Multienergy microgrids are a promising solution to improve overall energy (electricity, cooling, heating, etc.) efficiency. In this paper, a new optimal energy trading strategy is developed considering the risk from uncertain energy supply and demand in a set of individual multienergy microgrids. According to the historical data about energy supply of each microgrid, an aggregator aims to maximize each microgrid's profit while minimizing the risk of overbidding for renewable energy resources trading based microgrids. A novel two-stage stochastic game model with Cournot Nash pricing mechanism and the conditional value-at-risk criterion is proposed to characterize the payoff function of each microgrid. The sample average approximation (SAA) technique is employed to approximate the stochastic Nash equilibrium of the game model. The existence of the SAA Nash equilibrium is investigated and the corresponding Nash equilibrium seeking algorithm is also realized in a distributed manner. The proposed method is validated by numerical simulations on real-world data collected in Australia, and the results show that the SAA Nash equilibrium based strategy can effectively reduce the risk of not meeting the demand and improve the economic benefits for each microgrid.

The feasibility of using adaptive object migration to enable the execution of heavy applications in pervasive environments, is determined by the computational efficiency of adaptation algorithms and the efficacy of their decisions. These two factors, which are largely predicated by the resource constraints of devices, are heavily influenced by the granularity at which adaptation decisions are performed. This paper proposes a new type of adaptation granularity which combines the efficiency of coarse level approaches with the efficacy of fine-grained adaptation. A novel approach for achieving this level of granularity through the dynamic decomposition of runtime class graphs is presented and empirically evaluated on a corpus of real world applications. It is shown that the approach improves the efficacy of adaptation decisions by reducing network overheads by a minimum of 17% to as much 99%, while maintaining comparable decision making efficiency to class level adaptation.

Adaptive offloading dynamically distributes portions of a computationally heavy application to remote devices to achieve context specific optimisations. However, since existing state-of-the-art approaches incur significant overhead from storing, updating and partitioning application graphs this paper proposes a novel distributed approach to alleviate much of this overhead. Specifically, each device maintains a graph consisting only of components in its own memory space, while maintaining abstraction elements for components in remote devices. This approach removes the need to store and update complete application graphs on each device and reduces the cost of partitioning an application during adaptation. An evaluation involving computationally heavy open-source applications adapting in a heterogeneous collaboration showed that the new approach reduced graph update network cost by 100%, collaboration-wide memory cost by between 37% and 50%, battery usage by between 63% and 93%, and adaptation time by between 19% and 98%, while improving efficacy of adaptation by 12% and 34% for two of the considered applications.

Due to the high cost of replication, incurred mainly by consistency management, it may be infeasible to use uninformed object replication in collaborative applications in pervasive environments, since resources are constrained, nodes are heterogeneous, and application behaviour is highly dynamic. Consequently, this paper proposes an adaptive replication scheme that dynamically creates and releases replicas on client nodes during runtime, with the objective of improving response time performance. This is based on both the runtime information of the execution environments, and application behaviours of both client and server nodes. The adaptive replication algorithm is simulated with resource utilisation and response times compared to no replication and full replication schemes. Results show that the adaptive scheme is able to improve response time in a greater range of execution contexts than either no or full replication, while introducing less synchronisation overhead than the full replication scheme.

One of the goals of Service-Oriented Computing (SOC) is to improve software maintainability as businesses become more agile, and thus underlying processes and rules change more frequently. This paper presents a controlled experiment examining the relationship between coupling in service-oriented designs, as measured using a recently proposed suite of SOC-specific coupling metrics and software maintainability in terms of the specific subcharacteristics of analyzability, changeability, and stability. The results indicate a statistically significant causal relationship between the investigated coupling metrics and the maintainability of service-oriented software. As such, the investigated metrics can facilitate coupling related design decisions with the aim of producing more maintainable service-oriented software products.

Service-Oriented Computing (SOC) is intended to improve software maintainability as businesses become more agile and underlying processes and rules change more frequently. However, to date, the impact of service cohesion on the analyzability subcharacteristic of maintainability has not been rigorously studied. Consequently, this paper extends existing notions of cohesion in the Procedural and OO paradigms in order to account for the unique characteristics of SOC, thereby supporting the derivation of design-level software metrics for objectively quantifying the degree of service cohesion. The metrics are theoretically validated, and an initial empirical evaluation using a small-scale controlled study suggests that the proposed metrics could help predict analyzability early in the Software Development Life Cycle. If future industrial studies confirm these findings, the practical applicability of such metrics is to support the development of service-oriented systems that can be analyzed, and thus maintained, more easily. In addition, such metrics could help identify design problems in existing systems.

Real time mobile collaboration systems allow two or more geographically separated users to work on a shared document at the same time. There has been a large amount of work on consistency management for real-time collaboration in replicated architectures, however, the algorithms depend on fixed membership, and are not suited to ad-hoc mobile networks. This paper builds on the previous work of the authors by enhancing the algorithm to handle membership events. The proposed algorithm is built on point to point membership management, and uses a state map to handle multiple sites joining the session at the same time. It is able to manage all membership events while maintaining document consistency. The algorithm has been implemented and evaluated across a number of mobile collaboration scenarios. The results show that the algorithm is able to handle membership events correctly with a relatively low impact on resource consumption