ACE: Availability-Aware CloudSim Extension

• Published in: IEEE eTransactions on network and service management Vol. 15; no. 4; pp. 1586 - 1599
• Main Authors: Jammal, Manar, Hawilo, Hassan, Kanso, Ali, Shami, Abdallah
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Availability; Cloud computing; CloudSim; Computational modeling; computational path; Computer simulation; failover; Failure; Failure analysis; failure injection; High availability; load balancing; Load management; Load modeling; Maintenance engineering; Mathematical models; Modelling; Placement; Policies; Redundancy; repair; Scheduling; Servers; simulation; software components; virtual machines
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2018.2879665

In the interconnected globe where service delivery is the success measure, cloud high availability (HA) is an indispensable area for enterprises. An HA-aware cloud system provides different approaches to handle the outages. This includes geo-redundancy, failover schemes, and HA-aware placement solutions. However, using real-cloud platforms to model HA-aware approaches is hindered by the configuration settings. To this end, simulation tools, such as CloudSim, can be used to evaluate HA solutions and a cloud resiliency against failures. CloudSim allows implementing of scheduling policies, but it does not support HA properties. This paper provides availability-aware CloudSim extension (ACE). ACE extends CloudSim with a graphical and textual modeling to ensure simplicity and reusability of cloud scenarios. ACE has added HA-aware modeling (HA metrics and failure/redundancy/interdependency models) and HA-aware scheduling (HA-aware placements, failover, repair, and load balancing policies) into CloudSim. With ACE, the creation of cloud scenarios is facilitated, and multiple HA-aware deployment solutions can be evaluated under different stochastic and deterministic events. ACE can assess the impact of different redundancy/failure models, and other performance policies to extract HA-aware lessons. In this paper, ACE is assessed on a cloud application to evaluate different redundancy/failure models and provide availability analysis of the HA-aware placement solution.