Deadline Constrained Adaptive Multilevel Scheduling System in Cloud Environment

Abstract

In cloud, everything can be provided as a service wherein a large number of users submit their jobs and wait for their services. Thus, scheduling plays major role for providing the resources efficiently to the submitted jobs. The brainwave of the proposed work is to improve user satisfaction, to balance the load efficiently and to bolster the resource utilization. Hence, this paper proposes an Adaptive Multilevel Scheduling System (AMSS) which will process the jobs in a multileveled fashion. The first level contains Preprocessing Jobs with Multi-Criteria (PJMC) which will preprocess the jobs to elevate the user satisfaction and to mitigate the jobs violation. In the second level, a Deadline Based Dynamic Priority Scheduler (DBDPS) is proposed which will dynamically prioritize the jobs for evading starvation. At the third level, Contest Mapping Jobs with Virtual Machine (CMJVM) is proposed that will map the job to suitable Virtual Machine (VM). In the last level, VM Scheduler is introduced in the two-tier VM architecture that will efficiently schedule the jobs and increase the resource utilization. These contributions will mitigate job violations, avoid starvation, increase throughput and maximize resource utilization. Experimental results show that the performance of AMSS is better than other algorithms.

Cognitive Fuzzy-based Behavioral Learning System for Augmenting the Automated Multi-issue Negotiation in the E-commerce Applications

“…It gives the taxonomy of negotiation strategy in the trade-off, dynamic, learning, concession, dependency, constraint, and hybrid behaviors. The static trade-off strategy generates the sequence of negotiation proposals or offers with the same aggregated utility value by differing the attributes like price, time-slot, and QoS parameters [9]. In the case of adaptive and similaritybased trade-off strategy, adaptive opponents based on expected utility are chosen and enhance the trade-off through a fuzzy-based similarity concept [10].…”

“…Let ∑ ij T Aτ (Si→ Sj) =1 denote the total probability constraints. The linguistic terms of fuzzy variables belonging to a sequence of negotiation offers ρ τ ={ρ [x(1)-,x(1)+] }, ρ [x(2)-,x(2)+] , …, ρ [x(n)-,x(n)+] } can be modeled as fuzzy membership functions as presented in equation (9).…”