CLOUDLIGHTNING: A Framework for a Self-organising and

Self-managing Heterogeneous Cloud

Theo Lynn

, Huanhuan Xiong

, Dapeng Dong

, Bilal Momani

, George Gravvanis

Christos Filelis-Papadopoulos

, Anne Elster

, Malik Muhammad Zaki Murtaza Khan

Dimitrios Tzovaras

, Konstantinos Giannoutakis

, Dana Petcu

, Marian Neagul

, Ioan Dragan

Perumal Kuppudayar

, Suryanarayanan Natarajan

, Michael McGrath

, Georgi Gaydadjiev

Tobias Becker

, Anna Gourinovitch

, David Kenny

and John Morrison

Irish Centre for Cloud Computing and Commerce, DCU, Dublin, Ireland

Irish Centre for Cloud Computing and Commerce, UCC, Cork, Ireland

Democritus University of Thrace, Xanthi, Greece

Norwegian University of Science and Technology, Trondheim, Norway

The Centre for Research and Technology, Hellas, Thessaloniki, Greece

Institute e-Austria Timisoara and West University of Timisoara, Timisoara, Romania

Intel, Leixlip, Ireland

Maxeler, London, U.K.

Keywords: Cloud Computing Models, Cloud Infrastructures, Cloud Architecture, Cloud Computing, Cloud Services

Self-organisation, Self-management, Heterogeneous Resources, Resource as a Service, Cloud Orchestration,

Data Flow Engine, Many-integrated Cores, MIC, GPU, FPGA, DFE.

Abstract: As clouds increase in size and as machines of different types are added to the infrastructure in order to

maximize performance and power efficiency, heterogeneous clouds are being created. However, exploiting

different architectures poses significant challenges. To efficiently access heterogeneous resources and, at the

same time, to exploit these resources to reduce application development effort, to make optimisations easier

and to simplify service deployment, requires a re-evaluation of our approach to service delivery. We

propose a novel cloud management and delivery architecture based on the principles of self-organisation

and self-management that shifts the deployment and optimisation effort from the consumer to the software

stack running on the cloud infrastructure. Our goal is to address inefficient use of resources and

consequently to deliver savings to the cloud provider and consumer in terms of reduced power consumption

and improved service delivery, with hyperscale systems particularly in mind. The framework is general but

also endeavours to enable cloud services for high performance computing. Infrastructure-as-a-Service

provision is the primary use case, however, we posit that genomics, oil and gas exploration, and ray tracing

are three downstream use cases that will benefit from the proposed architecture.

1 INTRODUCTION

Current cloud infrastructures are typically centrally

managed and composed of a large number of

machines of the same type, made available to the

end user using the three standard service models:

Infrastructure as a Service (IaaS), Platform as a

Service (PaaS) and Software as a Service (SaaS). As

clouds increase in size and as machines of different

types are added to the infrastructure to maximize

performance and power efficiency, heterogeneous

clouds are being created; posing significant

challenges. To efficiently access heterogeneous

resources and, at the same time, to exploit these

resources to reduce application development effort,

to make optimisations easier and to simplify service

deployment, requires a re-evaluation of our approach

to service delivery. The adoption of heterogeneous

computing resources by cloud consumers will allow

Lynn T., Xiong H., Dong D., Momani B., Gravvanis G., Filelis-Papadopoulos C., Elster A., Khan M., Tzovaras D., Giannoutakis K., Petcu D., Neagul M., Dragon I., Kuppudayar P., Natarajan

S., McGrath M., Gaydadjiev G., Becker T., Gourinovitch A., Kenny D. and Morrison J.

CLOUDLIGHTNING: A Framework for a Self-organising and Self-managing Heterogeneous Cloud.

DOI: 10.5220/0005921503330338

In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016), pages 333-338

ISBN: 978-989-758-182-3

333

for improved resource efficiency and hence reduced

energy use. Market demand for better resource

management at the PaaS and IaaS layers, combined

with both demand and adoption of heterogenous

resources is rapidly increasing complexity of the

cloud ecosystem, which over time will push the

boundaries of traditional cloud management

techniques.

In this paper, we present our research agenda and

ongoing work for the development of

CloudLightning, a new cloud management and

delivery model designed on the principles of self-

organisation and self-management. We propose a

novel architecture for provisioning heterogeneous

cloud resources to deliver services, specified by the

user, using a bespoke service description language.

Service descriptions, provided by prospective cloud

consumers, will result in the cloud evolving to

deliver the required services. The self-organising

behaviour built into, and exhibited by, the cloud

infrastructure will result in the formation of a

heterogeneous collection of homogeneous resource

coalitions; capable of meeting service needs.

Typically, the quality, cost and performance of each

heterogeneous service will differ. The user will

choose from these offerings and appropriate

resources will be commissioned to deliver the

desired service.

An important objective in creating this system is

to remove the burden of low-level service

provisioning, optimisation and orchestration from

the cloud consumer and to vest them in the

collective response of the individual resource

elements comprising the cloud infrastructure. A

related objective is to locate decisions pertaining to

resource usage with the individual resource

components, where optimal decisions can be made.

Currently, successful cloud service delivery relies

heavily on the over-provisioning of resources. Our

goal is to address this inefficient use of resources

and consequently to deliver savings to the cloud

provider and the cloud consumer in terms of reduced

power consumption and improved service delivery,

with hyperscale systems particularly in mind.

The rest of the paper is organised as follows.

Section 2 summarises related work. Section 3

briefly mentions three challenging motivating

examples used to guide the architecture design

presented in Section 4. The paper concludes with a

discussion of work currently in progress.

2 RELATED WORK

The abstraction of hardware architecture from end

user applications and indeed from end users and

programmers is seen as a key benefit of the cloud

computing paradigm (Crago and Walters, 2015).

This abstraction allows data centers and service

providers to maintain, enhance and expand

underlying infrastructure without the absolute

necessity of making associated changes in software.

As such cloud service providers have been typically

able to achieve significant performance

improvements in cloud computing architecture

performance and scalability by natural

improvements in microprocessor capability in line

with Moore’s Law (Crago and Walters, 2015). These

clouds often make use of homogenous resources;

typically identical general purpose microprocessors

that are relatively inexpensive. In these systems,

over-provisioning as a method for assuring service

availability and performance is used extensively.

Consequently, servers are largely underutilised,

relative to their peak load, typically with frequent

idle times resulting in energy inefficiencies. These

inefficiencies can be further exacerbated by non-

uniform energy consumption by servers (Barroso

and Holzle, 2007; Awada et al. 2014). In addition,

associated energy costs related to cooling and air

conditioning and the need for multiple data

conversions in the power distribution system,

particularly for backup systems result in a dramatic

cost and environmental impact (Awada et al. 2014).

With the explosion in cloud computing usage,

infrastructure providers face increased market

demand for improved performance at lower costs

and at the same time are under increased scrutiny

from policymakers relating to their environmental

impact. Unsurprisingly, this area has been a fruitful

one for academic research. In addition to new

cooling methods, data center location, layout and

equipment design, a number of solutions have been

proposed to address resource management in cloud

computing including data voltage and frequency

scaling (DVFS), dynamic power management

(DPM), virtual machine (VM) consolidation and

allocation, task consolidation amongst others

(Kliazovich et al. 2010; Lee and Zomaya, 2010;

Beloglasov et al. 2012; Awada et al. 2014; Alzamil

et al. 2015).

Research suggests that while transistors continue

to shrink, concurrent limitations on power density,

heat removal and related considerations require a

different architecture strategy for improved

processor performance by adding identical, general-

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purpose cores (Esmaeilzadeh et al. 2011; Crago and

Walters, 2015). One solution is the heterogeneous

cloud. Unlike traditional cloud infrastructure built on

the same processor architecture, heterogeneity

assumes a cloud that makes use of different

specialist hardware devices that can accelerate the

completion of specific tasks or can be put in a state

where less power is used (or indeed deactivated if

possible) when not required; thus maximising both

performance and energy efficiency (Scogland et al.

2014). This is particularly relevant to compute-

intensive and high performance computing (HPC)

workloads. Three such common architectures with

relatively high computation/power consumption

ratios include graphics processing units (GPUs),

many integrated cores (MICs) and data flow engines

(DFEs). The programmable nature of GPUs, MICs

and DFEs/FPGAs gives them wide range of

application use cases and particularly for HPC tasks.

The heterogeneous cloud is still at a nascent

stage, however, larger cloud infrastructure providers

are offering commercial services e.g., Amazon Web

Services offers a variety of GPU services. As

demand for better processor, price and power

performance increases, it is anticipated that larger

infrastructure providers will need to cater for several

of these processor types and specifically for the

emerging HPC public cloud market (IDC, 2014a).

However, integrating and managing these different

architectures independently and with an existing

general purpose cloud architecture is not without

challenges; not least of which is access to a pool of

qualified engineers with deep IT knowledge. The

adoption of heterogeneous resources will

dramatically increase the complexity of an already

complex cloud ecosystem. We present self-

organisation and self-management as powerful

techniques for addressing this complexity.

Self-organisation is a powerful technique for

addressing complexity and borrows heavily from the

natural sciences and the study of natural systems

(Gell-Mann, 1988; Schuster, 2007). In the

computing context, Heylighen and Gershenson

(2003) define organisations as “structure with

function” and self-organisation as a functional

structure that appears and maintains spontaneously.

Control is distributed over all of the large number of

participating components in the system. Of self-

organisation, Alan Turing (1952) once observed that

“global order arises from local interactions”. In this

context, global order is achieved through

propagation and adaptation. Components in a self-

organising system are mutually dependent and

typically only interact with nearby components.

However, the system is dynamic and therefore the

components can change state to meet mutually

preferable, satisfactory or stable states (Heylighen

and Gershenson, 2003). As they meet these states,

they adapt and achieve “fit” and this propagation of

“fit” results in system growth. Structural complexity

is driven by increasing the interchangeability and

individuality of components capable of achieving fit.

As more and more components adapt and become

assimilated within the system, complexity increases

to incorporate the individual characteristics of

components. Growth only stops when resources

have been exhausted and self-maintenance is the de

facto purpose of the system. As such, self-organising

systems are defined by their robustness, flexibility,

and adaptivity (Heylighen, 2001).

Self-management has been posited as a solution

to complexity in IT infrastructure development

generally and cloud computing specifically (Kramer

and Magee, 2011; Puviani and Frei, 2013). It has its

roots in autonomic computing; these systems are

designed to react to internal and external

observations without human intervention to

overcome the management complexity of computer

systems (Zhang et al, 2010). As such, self-managing

systems are described in terms of four aspects of

self-management, namely, self-configuration, self-

optimisation, self-protection and self-healing

(Parashar and Hariri, 2005). In line with autonomic

computing, use of control or feedback loops such as

MAPE-K is regularly referenced in self-management

literature (Kephart at al. 2007).

The application of self-organising and self-

management principles to cloud computing is at an

early stage. Zhang et al. (2010) posit that cloud

computing systems are inherently self-organising

and, while they exhibit autonomic features, are not

self-managing as they do not have reducing

complexity as a goal. Marinescu et al. (2013) argue

that cloud computing represents a complex system

and therefore self-organisation is an appropriate

technique to address this complexity. They propose

an auction-driven self-organising cloud delivery

model based on the tenets of autonomy of individual

components, self-awareness, and intelligent

behaviour of individual components. They simulate

a new model of a complex heterogeneous system

with a very large number of components and with

many interaction channels among them. Preliminary

results suggest that the self-organising architecture

was scalable and the bidding mechanisms and

coalition formation algorithms are feasible at scale.

Extending work on self-manageable cloud

services by Brandic (2009) at an individual node

CLOUDLIGHTNING: A Framework for a Self-organising and Self-managing Heterogeneous Cloud

335

level, Puviani and Frei (2013) propose self-

management as a solution for managing complexity

in cloud computing at a system level. They propose

a catalogue of adaptation patterns based on

requirements, context and expected behaviour.

These patterns are classified according to the service

components and autonomic managers. Control loops

following the MAPE-K approach enact adaptation.

In their approach, each service component is

autonomous and autonomic and has its own

autonomic manager that monitors itself and the

environment. The service is aware of changes in the

environment including new and disappearing

components and adapts on a negotiated basis with

other components to meet system objectives. Initial

simulations suggest that self-management is a

promising approach but like Marinescu et al. (2013)

further research in to a real implementation is

necessary to reach final conclusions.

3 POTENTIAL IAAS AND HPC

USE CASES

Industry research suggests that a significant feature

of future HPC purchases will emphasise GPUs, Intel

MICs/Xeon Phi processors and FPGAs (Intersect360

Research, 2015; IDC, 2014b). The top attributes of

future HPC systems from a purchasing perspective

were price, total cost of ownership, performance and

hardware/software compatibility with existing HPC

systems (IDC, 2014b).

The primary use case for CloudLightning is

Infrastructure-as-a-Service (IaaS) provision,

however, three preliminary HPC use cases have

been identified as suitable to validate

CloudLightning, namely (i) genomics, (ii) oil and

case exploration, and (iii) ray tracing. IaaS

provision may include public cloud service

providers and/or companies providing, configuring

or using private and hybrid clouds. In each case,

CloudLightning is anticipated to offer better

performance and greater energy efficiency. By

exploiting heterogeneous computing technologies,

we anticipate significantly improved

performance/cost and performance/watt but also

enabling computation to be hosted at large-scale in

the cloud, making large-scale compute-intensive

application and by-products accessible and practical

from a cost and time perspective for a wider group

of stakeholders. In each use case, relatively small

efficiency and accuracy gains can result in

competitive advantage for industry.

4 PROPOSED ARCHITECTURE

Figure 1 depicts the high level architecture for the

proposed self-organising self-managing

heterogeneous cloud. The life-cycle of a proposed

cloud service is initiated by an Enterprise

Application Developer (“EAD”) by submitting a

Blueprint to a Gateway Service. A Blueprint is a

graph representing a workflow of Services

collectively composed to automate a particular task

or business process.

The Gateway Service is a front-facing

component of the CloudLightning system,

abstracting the inner workings of the backend

components and providing a unified access interface

to the CloudLightning system. The Gateway Service

provides a graphical user interface for EADs. As

well as providing a workflow and an enabling

process for Blueprint development, it also sends

requests for resource options capable of running the

Blueprint, receives resource deployment option(s),

presents options to the EAD and ultimately enables

the EAD to select and deploy the Blueprint to the

chosen resources automatically.

The Gateway Service communicates to a self-

contained self-organising self-managing system,

which we define as a “Cell”. We envisage a Cell

being typically associated with one geographical

region and the CloudLightning system as a whole

being composed of multiple Cells.

Each Cell is composed of resource fabric

comprising heterogeneous compute, storage and

network resources organised in to groups, each of

which is called a vRack. A vRack Manager is used

to maintain information about groups of servers with

the same type of physical resources and manages a

logical group of these resources which is called a

Coalition. Coalitions can be formed either statically

or dynamically. Each vRack is self-managed. Local

decisions are made by the vRack Manager, to decide

on how coalitions are optimally formed (or

identified) to deliver the service - reflecting self-

optimisation at the vRack Manager Level. Self-

healing and Self-protection may be addressed by

feedback loops and fault tolerance, respectively. A

vRack Manager (or vRack Manager Group) can

deliver one type of the heterogeneous options for

implementing a service.

vRack Managers cooperate to form a vRack

Manager Group. vRacks Managers in the same

Group are capable of self-organising to meet the

objective of efficient power consumption based on

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336

Figure 1: CloudLightning Architecture.

their local knowledge of underlying resource

utilisations. Like Puviani and Frei (2013), each

vRack Manager (or vRack Manager Group)

autonomously monitors itself and the environment.

Similarly, vRack Managers are aware of changes in

the environment including new and disappearing

resources and adapts on a negotiated basis with other

vRack Managers within the same vRack Manager

Group to meet system objectives.

Back at the Cell-level, having received a request

from the Gateway Service for resource options

capable of running the Blueprint, a resource

discovery service propagates this request to

appropriate vRack Manager Groups. The Cell

Manager has the knowledge of the resource capacity

in each vRack Manager Group and therefore can

immediately communicate back to the Gateway

Service informing it of the various delivery options

available. These options can be agreed upon, using a

Resource Selection Service, or selected

automatically without the need to reserve the

underlying resources. Having been agreed, the

resources necessary for the service implementation

can be commissioned by the appropriate vRack

Manager (or vRack Manager Groups). This

commissioning process involves either the

identification of pre-defined, statically created,

coalitions or by dynamically creating new coalitions

in consultation with the Coalition Formation

Services.

5 CONCLUSIONS

This paper proposes a novel cloud management and

delivery architecture based on the principles of self-

organisation and self-management that addresses the

challenges of complexity introduced by

heterogeneous resources. This approach can be

beneficial by shifting the deployment and

optimisation effort from the consumer to the

software stack running on the cloud infrastructure

and reducing power consumption and improving

service delivery. This paper represents work in

process. Our ambition is to provide a live

implementation of a self-organising and self-

managing heterogeneous cloud that can be used to

validate the hypothesised efficiencies. Performance

metrics, such as reliability, cost and power

consumption, will be used to compare the self-

organising and self-managing approach to the

current state of the art in cloud delivery. We present

three HPC use cases where the proposed architecture

can have a significant positive impact on power

consumption and service delivery.

End user surveys of the HPC community have

repeatedly highlighted the growing complexity of

the domain and the need for “ease of everything” in

the management of HPC (IDC, 2014b). This

complexity is mirrored in the wider cloud computing

context as more specialist computing technologies

are introduced in to the marketplace and form

important components in new service provision.

Being powerful techniques for addressing

complexity, self organisation and self-management

may present a solution to this complexity by shifting

the deployment and optimisation effort from the

consumer to the software stack and in the process

hiding this complexity from the consumer.

ACKNOWLEDGEMENTS

This work is partially funded by the European

Union’s Horizon 2020 Research and Innovation

Programme through the CloudLightning project

(http://www.cloudlightning.eu) under Grant

Agreement Number 643946.

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337

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