Cost-Aware Cloud Bursting for Enterprise Applications

Abstract

The high cost of provisioning resources to meet peak application demands has led to the widespread adoption of pay-as-you-go cloud computing services to handle workload fluctuations. Some enterprises with existing IT infrastructure employ a hybrid cloud model where the enterprise uses its own private resources for the majority of its computing, but then “bursts” into the cloud when local resources are insufficient. However, current commercial tools rely heavily on the system administrator’s knowledge to answer key questions such as when a cloud burst is needed and which applications must be moved to the cloud. In this article, we describe Seagull, a system designed to facilitate cloud bursting by determining which applications should be transitioned into the cloud and automating the movement process at the proper time. Seagull optimizes the bursting of applications using an optimization algorithm as well as a more efficient but approximate greedy heuristic. Seagull also optimizes the overhead of deploying applications into the cloud using an intelligent precopying mechanism that proactively replicates virtualized applications, lowering the bursting time from hours to minutes. Our evaluation shows over 100% improvement compared to naïve solutions but produces more expensive solutions compared to ILP. However, the scalability of our greedy algorithm is dramatically better as the number of VMs increase. Our evaluation illustrates scenarios where our prototype can reduce cloud costs by more than 45% when bursting to the cloud, and that the incremental cost added by precopying applications is offset by a burst time reduction of nearly 95%.

Fault-Tolerant Resource Provisioning with Deadline-Driven Optimization in Hybrid Clouds

“…The output of their research indicated the need to research on the solutions for various optimization problems associated with the hybrid SaaS concept. Subsequently, related works such as [16] and [17] focused on the design of cost efficient VM migration algorithms which enables the scale up of local data center resources to the public Cloud in the context of Cloud bursting. However, their works considers workload characteristics as static.…”

“…As indicated in (steps [25][26][27][28][29][30], for each job found in W, the algorithm will first search for other VMs in the private Cloud which may be free and satisfies P1 to complete the job. Otherwise, the VM instance in the public Cloud which satisfies P2 will be rented On-demand to run the job (steps [16][17][18]. The solution of P2 is is simply the minimum resulting value that is yielded when the price of each VM rented from the public Cloud is multiplied by the runtime estimate α k of the k th job.…”

A novel cloud bursting technique

“…Seagull [6] et al provide a framework for cloud bursting that efficiently precopies and migrates applications to a public cloud when local infrastructure becomes overloaded. This solution enables performing agile provisioning of resources across private and public clouds.…”

Metodología de predicción de rendimiento de aplicaciones MapReduce iterativas sobre una nube híbrida

“…To address this issue, hybrid cloud bursting [48] (complement on-premise virtual machines with temporary off-premise virtual machines) has seen a rapid increase in popularity among big data analytics users.…”