Autonomic management in a distributed storage system
Distributed operating systems (Computers);Electronic data processing--Distributed processing;Peer to peer architecture (Computer networks);Autonomic computing
Tauber, Markus ; Kirby, Graham N. C. ; Kirby, Graham N. C.
This thesis investigates the application of autonomic management to a distributed storagesystem. Effects on performance and resource consumption were measured in experiments,which were carried out in a local area test-bed. The experiments were conducted withcomponents of one specific distributed storage system, but seek to be applicable to a wide range of such systems, in particular those exposed to varying conditions.The perceived characteristics of distributed storage systems depend on their configurationparameters and on various dynamic conditions. For a given set of conditions, one specific configuration may be better than another with respect to measures such as resourceconsumption and performance. Here, configuration parameter values were set dynamicallyand the results compared with a static configuration. It was hypothesised that under non-changing conditions this would allow the system to converge on a configuration that wasmore suitable than any that could be set a priori. Furthermore, the system could react toa change in conditions by adopting a more appropriate configuration. Autonomic management was applied to the peer-to-peer (P2P) and data retrieval components of ASA, adistributed storage system. The effects were measured experimentally for various workload and churn patterns. The management policies and mechanisms were implementedusing a generic autonomic management framework developed during this work.The motivation for both groups ofexperiments was to test management policies with the objective to avoid unsatisfactory situations with respect to resource consumption and performance. Such unsatisfactory situations occur when either the P2P layer or the data retrieval mechanism is configured statically. In a statically configured P2P system two unsatisfactory situations can be identified. The first arises when the frequency with which P2P node states are verified is low andmembership churn is high. The P2P node state becomes inaccurate due to a high membership churn, leading to errors during the routing process and a reduction in performance. In this situation it is desirable to increase the frequency to increase P2P state accuracy. The converse situation arises when the frequency is high and churn is low. In this situation network resources are used unnecessarily, which may also reduce performance, making it desirable to decrease the frequency.In ASA’s data retrieval mechanism similar unsatisfactory situations can be identified with respect to the degree of concurrency (DOC). The DOC controls the eagerness with which multiple redundant replicas are retrieved. An unsatisfactory situation arises when the DOC is low and there is a large variation in the times taken to retrieve replicas. In this situation it is desirable to increase the DOC, because by retrieving more replicas in parallel aresult can be returned to the user sooner. The converse situation arises when the DOC ishigh, there is little variation in retrieval time and there is a network bottleneck close to the requesting client. In this situation it is desirable to decrease the DOC, since the low variation removes any benefit in parallel retrieval, and the bottleneck means that decreasing parallelism reduces both bandwidth consumption and elapsed time for the user.The experimental evaluations of autonomic management show promising results, and suggest several future research topics. These include optimisations of the managed mechanisms, alternative management policies, different evaluation methods, and the application of developed management mechanisms to other facets of a distributed storage system. The findings of this thesis could be exploited in building other distributed storage systems that focus on harnessing storage on user workstations, since these are particularly likely to be exposed to varying, unpredictable conditions.
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Autonomic management in a distributed storage system