Knowledge-based adaptable scheduler for SaaS providers in cloud computing

Abstract

Software as a Service (SaaS) in Cloud Computing offers reliable access to software applications for end users over the Internet without direct investment in infrastructure and software. SaaS providers utilize resources of internal datacenters or rent resources from a public Infrastructure as a Service (IaaS) provider in order to serve their customers. Internal hosting can increase cost of administration and maintenance, whereas hiring from an IaaS provider can impact quality of service due to its variable performance. To surmount these challenges, we propose a knowledge-based admission control along with scheduling algorithms for SaaS providers to effectively utilize public Cloud resources in order to maximize profit by minimizing cost and improving customers' satisfaction level. In the proposed model, the admission control is based on Service Level Agreement (SLA) and uses different strategies to decide upon accepting user requests for that minimal performance impact, avoiding SLA penalties that are giving higher profit. However, because the admission control can make decisions optimally, there is a need of machine learning methods to predict the strategies. In order to model prediction of sequence of strategies, a customized decision tree algorithm has been used. In addition, we conducted several experiments to analyze which solution in which scenario fit better to maximize SaaS provider's profit. Results obtained through our simulation shows that our proposed algorithm provides significant improvement (up to 38.4 % cost saving) compared to the previous research works.

Secure Authentication-Management human-centric Scheme for trusting personal resource information on mobile cloud computing with blockchain

“…MRM service distributes a requested large-scale job and responds to the troubles of client or master nodes. When a large-scale job is distributed in the MRM, only the static resource information (MAC, IP, CPU, and memory) and dynamic information (MAC, IP, CPU, and memory utilization) of the mobile resources are used [30].…”

“…Therefore, more powerful authentication technology is required. While 2-factor and multi-factor authentication methods are being used, they require the input of users, which is cumbersome, and they are vulnerable to exposure by shoulder surfing attacks and smudge attacks [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Advanced algorithms and applications based on IoT for the smart devices

“…Gangman Yi et al proposed mobile resource management (MRM) without Cloud Server, which is the computing resource integrated management of mobile devices. It is able to establish mobile computing infrastructure without a cloud server and to provide computing services (Motavaselalhagh et al 2015). In order to measure the MRM availability during integration of the MRM resources and validate the service effectiveness, they evaluated the time required for resource integration and task assignments for computing service requests.…”

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

“…The only solution to this problem, however, is to discard surplus resources, which are connected to traditional on-premise systems, or to introduce virtualization technology for all surplus resources. Nevertheless, the latter solution drastically increases the cost of cloud computing [9][10][11][12][13][14][15][16][17][18][19].…”