Zeng04, "QoS-aware middleware for web services composition"
Experiment settings
硬件和基础软件: a cluster of PC with the same configuration: Pentium III 933MHz with 512 RAM, Windows 2000, Java 2 Enterprise Edition V1.3.0, Oracle XML Developer Kit, connected by LAN through 100Mbits/sec Ethernet cards
应用软件: IBM's Web Services Toolkit (develop web service), AgFlow, which uses IBM's Optimization Solutions and Library (OSL) as IP solver
Scenario
a travel planning application
(1) environment type
static environment:
no change in the QoS of any component service during a given composite service execution
dynamic environment:
QoS of component services may undergo changes during the execution of a composite service: (a) existing services become unavailable (b) new services with better QoS appear (c)component service not able to complete the task (d) component service may complete the task but without meeting the expected QoS
simulated by randomly changing the QoS of the component services
Test data
(1) composite service generation
"randomly repeating exsiting states in the composite service shown in Fig. 3"
number of states: 10, 20, 30, …, 80
number of candidate component services per task: 10, 20, 30, 40
(2) QoS data collection
execution duration, execution price: defined in the underlying service ontology (?)
reliability, reputation: calculated by the service composition manager(which logs QoS information during task executions)
availability: calculated by the service broker ( it records the up and down time of each service)
Experiments
(1) Measuring Computation Cost
比较三种服务选择方法(local optimization, global planning by exhaustive search, global planning by integer programming)在静态和动态环境中的计算代价.
目的是要提供一些依据, 以确定在不同条件下应使用何种选择方法.
(1.1) 静态环境中. Global方法只用执行一次, Local方法需要执行N次(N是task的数目). 实验结果: Global(exhausive) >> Global(IP) > Local
(1.2) 动态环境. Global每执行一个task后就要执行一次. 比较了Global(IP)方法在动态和静态环境中的计算代价.
(2) Measuring QoS of composite services
Composite service has 20 or 80 tasks and one execution path, in static and dynamic environment.
实验的结果自然是全局优化方法要好于局部优化方法.
Alrifai09, "Combining Global Optimization with Local Selection for Efficient QoS-aware Service Composition" Experiment settings
硬件和基础软件: HP ProLiant DL380 G3 machine with 2 Intel Xeon 2.80GHz processors and 6 GB RAM, Linux (CentOS release 5), Java 1.6
应用软件: lpsolve version 5.5
Scenario
(1) create several test cases of the QoS-based service composition problem, each test case: n service classes, l service candidates per class, m global QoS constraints
(2) 首先使用global optimization方法获得最优解, 然后使用本文提出的方法来解决相同的case, 并将结果进行比较.
(3) number of quality levels: 10, 20, 30, 40, 50
Test data
(1) First dataset: QWS real dataset (这个是Web服务集, 但是没有组合服务集, 不知道作者是怎么用用在这个实验里的)
(2) Second dataset: 20000 artificial web services by assigning arbitrary QoS values (normally distributed in the range between 1 and 100)
Experiments
实验(1)和(2)比较了Global方法和hybrid方法(10, 20, 30, 40, 50)的computation cost, 结果显示后者要优于前者.
实验(3)和(4)比较了这两种方法的有效性, 结果显示本文方法能获得近似全局方法得到的解.
(1) Performance comparison w.r.t. the number of web service candidates
实验参数:
| | QWS | random dataset |
| number of service classes | 5 | 10 |
| candidate per class | 50, 100, …, 500 | 100, 200, …, 2000 |
(2) Performance comparison w.r.t. the number of web service classes
实验参数:
| | QWS | random dataset |
| number of service classes | 5, 10, …, 25 | 10, 20, …, 100 |
| candidate per class | 100 | 500 |
(3) Optimality w.r.t. the number of web service candidates
(4) Optimality w.r.t. the number of web service classes