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

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

Extending an open source enterprise service bus for cloud data access support

In the last years Cloud computing has become popular among IT organizations aiming to reduce its operational costs. Applications can be designed to be run on the Cloud, and utilize its technologies, or can be partially or totally migrated to the Cloud. The application's architecture contains three layers: presentation, business logic, and data layer. The presentation layer provides a user friendly interface, and acts as intermediary between the user and the application logic. The business logic separates the business logic from the underlaying layers of the application. The Data Layer (DL) abstracts the underlaying database storage system from the business layer. It is responsible for storing the application's data. The DL is divided into two sublayers: Data Access Layer (DAL), and Database Layer (DBL). The former provides the abstraction to the business layer of the database operations, while the latter is responsible for the data persistency, and manipulation. When migrating an application to the Cloud, it can be fully or partially migrated. Each application layer can be hosted using different Cloud deployment models. Possible Cloud deployment models are: Private Cloud, Public Cloud, Community Cloud, and Hybrid Cloud. In this diploma thesis we focus on the database layer, which is one of the most expensive layers to build and maintain in an IT infrastructure. Application data is typically moved to the Cloud because of , e. g. Cloud bursting, data analysis, or backup and archiving. Currently, there is little support and guidance how to enable appropriate data access to the Cloud. In this diploma thesis the we extend an Open Source Enterprise Service Bus to provide support for enabling transparent data access in the Cloud. After a research in the different protocols used by the Cloud providers to manage and store data, we design and implement the needed components in the Enterprise Service Bus to provide the user transparent access to his data previously migrated to the Cloud

A Generic Framework for Deploying Video Analytic Services on the Edge

This paper introduces a novel distributed model for handling in real-time, edge-based Artificial Intelligence analytics, such as the ones required for smart video surveillance. The novelty of the model relies on decoupling and distributing the services into several decomposed functions which are linked together, creating virtual function chains (VFC model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the VFC model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the VFC model have shown that it can reduce the total edge cost, compared with a Monolithic and a Simple Frame Distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Spark ©) and an edge-deployement framework (Kubernetes©), have been deployed. A cloud service has also been considered. Experiments have shown that VFC can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a caching and a QoS monitoring service based on Long-Term-Short-Term models are introduced and evaluated

UAV Trajectory Optimization in Modern Communication Systems: Advances and Challenges

UAV trajectory optimization in modern communication systems is crucial as many research efforts are recorded in integrationof5GinUAVs.Thishasattractedsignificantattention from wireless communication research community around the world. With the rapid advancement in UAV-assisted communication systems, UAV's trajectory optimization has become important due to intrinsic constraints facing in modern communication systems. Notable research activities have been conducted in the direction of UAV trajectory optimization in different communication setups during last few years. Despite the importance of the topic, there are no extensive reviews available in open literature related to UAV trajectory optimization techniques used in 5G. Thus, this paper provide a comprehensive survey on UAV trajectory optimization techniques used in the open literature and advancement to date, with identified research issues and challenges. This provides a valuable reference and new avenues for the future research in this direction