CAUSAS CRIMINALES

The effects of the autonomic manager, outlined in section 7.2, on StAChord’s performance and network usage were measured in various experiments. Each experiment involved a particular workload (a temporal pattern of lookup requests) and membership churn (a tem- poral pattern of nodes joining and leaving the overlay). As the motivation for this research is the use of P2P overlays in distributed storage systems, workloads and churns were de- rived from distributed storage use cases. In each of the use cases a P2P overlay as used in ASA was considered. This means that the nodes of which the P2P overlay was comprised represent individual storage servers. One of these storage servers acted as a dedicatedgate- wayto issue lookups during the application of workloads. Here the general concepts and motivations for both workload and churn patterns are explained as well as the machinery used for applying them. The configurations used in the experiments can be found in section 7.4.

7.3.1

Churn Pattern

Each churn pattern modelled the behaviour of a set of nodes, in terms of a sequence of alternating on-line and off-line phases for each node.

• During on-line phases a node can be routed to by its key.

The durations of these phases were pseudo-randomly generated according to two normal distributions, one for on-line and one for off-line phases. Thus the churn pattern was de- fined by the two distributions.

Example Churn Pattern

The following example shows how a churn pattern was applied to an individual node by the machinery. The churn pattern was given by the on-line and off-line phase-durations. Each phase-duration was selected from a normal distribution of values which was specified by its mean and standard deviation (µton/of f−line±σton/of f−line). Adurationdefined the minimum

overall length of all alternating phases. All durations are given in seconds,[s].

• duration: 500[s]

• ton−line : 102±3[s]

• tof f−line : 106±10[s]

Figure 7.1 illustrates the behaviour of an individual node due to the above churn pattern, which resulted in alternating on-line and off-line phases for an overall duration of518 [s].

All participating nodes in an overlay, as used in the experiments, exhibited similar be- haviour to that illustrated in figure 7.1 as a result of the churn pattern just described. The particular length of each phase was pseudo-randomly selected from the corresponding dis- tribution. Additionally the type of the first phase (on-line or off-line) was also selected pseudo-randomly. The motivation for the pseudo-randomness was that churn patterns were required to exhibit variation during the course of an experiment and between nodes but not between repetitions of experiments with the same churn pattern configuration.

Distributed Storage Usage Scenarios/Churn Patterns

The conceptual approach towards the simulation of specific churn patterns as described above was used to support the following four usage scenarios. The four scenarios represent edge cases and combinations of edge cases in order to have significant variations between the tested scenarios. In each scenario one of the nodes was a dedicated gateway through which a workload was executed. All other nodes exhibited behaviour corresponding to the scenarios. The gateway was permanently on-line because it was required by any node to join the overlay at the initiation of any node’s on-line phase and by the machinery which applied the workload, for issuing lookups. The scenarios evaluated were:

• Alow membership churn in which the overlay is composed of nodes representative of dedicated servers that join the network and rarely leave it. In this scenario a high interval between maintenance operations is desired.

of workstations that join and leave with a high frequency. In this scenario a short interval between maintenance operations is desired.

• A locally varying membership churn in which the overlay is composed of nodes representative of either servers or workstations with different joining and leaving patterns. In this scenario it is desired that nodes which are exposed to high churn maintain links to their peers with a higher frequency than those exposed to low churn.

• Atemporally varying membershipin which the overlay is composed of nodes which change their behaviour over time, from behaving like workstations to behaving like servers and vice versa. Workstations exhibit high churn whereas servers exhibit low churn. In this scenario small intervals are desirable when nodes exhibit high churn and vice versa in periods of low churn.

7.3.2

Workload

Each workload was specified as a temporal pattern of P2P lookups, which were executed via the previously introduced gateway. Machinery was developed which allowed the ex- pression of various temporal lookup patterns representing four scenarios which starkly dif- fer from each other. These can be categorised as synthetic or file system specific. In each type of workload the keys were pseudo-randomly generated, to allow a variation of keys within an individual experiment but not between repetitions.

Synthetic Workloads

The evaluated synthetic workloads were:

• To represent scenarios in which no or very few lookups were executed, asynthetic light weight workload was defined. Such a workload was specified by a number of

l lookups which were spread evenly over the experimental duration. The interval between lookups wasdseconds.

• To represent scenarios in which lookups were executed at a high rate a synthetic heavy weight workload was defined. Such a workload was specified by l lookups which were executed sequentially without any delay between them.

• To represent scenarios in which a workload exhibits alternating phases of heavy and light weight workloads, a synthetic variable weight workload was defined. Such a workload was specified byl sequential lookups which were spread over the experi- mental duration. The spreading factor was specified bysandd. ssequential lookups were executed without any delay between them, and after everysth lookup a delay ofdseconds was exhibited.

File System Specific Workload

To represent a distributed storage usage scenario, a workload specific for a file system workload was defined. File system operations were extracted from existing “real world” file system traces and the corresponding ASA operations were generated. These ASA

operations corresponded with temporal patterns of lookup operations in ASA’s P2P layer. This transformation was based on ASA semantics as described in chapter 3. More details are available in the appendix A.1.4.