Reducing Runtime Complexity of Long-Running Application Services via Dynamic Profiling and Dynamic Bytecode Adaptation for Improved Quality of Service

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1 Reduing Runtime Complexity of Long-Running Appliation Servies via Dynami Profiling and Dynami Byteode Adaptation for Improved Quality of Servie ABSTRACT John Bergin Performane Engineering Laboratory University College Dublin Dublin, Ireland We present a transparent optimisation framework for automatially improving run-time performane of omponentbased enterprise appliations. Run-time performane is improved by automatially identifying and dynamially swithing to an optimised but funtionally equivalent program flow, for a speifi transation type. More preisely, one or more appliation omponents an have both stati and dynami inputs, where the former and its output remain mostly fixed from invoation-to-invoation, while the latter is unonstrained. Through dynami profiling and run-time adaptation, an optimised program flow is derived, that allows only future exeution of program points that operate on dynami inputs and not stati inputs. During exeution, a target appliation is instrumented to swith to an optimised program flow. Additional instrumentation ours to profile key program points that may invalidate an optimised program flow. If these points exeute, then the appliation reverts bak to its pre-optimised program flow. We evaluate and demonstrate how run-time performane for a typial thre-tired enterprise system an be improved for frequently aessed data that remains mostly stati. For the appliation studied we showed a 49% marked improvement in the number of users servied per seond. Categories and Subjet Desriptors D.3.4 [Programming Languages]: Proessors - Optimisation, Code generation, Run-time environments ; D.2.9 [Software Engineering]: Management -Life yle ; D.2.8 [Software Engineering]: Metris -Proess metris The presented work is funded by Enterprise Ireland Informatis Initiative 2004 Permission to make digital or hard opies of all or part of this work for personal or lassroom use is granted without fee provided that opies are not made or distributed for profit or ommerial advantage and that opies bear this notie and the full itation on the first page. To opy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speifi permission and/or a fee. ASE Workshop on Automating Servie Quality, November 2007, Atlanta, Georgia, USA Copyright 2007 ACM ISBN: /07/11...$5.00. Liam Murphy Performane Engineering Laboratory University College Dublin Dublin, Ireland liam.murphy@ud.ie General Terms Performane, Profiling, Cahing, Measurement Keywords adaptation, Optimisation, Cahing, Profiling, Objet Cahing, Java Language 1. INTRODUCTION Large-Sale Enterprise Systems (LSES) typially exeute as managed Objet-Oriented (OO) programs on High-Level- Language Virtual Mahines (HLLVM) [16]. HLLVM use Garbage Colletion (GC) [9] to automatially dealloate unused run-time objets. GC offers numerous software engineering benefits, but often inurs run-time penalties of poor performane, during periods of redued memory availability [9]. OO languages enourage highly modularised programs, that result in large numbers of objet alloations and short lived method invoations, plaing inreased demands on GC. In addition, LSES often leads to exessive run-time ativity for simple funtionality. Suh omplex run-time behaviour arises from ineffiient oupling of heterogeneous, off-the-shelf omponents, ausing repliated movement and manipulation of system data during run-time [18]. Furthermore, due to the long-running nature of LSES suh ineffiient run-time exeution persists aross repliated invoations of idential user interations. Ineffiient program behaviour is beoming inreasingly diffiult to asses [18] and even more problemati to remove, due to a rise in dynami, distributed, omponent-based software engineering. Component-based frameworks (CBF), suh as Enterprise Java Beans (EJB), are being inreasingly adopted for building LSES. CBF yield numerous software engineering benefits, but inur run-time penalties of poor performane. This is due to an inreased reuse and oupling of blak-box omponents. Run-time modifiation of system omponents (dynami lass loading) and hanging ontexts (user s state) typially renders initial optimisations to prevent redundany obsolete. Cahing is key to preventing repeated run-time ativity for idential events. However, designing an effiient appliation independent ahing system for LSES is espeially diffiult due to their inherent heterogeneity. Cahing strategies used in appliation servers and supported middleware are too rudimentary to observe unneessary exeution of (a). fine grained events

2 and (b). program flow spanning multiple frameworks, leading to self-ontained and loalised ahing behaviour. In addition, ahing is ontext sensitive and requires multiple ahe types to servie varying appliation workloads and appliation-speifi knowledge to effiiently ahe program behaviour. Knowing whih program points reeives no further exeution is typially based on a simplified understanding of global omponent-to-omponent run-time ontexts. Suh oarse-grained ahing misses out on opportunities for fine-grained (method-to-method) ahing opportunities. We present a framework that an identify opportunities for finegrained ahing of multi-tiered enterprise appliations at varying levels of granularity that are loal and or distributed. Our framework makes the following ontributions: (a). automati elimination of redundant exeution (no progammer annotataion is needed), (b). redundany elimination is dynami and an adapt to a hanging system, (). our solution is appliation indenpendent and portable aross Java Virtual Mahines (JVM), (d). finally our framework an detet end-to-end redundany, i.e. aross distributed JVM. 2. BACKGROUND: OBJECT ALLOCATION The Java Virtual Mahine (JVM) [16] defines the following data types: primitive data types, referenes and objets, where objets are omposed of primitive and referene types. Primitive data types inlude: short, byte, int, double, long, boolean, double, float and return-address. Referene values point to objets, and objets are either an instane of a lass or an array. Three data storage areas are defined for the JVM - global, loal and operand. Heap: Global storage, also ommonly referred to as the heap is where objets and arrays are alloated. Heap alloations are ostly, sine objets and arrays are not expliitly dealloated. An automati memory manager known as the garbage olletor is invoked at speifi periods of time (depending on the olletion algorithm) to find all reahable objets. Those that are not reahable from global root objets are marked for olletion, dealloated and free up heap spae. Compation based olletors will redue heap fragmentation by organising heap objets into a non-fragmented ontiguous blok of objets. The heap memory does not have to be ontiguous but a defragmented heap results in fewer ahe misses and page faults, that greatly effet program performane [5, 12]. In addition, aess to heap objets require objet loks, that are ostly espeially given the large number of objets live at any time in the heap. Garbage Colletion Performane: Hertz et al. demonstrate the impat automati memory management has on program performane. The authors onlude that to math the performane of an expliit memory managed program (i.e. where the programer expliitly dealloates objets) an Appel-style generational olletor with a non-opying mature spae and a heap size five times greater is required [9]. Objet ahing frameworks [10, 6, 13] maintain their ahe on the heap, these ahes an be large and often ontain un-referened objets. Managing ahed data plaes further demands on the garbage olletor and it also redues the amount of available memory to a program, that an further degrade program performane if a large number of ahe misses our [5, 12]. Stak: Not all Java types are heap alloated: primitive data types, referenes, individual array elements, and proedure arguments are plaed on the stak. Some JVM implementations offer a run-time optimisation tehnique known as esape-analysis [4], whih fores stak alloation of normally heap alloated objets / arrays that do not esape the lifetime of the method in whih they are reated. When a method is invoked the JVM reates a new stak frame that enapsulates loal variables, operands and opodes for the entire life-yle of the method. One the method finishes exeuting, its staked data is simply overwritten and no garbage olletion is neessary. Foring stak alloation of normally staked objets an improve program performane greatly, but the potential to stak alloated objets is appliation dependant. 3. FRAMEWORK OVERVIEW We present a transparent optimisation framework for automatially improving run-time performane of omponentbased enterprise appliations and their underlying middleware. Run-time performane is improved by automatially identifying and migrating to an optimised (simplified) but funtionally equivalent ontrol flow for speifi user requests. More preisely, one or more appliation omponents an have both stati and dynami inputs, where the former and its output remain mostly fixed from invoation-to-invoation, while the latter is unonstrained. Through dynami profiling and run-time adaptation an optimised ontrol flow is derived that allows only future exeution of program points that operate on dynami inputs but not stati inputs. Our framework provides a transparent, appliation independent ahing tehnique for isolating and removing redundant program ativity resulting from user requests (end-toend transations) that results in idential output, or output that is largely stati from invoation-to-invoation. A transation defines both the ontrol flow (order of exeution - all paths) and data flow (data movement and transformation) resulting from a speifi user transation. A transation s run-time performane is improved by eliminating unneessary repetitive run-time behaviour, via on-line profiling and dynami byteode adaptation. We eliminate partial program exeution for speifi transation types based on redundant objet ativity, i.e. ostly objetion alloations, objet look ups, and objet modifiation for idential, repeated invoations. Optimisation is ahieved in 3 distint phases: (a) Profiling (b) Redundany Detetion and () Optimisation (dynami byteode adaptation). 3.1 Multi-threaded Call Path Profiling Call paths define program exeution at method entry and method exit. We extrat suh ativity by inserting byteode at the beginning and end of eah method points using the Java Virtual Mahine Tool Interfae (JVMTI) [11]. On method entry and exit points, the added byteode invokes a method to update a data struture used to reord the order of method exeution. A direted graph maintains a olletion of all paths for eah thread. Eah all path maintains the state of its all stak as a parent-hild node relationship, where eah node is assoiated with frequeny information and other run-time metris. Instrumentation ours by first speifying during run-time one or more root method(s) for profiling. Our framework identifies further methods to instrument by parsing a root method s byteode and statially

3 analysing the flow of exeution from lass to lass, expanding the number of edges and nodes as the analysis proeeds. The analysis will ontinue to a user defined depth or until the omplete transitive losure of the all path is identified, i.e. no further method invoations are made. One profiling is initiated, all paths are not olleted until an appliation s thread enters a root method. If a method hosen for profiling is a hild proess of an exeuting thread, profiling will not begin until an instrumented root method is re-invoked. 3.2 Objet Traking As well as apturing a program s flow of exeution, additional probes are instrumented to reord suh objet ativity as: instanes of objet alloations, values passed to and from method invoations, and objet modifying events. To determine if an objet s value is hanging, a tostring method is added to every lass loaded by the JVM. When an objet is passed to and from a method a hash of an objet s tostring value is ompared to previously hashed values for the urrent method invoation. If they math then the objets value has not hanged, but if they differ then an attribute s assoiated with this node is inremented, stating that the objet value has hanged. Additional byteode analysis is done to derive the ontrol flow of a method, suh analysis determines if an objet is esaping its enapsulated method. Depending on a method s ontrol flow an objet an be both esaping and non-esaping. If an objet esapes to a method M and M alters objet O, then an edge of type E w is plaed from M O. 3.3 Ativity Graph We introdue the notion of a run-time abstration alled the Ativity Graph, that aptures the relationship between methods and objets. We struture these relationships as a direted graph G = (N m, N l, N r, Ng, E r, E a, E m, E w), where N m represents the set of method all(s), and a program statement in a method that alloates, writes or referenes an objet - statement(n m). At least one invoation of N m must be made for G to exist. N l represents the set of heap alloated objets loal to a method. N g represents the set of heap alloated objets that are globally aessed by one or more methods, or objets passed to and from methods. N r represents the set of objet referenes from loalto-global objets or from global-to-global objets, and from loal-to-loal, but not loal-to-global referenes. E m represents the set of method edges, If x y ǫ E m, then x,y ǫ N m. E r represents the set of edges referening objets, If x y ǫ E r, then x ǫ N r y ǫ N l N g N r. E a represents the set of alloation edges, If x a y ǫ E a then x ǫ N m N l N r y ǫ N l N g N r x ǫ N m N g N r y ǫ N g N r. E w represents the set of edges that write to objets, If x w y ǫ E w, then x ǫ N m N l N r y ǫ N l N g N r x ǫ N m N g N r y ǫ N g N r. Given a reahable node o ǫ N g N r N l N m, the set of reahable nodes O G that o points to diretly and indiretly is determined by traversing the edge(s) leading from o until no further edges an be traversed. An edge between nodes an denote one of the following transation types: x y, ǫ E r, E m node x points to node (method / objet) y, x a y, ǫ E a denotes that x alloates an objet y, and x w y, ǫ E w states that x alters y. We formalise the transition between nodes as: Definition 1: Let x, y ǫ N m N g N l N r. The sequene of all nodes leading from x is denoted by x +p y, where x = x 0 x 1... y terminating at node y where no further edges an be traversed. We formalise the funtion used to determine the full transitive losure of all nodes leading from x as, let x ǫ N m N g N l N r., and the set of reahable nodes pointed to by x is: FullTransitiveClosure(x) = {y x +p y} If an appliation omponent x ontinuously reeives and returns the same objet y, then x is hosen as and initial node in a larger graph for potential redundany elimination. We apply the funtion F ullt ransitiveclosure to x, to determine the omplete transitive losure from x. The returned set of nodes is traversed, searhing for the initial point of exeution where the objet y is reated or where it s dependenies are reated. To understand how this is ahieved, the set of global objets needs to be further defined. The set of global objets N g ontains subsets of different global objet types. Eah sub-type differs in how objets behave within method invoations. It is important to know where objets are passed to and from. These additional properties are only required for global objets. Listing 1 illustrates using Java ode how suh sub-types are reognised and behave. S1 is the only objet not to esape its enapsulated method. The string new is forwarded to method M2 for proessing, we refer to this objet as globally forward (GF) moving. If the objet is passed from method-to-method and all edges pointing to the objet are of type N r, then the objets value is hard-oded into a method s byteode. Objet S3 is referred to as a globally forward and bakward moving, sine it aepts a value returned from a method invoation. Similarly S4 is referred to as a globally forward bakward and returned objet. Finally, the string equal is returned from M1. By knowing both an objets movement pattern outside the method in whih it was reated and all edges leading to and from an objet, one an determine if O s value remains onstant. for its entire life-yle for eah time it s instantiated. Figure 1 should help to illustrate the onnetion between the ativity graph and soure ode. 3.4 Redundany Detetion Eah node in the graph is augmented with two frequeny metris: i and s, where i ounts the number of node invoations, and s ounts the number of state hanges for a node (i.e. has an objets value hanged, is a method pulling the same value, is a method s aller pushing the same value). We look for a steady state to our in G, whereby the frequeny values for eah node are inreasing, and the state values remain onstant. We have found from analysing the

4 publi Objet M1() { String S1 = "s1"; M2(new String("new")); String S3 = "s3"; S3 = M3(S3); String S4 = "s4"; S4 = M3(S4); if(s4.equals("s4")) return S4; if(s3.equals(s4)) return "equal"; } Listing 1: Method M1 - Objet Sope S1 M2 (S2) M0 M1 (S4),( SR ) (S3),(S4)* M3 r w w S2 S3 S4 run-time Ativity Graph of numerous tightly oupled threetiered systems, that a large subset of a transation s nodes beome redundant after their initial invoation. By redundant we mean, that the node only serves to at as a support node for suessive invoations of larger transations to repeatedly pull the same data from objets, or from a database. This form of repeated objet ativity is extremely ostly. Our framework allows for on-the-fly definitions of redundant behaviour, by leveraging the type of information provided by the Ativity Graph. For example, we onsider method m to be the first node in a redundant data pulling senario, that is part of a transation U i, suh that U i G and m ontinues to retrieve objet o, whose value rarely hanges. We then hek to see if all nodes leading from m in U i have reahed a steady state, suh that, i,sequal(u i,s), where U ǫ N m N g ǫ N l. 3.5 Cahing and Byteode adaptation If the set of nodes in U [N 1,.., N n] have reahed a steady state, then U is hoosen for redundany elimination. If node N n ontinues to pull stati data from a repository (objet or database), and nodes N 1,., N n 1 only serve to instantiate an instane of N n then we refer to the set of redundant nodes (N 2,.., N n 1) in U i as U ir and the optimised set of paths for the next invoation of U i as U io = U U ir. The edge N m leading from N 1 to N 2 is removed, thus preventing further exeution of the redundant path. The objet returned to N 1 is stripped of it s primitive data types and their values are hard-oded into N 1. On future invoations of N 1 the stak alloated hard-oded data is returned. Alternatively there ould exist a path N 1,..., N n, N 1 whih passes an objet O to N 2 where N 2 and suessive nodes up to N n1 ontinue to reapply the same operations to O. One the altered objet reahes N n a new operation is always applied to O. In this ase the set of redundant path is N 2,..., N n 1. Byteode is added to rediret the all from N 1 N 2 to N 1 N n with the ahed objet O (taken from the output of N n 1) is sent diretly to N n. 3.6 Disussion We improve on standard objet ahing frameworks by observing the following program behaviour: program exeution based on a request, response olletion of method invoations, where a method often requests a speifi data value, that results in objets being alloated, populated and Figure 1: Ativity Graph for Method M1 returned to their aller. Objets serve only as entities to enapsulate and transfer sets of primitive data types between method invoations, upon where the aller strips an objet of it s primitive data types for further program manipulation. If this type of request, response exeution repeats, and repeats returning the same primitive data values, then suh ativity should be ahed. Current Plain Old Java Objet (POJO) ahing frameworks an observe (in some situations) and ahe suh ativity by preserving the final objet returned from a request, response senario [10, 6, 13, 14]. However, multiple instanes of suh ativity our frequently during exeution, and sine the ahed objet is still heap alloated, program performane still suffers from garbage olletion yles, objet loks, ahe misses and page faults [12]. We redue the possibility for suh performane degradation by stripping the would be ahed objet of its primitive data types and hard-oding their values into a method s body. On it s next invoation the data values are stak alloated. In doing so, we must ensure that no external objets or methods on the same thread or onurrent threads require the would be ahed objet s values. We perform esape analysis [4] to ensure that an objet s life-yle does not esape the method in whih it s reated. If an external method or thread of exeution strips a primate data type from the objet, the external method / objet is also hardoded with the primitive onstant. 4. RELATED WORK The following related work also seeks to redue the runtime omplexity of managed languages, by preventing repeated exeution of program events that ontinue to repeated idential behaviour aross invoations. However, despite the usefullness of eah approah, eah has a limiting fator for removing redundant run-time exeution in multitiered enterprise appliations. In-lining, Salar replaement: The JVM an perform a number of optimisation proedures to improve the performane of aessing and manipulating heap-alloated

5 objets. Aessing fields held in objets often involves levels of address indiretion, individual objet field aess suffer small overhead that an aumulate. To ope with reation overhead, if profiling indiates a partiular objet is frequently alloated, then the ode for the heap alloation and objet initialisation an be inlined. Further optimisations inlude salar replaement [7] that an redue objet aess delays, under speifi irumstanes. This optimisation replaes an objet s field with a salar value. For example the program statement objet.value = 10; is replaed with int s = 10 and printing suh values moves from print(objet.value) to print(s). Speialisation, Multiversioning: Some virtual mahines employ a more sophistiated and aggressive dynami optimisation tehnique known as Speialisation [20], where hot-portions of program exeution are heavily optimised by hard-oding frequently ourring values that depend on them bak into a method s byteode. In [8] the authors quantify the performane improvement gained from appliations suitable for speialisation, they demonstrate that program exeution an have marked speedups of five fold. Despite suh powerful optimisation potential, speialisation tehniques are not inluded in high-level-language Java Virtual Mahines, sine urrent speialisation tehniques are staged, they require multiple off-line preproessing runs and also require the need for programr annotation [3, 15]. [19] presents a dynami transparent run-time speialiser that is integrated with the Just-in-Time (JIT) ompiler of the Jike s Researh Virtual Mahine (JRVM). This work is losely related to our efforts. However, the speialiser presented is speifi to JVM implementation. The framework requires monitoring of the heap (to detet when objet values hange) that an t be done using a Java Virtual Mahine Tool Interfae (JVMTI) [11] based approah. Our framework profiles program exeution to detet a hange in objet values. Furthermore, the authors use the Arnold-Ryder sampling framework [2], we use byteode instrumentation that aptures all instrumented program exeution rather than sampling programming points. Beause of this we believe that our framework an identify more points for potential ahing (redundany elimination / speialisation). A sampling profiler is not suitable for the appliations we intend to optimise, sine an update to a ahed objet may our one in thousands of program events, rendering hard-oded results returned for speialised events invalid. Redundany Elimination: Enterprise systems are ideal andidates for potential ahing, as they frequently re-exeute idential program exeution for popular user requests. Trofin et al. [21] automatially removes redundant Enterprise Java Bean (EJB) ontainer servies for ontextually bounded omponent based systems, leading to redued program ativity. But the optimisation framework requires that the underlying appliation framework desribe all possible transation types and boundary onditions. In [17] a novel optimisation tehnique is presented, that disovers opportunities for replaing a set of equivalent objet instanes with a single representative objet. This type of optimisation reports similar results as program speialisers. Enterprise Cahing Frameworks: There are many advantages to using objet ahing for LSES, but there are many disadvantages to urrent ahing systems [10, 6, 13, 14]. Current appliation ahing frameworks are appliation dependant and require ontinuous onfiguration. In addition, multiple ahing systems are often need to maintain effiient ahing of one LSES, due to the appliations heterogeneity and distributed system omponents. Furthermore, suh ahing frameworks heap alloated ahed data; heap size an grow unaeptably if there a ahe is ineffiiently managed. 5. EVALUATION This setion presents an empirial evaluation of our urrent framework. We measure its ability to detet and eliminate redundant program behaviour for a three-tiered JEE appliation. We measure the appliations performane and its effet on system behaviour for a non-optimised and an optimised run where redundant behaviour is removed. 5.1 Sample Appliation A JEE Enterprise banking appliation, Duke s Bank [1], was used to demonstrate our framework s performane ahing apabilities. Duke s Bank is a sample JEE appliation that allows ustomers to perform banking operations online. Suh operations inlude: aessing aount histories and performing banking transations. Duke s Bank is designed as a typial three-tiered enterprise appliation, with a web, appliation and a database tier. The appliation ontains three main business entities: ustomer, aount and banking transation. Eah of these business entities is implemented by a separate Entity bean in the appliation tier and by a orresponding table in the relational database. The appliation is designed so that all Entity beans are aessed via Stateful Session beans. Conforming to the EJB speifiation, instanes of Stateful Session EJBs maintain their state for the entire duration of a lient session. Thus, in Duke s Bank, the Entity bean instanes must also remain available for the entire duration of the lient s session. 5.2 Experimental Environment The following distributed testbed was used to arry out our experiments. Three mahines were used. MySQL and JBoss appliation server ran on separate but idential Intel Pentium IV 2.4Ghz based mahines that had 512MB or RAM and both ran Linux kernel as their supporting operating system. A third and idential mahine ran Jmeter to load both the database and appliation server with a syntheti real world workload. The workload repliated five users logging in every seond, heking their bank aount balanes and then logging off. This workload was repeated for a duration of a half hour. 5.3 Results and Disussion The results presented in table 1, depited the differene in run-time performane for an optimised and non-optimised instane of Duke s Bank responding to a syntheti workload of twenty users logging in every seond, heking their bank aount balanes and then logging off. Our framework deteted repeated invoations of idential program ativity resulting from multiple user s requesting their aount information. The initial point in the redundant path begins at the Dispather.doGet( aountlist ) method, where a request for aounlist is made to return a user s aount balane. The full transitive losure of FullTransitive-

6 Duke s Bank (DB) DB DB (optimised) I Objet alloations (#) 826, ,260 69% Method invoations (#) 189,729 78,008 59% Garbage olletions (#) % Average GC time (ms) 27,311 19,986 27% Peak heap size (MB) % Response time (ms) 1, % Users per seond (#) % Table 1: Improved (I ) runtime performane Closure(Dispather.doGet()) returns a set of nodes that desribe the relationship between method and objet ativity, we apture this information in an ativity graph and begin to profile a hange in state for eah event in the graph. The ativity graph for nodes leading from Dispather.doGet() show that N m, N g nodes resulting from alls to Aount- ControllerBean, AountBean and alls to the database to populate AountBean have all reahed a steady state, i.e. i is inreasing for all nodes and s remains onstant. Sine a steady state has been reahed, Dispather.doGet() is hosen for ahing (redundany elimination). A ontrol statement is added to Dispather.doGet() to immediately return the ahed value (now a set of one or more loal variables in Dispather.doGet()) iff the request is of type aountlist and for a user who s aount balane has not hanged. Table 1 shows a 69% and 59% redution in objet alloations and method invoations for the optimised Duke s Bank instane. This in turn leads to a derease in: garbage olletions, heap size and response time. As a result, the number of users servied per seond inreased by 49%. We leave automati detetion of program ativity that updates a user s aount balane and renders ahed data inaurate as future work. 6. CONCLUSIONS AND FUTURE WORK We present a transparent, appliation independent ahing tehnique for isolating and extrating a user s end-to-end transation. A transation s run-time performane is improved by eliminating unneessary repetitive run-time behaviour, via on-line profiling and dynami byteode adaptation. We eliminate partial program exeution for speifi transation types based on redundant objet ativity, i.e. ostly objetion alloations, objet look ups, and objet modifiation for idential, repeated invoations. The optimisation senario presented dealt with a set of single threaded events at eah tier. Future work inludes: addressing the robustness (sensitivity to hange in ahed data), orretness (verifing program orretness after adaptation) and performane overhead of our framework for a real world appliation under a realisti workload senario. 7. REFERENCES [1] Dukes s bank. 3-fs/do/Ebank.html. [2] M. Arnold and B. Ryder. A framework work for reduing the ost of instrumented ode. Programming Languages Design and Implementation, June [3] M. Arnoldand, S. Finkand, D. Groveand, M. Hindand, and P. F. Sweeney. Adaptive optimisation in the Jalapeno JVM. Objet-Oriented Programming, Systems, Languages, and Appliations, Otober [4] B. Blanhet. 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