A Methodology for Analyzing Availability Weak Points in SOA Deployment Frameworks Jing Luol,Ying Lil,John A Pershing2,Lei Xie3,and Ying Chenl 1 IBM China Research Lab,China jingluo,lying,yingch@cn.ibm.com 2 IBM T.J.Watson Research Center,Hawthorne,NY 10532,USA pershing@alum.mit.edu 3 Department of Computer Science,Nanjing University,China xielei@dislab.nju.edu.cn Abstract-The fundamental characteristics of SOA,loose To ensure availability,redundancy-based High Availability coupling and on-demand integration,enable organizations to (HA)solutions are the primary approach,including clustering seek more flexibility and responsiveness from their business IT [2],hot-failover [3],recursive restartability [4],and Redundant systems.However,this brings challenges to assure QoS,especially availability,which should be considered in an integrated way in Array of Independent Disk (RAID).However,these solu- an SOA environment.Traditionally,availability is measured for tions are usually expensive and their cost,capabilities and each IT resource,but within SOA environments,rather than implementation difficulties also vary greatly.Thus,it becomes being considered individually,availability should be analyzed quite difficult to plan HA solutions in a cost-effective manner. from an end-to-end view from both business and IT perspectives. Traditionally,IT architects rely on experience to decide which In this paper,to address the availability problem of SOA,we propose a methodology that analyzes availability weak points HA solutions should be applied to which IT resources with in SOA deployment frameworks,leveraging workflow defini- what degree of redundancy.However,this experience-based tions that specify availability requirements at business level. approach to HA is difficult to apply to SOA environments, This methodology includes an effective way to calculate high- because the large number of involved IT systems and the com- availability enhancement recommendations for a given SOA plex relationships among them are beyond the comprehension deployment topology with near-minimum cost,while meeting the business-level availability requirements.A prototype has of most humans. been implemented as an extension to IBM's SOA deployment Moreover,even if all single points of failure have been framework.Its efficiency and performance are analyzed here. eliminated,some of the (redundant)IT resources still may not exhibit the necessary availability level to satisfy the I.INTRODUCTION requirements of the business,so it may be necessary to intro- The Service-Oriented Architecture (SOA)provides on- duce even more redundancy in order to meet the availability demand integration capabilities by loosely composing one requirements.Hence,the key to delivering a cost-effective HA architecture is to determine the right HA level for each IT or more services.This loose coupling enables SOA to offer system,based on the trade-off between its outage loss and clear benefits [1]and opens up new opportunities for orga- redundancy cost:too little redundancy could result in costly nizations to become more flexible and responsive.However, outages,and too much could be an expensive waste. this architecture brings challenges to Information Technology (IT)management and complicates Quality of Service (QoS) In this paper,we define an availability weak point as an IT resource that is not providing sufficient availability to meet measurement precisely because of its loosely coupled nature:it is frequently difficult to determine which systems and services current business requirements,but for which we can provide a cost-effective HA enhancement in order to meet these avail- are contributing to an SOA service,and how they may be ability requirements.Therefore,to apply HA solutions [2][3] failing to deliver the required quality of service.Traditionally, over SOA environments in a cost-effective manner,identifying availability is measured for each IT resource.But within and analyzing availability weak points is the starting point.In SOA environments,rather than merely considering the avail- ability of individual IT resources,one must take an end-to- a previous article [5].we proposed a workflow-based weak- end viewpoint.Moreover,the relationships between business point analysis methodology to address this challenge.The methodology determines which deployed IT resources need workflows and the supporting IT resources are complex and dynamic.For example,one service can be invoked by several to have their availability enhanced,and to what extent,in business workflows.while each business workflow usually order to satisfy the business-level availability requirements while keeping the overall cost close to the minimum.In this invokes multiple services. paper,we further refine and evaluate our methodology,and IManuscript received 1 April 2008,revised 29 October 2008.accepted 17 analyze the efficiency and performance of the prototype that February 2009.The Associate Editor coordinating the review of this paper implements it as an extension to IBM's SOA deployment and approving it for publication was J.P.Martin-Flatin. framework.A Methodology for Analyzing Availability Weak Points in SOA Deployment Frameworks Jing Luo1 , Ying Li1 , John A Pershing2 , Lei Xie3 , and Ying Chen1 1 IBM China Research Lab, China {jingluo, lying, yingch}@cn.ibm.com 2 IBM T. J. Watson Research Center, Hawthorne, NY 10532, USA pershing@alum.mit.edu 3 Department of Computer Science, Nanjing University, China xielei@dislab.nju.edu.cn 1Abstract— The fundamental characteristics of SOA, loose coupling and on-demand integration, enable organizations to seek more flexibility and responsiveness from their business IT systems. However, this brings challenges to assure QoS, especially availability, which should be considered in an integrated way in an SOA environment. Traditionally, availability is measured for each IT resource, but within SOA environments, rather than being considered individually, availability should be analyzed from an end-to-end view from both business and IT perspectives. In this paper, to address the availability problem of SOA, we propose a methodology that analyzes availability weak points in SOA deployment frameworks, leveraging workflow defini￾tions that specify availability requirements at business level. This methodology includes an effective way to calculate high￾availability enhancement recommendations for a given SOA deployment topology with near-minimum cost, while meeting the business-level availability requirements. A prototype has been implemented as an extension to IBM’s SOA deployment framework. Its efficiency and performance are analyzed here. I. INTRODUCTION The Service-Oriented Architecture (SOA) provides on￾demand integration capabilities by loosely composing one or more services. This loose coupling enables SOA to offer clear benefits [1] and opens up new opportunities for orga￾nizations to become more flexible and responsive. However, this architecture brings challenges to Information Technology (IT) management and complicates Quality of Service (QoS) measurement precisely because of its loosely coupled nature: it is frequently difficult to determine which systems and services are contributing to an SOA service, and how they may be failing to deliver the required quality of service. Traditionally, availability is measured for each IT resource. But within SOA environments, rather than merely considering the avail￾ability of individual IT resources, one must take an end-to￾end viewpoint. Moreover, the relationships between business workflows and the supporting IT resources are complex and dynamic. For example, one service can be invoked by several business workflows, while each business workflow usually invokes multiple services. 1Manuscript received 1 April 2008, revised 29 October 2008, accepted 17 February 2009. The Associate Editor coordinating the review of this paper and approving it for publication was J.P. Martin-Flatin. To ensure availability, redundancy-based High Availability (HA) solutions are the primary approach, including clustering [2], hot-failover [3], recursive restartability [4], and Redundant Array of Independent Disk (RAID). However, these solu￾tions are usually expensive and their cost, capabilities and implementation difficulties also vary greatly. Thus, it becomes quite difficult to plan HA solutions in a cost-effective manner. Traditionally, IT architects rely on experience to decide which HA solutions should be applied to which IT resources with what degree of redundancy. However, this experience-based approach to HA is difficult to apply to SOA environments, because the large number of involved IT systems and the com￾plex relationships among them are beyond the comprehension of most humans. Moreover, even if all single points of failure have been eliminated, some of the (redundant) IT resources still may not exhibit the necessary availability level to satisfy the requirements of the business, so it may be necessary to intro￾duce even more redundancy in order to meet the availability requirements. Hence, the key to delivering a cost-effective HA architecture is to determine the right HA level for each IT system, based on the trade-off between its outage loss and redundancy cost: too little redundancy could result in costly outages, and too much could be an expensive waste. In this paper, we define an availability weak point as an IT resource that is not providing sufficient availability to meet current business requirements, but for which we can provide a cost-effective HA enhancement in order to meet these avail￾ability requirements. Therefore, to apply HA solutions [2][3] over SOA environments in a cost-effective manner, identifying and analyzing availability weak points is the starting point. In a previous article [5], we proposed a workflow-based weak￾point analysis methodology to address this challenge. The methodology determines which deployed IT resources need to have their availability enhanced, and to what extent, in order to satisfy the business-level availability requirements while keeping the overall cost close to the minimum. In this paper, we further refine and evaluate our methodology, and analyze the efficiency and performance of the prototype that implements it as an extension to IBM’s SOA deployment framework