MORPHOSYS: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the IaaS provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MorphoSys: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of workloads. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. The results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MorphoSys.First author draf

Planning Live-Migrations to Prepare Servers for Maintenance

International audienceIn a virtualized data center, server maintenance is a common but still critical operation. A prerequisite is indeed to relocate elsewhere the Virtual Machines (VMs) running on the production servers to prepare them for the maintenance. When the maintenance focuses several servers, this may lead to a costly relocation of several VMs so the migration plan must be chose wisely. This however implies to master numerous human, technical, and economical aspects that play a role in the design of a quality migration plan. In this paper, we study migration plans that can be decided by an operator to prepare for an hardware upgrade or a server refresh on multiple servers. We exhibit performance bottleneck and pitfalls that reduce the plan efficiency. We then discuss and validate possible improvements deduced from the knowledge of the environment peculiarities

A Survey of Virtual Machine Placement Techniques and VM Selection Policies in Cloud Datacenter

The large scale virtualized data centers have been established due to the requirement of rapid growth in computational power driven by cloud computing model . The high energy consumption of such data centers is becoming more and more serious problem .In order to reduce the energy consumption, server consolidation techniques are used .But aggressive consolidation of VMs can lead to performance degradation. Hence another problem arise that is, the Service Level Agreement(SLA) violation. The optimized consolidation is achieved through optimized VM placement and VM selection policies . A comparative study of virtual machine placement and VM selection policies are presented in this paper for improving the energy efficiency

Flauncher and DVMS -- Deploying and Scheduling Thousands of Virtual Machines on Hundreds of Nodes Distributed Geographically

International audienceAlthough live migration of virtual machines has been an active area of research over the past decade, it has been mainly evaluated by means of simulations and small scale deployments. Proving the relevance of live migration at larger scales is a technical challenge that requires to be able to deploy and schedule virtual machines. In the last year, we succeeded to tackle such a challenge by conducting experiments with Flauncher and DVMS, two frameworks that can respectively deploy and schedule thousands of virtual machines over hundreds of nodes distributed geographically across the Grid'5000 testbed

Strategic and operational services for workload management in the cloud

In hosting environments such as Infrastructure as a Service (IaaS) clouds, desirable application performance is typically guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated by a service provider for unencumbered use by customers to ensure proper operation of their workloads. Most IaaS offerings are presented to customers as fixed-size and fixed-price SLAs, that do not match well the needs of specific applications. Furthermore, arbitrary colocation of applications with different SLAs may result in inefficient utilization of hosts' resources, resulting in economically undesirable customer behavior. In this thesis, we propose the design and architecture of a Colocation as a Service (CaaS) framework: a set of strategic and operational services that allow the efficient colocation of customer workloads. CaaS strategic services provide customers the means to specify their application workload using an SLA language that provides them the opportunity and incentive to take advantage of any tolerances they may have regarding the scheduling of their workloads. CaaS operational services provide the information necessary for, and carry out the reconfigurations mandated by strategic services. We recognize that it could be the case that there are multiple, yet functionally equivalent ways to express an SLA. Thus, towards that end, we present a service that allows the provably-safe transformation of SLAs from one form to another for the purpose of achieving more efficient colocation. Our CaaS framework could be incorporated into an IaaS offering by providers or it could be implemented as a value added proposition by IaaS resellers. To establish the practicality of such offerings, we present a prototype implementation of our proposed CaaS framework

Higher SLA Satisfaction in Datacenters with Continuous Placement Constraints

National audienceIn a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary constraint violations. In this thesis, we present the causes of these violations. We then advocate for continuous placement constraints to restrict also the action schedule. We discuss why the development of continuous constraints requires more attention but how the extensible placement algorithm BtrPlace can address this issue

Modeling response times in the Google ROADEF/EURO Challenge

In this paper, we extend the machine reassignment model proposed by Google for the ROADEF/EURO Challenge. The aim of the challenge is to develop algorithms for the efficient solutions of data-center consolidation problems. The problem stated in the challenge mainly focus on dependability requirements and does not take into account performance requirements (end-to-end response times). We extend the Google problem definition by modeling and constraining end-to-end response times. We provide experimental results to show the effectiveness of this extension. Copyright is held by author/owner(s)