scholarly journals Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lingling Li ◽  
Huixia Liu
Keyword(s):  
Cloud Computing ◽  
Reinforcement Learning ◽  
Streaming Media ◽  
Video Stream ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Access Problem ◽  
User Request ◽  
Migration Method ◽  
The Cost

In the resource scheduling of streaming Media Edge Cloud (MEC), in order to balance the cost and load of migration, this paper proposes a video stream session migration method based on deep reinforcement learning in cloud computing environment. First, combined with the current popular OpenFlow technology, a novel MEC architecture is designed, which separates streaming media service processing in application layer from forwarding path optimization in network layer. Second, taking the state information of the system as the attribute feature, the session migration is calculated, and gradient reinforcement learning is combined with in-depth learning and deterministic strategy for video stream session migration to solve the user request access problem. The experimental results show that the method has a better request access effect, can effectively improve the request acceptance rate, and can reduce the migration cost, while shortening the running time.

scholarly journals Efficient Cost Optimization using Transportation Mechanisms for Cloud Computing Environment

2020 ◽  
Vol 9 (4) ◽  
pp. 1630-1639
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Low Cost ◽  
Cost Optimization ◽  
Transportation Cost ◽  
Cloud Service ◽  
Computing Environment ◽  
Cloud Service Provider ◽  
Cloud Computing Environment ◽  
The Cost

Efficient computations are increasing now a day, so their need is very high in the world. Infrastructure and computation techniques are not as much as efficient in conventionally or in present scenario, therefore the cloud computing is new to deal this type of problems. Sequencing of hardware and software technologies, for giving scalable and low cost computational understandings in cloud computing. The major focus of this research is to diminish the transportation cost of resource allocation along with various virtual machines in cloud computing environment. In this research paper, implementation of Vogel's Approximation Method (VAM) to obtain an Initial Basic Feasible Solution (IBFS) and an algorithm to optimize the cost of resource transportations for cloud service provider (CSP) as well as present an example also to understand the proposed method for total supply values and total demand values. Although the calculation of cost reduction until the iteration still has a non-negative values, and the calculation is done again until the last iteration. A comparison has been shown the cost of the proposed mechanism is much less from other technique.

Adapting Market-Oriented Policies for Scheduling Divisible Loads on Clouds

2020 ◽  
Vol 11 (2) ◽  
pp. 45-55
Author(s):  
Mimi Liza Abdul Majid ◽  
Suriayati Chuprat
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Cloud Service ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Divisible Loads ◽  
Workload Allocation ◽  
The Cost ◽  
Baseline Approach ◽  
Allocation Approach

Cloud computing has become an important alternative for solving big data processing. Nowadays, cloud service providers usually offer users a virtual machine with various combinations of prices. As each user has different circumstances, the problem of choosing the cost-minimized combination under a deadline constraint as well as user's preference is becoming more complex. This article is concerned with the investigation of adapting a user's preference policies for scheduling real-time divisible loads in a cloud computing environment. The workload allocation approach used in this research is using Divisible Load Theory. The proposed algorithm aggregates resources into groups and optimally distributes the fractions of load to the available resources according to user's preference. The proposed algorithm was evaluated by simulation experiments and compared with the baseline approach. The result obtained from the proposed algorithm reveals that a significant reduction in computation cost can be attained when the user's preferences are low priority.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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