Some potential future work packages were already suggested in chapters 7 and 8. They include suggestions for improvements of the managed components in combination with the management; alternative management approaches; and different evaluation approaches. All of them are outlined in the corresponding chapters. An addition to these future work packages might be the evaluation of the effects of autonomic management on other facets of distributed storage systems. Such facets have been identified during the course of this work but not experimentally evaluated. Examples include the autonomic management of the scheduling of storage maintenance operations or the autonomic management of the
degree of fragmentation. Both examples are briefly outlined in the following to motivate future research projects.
9.3.1
Autonomic Management of Storage Maintenance Operations
Data in decentralised distributed storage systems such as ASA is replicated on multiple host machines in order to improve its availability in the event of host machines failing. A certain number of replicas must always be available to maintain fault-tolerance. The detec- tion of failed hosts and the copying of corresponding replicas to new hosts, which take over the key space of the failed host, is initiated, for instance, in ASA when P2P routing errors are observed. It is possible that replicas can be missed out which might have been agreed to be stored at such new hosts while the host was transiently unavailable or running slowly. Such issues need to be addressed by a maintenance operation which regularly searches for missing replicas. The responsible mechanism is referred to as astorage maintenance mech- anismin ASA. In the original ASA design, every host runs a storage maintenance mecha- nism which periodically searches for and fetches missing replicas. Similar unsatisfactory situations can be identified here as for a periodic P2P peer-state maintenance operation, executed at statically configured intervals (see chapter 4). Autonomic management may be able to discover and correct unsatisfactory situations, similarly to the ones outlined in chapter 4, by autonomically adapting the corresponding interval.
9.3.2
Autonomic Management of Data Fragmentation
In storage systems in general, data items are often split up into smaller blocks. In a dis- tributed storage system like ASA, the sizes of individual data blocks affect how data is distributed over the network; this specifies how evenly the storage hosts’ storage capacities are utilised (degree of data distribution). The sizes of the data blocks also affect the time it takes to return a requested data item (get time). The amount of data transferred over the network is also affected, mainly due to maintenance and administrative overhead. As in any of the investigations carried out as part of this thesis, an optimal data block size de- pends on various conditions and cannot be predicted statically. Even if a data block size seems to be ideal initially, it may cease to be so as conditions vary. Clearly this issue needs to be analysed in more depth by, for instance, developing an analytic model as in chap- ter 5. However, autonomic management may be able to adapt the data block size in the presence of a changing environment in order to achieve better performance and resource consumption than a statically configured system would do.
9.3.3
Combination of Autonomic Elements
In chapter 2, the behaviour of an autonomically controlled system is described as being dependent on the autonomic behaviour of its constituent parts (autonomic elements). The autonomically managed scheduling mechanisms of ASA’s P2P component and the man- aged DOC of the data retrieval mechanism correspond to individual autonomic elements. It was not within the scope of this work to evaluate the effect of autonomic management on
performance and resource consumption if more than one facet was autonomically managed concurrently. It would however be an interesting future experiment to do so.
9.4
Conclusions
In chapter 1 the hypothesis was made that autonomic management may be able to set a configuration which results in better performance and resource consumption than any that can be set a priori. An autonomic manager was envisioned towork without the need for a human operator. One of ASA’s design goals was stated in chapter 3 as: A general autonomic tuning mechanism should be provided to allow low-level aspects of the system’s operation to be managed automatically, controlled by policies that are driven by high-level user preferences.
The hypothesis about the effects of autonomic management on resource consumption and performance was experimentally evaluated, analysed and reported in chapters 7 and 8. In both evaluations (a relatively small number of) cases have been identified in which the stat- ically configured ASA components resulted in better performance and resource consump- tion than the autonomically managed ones. Following this, improvements of the specific manager (in combination with the managed component) were made in the corresponding chapters. However in the majority of the experiments the autonomically managed ASA components successfully identified and corrected unsatisfactory situations with respect to performance and resource consumption. This allows the conclusion that the hypothesis, that autonomically managed components may result in better performance and resource
consumption than statically configured ones in various (changing) conditions, was suc- cessfully tested and found to hold.
The autonomic manager was envisioned to adapt any target system in response to vari- ous conditions without human interaction. The above outlined detection and correction of unsatisfactory situations happened autonomically by the manager without the need of a hu- man administrator. Thus the corresponding design goal of the autonomic manager can be considered to have been met. An ASA design goal with respect to the autonomic manager stated that the manager’s behaviour was envisioned to be governed by high-level policies. This is also considered as having been fully met as the autonomic management cycle and the developed framework use policy evaluators as fundamental building blocks.