Towards high-available and energy-efficient virtual computing environments in the cloud

Abstract

h i g h l i g h t s• A review of failure-aware algorithms for resource provisioning on clouds.• The development of power-and failure-aware cloud scheduling algorithms that implement vertical and horizontal platform elasticity.• Autonomous readjustment of virtual-to-physical mappings on-the-fly.• Extensive evaluation of the dynamic strategy and of the scheduling algorithms.• Random workloads and workloads that follow the Google cloud tracelogs. a r t i c l e i n f o t r a c tEmpowered by virtualisation technology, cloud infrastructures enable the construction of flexible and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial requirement for effective consolidation is the ability to efficiently utilise system resources for high-availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust virtual clusters to enable the execution of users' jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decisionmaking algorithms leverage virtualisation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-ofthe-art algorithms.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

“…The cloud architecture, information flow, and key components involved in performance estimation, power-efficiency, and the scheduling of applications are illustrated in Fig. 1 [34]. For simplicity, a VM encapsulates one single application or task and is the unit of migration in the system.…”

“…Energy efficiency is achieved by means of consolidation, transferring VMs from lower loaded PMs to other PMs so that the first PMs can transit to sleep mode. This method improves resource utilization by increasing the load rate of the active PMs [34], but it can result in high levels of performance degradation. In turn, performance deviations can be estimated based on techniques that differ according to the type of application under consideration.…”

“…It extends the POFARE algorithm introduced in [34] by taking into account the interference among co-located tasks and the diversity of QoS requirements. The cloud manager considers power efficiency estimations and performance deviations provided by the modules described in Sections 3.3.1 and 3.3.2.…”

“…At the end, the algorithm applies a cap resource usage to assign to tasks the strictly necessary amount of CPU to complete them by the deadline and the network bandwidth to maintain the throughput. The use of resource limiting was already demonstrated to be effective in the managing of resources [34,36]. During execution, the cap parameter is dynamically updated to explore the available fraction of resources in the physical server.…”

Reliability and high availability in cloud computing environments: a reference roadmap

“…Although many research papers and studies have been conducted in recent years such as studies in [26,[29][30][31][32][33][34], but each of these previous works focused on a special aspect of HA in cloud computing and there are no comprehensive studies, which cover all aspects of HA problem in cloud computing environment based on all cloud actors' requirements. In this paper, a comprehensive study results about cloud computing reliability and HA problems as a 'Reference Roadmap' for future researchers will be proposed.…”