SAMEVED

2013 ◽  
Vol 5 (2) ◽  
pp. 27-42 ◽  
Author(s):  
Shao-Jui Chen ◽  
Jing-Ying Huang ◽  
Cheng-Ta Huang ◽  
Wei-Jen Wang
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Web Interface ◽  
Computing Paradigm ◽  
Elastic Computing ◽  
Virtual Routers ◽  
User Friendly ◽  
Cloud Applications ◽  
Remote Invocation

Cloud computing is an emerging computing paradigm that provides all kinds of services through the Internet. Existing elastic computing approaches are popular in cloud computing. They can fulfill the requirements of some cloud applications, but usually fail to provide an isolated computing environment consisting of connected virtual machines over a user-defined network topology. This paper presents a system architecture, namely SAMEVED, which exposes a cloud service that can allocate and manage a private, virtual elastic datacenter by integrating VPN and virtual routers into existing virtualization technologies. Authentication is required by user login while using SAMEVED. A user-friendly web interface and remote invocation interface are provided to support different operations for different users with different privileges.

scholarly journals ALGORITHM FOR TASK CONSOLIDATION IN CLOUD COMPUTING: A COMPARATIVE SURVEY

2018 ◽  
Vol 6 (5) ◽  
pp. 340-345
Author(s):  
Rajat Pugaliya ◽  
Madhu B R
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Utilization ◽  
Service Provider ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Dynamic Task ◽  
Main Challenge ◽  
High Level

Cloud Computing is an emerging field in the IT industry. Cloud computing provides computing services over the Internet. Cloud Computing demand increasing drastically, which has enforced cloud service provider to ensure proper resource utilization with less cost and less energy consumption. In recent time various consolidation problems found in cloud computing like the task, VM, and server consolidation. These consolidation problems become challenging for resource utilization in cloud computing. We found in the literature review that there is a high level of coupling in resource utilization, cost, and energy consumption. The main challenge for cloud service provider is to maximize the resource utilization, reduce the cost and minimize the energy consumption. The dynamic task consolidation of virtual machines can be a way to solve the problem. This paper presents the comparative study of various task consolidation algorithms.

scholarly journals An analysis of software aging in cloud environment

2020 ◽  
Vol 10 (6) ◽  
pp. 5985
Author(s):  
Shruthi P. ◽  
Nagaraj G. Cholli
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Service ◽  
Software Systems ◽  
System Failure ◽  
Dynamic Nature ◽  
Software Aging ◽  
Software Rejuvenation ◽  
The Impact

Cloud Computing is the environment in which several virtual machines (VM) run concurrently on physical machines. The cloud computing infrastructure hosts multiple cloud service segments that communicate with each other using the interfaces. This creates distributed computing environment. During operation, the software systems accumulate errors or garbage that leads to system failure and other hazardous consequences. This status is called software aging. Software aging happens because of memory fragmentation, resource consumption in large scale and accumulation of numerical error. Software aging degrads the performance that may result in system failure. This happens because of premature resource exhaustion. This issue cannot be determined during software testing phase because of the dynamic nature of operation. The errors that cause software aging are of special types. These errors do not disturb the software functionality but target the response time and its environment. This issue is to be resolved only during run time as it occurs because of the dynamic nature of the problem. To alleviate the impact of software aging, software rejuvenation technique is being used. Rejuvenation process reboots the system or re-initiates the softwares. This avoids faults or failure. Software rejuvenation removes accumulated error conditions, frees up deadlocks and defragments operating system resources like memory. Hence, it avoids future failures of system that may happen due to software aging. As service availability is crucial, software rejuvenation is to be carried out at defined schedules without disrupting the service. The presence of Software rejuvenation techniques can make software systems more trustworthy. Software designers are using this concept to improve the quality and reliability of the software. Software aging and rejuvenation has generated a lot of research interest in recent years. This work reviews some of the research works related to detection of software aging and identifies research gaps.

Policy Management in Cloud

2013 ◽  
pp. 814-834
Author(s):  
Hassan Takabi ◽  
James B.D. Joshi
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Control Point ◽  
Cloud Service ◽  
Cloud Provider ◽  
Policy Management ◽  
Computing Paradigm ◽  
Access Control Policies ◽  
Computing Environments ◽  
Significant Cost Reduction

Cloud computing paradigm is still an evolving paradigm but has recently gained tremendous momentum due to its potential for significant cost reduction and increased operating efficiencies in computing. However, its unique aspects exacerbate security and privacy challenges that pose as the key roadblock to its fast adoption. Cloud computing has already become very popular, and practitioners need to provide security mechanisms to ensure its secure adoption. In this chapter, the authors discuss access control systems and policy management in cloud computing environments. The cloud computing environments may not allow use of a single access control system, single policy language, or single management tool for the various cloud services that it offers. Currently, users must use diverse access control solutions available for each cloud service provider to secure data. Access control policies may be composed in incompatible ways because of diverse policy languages that are maintained separately at every cloud provider. Heterogeneity and distribution of these policies pose problems in managing access policy rules for a cloud environment. In this chapter, the authors discuss challenges of policy management and introduce a cloud based policy management framework that is designed to give users a unified control point for managing access policies to control access to their resources no matter where they are stored.

Cloud-Based Platforms and Infrastructures

2017 ◽  
pp. 47-90
Author(s):  
Marcus Tanque
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Virtual Networks ◽  
Network Resources ◽  
Computing Services ◽  
Service Models ◽  
Cloud Computing Services ◽  
Shared Network

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

A Cloud Trusting Mechanism Based on Resource Ranking

2020 ◽  
pp. 130-155
Author(s):  
Ajai K. Daniel
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Storage And Retrieval ◽  
Computing Paradigm ◽  
Efficient Resource ◽  
Software And Hardware ◽  
Cloud User

The cloud-based computing paradigm helps organizations grow exponentially through means of employing an efficient resource management under the budgetary constraints. As an emerging field, cloud computing has a concept of amalgamation of database techniques, programming, network, and internet. The revolutionary advantages over conventional data computing, storage, and retrieval infrastructures result in an increase in the number of organizational services. Cloud services are feasible in all aspects such as cost, operation, infrastructure (software and hardware) and processing. The efficient resource management with cloud computing has great importance of higher scalability, significant energy saving, and cost reduction. Trustworthiness of the provider significantly influences the possible cloud user in his selection of cloud services. This chapter proposes a cloud service selection model (CSSM) for analyzing any cloud service in detail with multidimensional perspectives.

Optimal Management of Cloud Centers with Different Arrival Modes for Cloud Computing Environment

2012 ◽  
Vol 2 (3) ◽  
pp. 86-97
Author(s):  
Veena Goswami ◽  
Sudhansu Shekhar Patra ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Finite Buffer ◽  
Web Browser ◽  
Computing Paradigm ◽  
Computing Services ◽  
Multi Server

Cloud computing is a new computing paradigm in which information and computing services can be accessed from a Web browser by clients. Understanding of the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver guaranteed Quality of Services (QoS) is crucial. Based on the Service level agreement, the requests are processed in the cloud centers in different modes. This paper analyzes a finite-buffer multi-server queuing system where client requests have two arrival modes. It is assumed that each arrival mode is serviced by one or more Virtual machines, and both the modes have equal probabilities of receiving service. Various performance measures are obtained and optimal cost policy is presented with numerical results. The genetic algorithm is employed to search the optimal values of various parameters for the system.

scholarly journals A Strong Law for the Rate of Growth of Long Latency Periods in a Cloud Computing Service

2012 ◽  
Vol 44 (4) ◽  
pp. 995-1017 ◽  
Author(s):  
Souvik Ghosh ◽  
Soumyadip Ghosh
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Service Providers ◽  
Large Deviation ◽  
Cloud Service ◽  
Strong Law ◽  
Customer Expectations ◽  
Service Guarantees ◽  
Long Latency ◽  
Cloud Computing Service

Cloud-computing shares a common pool of resources across customers at a scale that is orders of magnitude larger than traditional multiuser systems. Constituent physical compute servers are allocated multiple ‘virtual machines' (VMs) to serve simultaneously. Each VM user should ideally be unaffected by others’ demand. Naturally, this environment produces new challenges for the service providers in meeting customer expectations while extracting an efficient utilization from server resources. We study a new cloud service metric that measures prolonged latency or delay suffered by customers. We model the workload process of a cloud server and analyze the process as the customer population grows. The capacity required to ensure that the average workload does not exceed a threshold over long segments is characterized. This can be used by cloud operators to provide service guarantees on avoiding long durations of latency. As part of the analysis, we provide a uniform large deviation principle for collections of random variables that is of independent interest.

A Survey of Cloud Computing Data Virtualization Service

2013 ◽  
Vol 441 ◽  
pp. 1016-1019 ◽  
Author(s):  
Lei Xiao ◽  
Wei Jiang ◽  
Fang Xin Chen ◽  
Le Jiang Guo ◽  
Ya Hui Hu
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Network Virtualization ◽  
Cloud Service ◽  
Cloud Services ◽  
Support Network ◽  
Research Progress ◽  
Key Technologies ◽  
Cloud Applications ◽  
Recent Research Progress

Cloud computing is becoming a mainstream aspect of information technology. How to efficiently manage the cloud resources across multiple cloud domains is critical for providing continuous cloud services. This paper introduces the principle and review recent research progress on cloud service to support network virtualization. It presents a framework of network-Cloud convergence based on data center network and gives a survey on key technologies for realizing cloud center and service; the reliability of cloud applications can be greatly improved.

scholarly journals Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Pradeep Singh Rawat ◽  
◽  
Robin Singh Bhadoria ◽  
Punit Gupta ◽  
G. P. Saroha ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cloud Computing ◽  
Big Bang ◽  
Ann Model ◽  
Computing Paradigm ◽  
Big Crunch ◽  
Artificial Neural ◽  
Cloud Applications ◽  
The Way

High-performance computing is changing the way we compute. In the past decade, the cloud computing paradigm has changed the way we compute, communicate, and technology. Cover real-world problems. There are still many complex challenges in the cloud computing paradigm. Improving effective planning strategies is a complex problem in the service-oriented computing paradigm.In this article, our research focuses on improving task scheduler strategies to improve the performance of cloud applications. The proposed model is inspired by an artificial neural network-based system and astrology base scheduler Big-Bang Big-Crunch. The results show that the proposed strategy based on BBBC and neural network is superior to the method based on astrology (BigBang BigCrunch costaware), genetic cost and many other existing methods.The proposed BB-BC-ANN model is validated using standard workload file (San Diego Supercomputer Center (SDSC) Blue Horizon logs). The results show that the proposed BB-BC-ANN model performs better than some of the existing approaches using performance indicators like total completion time (ms), average start time (ms), average finish time(ms), scheduling time(ms), and total execution time(ms).

scholarly journals Automated Clustering of Virtual Machines based on Correlation of Resource Usage

2012 ◽  
Vol 8 (4) ◽  
pp. 102 ◽  
Author(s):  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Virtual Machines ◽  
Resource Usage ◽  
Cloud Data ◽  
Multiple Resources ◽  
Computing Paradigm ◽  
Innovative Methodology ◽  
Cloud Data Centers

The recent growth in demand for modern applicationscombined with the shift to the Cloud computing paradigm have led to the establishment of large-scale cloud data centers. The increasing size of these infrastructures represents a major challenge in terms of monitoring and management of the system resources. Available solutions typically consider every Virtual Machine (VM) as a black box each with independent characteristics, and face scalability issues by reducing the number of monitored resource samples, considering in most cases only average CPU usage sampled at a coarse time granularity. We claim that scalability issues can be addressed by leveraging thesimilarity between VMs in terms of resource usage patterns.In this paper we propose an automated methodology to cluster VMs depending on the usage of multiple resources, both systemand network-related, assuming no knowledge of the services executed on them. This is an innovative methodology that exploits the correlation between the resource usage to cluster together similar VMs. We evaluate the methodology through a case study with data coming from an enterprise datacenter, and we show that high performance may be achieved in automatic VMs clustering. Furthermore, we estimate the reduction in the amount of data collected, thus showing that our proposal may simplify the monitoring requirements and help administrators totake decisions on the resource management of cloud computing datacenters.

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