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This permits the H-metric of two different machines e.

The correction factor for different machine resources R e. Migration is similar to deployment, although in this case the query starts with a violating container detecting it has a problem, and then issuing a bounded H-metric query see Figure 2 to locate a new container to host the service that caused the or is expected to cause a violation. Algorithm 1 gives the pseudo-code for a responding to a service violation event. Firstly we stop registering SLA violations for this container, as subsequent violations will have the same outcome - migrate a service off this container.

Secondly we stop accepting incoming service migrations. Next we pick a random unconstrained service with a green SLA. Algorithm 2 gives the pseudo-code for the actual migration of the service S identified in algorithm1.

Next we do a bounded depth H-metric query and find the set of accepting containers A, from which we choose the minimum dest. The service S is then migrated to container dest and the algorithm terminates.

Development

The P2P functionality is provided by an extension of freePastry [15], however it is not critical which structured DHT package is used. The prototype has been deployed and tested on five ma- chines. However, to properly test the performance of the architecture we ran the deployable prototype code but used the freePastry overlay simulation mode to scale the simulation to containers. The experimental configuration was services deployed over containers. Each service had an expected response time of 10ms, with 75 gold, red and green level SLAs.

To explore how our architecture responds to, and resolves SLA violations, all services were initially deployed to containers at random.

It is worth pointing out however, that deployments are usually made much more carefully using the same H-metric query as migration. Hence these experiments show that the architecture can deal with a poor initial distribution and can resolve the distribution of services to provide the QoS dictated by the differenti- ated SLAs.

The vast bulk of violations and subsequent rectification migrations are finished in seconds after initial deployment when using a query depth of 3. Total number of SLA violations for bounded H-metric queries. Figure 4 shows how quickly the system manages to stabilise at the agreed QoS after the initial random deployments. The slowest to stabilise on the agreed QoS was when the destination was selected using a query depth of 1 random , with depth 2 and 3 improving things respectively.

There are some single violations long after the bulk of the migrations have finished, and this is due to the same destination being chosen by two offloading containers it is a fully decentralised algorithm. The deeper the query depth, the faster the system provides the agreed QoS to the vast majority of hosted services. Figure 5 shows the number of gold, red and green service violations after initial deployment for a H-metric query depth of 2.

The results for level 1 and level 3 both show similar decay curves but differ in the rate at which they stabilise on the agreed QoS as in Figure 4. It is worth remembering that in response to any of these violations, a green then red if no green, then gold service is chosen to migrate from the host. Each of these violations results in a service migration, however the vast bulk of the migrated services are green, with few reds and minimal gold services migrating.

Finally Figure 6 shows the total number of violations for each query depth and service level domain.

Here the reduction in the number total of violations as more effort is put into finding the Green Red Gold 30 25 Number of SLA Violations 20 15 10 5 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 Time 5 seconds per interval Fig.

SLA violations shown by service level for a H-metric query depth of 2. Total number of violations for each service level for bounded H-metric queries. They conclude that migration is a heavy-weight exercise and should be avoided whenever possible and that migrating services to satisfy the minimal resource consumption can lead to unnecessary overhead.

Like our approach, the principle is to migrate only when resource bottlenecks occur.

Pro Apache Geronimo

Hao [16] carries out migration of weblets, specialized Web services, that can be migrated, according to the round trip time, message size, data location and load of the weblet containers. Other projects have attempted to address scalability issues for, such as that by El-Darieby and Krishnamurthy [17], which partitions resources into individ- ual, cluster and grid resources. Dowlatshahi et. The key character- istics of their architecture are optimal search for both distant and close services, minimal overhead traffic, scalability, robustness, and easier QoS support.

Each resource manager periodically transmits a list of resources that it is willing to share to resource managers that are in close proximity. If a manager has insufficient resources to handle their jobs, they can forward some of their jobs to the advertising resource manager. Kang et. The percentile response time of the real server is used as base for determining whether to allocate more computing resources to clients de- manding a high level of service.

They do not consider service migration to meet the QoS targets. Lee and Lee[22] discuss how to integrate a service provider in a negotiation framework. Other projects have attempted to address scalability issues for, such as that by El-Darieby and Krishnamurthy [17], which partitions resources into individ- ual, cluster and grid resources.

Dowlatshahi et. The key character- istics of their architecture are optimal search for both distant and close services, minimal overhead traffic, scalability, robustness, and easier QoS support.

Each resource manager periodically transmits a list of resources that it is willing to share to resource managers that are in close proximity. If a manager has insufficient resources to handle their jobs, they can forward some of their jobs to the advertising resource manager.

Kang et. The percentile response time of the real server is used as base for determining whether to allocate more computing resources to clients de- manding a high level of service.

They do not consider service migration to meet the QoS targets. Lee and Lee[22] discuss how to integrate a service provider in a negotiation framework. An important aspect is the need for a quality measure- ment like the h-value developed in this paper.

Mikic-Rakic et. Berenbrink et. Agents would then be expected to migrate from overloaded to under loaded resources, until the allocation becomes balanced. This system is unlikely to scale well, as the resource discovery is centralised.

The research of Zeid and Gurguis [25] aims at proving that with autonomic Web services, computing systems will be able to manage themselves as well as their relationships with each other.

To achieve this objective, the research proposes a system that implements the concept of autonomic Web services but without service migration. However, although we share many of the high level goals such as scalability, transparency and fault tolerance, there are many significant differences in the architecture itself.

We use the decentralised, fault tolerant and dynamic properties of a structured P2P DHT to create a scalable decentralised autonomic web service middleware that complies with service level agreements and strives to deliver QoS in response to client SLA specifications.

Pro apache geronimo

We have demonstrated that our autonomic SLA aware containers, that mon- itor their SLA compliance and migrate excess services to other containers with spare capacity, can react to dynamic to runtime conditions. The rate at which the system is capable of redistributing services to find a QoS preserving distri- bution is very fast, and improves further as the H-metric query depth increases. We have also provided differentiated service level domains green, red and gold in our SLAs, and using the health metric and service level domains we have de- veloped a decentralised migration algorithm that redistributes services between containers to meet agreed QoS.

This is done in a distributed, scalable and robust way. Finally, we have implemented the architecture using standard modern tech- nologies and with high levels of transparency, indeed, conventional webservices can be deployed with the addition of a SLA specification.

Professional Apache Geronimo

References 1. Papazoglou, M. In Cubera, F. Parashar, M. In et al. Volume Li, Y. IBM: An architectural blueprint for autonomic computing. Sun: Jsr J2ee management specification. Hanson, J. August Fowler, M. Rowstron, A. In: Pastry: Scalable, decentralized object location and routing for large-scale peer-to-peer systems.

Apache: Geronimo user guide. Druschel, P. Hao, W. El-Darieby, M. International conference on Networking and Ser- vices, Dowlatshahi, M. September 51 — 56 It also has the capabilities for Spring compliance, so Spring developers can deploy their web applications using Geronimo. Skip to main content Skip to table of contents. Advertisement Hide. Pro Apache Geronimo. Front Matter Pages i-xvii. Getting Started.

Pages Geronimo Architecture.It works with Java EE services and components to build specific configurations—one of which is a full Java EE solution stack. The results for level 1 and level 3 both show similar decay curves but differ in the rate at which they stabilise on the agreed QoS as in Figure 4.

Web Application Development with Geronimo. The key character- istics of their architecture are optimal search for both distant and close services, minimal overhead traffic, scalability, robustness, and easier QoS support.

Migration is similar to deployment, although in this case the query starts with a violating container detecting it has a problem, and then issuing a bounded H-metric query see Figure 2 to locate a new container to host the service that caused the or is expected to cause a violation. This permits the H-metric of two different machines e. The machine specific health status hmachine is set relative to other contain- ers. The SLA specifies a set of resource conditions that must be satisfied.

If a new container advises that it has more memory or a higher MIPS performance than the current maximums, then its values are selected as the new maximums for normalisation and are propagated to all containers in the network.