Umeå University's logo

Current cloud computing infrastructure offerings are lacking in interoperability, which is a hindrance to the advancement and adoption of the cloud computing paradigm. As clouds are made interoperable, federations of clouds may be formed. Such federations are from the point of view of the user not burdened by vendor lock-in, and opens for business possibilities where a market place of cloud computing infrastructure can be formed. Federated clouds require unified management interfaces regarding the virtual machines (VMs) that comprise the services running in the cloud federation. Standardization efforts for the required management interfaces have so far focused on definition of description formats regarding VMs, and the control of already deployed VMs. We propose technology neutral interfaces and architectural additions for handling placement, migration, and monitoring of VMs in federated cloud environments, the latter as an extension of current monitoring architectures used in grid computing. The interfaces presented adhere to the general requirements of scalability, efficiency, and security in addition to specific requirements related to the particular issues of interoperability and business relationships between competing cloud computing infrastructure providers. In addition, they may be used equally well locally and remotely, creating a layer of abstraction that simplifies management of virtualized service components.

Cloud infrastructure providers may form Cloud federations to cope with peaks in resource demand and to make large-scale service management simpler for service providers. To realize Cloud federations, a number of technical and managerial difficulties need to be solved. We present ongoing work addressing three related key management topics, namely, specification, scheduling, and monitoring of services. Service providers need to be able to influence how their resources are placed in Cloud federations, as federations may cross national borders or include companies in direct competition with the service provider. Based on related work in the RESERVOIR project, we propose a way to define service structure and placement restrictions using hierarchical directed acyclic graphs. We define a model for scheduling in Cloud federations that abides by the specified placement constraints and minimizes the risk of violating Service-Level Agreements. We present a heuristic that helps the model determine which virtual machines (VMs) are suitable candidates for migration. To aid the scheduler, and to provide unified data to service providers, we also propose a monitoring data distribution architecture that introduces cross-site compatibility by means of semantic metadata annotations.

Virtual machine placement is the process of mapping virtual machines to available physical hosts within a datacenter or on a remote datacenter in a cloud federation. Normally, service owners cannot influence the placement of service components beyond choosing datacenter provider and deployment zone at that provider. For some services, however, this lack of influence is a hindrance to cloud adoption. For example, services that require specific geographical deployment (due e.g. to legislation), or require redundancy by avoiding co-location placement of critical components. We present an approach for service owners to influence placement of their service components by explicitly specifying service structure, component relationships, and placement constraints between components. We show how the structure and constraints can be expressed and subsequently formulated as constraints that can be used in placement of virtual machines in the cloud. We use an integer linear programming scheduling approach to illustrate the approach, show the corresponding mathematical formulation of the model, and evaluate it using a large set of simulated input. Our experimental evaluation confirms the feasibility of the model and shows how varying amounts of placement constraints and data center background load affects the possibility for a solver to find a solution satisfying all constraints within a certain time-frame. Our experiments indicate that the number of constraints affects the ability of finding a solution to a higher degree than background load, and that for a high number of hosts with low capacity, component affinity is the dominating factor affecting the possibility to find a solution.

This work addresses two related problems for on-line services, namely poor resource utilization during regular operating conditions, and low throughput, long response times, or poor performance under periods of high system load. To address these problems, we introduce our notion of quality-elasticity as a manner of dynamically adapting response qualities from software services along a fine-grained spectrum. When resources are abundant, response quality can be increased, and when resources are scarce, responses are delivered at a lower quality to prioritize throughput and response times. We present an example of how a complex online shopping site can be made quality-elastic. Experiments show that, compared to state of the art, improvements in throughput (57% more served queries), lowered response times (8 time reduction for 95th percentile responses), and an estimated 40% profitability increase can be made using our quality-elastic approach. When resources are abundant, our approach may achieve upwards of twice as high resource utilization as prior work in this field.

This experience article describes lessons learned as we conducted experiments in a Kubernetes-based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)-backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed-loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high-performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant (P < .05) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.

This position paper presents our vision for a distributed decentralized Kubernetes federation control plane. The goal is to support federations consisting of thousands of Kubernetes clusters, in order to support next generation edge cloud use-cases. Our review of the literature and experience with the current centralized state of the art Kubernetes federation controllers shows that it is unable to scale to a sufficient size, and centralization constitutes an unacceptable single point of failure. Our proposed system maintains cluster autonomy, allows clusters to collaboratively handle error conditions, and scales to support edge cloud use-cases. Our approach is based on a shared database of conflict-free replicated data types (CRDTs), shared among all clusters in the federation, and algorithms that make use of the data.