Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybrid Clouds
With the recent advent of cloud computing technologies, a growing number of content distribution applications are contemplating a switch to cloud-based services, for better scalability and lower cost. Two key tasks are involved for such a move: to migrate the contents to cloud storage, and to distribute the web service load to cloud-based web services. The main issue is to best utilize the cloud as well as the application provider’s existing private cloud, to serve volatile requests with service response time guarantee at all times, while incurring the minimum operational cost. While it may not be too difficult to design a simple heuristic, proposing one with guaranteed cost optimality over a long run of the system constitutes an intimidating challenge. Employing Lyapunov optimization techniques, we design a dynamic control algorithm to optimally place contents and dispatch requests in a hybrid cloud infrastructure spanning geo-distributed data centers, which minimizes overall operational cost over time, subject to service response time constraints. Rigorous analysis shows that the algorithm nicely bounds the response times within the preset QoS target, and guarantees that the overall cost is within a small constant gap from the optimum achieved by a T-slot lookahead mechanism with known future information. We verify the performance of our dynamic algorithm with prototype-based evaluation.
Two major components exist in a typical content distribution application, namely back-end storage for keeping the contents, and front-end web services to serve the requests. Both can be migrated to the cloud: contents can be stored in storage servers in the cloud, and requests can be distributed to cloud-based web services.
Hajjat et al. developed an optimization model for migrating enterprise IT applications onto a hybrid cloud. Their model takes into account enterprise-specific constraints, such as transaction delays and security policies.
Zhang et al. propose an intelligent algorithm to factor workload and dynamically determine the service placement across the public cloud and the private cloud.
Chen et al. propose to build CDNs in the cloud in order to minimize cost under the constraints of QoS requirement
DISADVANTAGES OF EXISTING SYSTEM:
Onetime optimal service deployment is considered.
They only propose greedy-strategy based heuristics without provable properties.
It focuses on balancing the data access load, by considering social relationships and user access patterns in the data storage.
In this paper, we present a generic optimization framework for dynamic, cost-minimizing migration of content distribution services into a hybrid cloud (i.e., private and public clouds combined), and design a joint content placement and load distribution algorithm that minimizes overall operational cost over time, subject to service response time constraints.
Our design is rooted in Lyapunov optimization theory, where cost minimization and response time guarantee are achieved simultaneously by efficient scheduling of content migration and request dispatching among data centers.
Lyapunov optimization provides a framework for designing algorithms with performance arbitrarily close to the optimal performance over a long run of the system, without the need for any future information.
We propose a generic optimization framework for dynamic, optimal migration of a content distribution service to a hybrid cloud consisting of a private cloud and public geo-distributed cloud services.
We design a joint content placement and load distribution algorithm for dynamic content distribution service deployment in the hybrid cloud. Providers of content distribution services can practically apply it to guide their service migration, with confidence in cost minimization and performance guarantee, regardless of the request arrival pattern.
ADVANTAGES OF PROPOSED SYSTEM:
We tailor Lyapunov optimization techniques in the setting of a hybrid cloud, to dynamically and jointly resolve the optimal content replication and load distribution problems.
We demonstrate optimality of our algorithm with rigorous theoretical analysis and prototype-based evaluation. The algorithm nicely bounds the response times (including queueing and round-trip delays) within the preset QoS target in cases of arbitrary request arrivals, and guarantees that the overall cost is within a small constant gap from the optimum achieved by a T-slot lookahead mechanism with information into the future.
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 44 Mb.
Monitor : 15 VGA Colour.
Ram : 512 Mb.
Operating system : Windows XP/7.
Coding Language : net, C#.net
Tool : Visual Studio 2010
Database : SQL SERVER 2008
Xuanjia Qiu, Hongxing Li, Chuan Wu, Zongpeng Liy and Francis C.M. Lau, “Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybrid Clouds”, IEEE Transactions on Parallel and Distributed Systems 2015.