simultaneously reduce total resource waste and energy consumption. A virtual machine placement algorithm named Max-BRU has been proposed for balanced resource utilization in cloud data centers, based on multiple resource constraint metrics that help to find a server that is ideally suited to deploying VMs in large cloud data centers. Its drive is that most algorithms consider a limited number of resource types which result in unbalanced loading or unnecessary physical server activation. Energy-efficient virtual machine placement using enhanced firefly algorithm is a load balancing algorithm. This solves VM positioning problems by proposing two meta-heuristic algorithms, namely the enhanced modified firefly algorithm and the modified firefly algorithm based on the Hierarchical Cluster. the efficient use of the exploitation function is ignored by many algorithms. The VM scheduling strategy is primarily discussed from the point of view of network traffic and three different scheduling algorithms have been implemented for intensively studying cloud load balancing in data centers. Heuristics was used as a standard approach between systems to allow balancing of loads between physical servers. Variations in performance were detected and monitored on a physical server that host VMs. In another existing solution a few simple VM placement algorithms, such as time-shared and space-shared, were presented and compared and introduced a framework for modelling and simulating cloud computing environments in which algorithms can be implemented. In another existing solution a new Vector Dot load balancing algorithm was implemented to deal with standardized and multidimensional resource limitations by taking servers and cloud storage into consideration. In another existing solution the information consolidation was thought-about a bin packing drawback and projected a VM-based algorithmic rule that may take under consideration the host 's collective resource demand wherever the VM is to be mounted and an overloaded resource-based approach to VM placement was presented in another existing solution. The comparison of VM scheduling algorithms indicated the need for a new efficient algorithm for VM placement. The primary goal of the VM placement task is to optimize the usage of the resources available. Previously, mapping of VMs to suitable PMs was possible manually when the number of VMs and PMS was limited. But the new situation has totally changed that the placement process automation is mandatory due to an unprecedented rise in the number of VMs and PMs. Current automated solutions need to analyse a range of potential mappings for a collection of VMs and PMs and thus need enhanced smart placement heuristics to narrow down the quest for a near-optimal placement plan solution.
In one prior art solution the invention discloses a request scheduling and optimization method for spatial detection in a distributed green cloud data center. The method comprehensively considers changes of factors, such as electric energy price, wind speed, solar radiation strength and field air density generated by a thermal power generation mode, at different geographical locations. Aiming at the requests of a plurality of applications, the method builds a framework for processing multiple types of application requests under a distributed green cloud data center environment, and accordingly, a non-linear constraint optimization model of request scheduling of an overall cost of a cloud provider, is built, and a penalty function is designed to convert the non-linear constraint optimization model into an unrestraint optimization model, then a mixed element heuristic optimization algorithm based on simulated annealing and bat algorithms is used for solving the model, and thus request scheduling of spatial detection under the distributed green cloud data center environment is achieved. According to the method provided by the invention, all reached application requests can be scheduled to a plurality of green cloud data centers for executing, so that the overall cost of the cloud provider is minimized and the delay time requirements of all application requests are ensured.
In another prior art solution the invention discloses a green cloud data center profit maximization method based on multi-objective optimization. The influence of the green cloud data center on different servers, different types of applications, different SLAs, application request arrival rates, task loss rates, power prices of different regions and other factors is comprehensively considered, and the influence of solar energy and wind energy on the income and cost of the cloud data center is comprehensively considered. And meanwhile, the income and the cost of the green cloud data center are balanced, so that a relative profit optimal value is achieved. The method comprises the following steps: firstly, calculating wind energy and solar energy which can be provided by each region according to acquired wind speeds and solar radiation quantities of different regions; and then establishing an income and cost model of each data center according to factors such as arrival rates and loss rates of different types of requests, and solving the model by adopting a multi-objective optimization method based on dynamic genetic parameters and simulated annealing. According to the invention, the cost and income of the greencloud data center can be optimized at the same time, thereby maximizing the profit of the green cloud data center.
However, we know cloud providers allow cloud users to access services as pay-per-use, these resources need to be optimally chosen to process the user request to optimize user satisfaction in the virtualized distributed environment. There may be an inequity between the actual uses and the records of billing, so any false accusation that may be claimed by each other for illegal compensation. There are plenty of scheduling algorithms proposed to achieve the various goals such as energy-saving, time-saving, Processor load balancing, performance enhancements, but very few consider the disc I / 0 load for performance improvements. Too many applications perform a great number of disc operations, such as data mining, signal processing, etc., often leading to a performance bottleneck. Therefore in order to avoid the aforementioned drawbacks there is a need of a system and method for cloud management for provisioning multiple services through smart virtual green cloud.
The present disclosure relates to a system and method for cloud management for provisioning multiple services through smart virtual green cloud. The present disclosure is proposed to consolidate the VMs with their assigned tasks using the Smart Virtual Green Cloud as a Service (SVGCaaS) model in such a way as to maximize the use of resources by assigning and performing multiple tasks with their service parameters in parallel on the dedicated Virtual Machine in order to minimize the execution time, whereby the power consumption, carbon emissions should be kept at minimum. The model allows the services to be consolidated to a smaller number of physical servers than was initially required, considering all the cases. In this model multiple services are assigned at a time that has never been used to check all possible parameters for the provision of resources before allocating the e-Service application using Tasks Scheduling under a Virtual Machine through pipelining (TSVMP) algorithm.
The present disclosure seeks to provide a method for cloud management for provisioning multiple services through smart virtual green cloud. The method comprises: requesting by user to VCSP for e-services and thereafter VCSP tests user authentication and SLA before deciding whether to accept or deny said request and forward to expected CSP to access cloud resources on request; sending periodic signals / record information by CSP on basis of agreement to smart service analyzer (SSA) and thereby storing record information in a log file by said SSA; using virtual cloud principle called smart virtual green cloud (SVGC) architecture to establish a new cloud agreement or authenticate an existing customer to satisfy user's requirements; verifying selected resources/used resources and checking service parameters to fulfill QoS requirements based on agreement; and performing fine tune by service management layer in case of dispute otherwise provision module allocate selected resources for invoking e-application instance, prudence module sends record details/consumed resources to user, and allow user to invoke services.
The present disclosure also seeks to provide a system for cloud management for provisioning multiple services through smart virtual green cloud. The system comprises: a user interface to allow for requesting said user to VCSP for e-services; a VCSP to check user authentication and SLA before deciding whether to accept or deny said request and forward to expected CSP to access cloud resources on request; a CSP to send periodic signals / record information on basis of agreement to smart service analyzer (SSA) and thereby store record information in a log file by said SSA; a virtual cloud principle called smart virtual green cloud (SVGC) architecture to establish a new cloud agreement or authenticate an existing customer to satisfy user's requirements; wherein selected resources/used resources are verified, and service parameters are checked to fulfill QoS requirements based on agreement; and a service management layer to perform fine tune in case of dispute otherwise a provision module to allocate selected resources for invoking e-application instance, a prudence module to send record details/consumed resources to user and allow user to invoke services.
An objective of the present disclosure is to provide a system and a method for cloud management for provisioning multiple services through smart virtual green cloud.
Another object of the present disclosure is to incorporate multiple service approaches under an umbrella for maximum resource utilization.
Anther object of the present disclosure is to manage the resources, data backup and recovery, power management, control carbon emission, security measures, billing standards.
Another object of the present disclosure is to maintain the overall quality of service for accessing the e-Applications.
Another object of the present disclosure is to consolidate the VMs with their assigned tasks using the Smart Virtual Green Cloud as a Service (SVGCaaS) model.
Yet, another object of the present disclosure is to maximize the use of resources by assigning and performing multiple tasks with service parameters in parallel on the dedicated Virtual Machine.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a flow chart of a method for cloud management for provisioning multiple services through smart virtual green cloud in accordance with an embodiment of the present disclosure;
Figure 2 illustrates a block diagram of a system for cloud management for provisioning multiple services through smart virtual green cloud in accordance with an embodiment of the present disclosure;
Figure 3 illustrates the Proposed Architecture of Smart Virtual Green Cloud as a Service (SVGCaaS) Model in accordance with an embodiment of the present disclosure;
Figure 4 illustrates the flow chart of the proposed method in accordance with an embodiment of the present disclosure;
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
Figure 1 illustrates a flow chart of a method for cloud management for provisioning multiple services through smart virtual green cloud in accordance with an embodiment of the present disclosure; At step 102 the method 100 includes requesting by user to VCSP for e services and thereafter VCSP tests user authentication and SLA before deciding whether to accept or deny said request and forward to expected CSP to access cloud resources on request.
At step 104 the method 100 includes sending periodic signals / record information by CSP on basis of agreement to smart service analyzer (SSA) and thereby storing record information in a log file by said SSA.
At step 106 the method 100 includes using virtual cloud principle called smart virtual green cloud (SVGC) architecture to establish a new cloud agreement or authenticate an existing customer to satisfy user's requirements.
At step108 the method 100 includes verifying selected resources/used resources and checking service parameters to fulfill QoS requirements based on agreement.
At step 110 the method 100 includes performing fine tune by service management layer in case of dispute otherwise provision module allocate selected resources for invoking e application instance, prudence module sends record details/consumed resources to user, and allow user to invoke services.
Figure 2 illustrates a block diagram of a system for cloud management for provisioning multiple services through smart virtual green cloud in accordance with an embodiment of the present disclosure. The system 200 includes a user interface 202 to allow for requesting said user to VCSP for e-services.
In an embodiment a VCSP 204 is used to check user authentication and SLA before deciding whether to accept or deny said request and forward to expected CSP to access cloud resources on request.
In an embodiment a CSP 206 is used to send periodic signals / record informationon basis of agreement to smart service analyzer (SSA) and thereby store record information in a log file by said SSA.
In an embodiment a virtual cloud principle 208 called smart virtual green cloud (SVGC) architecture is used to establish a new cloud agreement or authenticate an existing customer to satisfy user's requirements, wherein selected resources/used resources are verified, and service parameters are checked to fulfill QoS requirements based on agreement.
In an embodiment a service management layer 210 is used to perform fine tune in case of dispute otherwise a provision module to allocate selected resources for invoking e-application instance, a prudence module to send record details/consumed resources to user and allow user to invoke services.
Figure 3 illustrates the Proposed Architecture of Smart Virtual Green Cloud as a Service (SVGCaaS) Model in accordance with an embodiment of the present disclosure. The figure explains the proposed SVGCaaS service model, where VCSP delivers the services to the user using its own SVGC Architecture. The model is an extension to the SFaaS, VaaS, PMaaS, and EVaaS where these services pass through a pipeline process of validate the resource allocation task for the customer. Here the user's task is planned and grouped on an FCFS basis based on PMaaS. The task administrator (TA) selects the maximum resource requirements as the Virtual Machine (VM) model for each community and sends the VMs with the maximum task requirements to the Virtual Machine Administrator (VMA), where VMA maintains a VM queue as the order sent by the TA.
Figure 4 illustrates the flow chart of the proposed method in accordance with an embodiment of the present disclosure. User requests to VCSP for e-services, VCSP tests the user authentication and SLA before deciding whether to accept or deny the request and forward it to the expected CSP to access the cloud resources on request. CSP sends periodic signals / record information on the basis of the agreement to Smart Service Analyser (SSA) and SSA will store the record information in a log file. The virtual cloud principle called "Smart Virtual Green Cloud (SVGC)" architecture is used to establish a new cloud agreement or authenticate an existing customer, then check the resources needed from its Resource Management Layer before delivering any services to users where actual resources are used or any ambiguities are resolved by invoking actual services from its Service Management Layer for which ambiguity occurs.
The 4 stages pipeline architecture is proposed using "Tasks Scheduling under a Virtual Machine through Pipelining (TSVMP)" Algorithm for automatic Tasks Management where more than one task are assigned under one virtual machine at a time. Pipeline approach is used where all the user's tasks goes through the following stages- (a) Service Level Agreement (SLA) stage: Either new agreement or check an existing agreement (b) Resource Level Management (RLM) stage: Check the required resources (CPU core, RAM, Disk space, Software & Applications, Network Bandwidth, Power Consumptions etc.) (c) Service Level Management (SLM) stage: check the necessary services (PMaaS, EVaaS, SFaaS, VaaS, DaaS, BRaaS, LBaaS etc.). Before provisioning the actual resources the above three stages are required to fulfil the user's requirements and finally the (d) Resource Allocation (RA) stage where the resources are allocated to invoke the e-services by the user.
On the other hand upon receiving the user's request through VCSP, SSA categories the services based on the user's requirements- Requests for e-Application, Resolve ambiguities or on demand Service requests. For e-Application, Prudence module selects the on-demand e Application from e-Application Layer of SVGC. Using the Accumulate module SSA collects the periodic record details about the resources from its log file and analysis on the collected resources by its Analysis Module, then Prudence module selects the analysed resources. Now, Mediation Module invoke all the layers for verification of the selected resources / used resources and check all the service parameters to fulfil the QoS requirements based on the agreement and if required fine tuned by the Service Management Layer otherwise, Provision Module allocate the selected resources for invoking the e-Application instance. For Resolve ambiguities, Prudence Module forwards the user's requests to Mediation Module for clarification of user's ambiguities and after resolving prudence Module sends the record details / consumed resources to the user. For on-demand Service request, Prudence module selects the on-demand -Service instance from Service Management Layer
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (10)
1. A method for cloud management for provisioning multiple services through smart virtual green cloud, said method comprises:
requesting by user to VCSP for e-services and thereafter VCSP tests user authentication and SLA before deciding whether to accept or deny said request and forward to expected CSP to access cloud resources on request; sending periodic signals / record information by CSP on basis of agreement to smart service analyzer (SSA) and thereby storing record information in a log file by said SSA; using virtual cloud principle called smart virtual green cloud (SVGC) architecture to establish a new cloud agreement or authenticate an existing customer to satisfy user's requirements; verifying selected resources/used resources and checking service parameters to fulfill QoS requirements based on agreement; and performing fine tune by service management layer in case of dispute otherwise provision module allocate selected resources for invoking e-application instance, prudence module sends record details/consumed resources to user, and allow user to invoke services.
2. The method as claimed in claim 1, comprises a 4-stage pipeline architecture using tasks scheduling under a virtual machine through Pipelining (TSVMP) algorithm for automatic tasks management where more than one task are assigned under one virtual machine at a time.
3. The method as claimed in claim 2, wherein pipelining approach where all user's tasks goes through stages comprises:
a service level agreement (SLA) stage to check either new agreement or an existing agreement; a resource level management (RLM) stage to check required resources consist of CPU core, RAM, Disk space, Software & Applications, Network Bandwidth, and Power Consumptions; a service level management (SLM) stage to check necessary services consist of PMaaS, EVaaS, SFaaS, VaaS, DaaS, BRaaS, and LBaaS; wherein before provisioning actual resources three stages are required to fulfil user's requirements; and a resource allocation (RA) stage in which resources are allocated to invoke e-services by user.
4. The method as claimed in claim 1, wherein in case of successful authentication, VCSP accepted for services and sends an acknowledgement to user, and VCSP requests to intended CSP for getting on-demand cloud resources, wherein CSP checks for VCSP's authentication and SLA.
5. The method as claimed in claim 4, wherein in case of successful authentication, accepted for services and sends an acknowledgement to VCSP, based on agreement CSP sends periodic signals / record details to smart service analyzer (SSA), and thereafter SSA stores record details in a log file.
6. The method as claimed in claim 1, wherein services based on user's requirements in e application comprises:
Selecting on-demand e-Application from e-Application Layer of SVGC using a prudence module selects; using accumulate module SSA collects periodic record details about resources from its log file; performing analysis on collected resources by an analysis module; selecting analyzed resources using prudence module; invoking all layers through mediation module; and allocating selected resources for invoking e-Application instance using provision module.
7. The method as claimed in claim 1, wherein services based on user's requirements in resolving ambiguities comprises:
forwarding user's requests to mediation module for clarification of user's ambiguities through said prudence module; and sending record details / consumed resources to user through said prudence module.
8. The method as claimed in claim 1, wherein said prudence module selects on-demand e Service instance from service management layer.
9. The method as claimed in claim 1, wherein monitor module control all sub modules to invoke services for proper utilization of resources as and when required, wherein said user or CSPs' is not able to interacts with each other for resource allocation without said VCSP's permission.
10. A system for cloud management for provisioning multiple services through smart virtual green cloud, said system comprises: a user interface to allow for requesting said user to VCSP for e-services; a VCSP to check user authentication and SLA before deciding whether to accept or deny said request and forward to expected CSP to access cloud resources on request; a CSP to send periodic signals / record information on basis of agreement to smart service analyzer (SSA) and thereby store record information in a log file by said SSA; a virtual cloud principle called smart virtual green cloud (SVGC) architecture to establish a new cloud agreement or authenticate an existing customer to satisfy user's requirements; wherein selected resources/used resources are verified, and service parameters are checked to fulfill QoS requirements based on agreement; and a service management layer to perform fine tune in case of dispute otherwise a provision module to allocate selected resources for invoking e-application instance, a prudence module to send record details/consumed resources to user and allow user to invoke services.
Figure 3
Figure 4
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021102711A AU2021102711A4 (en) | 2021-05-20 | 2021-05-20 | System and method for cloud management for provisioning multiple services through smart virtual green cloud |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021102711A AU2021102711A4 (en) | 2021-05-20 | 2021-05-20 | System and method for cloud management for provisioning multiple services through smart virtual green cloud |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021102711A4 true AU2021102711A4 (en) | 2021-12-02 |
Family
ID=78716536
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021102711A Ceased AU2021102711A4 (en) | 2021-05-20 | 2021-05-20 | System and method for cloud management for provisioning multiple services through smart virtual green cloud |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021102711A4 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114764641A (en) * | 2022-04-29 | 2022-07-19 | 中国能源建设集团广东省电力设计研究院有限公司 | Two-ticket management method, system, computer equipment and medium based on security verification |
-
2021
- 2021-05-20 AU AU2021102711A patent/AU2021102711A4/en not_active Ceased
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114764641A (en) * | 2022-04-29 | 2022-07-19 | 中国能源建设集团广东省电力设计研究院有限公司 | Two-ticket management method, system, computer equipment and medium based on security verification |
| CN114764641B (en) * | 2022-04-29 | 2023-11-14 | 中国能源建设集团广东省电力设计研究院有限公司 | Two-ticket management method, system, computer equipment and medium based on security verification |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xu et al. | Dynamic resource allocation for load balancing in fog environment | |
| Amini Motlagh et al. | Task scheduling mechanisms in cloud computing: A systematic review | |
| US9336059B2 (en) | Forecasting capacity available for processing workloads in a networked computing environment | |
| Kwok et al. | Resource calculations with constraints, and placement of tenants and instances for multi-tenant SaaS applications | |
| US8862738B2 (en) | Reallocating resource capacity among resource pools in a cloud computing environment | |
| US9195509B2 (en) | Identifying optimal platforms for workload placement in a networked computing environment | |
| US8046767B2 (en) | Systems and methods for providing capacity management of resource pools for servicing workloads | |
| Baker et al. | Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications | |
| CN108399101A (en) | The methods, devices and systems of scheduling of resource | |
| Bermejo et al. | Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance | |
| Zhang et al. | Heterogeneity aware dominant resource assistant heuristics for virtual machine consolidation | |
| Donida Labati et al. | Computational intelligence in cloud computing | |
| Emeakaroha et al. | Cloud resource provisioning and SLA enforcement via LoM2HiS framework | |
| Kansal et al. | Classification of resource management approaches in fog/edge paradigm and future research prospects: A systematic review | |
| Kim et al. | Min-max exclusive virtual machine placement in cloud computing for scientific data environment | |
| Haidri et al. | A deadline aware load balancing strategy for cloud computing | |
| AU2021102711A4 (en) | System and method for cloud management for provisioning multiple services through smart virtual green cloud | |
| Cao et al. | Dynamic virtual resource allocation mechanism for survivable services in emerging NFV-enabled vehicular networks | |
| Neelakantan et al. | Proficient job scheduling in cloud computation using an optimized machine learning strategy | |
| Saravanan et al. | An enhanced QoS architecture based framework for ranking of cloud services | |
| Meng et al. | Service-oriented reliability modeling and autonomous optimization of reliability for public cloud computing systems | |
| Swain et al. | Interference aware workload scheduling for latency sensitive tasks in cloud environment | |
| Leena Sri et al. | An empirical model of adaptive cloud resource provisioning with speculation | |
| Gupta et al. | Trust & fault in multi layered cloud computing architecture | |
| Suresh et al. | User demand aware grid scheduling model with hierarchical load balancing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |