An Autonomic Approach to Risk-Aware Data Center Overbooking

Abstract

Abstract-Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to poor resource utilization, specially in combination with other factors, such as user overestimations and pre-defined VM sizes. Admission control mechanisms are thus needed to increase the number of services accepted, raising the utilization without affecting services performance. This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources. Fuzzy logic functions are used to estimate the associated risk to each overbooking decision. By using a distributed PID controller approach, the system is capable of self-adapting over time -changing the acceptable level of risk -depending on the current status of the cloud data center. The suggested approach is extensively evaluated using a combination of simulations and experiments executing real cloud applications with real-life available workloads. Our results show a 50% increment at both resource utilization and capacity allocated with acceptable performance degradation and more stable resource utilization over time.

Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

“…This may be a problem if workload characteristics are unknown -as is frequently the case for public Infrastructureas-a-Service providers -because setting such parameters wrongly can lead to substantial degradation of algorithm effectiveness. Similar conclusions can be drawn from the results presented by Tomás and Tordsson, who showed the effect of data center overbooking on resource utilization and application response time [18]: beyond a -workload-dependent -overbooking threshold, application response time abruptly increases. As a consequence, if the target overbooking rate is wrongly selected, this may lead to severe SLA violations.…”

“…For example, Beloglazov et al considered 70%, 80%, and 90% as possible values for the upper threshold of the MM algorithm [6]. Tomás and Tordsson suggested target utilization levels of 70-80% [18]. Lago et al try to keep utilization between 80% and 100% [28].…”

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

“…However, other resources like memory or disk space can also become a bottleneck [95]. Of particular interest is the cache, because current virtualization technologies do not ensure isolation of the cache usage of individual VMs accommodated by the same PM, leading to contention between them [58,100].…”

“…This constrains the consolidation opportunities because too aggressive VM consolidation and overbooking of PMs would increase the probability of SLA violations [95].…”

“…Several authors mentioned this deficiency as an area for future research [8,42]. Only few works take into account also memory [82,88] or memory and I/O as further dimensions [72,95,105]. Moreover, the sharing of cores of multi-core CPU-s was hardly addressed explicitly.…”