Improving I/O Performance using Cache as a Service on Cloud

Caching is gaining popularity in Cloud world. It is one of the key technologies which plays a major role in bridging the performance gap between memory hierarchies through spatial or temporal localities. In cloud systems, heavy I/O activities are associated with different applications. Due to heavy I/O activities, performance is degrading. If caching is implemented, these applications would be benefited the most. The use of a Cache as a Service (CaaS) model as a cost efficient cache solution to the disk I/O problem. We have built the remote-memory based cache that is pluggable and file system independent to support various configurations. The cloud Server process introduce, pricing model together with the elastic cache system. This will increase the disk I/O performance of the IaaS, and it will reduce the usage of the physical machines. DOI: 10.17762/ijritcc2321-8169.150516

IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems

Recommendation systems have proven to be useful in various application domains. However, current solutions are usually ad-hoc systems which are tightly-coupled with the application domain. We present the IC-Service, a recommendation service that can be included in any system in a loosely coupled way. The implementation follows the principles of service oriented computing and provides a solution to various problems arising in recommendation systems, e.g. to the problem of meta-recommendation systems development. Moreover, when properly configured, the IC-Service can be used by different applications (clients), and several independent instances of the IC-Service can collaborate to produce better recommendations. Service architecture and communication protocols are presented. The paper describes also ongoing work and applications based on the IC-Service

A New Efficient Cloud Model for Data Intensive Application

Cloud computing play an important role in data intensive application since it provide a consistent performance over time and it provide scalability and good fault tolerant mechanism Hadoop provide a scalable data intensive map reduce architecture Hadoop map task are executed on large cluster and consumes lot of energy and resources Executing these tasks requires lot of resource and energy which are expensive so minimizing the cost and resource is critical for a map reduce application So here in this paper we propose a new novel efficient cloud structure algorithm for data processing or computation on azure cloud Here we propose an efficient BSP based dynamic scheduling algorithm for iterative MapReduce for data intensive application on Microsoft azure cloud platform Our framework can be used on different domain application such as data analysis medical research dataminining etc Here we analyze the performance of our system by using a co-located cashing on the worker role and how it is improving the performance of data intensive application over Hadoop map reduce data intrinsic application The experimental result shows that our proposed framework properly utilizes cloud infrastructure service management overheads bandwith bottleneck and it is high scalable fault tolerant and efficien

Video on Demand Streaming Using RL-based Edge Caching in 5G Networks

Edge caching can significantly improve the 5G networks' performance both in terms of delay and backhaul traffic. We use a reinforcement learning-based (RL-based) caching technique that can adapt to time-location-dependent popularity patterns for on-demand video contents. In a private 5G, we implement the proposed caching scheme as two virtual network functions (VNFs), edge and remote servers, and measure the cache hit ratio as a KPI. Combined with the HLS protocol, the proposed video-on-demand (VoD) streaming is a reliable and scalable service that can adapt to content popularity.Comment: 3 pages, 1 figure One page version of this paper has been accepted to 2022 IEEE Conference on Standards for Communications and Networking (CSCN) - Demo submission