dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter

Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft

Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

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

Resource management for software defined data centers for heterogeneous infrastructures

Software Defined Data Center (SDDC) provides more resource management flexibility since everything is defined as a software, including the network as Software Defined Network (SDN).Typically, cloud providers overlook the network, which is configured in static way. SDN can help to meet applications goals with dynamic network configuration and provide best-efforts for QoS. Additionally, SDDC might benefit by instead of be composed by heavy Virtual Machines, use light-weight OS Containers. Despite the advantages of SDDC and OS Containers, it brings more complexity for resource provisioning. The goal of this project is to optimize the management of container based workloads deployed on Software defined Data Centers enabled with heterogeneous network fabrics through the use of network-aware placement algorithms that are driven by performance models

Transiency-driven Resource Management for Cloud Computing Platforms

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator. Transient computing resources are increasingly important, and existing fault-tolerance and resource management techniques are inadequate for transient servers because applications typically assume continuous resource availability. This thesis presents research in distributed systems design that treats transiency as a first-class design principle. I show that combining transiency-specific fault-tolerance mechanisms with resource management policies to suit application characteristics and requirements, can yield significant cost and performance benefits. These mechanisms and policies have been implemented and prototyped as part of software systems, which allow a wide range of applications, such as interactive services and distributed data processing, to be deployed on transient servers, and can reduce cloud computing costs by up to 90\%. This thesis makes contributions to four areas of computer systems research: transiency-specific fault-tolerance, resource allocation, abstractions, and resource reclamation. For reducing the impact of transient server revocations, I develop two fault-tolerance techniques that are tailored to transient server characteristics and application requirements. For interactive applications, I build a derivative cloud platform that masks revocations by transparently moving application-state between servers of different types. Similarly, for distributed data processing applications, I investigate the use of application level periodic checkpointing to reduce the performance impact of server revocations. For managing and reducing the risk of server revocations, I investigate the use of server portfolios that allow transient resource allocation to be tailored to application requirements. Finally, I investigate how resource providers (such as cloud platforms) can provide transient resource availability without revocation, by looking into alternative resource reclamation techniques. I develop resource deflation, wherein a server\u27s resources are fractionally reclaimed, allowing the application to continue execution albeit with fewer resources. Resource deflation generalizes revocation, and the deflation mechanisms and cluster-wide policies can yield both high cluster utilization and low application performance degradation

A Cloud Native Solution for Dynamic Auto Scaling of MME in LTE

Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%

Adaptable Service Oriented Infrastructure Provisioning with Lightweight Containers Virtualization Technology

Modern computing infrastructures should enable realization of converged provisioning and governance operations on virtualized computing, storage and network resources used on behalf of users' workloads. These workloads must have ensured sufficient access to the resources to satisfy required QoS. This requires flexible platforms providing functionality for construction, activation and governance of Runtime Infrastructure which can be realized according to Service Oriented Infrastructure (SOI) paradigm. Implementation of the SOI management framework requires definition of flexible architecture and utilization of advanced software engineering and policy-based techniques. The paper presents an Adaptable SOI Provisioning Platform which supports adaptable SOI provisioning with lightweight virtualization, compliant with the structured process model suitable for construction, activation and governance of IT environments. The requirements, architecture and implementation of the platform are all discussed. Practical usage of the platform is presented on the basis of a complex case study for provisioning JEE middleware on top of the Solaris 10 lightweight virtualization platform

Use of a virtualization in the transition of a telecommunication networks toward 5G

We are in the front of the next big step of a new generation of the telecommunications networks, called 5G. The 5G in still in the preparation, but the actual wide spread use is nearby. The move toward 5G is not possible without use of a cloud and a virtualization. In the paper we are dealing with the issues how to incorporate existing fixed networks to the mobile 5G network and how to use a virtualization technology when moving to 5G. From the example of a real telecommunication system we defined issues, dilemmas and suggestions when moving toward 5G networks using virtualization

Wide area network autoscaling for cloud applications

Modern cloud orchestrators like Kubernetes provide a versatile and robust way to host applications at scale. One of their key features is autoscaling, that automatically adjusts cloud resources (compute, memory, storage) in order to dynamically adapt to the demands of the application. However, the scope of cloud autoscaling is limited to the datacenter hosting the cloud and it doesn't apply uniformly to the allocation of network resources. In I/O-constrained or data-in-motion use cases this can lead to severe performance degradation for the application. For example, when the load on a cloud service increases and the Wide Area Network (WAN) connecting the datacenter to the Internet becomes saturated, the application experiences an increase in delay and loss. In many cases this is dealt by overprovisioning network capacity, which introduces significant additional costs and inefficiencies. On the other hand, thanks to the concept of "Network as Code", the WAN today exposes a programmable set ofAPIs that can be used to dynamically allocate and deallocate capacity on-demand. In this paper we propose extending the concept of cloud autoscaling into the network to address this limitation. This way, applications running in the cloud can communicate their networking requirements, like bandwidth or traffic profile, to an SDN controller or Network as a Service (NaaS) platform. Moreover, we aim to define the concepts of vertical and horizontal autoscaling applied to networking. We present a prototype that automatically allocates bandwidth in the underlay of an SD-WAN, according to the requirements of the applications hosted in Kubernetes. Finally, we discuss open research challenges