Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework

Don't Hurry be Happy: a Deadline-based Backfilling Approach

International audienceComputing resources in data centers are usually managed by a Resource and Job Management System whose main objective is to complete submitted jobs as soon as possible while maximizing resource usage and ensuring fairness among users. However, some users might not be as hurried as the job scheduler but only interested in their jobs to complete before a given deadline. In this paper, we derive from this initial hypothesis a low-complexity scheduling algorithm, called Deadline-Based Backlling (DBF), that distinguishes regular jobs that have to complete as early as possible from deadline-driven jobs that come with a deadline before when they have to nish. We also investigate a scenario in which deadline-driven jobs are submitted and evaluate the impact of the proposed algorithm on classical performance metrics with regard to state-of-the-art scheduling algorithms. Experiments conducted on four dierent workloads show that the proposed algorithm signicantly reduces the average wait time and average stretch when compared to Conservative Backlling