Cloud service providers enable enterprises with the ability to place their business applications into availability zones across multiple locations worldwide. While this capability helps achieve higher availability with smaller failure rates, business applications deployed across these independent zones may experience different quality of service (QoS) due to heterogeneous physical infrastructures. Since the perceived QoS against specific requirements are not usually advertised by cloud providers, selecting an availability zone that would best satisfy the user requirements is a challenge. In this paper, we introduce a predictive approach to identify the cloud availability zone that maximizes satisfaction of an incoming request against a set of requirements. The prediction models are built from historical usage data for each availability zone and are updated as the nature of the zones and requests change. Simulation results show that our method successfully predicts the unpublished zone behavior from historical data and identifies the availability zone that maximizes user satisfaction against specific requirements.

Cloud service providers enable enterprises with the ability to place their business applications into availability zones across multiple locations worldwide. While this capability helps achieve higher availability with smaller failure rates, business applications deployed across these independent zones may experience different Quality of Service (QoS) due to heterogeneous physical infrastructures. Since the perceived QoS against specific requirements are not usually advertised by cloud providers, selecting an availability zone that would best satisfy the user requirements is a challenge. In this paper, we introduce a predictive approach to identify the cloud availability zone that maximizes satisfaction of an incoming request against a set of requirements. The predictive models are built from historical usage data for each availability zone and are updated as the nature of the zones and requests change. Simulation results show that our method successfully predicts the unpublished zone behavior from historical data and identifies the availability zone that maximizes user satisfaction against specific requirements.

Large scale cloud-based services are built upon a multitude of hardware and software resources, disseminated in one or multiple data centers.Controlling and managing these resources requires the integration of several pieces of software that may yield a representative view of the data center status.Today's both closed and open-source monitoring solutions fail in different ways, including the lack of scalability, scarce representativity of global state conditions, inability in guaranteeing persistence in service delivery, and the impossibility of monitoring multi-tenant applications.In this paper, we present a novel monitoring architecture that addresses the aforementioned issues.It integrates a hierarchical scheme to monitor the resources in a cluster with a distributed hash table (DHT) to broadcast system state information among different monitors.This architecture strives to obtain high scalability, effectiveness and resilience, as well as the possibility of monitoring services spanning across different clusters or even different data centers of the cloud provider.We evaluate the scalability of the proposed architecture through a bottleneck analysis achieved by experimental results.

In large scale systems, real-time monitoring of hardware and software resources is a crucial means for any management purpose. In architectures consisting of thousands of servers and hundreds of thousands of component resources, the amount of data monitored at high sampling frequencies represents an overhead on system performance and communication, while reducing sampling may cause quality degradation. We present a real-time adaptive algorithm for scalable data monitoring that is able to adapt the frequency of sampling and data updating for a twofold goal: to minimize computational and communication costs, to guarantee that reduced samples do not affect the accuracy of information about resources. Experiments carried out on heterogeneous data traces referring to synthetic and real environments confirm that the proposed adaptive approach reduces utilization and communication overhead without penalizing the quality of data with respect to existing monitoring algorithms.

Data centers providing modern interactive applications are enriched by autonomous management decision systems that are able to clone and migrate virtual machines, to re-distribute resources or to re-map services in real-time. At the basis of all these decisions, there is the need of a continuous evaluation of the state of system resources and of detecting when some relevant changes are occurring. Unfortunately, the load of interactive applications reaching the system is intrinsically heterogeneous with consequent highly variable effects on the resource behavior emerging from system monitors. Hence, existing algorithms for online detection of state changes are affected by low precision and scarce robustness when they are applied to modern contexts. We propose a novel model for online detection of relevant state changes that combines a filtered representation of the raw measures with adaptive detection rules. Experiments carried out on real and emulated data sets confirm that the proposed model is able to timely signal all relevant state changes, to limit false detections and, even more important, its results are robust in highly variable contexts.