68 research outputs found
Performance prediction for burstable cloud resources
We propose ForeBurst, an open source tool for performance prediction for complex cloud-based applications. ForeBurst leverages queueing network models for predicting performance metrics such as resource utilizations, request response times, and credit usage in burstable resources, such as the Amazon EC2 T-family instances
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure
Recent technology advancements in the areas of compute, storage and
networking, along with the increased demand for organizations to cut costs
while remaining responsive to increasing service demands have led to the growth
in the adoption of cloud computing services. Cloud services provide the promise
of improved agility, resiliency, scalability and a lowered Total Cost of
Ownership (TCO). This research introduces a framework for minimizing cost and
maximizing resource utilization by using an Integer Linear Programming (ILP)
approach to optimize the assignment of workloads to servers on Amazon Web
Services (AWS) cloud infrastructure. The model is based on the classical
minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin
Sidecar based resource estimation method for virtualized environments
The widespread use of virtualization technologies in telecommunication system resulted in series of benefits, as flexibility, agility and increased resource usage efficiency. Nevertheless, the use of Virtualized Network Functions (VNF) in virtualized modules (e.g., containers, virtual machines) also means that some legacy mechanisms that are crucial for a telco grade operation are no longer efficient. Specifically, the monitoring of the resource sets (e.g., CPU power, memory capacity) allocated to VNFs cannot rely anymore on the methods developed for earlier deployment scenarios. Even the recent monitoring solutions designed for cloud environments is rendered useless if the VNF vendor and the telco solution supplier has to deploy its product into a virtualized environment, since it does not have access to the host level monitoring tools. In this paper we propose a sidecar-based solution to evaluate the resources available for a virtualized process. We evaluated the accuracy of our proposal in a proof of concept deployment, using KVM, Docker and Kubernetes virtualization technologies, respectively. We show that our proposal can provide real monitoring data and discuss its applicability
Cloud-scale VM Deflation for Running Interactive Applications On Transient Servers
Transient computing has become popular in public cloud environments for
running delay-insensitive batch and data processing applications at low cost.
Since transient cloud servers can be revoked at any time by the cloud provider,
they are considered unsuitable for running interactive application such as web
services. In this paper, we present VM deflation as an alternative mechanism to
server preemption for reclaiming resources from transient cloud servers under
resource pressure. Using real traces from top-tier cloud providers, we show the
feasibility of using VM deflation as a resource reclamation mechanism for
interactive applications in public clouds. We show how current hypervisor
mechanisms can be used to implement VM deflation and present cluster deflation
policies for resource management of transient and on-demand cloud VMs.
Experimental evaluation of our deflation system on a Linux cluster shows that
microservice-based applications can be deflated by up to 50\% with negligible
performance overhead. Our cluster-level deflation policies allow overcommitment
levels as high as 50\%, with less than a 1\% decrease in application
throughput, and can enable cloud platforms to increase revenue by 30\%.Comment: To appear at ACM HPDC 202
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