Along with an increase in the number of IoT devices and computations progressively shifting to the network edge and mobile systems, the complexity of computer systems is on the rise. In order to keep them in operation their monitoring becomes important. However, classic monitoring systems do not cope well with lightweight solutions due to the large volume of monitoring data sent to the cloud. In this paper we present mechanisms which can be applied to monitoring systems to make them suitable for IoT and mobile devices. We discuss and evaluate the benefits and drawbacks of both mechanisms as well as the results they produce. Finally, we propose the concept of function-driven log processor that can be used to control the proposed mechanisms.

In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administrator. It makes it possible to adapt reservation plans one or more weeks ahead. Hence, it allows time for the administrator to analyze the plan and discover potential problems with resource under-provisioning or over-provisioning, which may prevent server overload in the former case and unnecessary expenses in the latter. It also makes it possible to extract and analyze the knowledge learned, which may provide useful information about resource usage characteristics. The proposed solution is tested on OpenStack using real Wikipedia server traffic data. Experimental results demonstrate that machine learning enables an improvement in resource usage.

This paper presents the concept of an Adaptive SOA Solution Stack (AS3). It is an extension of the S3 model, implemented via uniform application of the AS3 element pattern across different layers of the model. The pattern consists of components constituting an adaptation loop. The functionality of each component is specified in a generic way. Aspects of these patterns are analyzed in relation to individual S3 layers. The ability to achieve multilayer adaptation, provided by several cooperating AS3 elements is also discussed. Practical usage of the proposed concepts for Adaptive Operational Systems, Integration, and Service Component layers are presented in the form of three case studies. Each study describes the architecture of the proposed system extensions, selected software technologies, implementation details, and sample applications. Related work is discussed in order to provide a background for the reported research. This paper ends with conclusions and an outline of future work.

The Java™ 2 Platform, Enterprise Edition (J2EE™), is established as the standard platform for hosting enterprise applications written in the Java programming language. Similar to an operating system, a J2EE server can host multiple applications, but this is problematic due to limitations on scalability, weak inter-application isolation and inadequate resource management facilities in the underlying Java platform. These limitations lead to a proliferation of server instances with a consequent dramatic increase in the total memory footprint and more complex system administration. The Multi-tasking Virtual Machine (MVM) solves this problem by providing an efficient and scalable implementation of the isolate API for multiple, isolated tasks, enabling the co-location of multiple server instances in a single MVM process. Isolates also enable the restructuring of a J2EE server implementation as a collection of isolated components, offering increased flexibility and reliability. The resulting system is a step towards a complete and scalable operating environment for enterprise applications. Copyright © 2006 John Wiley & Sons, Ltd.