V. ON-DEMAND SERVICES
User demand modelling based on domain modelling; domain demand models are the basis for modelling users personalized demands. A demand model that supports uncertainty, consumers may be unspecific or provide incomplete information, accurately predicting resource demands is a key concern of demand modelling. User scenario modelling, modelling demands in uncertain scenarios, a probabilistic-constrained fuzzy logic as well as its speculative method.
VI. PROVISIONING PLANS
The cloud broker considers the reservation plan as medium- to long-term planning, since the plan has to be reserved in advance such as 1 or 3 years and the plan can significantly reduce the total provisioning cost. Also, the broker considers the on-demand plan as short term planning, since the plan can be purchased anytime for short period of time such as one week when the resources reserved by the reservation-plan are insufficient.
VII. PROVISIONING STAGES
When a cloud provider accepts a request from a cloud customer, cloud must create the appropriate number of virtual machines (VMs) and allocate resources to support them. The services are provided by several different ways: advance provisioning, dynamic resource provisioning and self-service provisioning.
In advance resource provisioning, the customer contacts with the provider for services and the cloud provider prepares the appropriate resources in advance of start of service. The customer is charged for a resource they consumed either in a flat fee or is billed on a monthly basis.
In dynamic resource provisioning, the cloud provider allocates more resources as consumers needed and removes them when they do not want to use. The customer is billed on a pay-per-usage basis.
In user self-provisioning (also known as cloud self-service), the customer buy resources from the cloud provider by creating an account and paying for resources either with a credit card or net banking. The provider’s resources are available for customer use within an hour.
VIII. PSO PARTICLE SWARM OPTIMIZATION
PSO learned from the scenario and used it to solve the optimization problems. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms. It uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. Each particle is treated as a point in a N-dimensional space which adjusts its ???flying??? according to its own flying experience as well as the flying experience of other particles. Each particle keeps track of its coordinates in the solution space which are associated with the best solution (fitness) that has achieved so far by that particle. This value is called personal best , pbest. Another best value that is tracked by the PSO is the best value obtained so far by any particle in the neighborhood of that particle. This value is called gbest.
IX. ADVANCED ENCRYPTION STANDARD
AES is a block cipher with a block length of 128 bits. AES allows for three different key lengths: 128, 192, or 256 bits. Each round of processing includes one single-byte based substitution step, a column-wise mixing step, a row-wise permutation step and the addition of the round key. The order may differ for these four steps are executed for encryption and decryption. Unlike DES, the decryption algorithm differs substantially from the encryption algorithm. AES requires the block size to be 128 bits, the original rijndael cipher works with any block size that is a multiple of 32 as long as it exceeds 128. The state array for the different block sizes still has only four rows in the rijndael cipher. However, the number of columns depends on size of the block. For example, when the block size is 192, the rijndael cipher requires a state array to consist of 4 rows and 6 columns.
In Cloud Computing, the resource provisioning mechanism uses Stochastic Programming model. These models consider many numbers of scenarios which leads to time and computational. The utility model employed by commercial cloud providers has demotivated the need for efficient and responsive economic resource allocation in high-performance computing environments. Economic resource allocation provides a well-studied and efficient means of scalable decentralized allocation it has been stereotyped as a low performance solution due to the resource commitment overhead and latency in the allocation process. The high utilization strategies are designed to minimize the impact of these factors to increase occupancy and improve system utilization. The Scenario Reduction algorithm is applied to reduce the uncertainties in cloud computing and by formulating PSO particle swarm optimization algorithm, the total cost of the resources can be reduced.
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