Dynamic Replication in Service-Oriented Systems

Björkqvist, Mathias; Chen, Lydia Y.; Binder, Walter

doi:10.1109/ccgrid.2012.108

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…When consolidating virtual machines in a data center environment, resource utilization is a crucial parameter for virtual machine placement policies [14,44]. Resource provisioning policies [45,46] are validated using a request workload generated directly from utilization traces collected in production systems.…”

Section: Discussing Related Workmentioning

confidence: 99%

Robust partial-load experiments with Showstopper

Podzimek

Bulej

Chen

et al. 2016

Future Generation Computer Systems

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Section: Discussing Related Workmentioning

confidence: 99%

Robust partial-load experiments with Showstopper

Podzimek

Bulej

Chen

et al. 2016

Future Generation Computer Systems

Self Cite

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“…For a given period of time when the workload intensity is predicted as λ requests per second, one may want to keep the system 80% utilized, and set the target capacity to C * = λ/0.8. The choice of the target capacity is out of scope of this work, and we direct interested readers to our prior work [4,5]. n * vs.…”

Section: Trade-off Between Cost and Service Availabilitymentioning

confidence: 99%

QoS-Aware Service VM Provisioning in Clouds: Experiences, Models, and Cost Analysis

Björkqvist

Spicuglia

Chen

et al. 2013

Service-Oriented Computing

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Recent studies show that service systems hosted in clouds can elastically scale the provisioning of pre-configured virtual machines (VMs) with workload demands, but suffer from performance variability, particularly from varying response times. Service management in clouds is further complicated especially when aiming at striking an optimal trade-off between cost (i.e., proportional to the number and types of VM instances) and the fulfillment of quality-of-service (QoS) properties (e.g., a system should serve at least 30 requests per second for more than 90% of the time). In this paper, we develop a QoS-aware VM provisioning policy for service systems in clouds with high capacity variability, using experimental as well as modeling approaches. Using a wiki service hosted in a private cloud, we empirically quantify the QoS variability of a single VM with different configurations in terms of capacity. We develop a Markovian framework which explicitly models the capacity variability of a service cluster and derives a probability distribution of QoS fulfillment. To achieve the guaranteed QoS at minimal cost, we construct theoretical and numerical cost analyses, which facilitate the search for an optimal size of a given VM configuration, and additionally support the comparison between VM configurations.

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“…Web service applications are characterized not only by disparate resource requirements (e.g., a CPU-intensive browsing mix versus an I/O-intensive transaction mix), but also by time-varying request workloads [4,17]. Consequently, the overall system workloads fluctuate in terms of mixes of dissimilar applications and their volume of requests [16].…”

Section: Introductionmentioning

confidence: 99%

On load balancing

Spicuglia

Bjoerkqvist

Chen

et al. 2013

Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering

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Today's web services are commonly hosted on clusters of servers that are often located within computing clouds, whose computational and storage resources can be highly heterogeneous. The workload served typically exhibits disparate computation patterns (e.g., CPU-intensive or IO-intensive), that fluctuate both in terms of volume and mix. The system heterogeneity together with workload diversity further exacerbates the challenge of effective distribution of load within a computing cloud. This paper presents a novel, mixaware load-balancing algorithm, which aims to distribute requests sent by multiple applications in heterogeneous servers such that the application response times are minimized and system resources (e.g., CPU and IO) are equally utilized. To this end, the presented algorithm tries to not only balance the total number of requests seen by each server, but also to shape the requests received by each server into a certain "mix", that is analytically shown to be optimal for response time minimization. Our experimental resultsbased both on simulation and on a prototype implementationshow that the mix-aware algorithm achieves robust performance in most workload mixes as well as a consistent performance improvement in comparison with one of the most robust load-balancing schemes of the Apache server.

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Dynamic Replication in Service-Oriented Systems

Cited by 19 publications

References 18 publications

Robust partial-load experiments with Showstopper

Robust partial-load experiments with Showstopper

QoS-Aware Service VM Provisioning in Clouds: Experiences, Models, and Cost Analysis

On load balancing

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