Dynamic migration of virtual machines on a cluster of physical machines is designed to maximize resource utilization by balancing loads across the cluster. When the utilization of a physical machine is beyond a fixed threshold, the machine is deemed overloaded. A virtual machine is then selected within the overloaded physical machine for migration to a lightly loaded physical machine. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. We present in this paper a learning framework that autonomously finds and adjusts thresholds at runtime for different computing requirements. Central to our approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. We set up an experimental environment that consists of extensive real world benchmarking problems and a cluster of 16 physical machines each of which has on average eight virtual machines. We demonstrate through experimental results that our approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization.

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Abstract:Chord is a peer-to-peer look up algorithm based on a distributed hash table protocol in wired IP networks, exploiting the advantage of scalability for large-scale of distributed applications. However, deploying Chord into mobile networks should be inherently accompanied with supplementary network traffics to maintain the hash key mapping rules because of a high rate of joining and leaving nodes. This paper proposes an enhanced reactive Chord for mobile networks, which can reduce network traffics and achieve fast lookup services. For this purpose, a conventional Chord is modified to act reactively, and then the table activity checking feature is devised into it, called enhanced reactive Chord. Simulation results show that the proposed Chord can decrease network traffics by an average of 41.3% maintaining same setup latency, compared with conventional Chord in mobile networks.

SUMMARYDispatching a large number of dynamically changing requests directly to a small number of servers exposes the disparity between the requests and the machines. In this paper, we present a novel approach that dispatches requests to servers through virtual machines, called Dispatching Requests Indirectly through Virtual Environment (DRIVE). Client requests are first dispatched to virtual machines that are subsequently dispatched to actual physical machines. This buffering of requests helps to reduce the complexity involved in dispatching a large number of requests to a small number of machines. To demonstrate the effectiveness of the DRIVE framework, we set up an experimental environment consisting of a PC cluster and four benchmark suites. With the experimental results, we demonstrate that the use of virtual machines indeed abstracts away the client requests and hence helps to improve the overall performance of a dynamically changing computing environment.

Adaptive clustering aims at improving cluster utilization for varying load and traffic patterns. Localitybased least-connection with replication (LBLCR) scheduling that comes with Linux is designed to help improve cluster utilization through adaptive clustering. A key issue with LBLCR, however, is that cluster performance depends much on a single threshold value that is used to determine adaptation. Once set, the threshold remains fixed, regardless of the load and traffic patterns. If a cluster of PCs is to adapt to different traffic patterns for high utilization, a good threshold has to be selected and used dynamically. We present in this paper an adaptive clustering framework that autonomously learns and adapts to different load and traffic patterns at runtime with no administrator intervention. The cluster is configured once and for all. As the patterns change, the cluster automatically expands/contracts to meet the changing demands. At the same time, the patterns are proactively learned so that when similar patterns emerge in the future, the cluster knows what to do to improve utilization. We have implemented this autonomous learning method and compared it with LBLCR using published Web traces. Experimental results indicate that our autonomous learning method produces high performance scalability and adaptability for different patterns. On the other hand LBLCR-based clustering suffers from performance scalability and adaptability for different traffic patterns since it is not designed to obtain good threshold values and use them at runtime.