Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

A physical connection of computer network must be made reliably. Breaking connection will cause communication between nodes (for example routers, switches, hosts) can be disconnected. One of the solutions is implemention of link aggregation (LA). LA integrates several of physical ports together to make a single logical communication link. Accordingly, there is load sharing traffic among the member port of the group, high-throughput increasing via a single link, and redundancy providing for broken links. We present the implementation of link aggregation using Ryu controller on Software Defined Network (SDN [4]. OpenFlow is developed by Open Networking Foundation (ONF) Today, the performance of computer networks is a great concern by the customers. Network performance is influenced by the reliability of the physical network topology. When the physical connection between two nodes is failed, then the packet can not be sent through the link. Otherwise, if a physical connection is a good condition with limited bandwidth, it can cause congestion since several nodes using the link for delivery. This problem can be solved by Link Aggregation (LA). LA is a method that aggregates multiple network connections acts as a single logical interface using software. LA provides redundancy for link failure, for example: if one or more physical interface on a logical aggregate loss, the other physical connection still up and operate. Moreover, LA increases throughput passing through a single link which can reduce bottleneck by allowing packets to be traversed over multiple interfaces. LA is defined in IEEE 802.3ad that allows to group Ethernet interface at the physical layer to form a single link interface, namely Link Aggregation Group (LAG). Link Aggregation Control Protocol (LACP) is a mechanism for changing the port to preserve LAG bundles. LACP provides a dynamic configuration that means the other end can handle link aggregation. In addition, it handles failover automatically when a link failed [1].Software Defined Network (SDN) is a new architecture for a computer network that separates network control and forwarding functions, which allowing the controller can connect directly programmable. SDN's characteristics are manageable, adaptable, scalable, appropriate for high bandwidth, and dynamic features of recent applications compared to traditional network architecture [2]. Hence, users can assign the logical network topology using software, regardless of underlying network structure [3]. In general, SDN

Link Aggregation Control Protocol on Software Defined Network

Irawati¹,

Hadiyoso²,

Hariyani³

2017

An Empirical Study on Peer-to-Peer Sharing of Resources in Mobile Cloud Environment

Nadesh

Aramudhan²

2018

The increase usage of mobile users with internet and interoperability among the cloud services intensifies the role of distributed environemtnt in today's real world application. Modern technologies are important for building rich, scalable and interoperable applications. To meet the requirements of client,the cloud service provider should offer adequate infrastructure especially under heavy multi-client load.To provide solution for large scale requirements and to statisfy the mobile client from the critical situation like lacking with bandwidth,connectivity issues,service completion ratio, we present adhoc virtual cloud model for different scenarios that include single and multiple client configurations with various file sizes of various file formats for retrieving files in the mobile cloud environement.We evaluate the strategies with the socket and RMI implemented using java and identify the best model for real world applications. Performance evaluation is done with the results obtained and recommends that when sockets and RMI can be appropriately used in peer-to-peer environment when the mobile user cannot connect directly to the cloud services.

A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services

Annette¹,

W²,

P³

2018

Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion.