بایگانی برچسب برای: quality of service

A-framework-for-ranking-of-cloud-computing-services.[taliem.ir]

A framework for ranking of cloud computing services

Cloud computing is revolutionizing the IT industry by enabling them to offer access to their infrastructure and application services on a subscription basis. As a result, several enterprises including IBM, Microsoft ,Google, and Amazon have started to offer different Cloud services to their customers. Due to the vast diversity in the available Cloud services, from the customer’s point of view, it has become difficult to decide whose services they should use and what is the basis for their selection. Currently, there is no framework that can allow customers to evaluate Cloud offerings and rank them based on their ability to meet the user’s Quality of Service (QoS) requirements. In this work, we propose a framework and a mechanism that measure the quality and prioritize Cloud services. Such a framework can make a significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their QoS. We have shown the applicability of the ranking framework using a case study.
ANFIS and agent based bandwidth and delay aware anycast routing[taliem.ir]

ANFIS and agent based bandwidth and delay aware anycast routing in mobile ad hoc networks

Anycast is a point to point flow of packets for obtaining services or sending data to one of a multitude of destinations that share one address. To meet needs of real time and multimedia applications, anycast routing in Mobile Ad hoc Networks (MANETs) must provide faster service with better Quality of Service (QoS). This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) based multiple QoS constrained anycast routing in MANETs by using a set of static and mobile agents. Three types of agents are used in the scheme: static anycast manager agent, static optimization agent, and mobile anycast route creation agent. The scheme operates in the following steps. (1) Optimization agent at the client optimizes membership functions for bandwidth, link delay and packet loss rate to develop Fuzzy Inference System (FIS) by using ANFIS. (2) Anycast route creation agents are employed by the client to explore multiple paths from source (client) to all anycast members (servers) through intermediate nodes. These agents gather intermediate node's information such as available bandwidth, link delay, residual battery power, and stability of anycast servers. The information is passed on to the client. (3) Anycast manager agent at the client performs finding QoS factor by using optimized FIS for every path, and selects QoS anycast path based on QoS and server stability factor, and (4) Anycast route creation agent is also employed for maintaining the QoS path in the event of node/link failures. The simulation results demonstrate reduction in end-to-end delay and control overhead, improvement in packet delivery ratio and path success ratio, as compared to shortcut tree based anycast routing (SATR) in MANETs.
Probabilistic Modeling to Achieve Load balancing in Expert Clouds[taliem.ir]

Probabilistic Modeling to Achieve Load balancing in Expert Clouds

Expert Cloud as a new class of Cloud computing systems enables its users to request the skill, knowledge and expertise of people without any information of their location by employing Internet infrastructures and Cloud computing concepts. Effective load balancing in a heterogeneous distributed environment such as Cloud is important. Since the differences in the human resource (HRs) capabilities and the variety of users' requests causes that some HRs are overloaded and some others are idle. The task allocation to the HR based on the announced requirements by the user may cause the imbalanced load distribution among HRs as well. Hence resource management and scheduling are among the important cases to achieve load balancing. Using static and dynamic algorithms, the ant colony, and the method based on searching tree all are among the methods to achieve load balancing. This paper presents a new method in order to distribute the dynamic load based on distributed queues aware of service quality in the Cloud environment. In this method, we utilize the colorful ants as a ranking for making distinction among the HRs capabilities. In this paper, we perform the mapping among the tasks and HRs using allocating a label to each HR. We model the load balancing and mapping process based on Poisson and exponential distribution. This model allows us to allocate each task to the HR which is able to execute it with maximum power using the distributed queues aware of the service quality. Simulation results show that the expert Cloud can reduce the execution and tardiness time and improve HR utilization. The cost of using resources as an effective factor in load balancing is also observed.