Request Distribution Algorithm Research Articles

Locality sensitive request distribution for fog and cloud servers

Fog computing is meant to bring the cloud resource closer to the edge of the Internet so that devices can access the back end services much faster. Additionally, the services hosted at the fogs can be customized to fit the local needs. Because fogs are dispersed throughout the network, each installation will have limited resources. This makes resource management a very critical issue. In this paper, we present a two-step resource management approach for fog computing. The first step decides the allocation of the devices to the fogs. The fogs are allocated in a two-tiered manner. That is, for each device, a home fog and a pool of backup fogs are allocated. In the second step, the requests from the devices are distributed to the allocated fogs or the cloud. Using simulation studies, we compared the performance of the proposed request distribution algorithm against existing ones. The results indicate that our request distribution algorithm outperforms existing ones.

Scaling Social Media Applications Into Geo-Distributed Clouds

Federation of geo-distributed cloud services is a trend in cloud computing that, by spanning multiple data centers at different geographical locations, can provide a cloud platform with much larger capacities. Such a geo-distributed cloud is ideal for supporting large-scale social media applications with dynamic contents and demands. Although promising, its realization presents challenges on how to efficiently store and migrate contents among different cloud sites and how to distribute user requests to the appropriate sites for timely responses at modest costs. These challenges escalate when we consider the persistently increasing contents and volatile user behaviors in a social media application. By exploiting social influences among users, this paper proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geo-distributed cloud. Our key contribution is an online content migration and request distribution algorithm with the following features: 1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; 2) one-shot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand; and 3) a Δ(t)-step look-ahead mechanism to adjust the one-shot optimization results toward the offline optimum. We verify the effectiveness of our online algorithm by solid theoretical analysis, as well as thorough comparisons to ready algorithms including the ideal offline optimum, using large-scale experiments with dynamic realistic settings on Amazon Elastic Compute Cloud (EC2).

A Request Distribution Algorithm for Web Server Cluster

With the explosively increasing of web-based applications’ workloads, Web server cluster encounters challenge in response time for requests. Request distribution among servers in web server cluster is the key to address such challenge, especially under heavy workloads. In this paper, we propose a new request distribution algorithm named llac (least load active cache) for load balancing switch in web server cluster. The goal of llac is to improve the cache hit rate and reduce response time. Packets are parsed in IP level, and back-end servers are notified to cache hot files using link change technology, neither changing URL information nor modifying the service program. This avoids switching overhead between user mode and kernel mode. The load balancing switch directly creates connection with the selected server, avoiding migrating connection overhead. This policy estimates the current composited load of each server and selects the server with the least load to serve the request. It also improves the resource utilization of web servers. Experimental results show that llac achieves better performance for web applications than wrr (weight round robin) which is a popular request distribution.

ADAPTIVE AND INTELLIGENT REQUEST DISTRIBUTION FOR CONTENT DELIVERY NETWORKS

Today's knowledge and content Web-based technologies call for adaptive and intelligent algorithms for accessing the resources, especially to respond to Semantic Web, as well to performance challenges. This work presents the application of two machine learning techniques in an adaptive decision making framework, namely a fuzzy logic and neural networks, to deploy the adaptive and intelligent dispatching algorithm for resource requesting within a geographically distributed fully replicated Web sites. Our approach and algorithm can be used in content distribution and delivery networks where due to content replication each Web server can respond to the client request. Intelligent redirection mechanism redirects the request from the originating site's server to the “best” content source to deliver the content in the fastest manner. First, we introduce a broker-based system architecture. Next, we develop a new global adaptive intelligent request distribution algorithm. Our algorithm called GARDiB by a means of the fuzzy-neural techniques redirects each incoming request to local service with the least expected response time, which takes into account, the transfer time of the request and the response, as well as the time elapsed on the local service that responds to the request. Our simulations showed the effectiveness of the proposed adaptive distribution.