A Smart Checkpointing Scheme for Improving the Reliability of Clustering Routing Protocols

In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency

Towards priority-awareness in autonomous intelligent systems

In Autonomous and Intelligent systems (AIS), the decision-making process can be divided into two parts: (i) the priorities of the requirements are determined at design-time; (ii) design selection follows where alternatives are compared, and the preferred alternatives are chosen autonomously by the AIS. Runtime design selection is a trade-off analysis between non-functional requirements (NFRs) that uses optimisation methods, including decision-analysis and utility theory. The aim is to select the design option yielding the highest expected utility. A problem with these techniques is that they use a uni-scalar cumulative utility value to represent a combined priority for all the NFRs. However, this uni-scalar value doesn't give information about the varying impacts of actions under uncertain environmental contexts on the satisfaction priorities of individual NFRs. In this paper, we present a novel use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to support reasoning of separate NFR priorities. We discuss the use of rewards in MR-POMDPs as a way to support AIS with (a) priority-aware decision-making; and (b) maintain service-level agreement, by autonomously tuning NFRs' priorities to new contexts and based on data gathered at runtime. We evaluate our approach by applying it to a substantial Network case

Towards an architecture integrating complex event processing and temporal graphs for service monitoring

Software is becoming more complex as it needs to deal with an increasing number of aspects in volatile environments. This complexity may cause behaviors that violate the imposed constraints. A goal of runtime service monitoring is to determine whether the service behaves as intended to potentially allow the correction of the behavior. It may be set up in advance the infrastructure to allow the detections of suspicious situations. However, there may also be unexpected situations to look for as they only become evident during data stream monitoring at runtime produced by te system. The access to historic data may be key to detect relevant situations in the monitoring infrastructure. Available technologies used for monitoring offer different trade-offs, e.g. in cost and flexibility to store historic information. For instance, Temporal Graphs (TGs) can store the long-term history of an evolving system for future querying, at the expense of disk space and processing time. In contrast, Complex Event Processing (CEP) can quickly react to incoming situations efficiently, as long as the appropriate event patterns have been set up in advance. This paper presents an architecture that integrates CEP and TGs for service monitoring through the data stream produced at runtime by a system. The pros and cons of the proposed architecture for extracting and treating the monitored data are analyzed. The approach is applied on the monitoring of Quality of Service (QoS) of a data-management network case study. It is demonstrated how the architecture provides rapid detection of issues, as well as the ability to access to historical data about the state of the system to allow for a comprehensive monitoring solution

Index selection: A query pattern mining approach

Indexing technique has been used extensively in order to facilitate and optimize query processing in various data retrieval and storage systems. Although an index technique can be used to reduce searching cost in both horizontal and vertical dimensions, it incurs both storage and maintenance costs. Index selection has been an active research subject, and various index selection methods have been given in literature. This paper will present a framework to mine frequent query patterns to select most frequently used access paths as candidates for index selection, and a Bayesian based method will be used to select index fields from the candidate set

Index Selection: A Query Pattern Mining Based Approach

Indexing technique has been used extensively in order to facilitate and optimize query processing in various data retrieval and storage systems. Although an index technique can be used to reduce searching cost in both horizontal and vertical dimensions, it incurs both storage and maintenance costs. Index selection has been an active research subject, and various index selection methods have been given in literature. This paper will present a framework to mine frequent query patterns to select most frequently used access paths as candidates for index selection, and a Bayesian based method will be used to select index fields from the candidate set

Impact of Mobility on Routing Energy Consumption in Mobile Sensor Networks

Mobility in mobile sensor networks causes frequent route breaks, and each routing scheme reacts differently during route breaks. It results in a performance degradation of the energy consumption to reestablish the route. Since routing schemes have various operational characteristics for rerouting, the impact of mobility on routing energy consumption shows significantly different results under varying network dynamics. Therefore, we should consider the mobility impact when analyzing the routing energy consumption in mobile sensor networks. However, most analysis of the routing energy consumption concentrates on the traffic condition and often neglects the mobility impact. We analyze the mobility impact on the routing energy consumption by deriving the expected energy consumption of reactive, proactive, and flooding scheme as a function of both the packet arrival rate and topology change rate. Routing energy consumption for mobile sensor networks is analytically shown to have a strong relationship with sensor mobility and traffic conditions. We then demonstrate the accuracy of our analysis through simulations. Our analysis can be used to decide a routing scheme that will operate most energy efficiently for a sensor application, taking into account the mobility as well as traffic condition

Space-efficient page-level incremental checkpointing

Incremental checkpointing, which is intended to minimize checkpointing overhead, saves only the modified pages of a process. However, the cumulative size of incremental checkpoints increases at a steady rate over time because a number of updated values may be saved for the same page. In this paper, we present a comprehensive overview of Pickpt, a page-level incremental checkpointing facility. Pickpt provides space-efficient techniques aiming to minimizing the use of disk space. For our experiments, the results showed that the use of disk space using Pickpt was significantly reduced, compared with existing incremental checkpointing