scholarly journals Cost-efficient and network-aware dynamic repartitioning-based algorithms for scheduling large-scale graphs in cloud computing environments

2018 ◽  
Vol 48 (12) ◽  
pp. 2174-2192 ◽  
Author(s):  
Safiollah Heidari ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Computing Environments ◽  
Cost Efficient

scholarly journals Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5706
Author(s):  
Muhammad Shuaib Qureshi ◽  
Muhammad Bilal Qureshi ◽  
Muhammad Fayaz ◽  
Muhammad Zakarya ◽  
Sheraz Aslam ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Real Time ◽  
Large Scale ◽  
Smart Devices ◽  
Budget Constraints ◽  
Smart Systems ◽  
Data Intensive ◽  
Cost Efficient ◽  
And Storage

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

scholarly journals Load Balancing With Max-Min of Summations

2019 ◽  
Vol 8 (6) ◽  
pp. 5224-5226
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Large Scale ◽  
Modern Method ◽  
Cloud Services ◽  
It Services ◽  
Internet Applications ◽  
Modern Times ◽  
Cost Efficient ◽  
Scalable Network

Cloud computing is a computing tool for humankind. In recent years, it is using to generate IT services, appliances for higher activities computing and outsourcing in a cost-efficient and flexible way. In modern times, a variety of types of bandwidth eater are growing speedily Cloud computing is growing phenomenal gradually to supply the different kinds of cloud services and applications to the internet-based customer. Cloud computing utilizes Internet applications to execute the large-scale jobs. The most important objective of cloud computing is to allocate and calculate different services transparently throughout a scalable network of machines. Load balancing is one of the significant issues in Cloud Computing. Loads should be divided as CPU load, the capacity of memory and system load which is the measurement of work that a computation system performs. Load balancing is a modern method where the load is being shared amongst several machines of a distributed system to enhance the utilization of various applications and response time of multiple tasks and prevent overloading situation and under loading situation. In or approach, we developed an algorithm, LBMMS, which combines all least completion time. For this study, LBMMS presents the proficient deployment of various resources in cloud computing

Large Scale Graph Data Processing on Cloud Computing Environments

2011 ◽  
Vol 34 (10) ◽  
pp. 1753-1767 ◽  
Author(s):  
Ge YU ◽  
Yu GU ◽  
Yu-Bin BAO ◽  
Zhi-Gang WANG
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Large Scale ◽  
Graph Data ◽  
Computing Environments

Intelligent Monitoring System for Solving Large-Scale Scientific Problems in Cloud Computing Environments

2017 ◽  
Vol 2 (87) ◽  
pp. 19-25 ◽  
Author(s):  
A. V. Skatkov ◽  
◽  
A. A. Brjuhoveckij ◽  
D. V. Moiseev ◽  
T. A. Abramov ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Monitoring System ◽  
Large Scale ◽  
Intelligent Monitoring ◽  
Computing Environments

scholarly journals Analysis of the major issues in Cloud Computing Environments

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
S. Selvam ◽  
S. Thabasu Kannan
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Innovative Technology ◽  
Future Trends ◽  
Network Access ◽  
Configurable Computing ◽  
Promising Technology ◽  
On Demand ◽  
The Future ◽  
Computing Environments

Cloud computing is a model for enabling service user’s ubiquitous, convenient and on-demand network access to a shared pool of configurable computing resources. Cloud computing is a promising technology to facilitate development of large-scale, on-demand, flexible computing infrastructures. But without security embedded into innovative technology that supports cloud computing, businesses are setting themselves up for a fall. The trend of frequently adopting this technology by the organizations automatically introduced new risk on top of existing risk. Obviously putting everything into a single box i.e. into the cloud will only make it easier for hacker. This paper presents an overview and the study of the cloud computing. Also include the several security and challenging issues, emerging application and the future trends of cloud computing.

Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments

2020 ◽  
pp. 190-213
Author(s):  
Maria Rodriguez ◽  
Rajkumar Buyya
Keyword(s):  
Cluster Size ◽  
Large Scale ◽  
Management Approach ◽  
Public Cloud ◽  
Cloud Infrastructure ◽  
Initial Placement ◽  
Cloud Environments ◽  
Computing Environments ◽  
Cost Efficient ◽  
Container Orchestration

Containers are widely used by organizations to deploy diverse workloads such as web services, big data, and IoT applications. Container orchestration platforms are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms optimize the scheduling of containers on a fixed-sized cluster and are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. This chapter presents a resource management approach with three objectives: 1) optimize the initial placement of containers by efficiently scheduling them on existing resources, 2) autoscale the number of resources at runtime based on the cluster's workload, and 3) consolidate applications into fewer VMs at runtime. The framework was implemented as a Kubernetes plugin and its efficiency was evaluated on an Australian cloud infrastructure. The experiments demonstrate that a reduction of 58% in cost can be achieved by dynamically managing the cluster size and placement of applications.

Trust and Reliability Management in Large-Scale Cloud Computing Environments

2021 ◽  
pp. 66-89
Author(s):  
Punit Gupta
Keyword(s):  
Cloud Computing ◽  
Trust Management ◽  
Large Scale ◽  
Cloud Environment ◽  
Future Prospects ◽  
Distributed Environment ◽  
Reliability Management ◽  
Trust Models ◽  
Management Techniques ◽  
Computing Environments

Trust is a firm belief over a person or a thing in distributed environment based on its feedback on review based on its performance by others. Similarly, in cloud, trust models play an important role in solving various open challenges in cloud environment. This chapter showcases all such issues that can be solved by trust management techniques. This work discourses various trust management models and its categorization. The work discourses existing work using trust models from the field of grid computing, cloud computing, and web services because all these domains are sub child of each other. The work provides an abstract view over all trust models and find the suitable one for cloud and its future prospects.

Sign in / Sign up

Export Citation Format

Share Document