Presentation Title SLA Decomposition: Translating Service Level Objectives to System Level Thresholds Thanks Dejan. Very glad to see my advisor karsten.

Presentation Title SLA Decomposition: Translating Service Level Objectives to System Level Thresholds Thanks Dejan. Very glad to see my advisor karsten here. Today I am going to present some early results of my work with Tyche project during the last 3 months. I would like to get feedback from you all. My talk is “SLA…” This is a jont work with 30 secs Yuan Chen, Subu Iyer, Xue Liu, Dejan Milojicic, Akhil Sahai Enterprise Systems and Software Lab Hewlett Packard Labs Company Confidential

Introduction Service Level Agreements (SLAs)
Presentation Title Service Level Agreements (SLAs) service behavior guarantees, e.g. performance, availability, reliability, security penalties in case the guarantees are violated The ability to deliver according to pre-defined SLAs agreements is a key to success SLAs management capturing the guarantees between a service provider and a customer meeting these service level agreements, by designing systems/services accordingly monitoring for violations of agreed SLAs enforcing SLAs in case of violations A SLA is a formal negotiated agreement between two parties. It is a contract that exists between customers and their service provider, or between service providers. It transcripts the common understanding about services, priorities, responsibilities, guarantee, etc. with the main purpose to agree on the level of service. For example, it may specify the levels of availability, serviceability, performance, operation or other attributes of the service like billing and even penalties in the case of violation of the SLA. Historically, SLAs have been used since late 80's by fixed line telecom operators as part of their contracts with their corporate customers. More recently, IT departments in larger enterprises have adopted the idea of using service level agreements with their customers, i.e. users in other departments within the same enterprise, to allow for comparing the delivered quality of service with the one promised, and potentially consider the alternative of outsourcing IT services to an external company. Company Confidential

SLA Management SLA Specification Design SLA Negotiation SLA Monitoring
Presentation Title SLA Specification Design Clients SLA Negotiation Applications Virtual resources Physical resources SLA Monitoring SLA Enforcement Company Confidential

Design Services/systems need to be designed to meet the agreed SLAs
Presentation Title Services/systems need to be designed to meet the agreed SLAs to ensure that the system/service behaves satisfactorily before putting it in production Enterprise systems and services are comprised of multiple sub-components each sub system or component potentially affects the overall behavior any high level goals specified for a service in SLA potentially relates to low level system components Traditional designs usually involve domain experts manual and ad-hoc costly, time-consuming, and often inflexible Company Confidential

Motivational Scenario
Presentation Title Virtualized data center on demanding computing application share resources using virtual technologies Scenario a 3-tier (Apache-Tomcat-Mysql) application SLO: average response time < 10 secs determine the percentage of CPU assigned to each VMs to meet the SLO with reasonable CPU utilization Today’s enterprise data centers are designed with on-demand computing and resource sharing in mind, where resources are pooled into a common shared infrastructure and enable applications to share computing resources using virtualization technology. Business can flex their computing resources based on needs. Let’s look at some scenarios in a virtualized data center. Consider a typical application is a 3-tier e-commerce service consisting of a web server, an application server and a database server. This figure shows the application’s average response time with three different COPU shares assigned to the virtual machine hosting the application server tier Gvien the slo of average response time less than 10 seconds, the configuration with CPU assignment of 20% fails to meet the SLO while the CPU assignment of 90% meets the SLO but the system is over-provisioned since CPU assignment of 50% is sufficient to ensure the SLO. One key task of designing such an application is to determine the resource requirement of each tier to meet high level SLOs while achieve high resource utilization 150 s Company Confidential

Problem Statement Presentation Title SLA Decomposition: given high level Service Level Objectives (SLOs), translate the SLOs to low level system thresholds The system thresholds are used to created an effective design to meet the SLOs determine resource allocation for each individual component determine software configuration SLOs monitoring and assessment SLA Decomposition Service Level Objectives (SLOs) response time throughput workload system metrics application attributes healthy ranges low level system thresholds Enterprise applications and services have to be designed to meet service level agreements or SLA Enterprise applications and services are typically comprised of a number of components or systems, which interact with one another in a complex manner. Each sub-system or component potentially affects the overall behavior of the service, and the high level goal. Any high level goal specified for a service in SLA potentially relates to low level system components. One of the key tasks during the design state is to ensure the service to behave satisfactorily before putting in production The problem we are trying to solve is the given an SLA, translate the service level objectives specified in the SLA to low level system thresholds Resource requirements of each component involved in providing the service, Configuration parameters for software Healthy ranges or bounds of low level metrics These low level thresholds can be then used to create an effective design and ensure the SLAs For example The system thresholds are used to determine how much and how many of the resources should be allocated to satisfy the proposed SLOs. For configure the software and for online SLA monitoring and assessment 60 sec Company Confidential

Presentation Title Challenges The decomposition problem requires domain experts to be involved, which makes the process manual, complex, costly and time consuming Complex and dynamic behavior of multi-component applications components interact with each other in a complex manner multi-thread/multi-server, various configurations, cache & optimization various workload different software architectures, e.g., 2- vs 3-tier, 3-tier Servlet vs 3-tier EJB different kinds of software components and performance behaviors, e.g., Microsoft IIS, Apache, JBoss, WebLogic, WebSphere; Oracle, MySQL Microsoft SQL server Impact of virtualization and application sharing, e.g., Xen, VMware granular allocation of resources environments are dynamic Different kinds of SLOs, e.g., performance, availability, security, … System administrators and experts normally apply their domain knowledge to implicitly map high level goals to lower level thresholds, ie. They use past experiences with specific applications this procedure involves manual intervention Automatically deriving and inferring low level thresholds from high level goals are difficult talks due to the complexity and dynamisms inherent in such systems. Enterprise applications and services are typically comprised of a large number of components, which interacts with one another in a complex manner, hardware and software are complex, cache, multi-threaded, The range of design choices in terms of middleware, shared infrastructures, software structures further complicates the problem. Virtualization technologies makes the decomposition even more challenging with the finer-grain resource allocation and more dynamic environments SLO requirements may involve performance, availability, security Our approach Effective: ensures that the overall SLO goals are met reasonably well Automated: eliminates the involvement of domain experts Extensible: applicable to commonly used multi-component applications Flexible: easily adapts to changes in SLOs, application topology, software configuration and infrastructure and workloads 90 sec Company Confidential

Presentation Title Goal Develop a SLA decomposition approach for multi-component applications, which translates high level SLOs to the state of each component involved in providing the service Effective: ensures that the overall SLO goals are met reasonably well Automated: eliminates the involvement of domain experts Extensible: applicable to commonly used multi-component applications Flexible: easily adapts to changes in SLOs, application topology, software configuration and infrastructure Company Confidential

Outline Problem Statement and Challenges Our Approach Validation
Presentation Title Problem Statement and Challenges Our Approach Overview Analytical Model for Multi-tier Applications Component Profile Creation SLA Decomposition Validation Related Work Summary and Future Work Company Confidential

Our Approach Presentation Title Combine performance model and component characterization to create decomposition model model the behavior of the service characterize the behavior of each component combine them to create decomposition model Given a service instance and SLOs, use the decomposition model to derive low level thresholds Create an effective design of the service to meet the SLOs based on low level thresholds While the high level SLA requirements may include performance, availability, security, etc., we currently focus on the performance goals and leave other SLOs as our future work . In this work, we propose an approach which combines performance model and component characterization to undertake SLA decomposition in a systematical way Our approach uses analytical models to capture the relationship between high level performance goals (e.g., the response time of the overall system and performance metrics of each component (e.g., average service time of each component) Build profiles characterizing per-component performance metrics as functions of resource allocation (e.g., CPU , memory) and configuration parameters ( e.g., max connections) With the analytical models and the component profiles, a decomposition model is derived. Some of the output are used for monitoring the systems and identify bottlenecks in case of SLOs violation 120 sec SLOs Performance Modeling Decomposition Component Profiling & Regression Analysis low level system thresholds Resource Allocation Configuration SLA monitoring Assessment Company Confidential

SLA Decomposition Decomposition
Presentation Title Performance modeling create analytical model that captures the relationship between each single component and the overall system performance. For example, given performance characteristics of each of the components in a 3-tier application and the workload characteristics , model g predicates the response time of the 3-tier applications. We propose a novel queueing network model for multi-tier e-business application Our model is sufficiently general to capture typical common used multi-tier applications with different application topology, configuration, and performance characteristics. vary the resource allocation and configuration parameters and collect detailed performance characteristics for each component apply statistics techniques such as regression analysis to derive profile functions for each component Profiling creates detailed profiles of each component which is independent of other components. A test system is put in place and then vary the resource allocated to the component and configuration parameters and collect detailed performance characteristics under each configuration We then apply statistics techniques such as regression analysis to derive the performance characteristics functions of each component Once we have the component profile and the model, the decomposition model is created by plug-in the profile to the performance model Given thigh level goals response time R< r and throughput X> x, find the set of CPU satisfying the following constraints, It’s somehow a reverse problem of traditional problem modeling The problem becomes a constraint satisfaction problem or optimization problems, various constraint satisfaction algorithms and optimization techniques can be used to solve such problems. Typically, the solution is underterministic and the solution space is large. Also, we are often interested in finding a feasible and reasonable solution. Enumerate the entire solution space to find the solutions. Other heuristic techniques can be used during the search. The optimization algorithms has yet to be investigate. If the high level goals or application structures change, reapply decomposition model Similarly, if the application is deployed to a new environment, regenerate a profile for new components in that environment. Further, assume we have resource availability (profile), and the decomposition model, given high level goals, we can apply the decomposition model for automatic selection of resources and for generation of sizing specifications. Next I am going to present the detailed implementations of modeling, profiling and decomposition of multi-tier applications in a virtual data center. 180 secs Decomposition given SLOs, R < r, X > x, find the set of cpu, mem, … n_clients, n_threads, s_cache g1(f1(cpuhttp,memhttp,n_clients),f2(cpuapp,memapp,n_threads),f3(cpudb,memdb,s_cache)) < r objective function, e.g. minimize (cpuhttp+ cpuapp+ cpudb) Company Confidential

Presentation Title Problem Statement and Challenges Our Approach Overview Analytical Model for Multi-tier Applications Component Profile Creation SLA Decomposition Validation Related Work Summary and Future Work Company Confidential

Modeling Multi-Tier Applications
Presentation Title Multi-tier architecture Closed multi-station queuing network general multi-station queue G/G/K representing each tier and the underlying server arbitrary service time distribution and visit rate to each tier capture multi-thread/multi-server structure and concurrency handle realistic user session based interactions Si: mean service time Vi: visit rate Ki: number of stations N: number of users Z: think time Modern web applications and e-business sites are usually structured into multiple logical tiers, each tier provides certain functionality to its preceding tier and uses the functionality provided by its successor to carry out its part of the overall request processing. Consider a multi-tier application consisting of M timer T1… Multiple server/threads structures Each request is processed by each tier and forwarded to succeeding tier for further processing. Once the result is processed by the final tier, the results are sent back in the reverse order until it reach T1, which then sends the results to the user. In more complex processing scenarios, each request at tier can trigger zero or multiple requests to tier t. a static web page request is processed by web tier entirely and will not be forwarded to the succeeding tiers. On the other hand, a keyword search may trigger multiple queries to the database tier. Service time distribution is arbitrary User-session based interactions, User session-based, where a user session consists of a succession of requests issued by the user with think time Z in between. At a time, multiple concurrent user sessions are interacting with the application. A user typically waits until the previous request response sent back to send next request, the average time elapsed between the response from a previous request and the submission of a new request by the same user is called “think time” We model the application using an closed queuing network of M queues Q1, .. Qm Each queue represents an individual tier of the application and the underlying server where it runs Modern servers typically utilize a multi-thread or multi-process structure, in order to capture the multi-thread architecture and concurrency , we use multi-station queue to model tier. Each work thread or server is represented by a station Our model is sufficiently general to capture a number of common multi-tier applications with different application topology, configuration and performance characteristics. 120 secs Company Confidential

Approximate Model for Mean Value Analysis (MVA)
Presentation Title Approximate Model for Mean Value Analysis (MVA) Analytical performance model (M, N, Z, S1,V1, KI , … SM,VM, KM)  R, X A queue with m stations and service demand D at each station is replaced with two tandem queues a single-station queue with service demand D/m a pure delay center, with delay D×(m-1)/m The proposed closed queueing network model can be solved analytically to predict the performance. Mva is an efficient algorithm for evaluating closed queueing network models. Traditional mva has the limitation that it only applied to sing-station queue. In our model, each tier is modeled with a multi-station queuing center, to solve this problem, we adopt an approximation method, a queueing center who has m stations and service demand D at station is replaced with two tandem queues. The first queues is a single station queue with service demand D/m (m times faster) and the second queue is a pure delay center with delay…it has been shown that the error introduced by this approximation is small. By using the approximation, the final queuing network model is 60 Company Confidential

Deriving Queuing Network Performance
Presentation Title Deriving Queuing Network Performance (M, N, Z, Ki, Si, Vi)  R, X Di = Si * (Vi / V0) Mean Value Analysis (MVA) Input N: number of users Z: think time M: number of tiers Ki: number of stations at tier i (i = 1,…, M) S: mean service time at tier i (i = 1,…, M) Vi: mean request rate of tier i (i = 1,…, M) Output R: average response time X: throughput Ri: response time of tier i (i = 1,…, M) Qi: queue length of tier i (i = 1,…, M) Complexity O(MN) MVa algorithm is an iterative algorithm. It begins from the initial conditions when the system population is 1 and derives the performance when the population is I from the performance with system population of (i-1) as the following equations. R is mean response time at tier K when population is I Qk(i) is the average number of requests at tier K when the system population i. 60 sec Company Confidential

Component Profiling Capture component performance characteristics
Presentation Title Capture component performance characteristics S= f(CPU, MEM,,nConnections, CacheSize …) independent of other components Profiling deploy the application on a testbed change the resource allocation and configurations of each component while profiling a component, configure other component at its maximum capacity apply certain workload and collect the performance and workload data apply statistical analysis to derive the correlation between a component’s performance and its resource assignments and configuration archive the result as the component’s profile Capture workload characteristics e.g., visit rate, think time Challenges measurement methodology: accurate, practical, general non-intrusive approach appropriate statistical analysis techniques, e.g., regression analysis One key task during profiling is to accurately estimate the service time of the component. How to effectively measure the performance, 60 secs Company Confidential

Decomposition Performance model of M-tier applications
Presentation Title Performance model of M-tier applications Profiles for each tier/component Si = fi (CPUi, MEMi, nConnectionsi) i =1,…, M Workload characteristics visit rate Vi , number of stations Ki ; think time Z Decomposition (M, Ki, Vi, Z, N, R, X)  (CPU1, MEM1, nConnection1…. CPUM, MEMM, nConnectionM) given a M-tier application with the SLOs of R < r, X > x, N users, find the set of CPUi, MEMi, nConnectionsi satisfying e.g., optimization problem with objective function Company Confidential

Presentation Title Problem Statement and Challenges Our Approach Overview Analytical Model for Multi-tier Applications Component Profile Creation Decomposition Validation Performance Model Validation SLA Decomposition Validation Related Work Summary and Future Work Company Confidential

Virtualized Data Center Testbed
Presentation Title Virtualized Data Center Testbed Setup a cluster of HP Proliant servers with Xen virtual machines (VMs) each of the server nodes has two processors, 4 GB of RAM, and 1G Ethernet interfaces each running Fedora 4, kernel , and Xen 3.0-teseting TPC-W and RUBiS VMs hosting different tiers on different server nodes Estimate component service time TS1: when an idle thread is assigned or when a new thread is created TS2: when a thread is returned to the thread pool or destroyed T = TS2 – TS1,S = T – waiting-time fine grained, works well for both light load and overload conditions Estimate number of stations Max clients for Apache, Max threads for Tomcat, MySQL: average number of running threads We use a cluster of x86 servers with Xen virtual machines to emulate a data center where applications share a common pool of resources. The testbed consists of multiple HP Proliant servers, each running .. Each server has a bunch of Vm images, database images and swap images. The hardware resources are shared between the virtual machines that host the applications Alternatively, service times can be estimated using apache, tomcat and mysql logging facilities , the basic idea is apply light workload to the application and use the residence time to approximate the service time. This approach need to take extra care to handle overload conditions because software overheads have significant impacts on the service time as the load grows. Company Confidential

Performance Model Validation (1)
Presentation Title Experiment setup TPC-W, an industry standard e-commerce application Apache 2.0, Tomcat 5.5 and MySQL 5.0 10,000 items, 288,000 customers in DB exponential distribution with a mean 3.5ms think time The model predicts the response time and throughput very accurately. The model works well even when the system load is high. To validate the correctness and accuracy of our model, we experimented with two-open source 3-tier applications running on our virutalized linux-based servers. The testbed is composed of four machines. One of them is used as client workload generator and other three are used as apache web server, application server and database server. The first applications we use is TPC-W, an industry standard e-commerce applications. ]We change workload by varying the number of concurrent sessions We measure different model input and output parameters and then apply mva algorithm to derive the response time and throughput. We can see that the analytical model does predict the performance of tpc-w accurately. Under different workloads, the results predicted by our model are close to the measurement even when the application reaches its maximum throughput Company Confidential

Performance Model Validation (2)
Presentation Title Experiment setup RUBiS, an eBay like auction site application Apache 2, Tomcat 5.5, MySQL 5.0 1,000,000 users and 1,000,000 items in DB exponential distribution with a mean 3.5s think time same set of model parameters profiled with 200 users Using the same set of model input parameters, the model still predicts the performance for different workloads The model works for different applications with different performance characteristics To further validate the effectiveness of our model, we experiment with second application RUBiS, an eBay like aunction site developed at Rice university. Think time is exponential distribution with a mean of 3.5 seconds We vary the workload from100 to 300 concurrent users. We use the same of input parameters we obtain during profiling to predict the performance of different workload Even using the same set of model input parameters, the model can still predicts the performance of different workloads well. Company Confidential

Component Profiles Creation
Presentation Title Change CPU assignments to VMs management domain (dom0) uses one CPU and VMs use the other CPU Simple Earliest Deadline First scheduling (SEDF) to set the CPU share capped mode enforces a VM cannot use more than its share of the total CPU VMs hosting different tiers run on different servers. While profiling a component, fix the CPU assignment of other components at 100% Change the CPU assignment from 10% to 60% with an increase of 5% and collect the performance data Derive the component service time (workload independent) from the measurements To isolate performance interference SEDF is used to control the percentage of total CPU assigned to a vm, In our experiment, we built profiles for apache, tomcat and mysql with different CPU assignments to vms hosting them. When we profile a tier, apache, we fix other tiers’ CPU assignments as 100%. Apache was not found to be a bottleneck, so we ignored the results for it. As the cpu assignment increase, the service time drops initially and remains constant after getting enough CPU. The results are then save as tomcat and mysql’s profiles. Company Confidential

Designing a 3-tier RUBiS
Presentation Title We apply SLA decomposition approach to design RUBis applications with different SLA goals and software architecture. Given high level SLA goals, we generate low level CPU requirements through SLA decomposition model and then configure VMs based on the derived low level CPU requirements. We then validate our deign by measuring the actual performance of the system and the CPU utilization, and compare the results with the SLA goals. In the experiments, we consider the high level SLA goals defined as number of concurrent users, average response time and maximum throughput. We use 5% of the total CPu capacity for CPU assignment. 3-tier RUBis. The results of different designs (ie. Different CPu assignment) for the sla goals of 300 users, response time < 5s and throughput > 20 . System0 is the system designed based on the proposed SLA. System0 meets the SLAs with reasonable CPU utilization. Two other systems are used for the purpose of comparisons. System1 is underprovisioned case while system2 is the overprovisioned scenarios. System 1 fails to meet sla since the system is completely overloaded while system 2 meet the SLAs but is highly under-utilzed with less than 25% cpu System0 reduce the resource usage from 180 to 70%. The first column show the SLA goals, the column of CPU assignments describes the system design parameters in terms of percentage of CPu assigned to each tier. The columns of response time, throughput, and cpu utilization show the measured … of the actual system. Compared with the over-provisioing system1, our system can meet the SLAs using less CPu resource (75% as opposed to 180%) and improve the utilization by 3 times (60% as opposed to 20%) We also experimented with another SLA of 100 uses, … system0 which is designed based on low level system thresholds derived by our approach can meet the high level sla with reasonable CPU utilization Company Confidential

Designing a 2-tier RUBiS
Presentation Title SLOs Design CPU Assignment Performance CPU Utilization Apache MySQL Total Resp (secs.) Throughput (reqs/sec) Users =100 Resp < 5 sec Throughput > 10 reqs/sec System0 10% 15% 25% 4.83 13 72% 53% Users=500 Resp < 10 sec. Throughput > 40 35% 30% 65% 8.2 42 76% 61% In order to further check the applicability of our approach, we apply our approach to design a 2-tier rubis implementation. The 2-tier runs php script at web server tier and puts much higher load on web tier than 3-tier. We evaluate our approach with two different slas. The results shown our approach can be effectively applied to design such a 2-tier system with different sla requirements, too In a summary, our approach can work well with multi-tier applications with different slOs and different software architectures and workloads. We plan to experiment with other implementations (e.g., 3-tier ejb rubis using jboss as middle tier) Meets the SLOs and optimizes the resource usage Applicable to multi-tier applications with different SLOs, different software architectures and different performance characteristics Company Confidential

Presentation Title Problem Statement and Challenges Our Approach Overview Analytical Model for Multi-tier Applications Component Profile Creation Decomposition Validation Related Work Summary and Future Work Company Confidential

Related Work Using Queuing theory models for provisioning
Presentation Title Using Queuing theory models for provisioning C. Stewart, and K. Shen, NSDI 2005 B. Urgaonkar, et. al., ICAC 2005 A. Zhang, P. Santos, D. Beyer, and H. Tang, HPL Performance models for multi-tier applications B. Urgaonkar, et. al., SIGMETRICS 2005 T. Kelley, WORLDS 2005 U. Herzog and J. Rolia, layered queueing model Classification-based decomposition Y. Udupi, A. Sahai and S. Singhal, IM 2007 ACTS: Automated Control and Tuning of Systems Previous studies have utilized performance models to guide resource provisioning and do capacity planning We propose a a more general and extensible systematical way to various high level goals and a variety of allocation decisions regarding lower-level system configurations and designs Performance model are more accurate Virtualized environments Profile i/o and memory and explicit captures communication overheads A lot of research efforts have been made to develop performance models for multi-tier applications. Most such models concern single-tier . A few recent efforts have extended single-tier models to multi-tier. The most recent and accurate performance model is proposed .similar to our model…our model use multi-station, enable us to model multi-server the same way. The approximate mva for multi-station is more accurate than simply adjust the total workload. We derive model parameter in a fine-grained manner. Our measurement methodology can work well for both light load and heavy load conditions. We systematically study the performance and validate our models in a virtualized environment. Model application in a more fine-grained manner and handle various workload and conditions in a consistent way. Imbalance across tier replicas and multiple session classes, we have not explored these areas yet. The two model were developed concurrently Does not require knowledge of internal application component structure and uses only transaction type information instead. It works well for realistic workload under normal system load. It’s not clear how ell it will perform under high system load The specific performance model used in this paper is based on queueing model, conceptually, any model which can help determine the performance can be incorporated into our solution, it would be interesting to investigate it further. Company Confidential

Summary Presentation Title Proposed a systematic approach to combine performance modeling and component profiling to derive low level system thresholds from performance oriented SLOs create an effective design (e.g., resource selection and allocation, software configuration) to ensure SLAs SLA monitoring and assessment Presented an effective analytical performance model for multi-tier applications accurately predict the performance work well for applications with different software architectures, workloads and performance characteristics Validated the proposed approach for multi-tier applications in virtualized environment design the system to meet the given SLOs with reasonable resource usage work for common multi-tier applications with different SLOs goals and software architectures easy to adapt to changes in applications and environments Company Confidential

Open Issues Presentation Title Extensions to other parameters like memory and configuration parameters “nice” regression function? Non-stationary workload multi-class queueing network, layering queueing model combine regression model and queueing model Profiling and measurement tools and technologies from Mercury Interactive non-intrusive approach: derive model parameters via regression model Long running transactions e.g., HPC applications, complex composed service Non-performance based SLOs e.g., availability goals tradeoff analysis Complex and large scale systems advanced constraint solving and optimization algorithms required Company Confidential

Future Work Extend profiling to other parameters
Presentation Title Extend profiling to other parameters other system resources in addition to CPU resources, e.g., memory, I/O, cache software configuration parameters apply regression analysis on the profiling results general and practical measurement methodology Apply the approach to realistic applications and workloads non-stationary workload: multi-class queueing network model non-intrusive profiling and measurement enterprise applications, HPC applications, and composed services non-performance SLOs, e.g., availability non-traditional SLOs, e.g., represented as utility functions Use advanced constraint solving and optimization algorithms for complex and large scale problems Integrate SLA decomposition into SLA life-cycle management integrate with tradeoff analysis, SLA monitoring and SLA assessment Company Confidential

Papers Presentation Title SLA Decomposition: Translating Service Level Objectives SLOs) to Low-level System Thresholds. Yuan Chen, Subu Iyer, Xue Liu, Dejan Molojicic, and Akhil Sahai. To appear in Proceedings of the 4th IEEE International Conference on Autonomic Computing (ICAC 2007), June 2007. HP Technical Report: Company Confidential

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Presentation Title SLA Decomposition: Translating Service Level Objectives to System Level Thresholds Thanks Dejan. Very glad to see my advisor karsten.

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Presentation Title SLA Decomposition: Translating Service Level Objectives to System Level Thresholds Thanks Dejan. Very glad to see my advisor karsten.

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