Implementation of a Generic Autonomic Framework

Implementation of a Generic Autonomic Framework

Radu Calinescu

Computing Laboratory,University of Oxford

Wolfson Building,Parks Road,Oxford OX13QD,UK

Radu.Calinescu@https://www.wendangku.net/doc/c716086324.html,

Abstract—Based on insights from the implementation of commercial products for data-centre resource management,we identi?ed key challenges in the development of cost-effective autonomic solutions,and best practices for overcoming these challenges.In a related paper,we proposed a generic autonomic framework that complies with these best practices,and suggested ways in which existing technologies could be used to realise this framework.In this paper,we describe the actual implementation of our autonomic framework as a service-oriented architecture, and we show how the universal policy engine at its core can be con?gured to manage the allocation of server capacity to services of different priorities.This case study demonstrates the effectiveness of our generic approach to autonomic solution development in an area of great interest for commercial data centres,research laboratories and application service providers.

I.I NTRODUCTION

Today’s economy is characterised by revolutionary transfor-mations to the way in which Information and Communication Technologies(ICT)are used to conduct business and research and to provide services in all sectors of the society[1].The ability to accomplish more,faster and on a broader scale through expert use of ICT is at the core of today’s scienti?c discoveries,newly emerged electronic services and everyday life.Due to unprecedented advances in ICT,business needs are attended to by ever more sophisticated and feature-rich systems and systems of systems[2].Notwithstanding the bene?ts of such developments,the spiralling complexity of these systems led to unsustainable increases in the cost and expertise required for their management.In an attempt to address this problem,autonomic computing was proposed as a technology for delegating the management of complex systems to the machines themselves[3].Over the past few years, signi?cant progress has been made in de?ning what autonomic systems should look like[4],[5],[6]and in successfully implementing domain-speci?c autonomic solutions[7],[8], [9],[10].

Several such solutions[8]were implemented using a com-mercial autonomic system for policy-based resource man-agement that was co-developed by the author[11].Multiple challenges were encountered in this work,including[12]:?The lack of standardisation in ICT resource interfaces.

Despite an increasing trend to add management interfaces to new ICT components and devices,and to make ex-isting interfaces public,autonomic system development is hindered by the broad diversity of architectures and technologies these interfaces are based upon.

?The tendency to hardcode ICT resource metadata within the control component of the autonomic system.Man-agement frameworks are often intended for handling particular types of resources,and the properties of these resource types are hardcoded in the control element of the system.With careful design,complex systems consisting of supported resources can be successfully managed,however adding in support for additional types of resources cannot be achieved in a cost-effective way.?The complexity of business policies.Policy design for au-tonomic solutions is a complex,error-prone and iterative process.A high level of expertise is required to devise and?ne-tune the set of policies for a complete autonomic solution.

?The high scalability expectations.As simple,small ICT systems are easy to manage by low-skilled human op-erators,autonomic solutions are required in areas where the systems to manage are complex and comprise large numbers of resources.

Based on best practices identi?ed while addressing these chal-lenges[12],we proposed a generic autonomic framework for developing cost-effective autonomic solutions,and suggested ways in which existing technologies could be used to realise this framework[13].

In this paper,we describe the actual implementation of our autonomic framework as a service-oriented architecture (SOA),and we show how the universal policy engine at its core can be con?gured to manage the allocation of server capacity to services.A SOA solution was chosen as the target for the prototype implementation of the framework in order to leverage web service technology bene?ts such as platform independence,loose coupling and security support—all of which are of uttermost importance in an autonomic solution. The choice of server capacity allocation for our initial case study was motivated by the importance that this real-world application has had since the release of server-level capacity control APIs such as[14],[15].Additionally,our prior experience with data-centre resource management[11] helped signi?cantly during the implementation of the solution, and in the interpretation of the case study results.

II.R ELATED WORK

The autonomic infrastructure proposed in[16]is retro?tting autonomic functionality onto legacy systems by using sensors to collect resource data,gauges to interpret these data and controllers to decide the“adaptations”to be enforced

on

Fourth International Conference on Autonomic and Autonomous Systems 0-7695-3093-1/08 $25.00 ? 2008 IEEE

DOI 10.1109/ICAS.2008.21

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the managed systems through effectors.This infrastructure was successfully used to monitor,analyse and control legacy systems in applications such as spam detection,instant mes-saging quality-of-service management and load balancing for geographical information systems[17].Our generic autonomic framework addresses several key areas that are not supported by the approach in[16],[17].By using a system model for the con?guration of its policy engine,our architecture can be used for the autonomic management of heterogeneous types of resources.Moreover,our managed system can include resources beyond the software components handled by the infrastructure in[16].

In[18],the authors de?ne an autonomic architecture meta-model that extends IBM’s autonomic computing blueprint [19],and use a model-driven process to partly automate the generation of instances of this meta-model.Each instance is a special-purpose organic computing system that can handle the use cases de?ned by the model used for its generation.Our general-purpose autonomic architecture eliminates the need for the19-step generation process described in[18]by using a universal policy engine that can be dynamically repurposed to handle any use cases encoded within its system model and policy set.

A number of other projects have investigated isolated as-pects related to the development of autonomic systems out of non-autonomic components.Some of these projects addressed the standardisation of the policy information model,with the Policy Core Information Model[20]representing the most prominent outcome of this work.Recent efforts such as Oasis’Web Services Distributed Management(WSDM)project were directed at the standardisation of the interfaces through which the manageability of a resource is made available to other ap-plications[21].An integrated development environment for the implementation of WSDM-compliant interfaces is currently available from IBM[22].

In a different area,expression languages were proposed for the speci?cation of policy conditions and actions,and used to implement a range of policies[23],[24],[25],[26].In addition to the development of standards and technologies,complete autonomic computing solutions have been produced recently [14],[11],[15],typically for the management of speci?c systems,and with limited ability to function in different scenarios from those they were originally intended for.

III.O VERVIEW OF THE G ENERIC A UTONOMIC

F RAMEWORK

Fig.1depicts the general-purpose autonomic architecture used by our framework.This architecture,originally intro-duced in[13],is designed around a recon?gurable,model-driven policy engine that organises heterogeneous collections of legacy and autonomic ICT resources into self-managing systems.In order to support the great diversity of existing and future ICT resources encountered in real-world applications, the policy engine is capable of handling resources whose types are unknown at implementation and deployment time.This unique(re)purposing capability is achieved through

runtime

Fig.1.General-purpose autonomic architecture.

con?guration:a comprehensive speci?cation(or model)of the system to be managed is supplied to the policy engine for this purpose.The engine will then implement the high-level goals given by a set of user-speci?ed policies that are expressed in a declarative language,and which make reference to the resources de?ned in the system model.The policy engine performs this task by monitoring the state of the managed resources and controlling their con?guration parameters accordingly,while resource-speci?c manageability adaptors ensure that this is achieved without any modi?cation to existing ICT resources.

The generality of the architecture allows the implementation of the engine using different technologies,e.g.,as a software agent running on a data-centre server or a physical device incorporated into a factory automation equipment or a mobile phone.A full description of this novel autonomic framework is provided in[13].

IV.P ROTOTYPE P OLICY E NGINE

The prototype policy engine and the manageability adap-tors enabling its interoperation with legacy resources were implemented as web services in order to leverage the platform independence,loose coupling and security features of this technology.The runtime recon?guration of the policy engine necessitated the extensive use of techniques available only in an object-oriented environment:

?dynamic data type generation was required to support new types of resources when the policy engine was repurposed through con?guration with a new system model;

Fig.2.UML meta-model of a managed system

?runtime generation of web service proxies was required to enable the policy engine to interoperate with new, resource-speci?c manageability adaptors;

?re?ection was heavily used to access the values of the re-source properties,both to read their values once the policy engine obtained them from the manageability adaptors and to set new values for the modi?able properties;?generics were used to encode most of the functionality of manageability adaptors in a base abstract class,and to obtain resource-speci?c manageability adaptors by parameterising this abstract class with the resource data types.

Based on these requirements,J2EE https://www.wendangku.net/doc/c716086324.html, were selected as candidate development environments for the prototype engine,https://www.wendangku.net/doc/c716086324.html, being eventually preferred due to its better handling of dynamic proxy generation and slightly easier-to-use implementation of re?ection.

A model was developed for the system model used to supply a speci?cation of the ICT resources to be managed to the policy engine.As shown in Fig.2,this meta-model of a managed system speci?es a managed system as a named set of resource de?nitions.Each resource de?nition(i.e., resourceDe?nition in the UML diagram)comprises a unique identi?er ID,a description and a set of resource properties with their characteristics.A resource property has a data type (i.e.,propertyDataType),and is associated a unique ID and the metadata repository URL where its de?nition is available.Several other property characteristics are exposed:

?modi?ability—taken from the WS-ResourceMetadata-Descriptor(WS-RMD)1.0speci?cation[27],speci?es if the property is“read-only”or“read-write”;?mutability—the WS-RMD MutabilityType[27]speci?es if the property is“constant”,“mutable”or“appendable”;?subscribeability—speci?es whether a client such as the policy engine can subscribe to receive noti?cations when the value of this property changes;?primaryKey—indicates whether the property is part of the property set used to identify a resource instance among all resource instances of the same type.

The prototype policy engine(class policyEngine in Fig.3) was implemented itself as an instance of a resourceDe?nition from the system meta-model in Fig.2,such that an instance of the engine can be con?gured to manage other policy engine instances.The four properties of the policy engine are:

1)the policy evaluation period(i.e.,’period’);

2)the set of policies to implement(’policySet’);

3)the locations of the resources to manage(’re-

sourceUrls’),which for the current version of the proto-type are set explicitly(the use of a discovery technique

[28]is intended for future versions);

4)the model of the managed system(’system’);note

that the type of the’system’property is precisely our managed-system meta-model(Fig.2).

The prototype supports operators for the manipulation of primitive data types and a few of the more sophisticated operators recommended in[12](e.g.,set comprehension and scheduling).Support for additional operators is added on a regular basis as new case studies are being explored.

The policy engine recon?guration is achieved through an instance of the PolicyEngine class in Fig.3.PolicyEngine is a subclass of ManagedResource<>,a generic abstract class that is the base class for the manageability adaptors in our architecture and which comprises three web methods:?SupportedResource returns the ID of the supported re-source type.

?GetResources returns the list of all available resource instances.The method takes as argument a list of resource property IDs,and only the values of these properties are assessed and returned to the caller,thus preventing unnecessary resource property evaluation.?SetResources takes as argument a list of resources of the supported type,and assigns any new values speci?ed by the caller for the resource properties declared modi?able in the system model.The resources whose properties need to be modi?ed are uniquely identi?ed by the value of the resource properties marked as“primary key”components in the system model.

These three web methods rely on resource-speci?c methods that are declared abstract in ManagedResource<>,and which any of its subclasses(including PolicyEngine)implements:?GetRawResources builds a list of all available resource instances.The values of the resource properties need not

Fig.3.The prototype policy engine—class diagram

be provided by this method.

?GetResourceProperty takes as arguments a resource in-stance and the ID of a resource property,and ensures that the property value is set in the resource object.The method is used by GetResources to ?ll in the required property values after obtaining a “raw”resource list from GetRawResources .

?SetResourceProperties takes a resource object and en-sures that the modi?able properties of the corresponding real-world resource are assigned any new values speci?ed in the resource object.

A web administration client was implemented to set the properties of the prototype policy engine (Fig.4).This client uses the three web methods of PolicyEngine to read and to modify the policy engine parameters.In particular,the SetResources web method is used to (re)con?gure the engine:?Changes to the system model result in a repurposing of the policy engine for the management of new types of ICT resources,which involves the dynamic generation of new data types,and of proxies for the manageability adaptors speci?c to the new resource types.

?Changes to the resource URLs trigger the engine to con-tact the manageability adaptors at the speci?ed locations in order to establish the type of resources they expose.?Policy changes lead to re-analysis of the new policies and to their parsing into an internal format that makes the pe-riodical evaluation of policies computationally ef?cient.?The web client can also be used to alter the policy evaluation period.V.C ASE S TUDY

In a realistic case study,we con?gured the policy engine to allocate the CPU capacity of a server to a set of services of different priority and subjected to variable workloads.The only resource de?ned in the server model was ’service’with the properties:

?name —a string used to distinguish among services;?priority —an integer value;

?cpuAllocation —the percentage of the server CPU allo-cated to the service;

?cpuUtilisation —the amount of CPU utilised by the ser-vice,expressed as a percentage of its CPU allocation.The policy depicted in Fig.4allocates a percentage of the CPU capacity of the server to each ’service’resource,as

selected

Fig.4.Snapshot of the web client used to con?gure the policy engine

by the policy scope.The ’TRUE’policy condition requires that the policy action is applied at all times (i.e.,in line with the policy evaluation period of the engine).The policy action is speci?ed by means of an expression that uses the SCHEDULE (R,ordering,property,capacity,min,max,optimal )operator that

?sorts the resources in R in non-increasing order of the comparable expressions in ordering ;

?in the sorted order,sets the speci?ed resource property to a value never smaller than min or larger than max ,and as close to optimal as possible;

?ensures that the overall sum of all property values does not exceed the available capacity .Accordingly,the policy action

SCHEDULE (service, service.priority ,

service.cpuAllocation,100,15,85,service.cpuAllocation +5?HYSTERESIS (service.cpuUtilisation,55,80))in Fig.4will set the cpuAllocation property of all services to a value between 15%and 100%subject to the overall CPU allocation staying within the 100%available capacity.Opti-mally,the cpuAllocation should be left unchanged if the 55≤

Fig.5.The server manageability adaptor cpuUtilisation≤85,decrease by5(%)if cpuUtilisation< 55and increase by5(%)if cpuUtilisation>85.1

Like the policy engine itself,the manageability adaptor used to interface the engine with the server simulator was implemented as a sub-class of ManagedResource<>—Fig.5. The policy engine was con?gured to manage remotely a server simulator running a high-priority’premier’service and a lower-priority’standard’service.The two services handled simulated user requests with normally-distributed CPU utili-sation and exponentially-distributed inter-arrival time.Fig.6 shows the change in the system parameters when the request inter-arrival time of the two services was varied to simulate different workloads,and the policy engine was con?gured to implement the policy described earlier in this section:

a Both services are lightly loaded(5000μs request inter-

arrival time)and have the minimum amount of CPU allocated(i.e.,15%each).

b The load increases for the standard service,and its allo-

cated CPU is increased by the policy engine accordingly.

c For a brief perio

d of time,th

e standard service uses its

allocated CPU completely;no requests timeout though as its CPU allocation is increased swiftly.

d Th

e premium service workload starts to increase,and the

policy engine grows its CPU allocation.Accordingly,the standard service starts to get less CPU.

e As the workload for the premium service peaks and the

policy engine schedules additional CPU capacity for this service,the standard service is allocated insuf?cient CPU and some of its client requests time out.2

f The inter-arrival time for the premium service increases,

and some of the CPU capacity allocated to it during the previous time interval is re-deployed by the policy engine to the standard service.No more requests time out.

g Under constant workload,the CPU allocation is mostly

stable.

h To explore the role of the hysteresis,we replaced

the hysteresis term in the policy action with HYSTERESIS(service.cpuUtilisation,80,80),

basically eliminating the hysteresis.This led to signi?cant oscillations in the CPU capacity allocated to 1The HYSTERESIS(val,lower,upper)operator used to achieve this behaviour returns-1,0or1if val

2Requests time out after spending T=5s in a service request queue.

the services.The reinstatement of the original policy after this time interval brings the system back into a stable state.

The policy evaluation period was set to3seconds for this experiment,so that the system could self-adapt to the rapid variation in the workload of the two services.This allowed us to measure the CPU overhead of the policy engine,which was under1%with the engine service running on a1.8GHz Windows XP machine.In a real scenario,such variations in the request inter-arrival time are likely to happen over longer intervals of time,and the system would successfully self-con?gure with far less frequent policy evaluations.Note also that since the policy engine service is implemented as a managed resource,its policy evaluation period can be adjusted by another policy engine instance,so that it stays in step with the rate of change in the request inter-arrival time—a scenario that we are in the process of experimenting with.

VI.C ONCLUSION

This paper described the SOA-based implementation of the general-purpose autonomic architecture originally introduced in[13].Based on previous commercial work[11],[12]and on recent advances in autonomic computing[16],[23],[26],our implementation can be used to build policy-based autonomic systems out of non-autonomic ICT resources.Experimental work was carried out to validate the effectiveness of the implementation in an application to allocate server capacity to services of different priorities and varying workloads.The ex-perimental results showed that our general-purpose framework could perform the planned management task successfully,and similarly to a dedicated commercial system for data-centre resource management[8],[11].However,unlike the commer-cial resource-management system,our novel approach has the unique ability to handle resources whose types are unknown at implementation and deployment time,therefore enabling the cost-effective development of autonomic solutions across a broad variety of application domains.

Work is in progress to extend the prototype policy engine with operators supporting the implementation of“utility func-tion”policies[29],and with functionality that takes advantage of the noti?cation mechanism permitted by the system model used for its con?guration.Additional case studies will be carried out to validate the applicability of the framework to a broader spectrum of use cases related to data-centre resource management in the?rst instance,and to other application domains in the longer term.

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肿瘤的免疫逃避 正常机体每天有许多细胞可能发生突变，并产生有恶性表型的瘤细胞，但一般都不会发生肿瘤。对此，Burner提出了免疫监视学说，认为机体免疫系统通过细胞免疫机制能识别并特异地杀伤突变细胞，使突变细胞在未形成肿瘤之前即被清除。但当机体免疫监视功能不能清除突变细胞时，则可形成肿瘤。 然而，尽管机体有如此多样的抗肿瘤免疫效应，但肿瘤仍可在体内发生、发展，并且随着肿瘤的增长，肿瘤细胞能产生一些细胞因子。这些因子可以诱导机体产生一些抑制免疫的细胞，并诱导机体产生体液抑制因子，从而抑制抗肿瘤的免疫功能。肿瘤细胞自身也可以分泌一些具有免疫抑制作用的产物，侵犯局部转移的淋巴结，导致机体局部乃至全身的免疫功能低下。提示肿瘤具有一系列的免疫逃避机制来对抗机体的免疫系统。 1、肿瘤抗原调变作用 在肿瘤免疫过程中，T细胞的免疫功能起着关键性的作用。可溶性肿瘤抗原经抗原提呈细胞（APC）摄入后经加工处理，然后将处理后的抗原提呈给辅助性T细胞，后者通过分泌细胞因子促进杀伤性T细胞增殖，并产生特异杀伤作用。然而许多肿瘤细胞并没有死于机体的免疫杀伤，可以通过多种机制逃逸T细胞的免疫监视作用。 大多数肿瘤抗原的免疫原性很弱，不能诱发有效的抗肿瘤免疫应答。机体对肿瘤抗原的免疫应答导致肿瘤细胞表面抗原减少或丢失，从而肿瘤细胞不被免疫系统识别，逃避了机体的免疫攻击，这种现象称为“抗原调变”（Antigen Modulation）。这一理论在小鼠白血病细胞系中得到证实，该细胞系经抗体、补体处理后，丧失了细胞表面的胸腺白细胞抗原（TL抗原）。抗TL抗原的抗体与肿瘤细胞结合后，细胞表面出现斑点样的抗原分布改变，以后该抗原逐渐消失。在其他实验中也发现抗肿瘤抗体导致肿瘤抗原消失的类似现象。将肿瘤细胞与特异抗体或者细胞毒性淋巴细胞（CTLs）共同培养，也可以迅速诱导肿瘤抗原的丢失。抗原调变这一现象在生长快速的肿瘤普遍存在，抗原调变可由于细胞自身的机制或抗原抗体复合物脱落所致。这种肿瘤细胞表面抗原丢失仅反映肿瘤细胞表型的改变，经调变的细胞再次进入原宿主，抗原将再次出现，并重新诱发抗体产生。

RCEP区域自贸区协议全文第8章_附件3_中文版

第八章附件三 专业服务 一、每一缔约方应当与其领土内相关机构进行协商，以寻求确认两个或两个以上缔约方共同有意就与承认专业资质、许可或注册有关的问题开展对话的专业服务。 二、每一缔约方应当鼓励其相关机构与另外一个或多个缔约方的相关机构开展对话，以期承认专业资质并为许可或注册程序提供便利。 三、每一缔约方应当鼓励其相关机构与另外一个或多个缔约方的相关机构在有共同利益的专业服务部门中相互承 认专业资质，许可或注册方面的任何形式的安排进行谈判。 四、每一缔约方应当鼓励其相关机构在制定与承认专业资质、许可和注册相关的协定时，考虑与专业服务相关的协定。 五、如有可能，一缔约方可以考虑基于外国服务提供者在其本国获得的许可或得到承认的专业机构会员身份，无需进行进一步的书面审查，来： （一）采取步骤，实施临时或针对具体项目的许可或注册制度；或者 （二）如适当，授予此类许可或者注册。 一旦外国服务提供者符合本地适用的许可要求，此类临时或有限的许可制度不得以阻止该外国服务提供者获得本 地许可的方式实行。 六、为便利第一款至第三款所指的活动，每一缔约方应当鼓励其相关机构在共同同意的领域内致力于制定共同可 接受的专业标准和准则，可以包括:

（一）教育； （二）考试； （三）经验； （四）行为和道德规范； （五）专业发展和再认证； （六）执业范围； （七）本地知识；以及 （八）消费者保护。 七、应另一缔约方请求，被请求缔约方应当在可行的情况下，提供与专业服务提供者获得许可和认证的标准和准则相关的信息，或者提供与合适的监管机构或者其他机构相关的信息，使相关缔约方就此类标准和准则进行咨询。 八、每一缔约方应当鼓励其相关机构在适当的情况下，在制定相关专业的一般标准和准则时参考国际框架。 九、缔约方可通过服务和投机委员会定期审议本附件的执行情况。