IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Cloud Data Access

© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1054
An Adaptive scheduling based VM with random key authentication on
cloud data access
C. Surya1, S. Nanthini2
1,2Assistant Professor (CSE), Sri Ramakrishna College of Engineering, Perambalur, TamilNadu
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ABSTRACT— With the tremendous growth of cloud computing, it is increasingly critical to provide quantifiable
performance to tenants and to improve resource utilization for the cloud provider. Those recent proposals focus on
guaranteeing job performance. In the cloud, they usually lack efficient utilization of cloud resource, or vice versa. In this paper
we present cloud, which leverages the (soft) deadlines of cloud computing jobs to enable flexible and efficient resource
utilization in data centers. With the deadline requirement of a job guaranteed, cloud employs both time sliding and
bandwidth scaling in resource allocation. So as to better match the resource allocated to the job with the cloud’s residual
resource. Extensive simulations and test bed experiments show that cloud can accept much more jobs than existing solutions,
and significantly increase the cloud provider’s revenue with less cost for individual tenants.
KEYWORDS— authentication, bandwidth scaling, time sliding, virtual machine, CSP, resource provisioning, key
generation.
I. INTRODUCTION
Cloud computing has provision progressed from a bold vision to massive deployments in various application
domains. However, the complexity of technology underlying cloud computing introduces novel security risks and
challenges. From an end-user point of view the security of cloud infrastructure implies unquestionable trust in the cloud
provider, in some cases corroborated by reports of external auditors. While providers may offer security enhancements
such as protection of data at rest, end-users have limited or no control over such mechanisms.
There is a clear need for usable and cost-effective cloud platform security mechanisms suitable for organizations
that rely on cloud infrastructure. One such mechanism is platform integrity verification for compute hosts that support the
virtualized cloud infrastructure. Several large cloud vendors have signalled practical implementations of this mechanism,
primarily to protect the cloud infrastructure from insider threats and advanced persistent threats. We see two major
improvement vectors regarding these implementations.
First, details of such proprietary solutions are not disclosed and can thus not be implemented and improved by
other cloud platforms. Second, to the best of our knowledge, none of the solutions provides cloud tenants a proof regarding
the integrity of compute hosts supporting their slice of the cloud infrastructure. To address this, we propose a set of
protocols for trusted launch of virtual machines (VM) in IaaS, which provide tenants with a proof that the requested VM
instances were launched on a host with an expected software stack.
Another relevant security mechanism is encryption of virtual disk volumes, implemented and enforced at
compute host level. While support data encryption at rest is offered by several cloud providers and can be configured by
tenants in their VM instances, functionality and migration capabilities of such solutions are severely restricted. In most
cases cloud providers maintain and manage the keys necessary for encryption and decryption of data at rest. This further
convolutes the already complex data migration procedure between different cloud providers, disadvantaging tenants
through a new variation of vendor lock-in.
II. LITERATURE REVIEW
This section reviews the literatures on the various scheduling based methods that are carried out by different
researches. WeizhanZhang,YuxuanChen,XiangGao, Zhichao Mo, QinghuaZheng approached the cluster-aware VM
collaborative migration scheme for media cloud, tightly integrating clustering, placement, and dynamic migration process.
Evaluation results demonstrate that our scheme can effectively migrate virtual media servers in media cloud, while
reducing the total internal traffic in DCN under the resource consumption constraints of media streaming applications [1].
Mohammad Al-Fares, Alexander Loukissas, Amin Vahdat carried how to leverage largely commodity Ethernet switches
to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements.Similar to how clusters of
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 08 | Aug 2018 www.irjet.net p-ISSN: 2395-0072

commodity computers have largely replaced more specialized SMPs and MPPs, end solutions. This approach requires no
modifications to the end host network interface, operating system, or applications; critically [2].
ChuanxiongGuo,HaitaoWu,Kun Tan, Lei Shiy, Yongguang Zhang approached the DCell, a novel network structure that has
many desirable features for data center networking. DCell is a recursively defined structure, in which a high-level DCell is
constructed from many low-level DCells and DCells at the same level are fully connected with one another. DCell also
provides higher network capacity than the traditional tree- based structure for various types of services [3].
Timothy Wood, PrashantShenoy, ArunVenkataramani, MazinYousif investigated various approaches conduct a detailed
evaluation using a mix of CPU, network and memory-intensive applications. Our results show that Sandpiper is able to
resolve single server hotspots within 20 s and scales well to larger, data center environment. Server resources in a data
center are multiplexed across multiple applications each server runs one or more applications and application
components may be distributed across multiple servers [4]. Vincenzo De Maio, RaduProdan proposed a
energyconsumption model for network transfers.Such models should take into account all the actors (e.g. VMs, physical
hosts, network hardware) and activities (e.g. VM migration, powering down/off physical hosts) involved in the
consolidation. [5].
UmeshDeshpande, Xiaoshuang Wang, and, KartikGopalan approached the a comparative analysis of the energy
consumptionof the software stack of two of today's most used NICs in data centres, Ethernet and Inffniband. Consequently,
live migration has become a key ingredient behind a number of cluster management activities such as load balancing,
failure recovery, and system maintenance [6]. RajkumarBuyya,Chee Shin Yeo, SrikumarVenugopal, James Broberg
presented a cloud computing and provide the architecture for creating clouds with market-oriented resource allocation by
leveraging technologies such as Virtual Machine(VMs). Risk managementto sustain Service Level Agreement(SLA)-
oriented resource allocation [7].
VivekShrivastava, Petroszerfos, Kangwon Lee, Hani Jamjoomyee introduces AppAware-a novel, computationally
efficient scheme for incorporating (1) inter-VM dependencies and (2) the underlying network topology into VM migration
decisions. Using simulations, we show that our proposed method decreases network traffic by up to 81%compared to a
well-known alternative VM migration method that is not application-aware [8]. Fengwang, Jiangchuan Liu, Minghua Chen
proposed CALMS (Cloud-Assisted Live Media Streaming), a generic framework that facilitates a migration to the
cloud.CALMS adaptively lease and adjust cloud server resources in a fine granularity to accommodate temporal and spatial
dynamics of demands from live streaming users. While each being effective in certain aspects, having an all-round scalable,
reliable, responsive and cost-effective solution remains an illusive goal [9].
III. EXPERIMENTAL SETUP AND PROCEDURE
A. Management Process
The problem scales up, VMs are allocated to lower ranked servers and their happiness decreases, and servers are
allocated with higher ranked VMs, due to the increased competition among VMs. Also note that Multistage DA is only able
to improve the matching. In the upload a file in the cloud the admin can process the files. In the admin uploading and the
user downloading the files, the admin are going to upload file between them. They can share the uploaded files. User for
download files. System showed very good Performance in terms of speed, accuracy, and ease of use. The downloaded files
can be automatically stored.The user can download a file details can be viewed by the admin.
Fig.1. Management Process

B. Secure Key Generation
Secure Key Processing module generates the random keys to the users and sends those keys to the user’s respective
mail, whenever the user gets the key the system asks for the submission of those keys. After submitting the key to the
system, it checks the identities of the users whether they are authorized user or not.
Fig.2. Generation of Secure Key (KS)
C. Client Process
The Admin Process can upload a file, the user can search the files.Based on User requirements the admin can
upload the files the user can search the files from the admin upload the files. The search time includes fetching the posting
list in the index, ordering each entry.
Our focus is on top-k retrieval. As the, server can process the top-k retrieval almost as fast as in the plaintext
domain. Note that the server does not have to traverse every posting list for each given trapdoor, but instead uses a tree-
based data structure to fetch the corresponding list. Therefore, the overall search time cost is almost as efficient as on data.
Fig.3. Client Process
D. Resource Provisioning
An aggressive resource provisioning strategy which encourages to substantially increase the resource allocation
in each adaptation cycle when workload increases. The strategy first provisions resources which are possibly more than
actual demands, and then reduces the over-provisioned resources if needed this paper proposes, a system that
dynamically adjusting the number of virtual machine (VM) instances to ensure the QOS by accelerating the resource
provisioning in virtualized cloud computing environments.
The key idea behind is exploiting an aggressive strategy, which likely provisions resources that may exceed the actual
needs, satisfies the performance requirement at the very beginning of the adaptation process, and then decreases the over
provisioned resources if needed. The amount of the resources to be allocated is determined during runtime according to
the workload intensity and the amount of provisioned resources rather than a fixed number.

Fig.4. Provisioning the resources
IV. CONCLUSION AND FUTURE ENHANCEMENT
A. Conclusion
Cloud-based media streaming applications in media cloud exhibit high resource consumption on server sides and
dynamic traffic flow among media servers. Such characteristics need to be fully considered during the VM placement and
migration processes. In this paper, we propose a cluster-aware VM collaborative migration scheme for media cloud, which
highly accommodates the characteristics of media streaming under cloud environment. In detail, the proposed scheme
simultaneously considers the infrastructure of the DCN and the application resource requirements of media servers. It
introduces a generic scheme of VM migration to optimize the internal traffic in media cloud, which tightly integrates
clustering, placement, and dynamic migration process. Simulation results demonstrate that the scheme can effectively
reduce the total internal traffic of DCN in media cloud. In this study, the proposed scheme does not fully consider the
application characters of media streaming to optimize the VM migration in media cloud.
B. Future Enhancement
The future work should try to devise a collaborative VM migration method focusing on the user experience of media
streaming application. Furthermore, due to the limitation of experimental environment, our scheme is just verified on the
simulation platform with the self-generated traffic patterns, and the scheme ignores some emergencies such as the failing
issue of VM migrations.
References
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Migration in Media Cloud,” in IEEE Transaction on Parallel and Distributes System, Vol 28, No 10, OCT 2017.
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