The advent of 5G drives the growth of multi-access edge computing (MEC), a revolutionary paradigm that utilises edge resources to enable low-latency mobile access and support complex service execution. Deploying services across geographically distributed edge nodes challenges providers to optimise performance metrics like end-to-end latency and resource efficiency, impacting user experience, operational cost, and environmental footprint. The energy harvesting (EH) technology provides clean and renewable energy at the edge, promoting the MEC system to minimise the impacts on the environment. However, the integration of EH can introduce energy limits and uncertainty to the powered devices. In the context of service scheduling with data flow dependencies, we propose two offline and heuristic-based service placement algorithms that balance minimizing latency and maximizing resource efficiency with fast execution. The two algorithms, evaluated in a simulated environment using state-of-the-art workload benchmarks, achieve significant energy consumption improvements while maintaining comparable latency. Based on the designed algorithms, we take a step further by developing an online dynamic resource scheduling and service offloading approach for MEC systems with EH capabilities. Simulation results demonstrate that the proposed strategy effectively utilise the harvested energy while granting a low user-experienced latency and low operational cost.

Blockchain adoption is reaching an all-time high, with a plethora of blockchain architectures being developed to cover the needs of applications eager to integrate blockchain into their operations. However, blockchain systems suffer from the trilemma trade-off problem, which limits their ability to scale without sacrificing essential metrics such as decentralisation and security. The balance of the trilemma trade-off is primarily dictated by the consensus protocol used. Since consensus protocols are designed to function well under specific system conditions, and consequently, due to the blockchain’s complex and dynamic nature, systems operating under a single consensus protocol are bound to face periods of inefficiency. The work presented in this paper constitutes part of an effort to design a Digital Twin-based blockchain management framework to balance the trilemma trade-off problem, which aims to adapt the consensus process to fit the conditions of the underlying system. Specifically, this work addresses the problems of extracting the blockchain system and mirroring it in its digital twin by proposing algorithms that overcome the challenges posed by blockchains’ decentralised and asynchronous nature and the fundamental problems of global state and synchronisation in such systems. The robustness of the proposed algorithms is experimentally evaluated.

Despite recent leaps in artificial intelligence and natural language generation, which have led to widespread adoption, the integration of large language models in modelling and simulation has been limited. This work discusses the use of pre-trained large language models for the augmentation of a discrete event blockchain simulation system and their possible implications.

Over the last few years, Digital Twin (DT) has emerged as an innovative concept that integrates multiple technologies to mirror physical assets, systems, and processes. Assisted by data analytics, predictive models, and optimisation techniques, DTs are suitable for enhancing operations in virtual space before transferring information into real-world counterparts. Moreover, initiatives considering DTs as part of a composite complex system might consider federated ecosystems for exchanging insights and relevant information. In this context, the Federated Digital Twin (FDT) concept is a potential solution to address interaction among virtual entities, enabling advanced operations and ensuring collaborative decision-making. This study describes a comprehensive FDT framework inspired by principles and methodologies considered by well-studied federated systems. Furthermore, a reference abstract architecture allowing seamless integration among multiple agent-based DTs is provided as a tool to develop a wide range of DT-based applications.

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system.

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

Despite the recent increase in the popularity of blockchain, the technology suffers from the trilemma trade-off between security decentralisation and scalability prohibiting adoption, and limiting the efficiency and effectiveness of the induced system. Addressing the trilemma trade-off calls for dynamic management and configuration of the blockchain system. In particular, choosing an effective and efficient consensus protocol for balancing the trilemma trade-off when inducing the blockchain-based system is acknowledged to be a challenging problem given the dynamic and complex nature of the blockchain environment. DDDAS approaches are particularly suitable for this challenge, and in previous work, the authors presented a novel DDDAS-based blockchain architecture and demonstrated that it offers a promising approach for dynamically adjusting the parameters of a system and optimising for the trade-off. This paper presents a novel simulation tool that can support and satisfy the DDDAS requirements for a dynamically re-configurable blockchain system. The tool supports the simulation and the dynamic switching of consensus protocols, analysing their trilemma trade-off. The simulator design is modular and allows the implementation and analysis of a wide range of consensus protocols and their implementation scenarios, along with the ability for parallelization. The paper also presents a quantitative evaluation of the tool.

In the interval scheduling problem, jobs have known start and end times (referred to as job intervals) and must be assigned to processing nodes for their whole duration. Although the problem originally stems from the resource allocation demands of resident processes in operating systems, it found a renewed interest in the Cloud context, both in IaaS and SaaS, since reservations for virtual machines and services often have known activation intervals. A common objective of interval scheduling is to minimize busy time of machines which relates (among others) to minimizing the number of machines participating in the computation. As a consequence, bin packing techniques have been applied in the past. In this paper we tackle the online version of the problem, whereby future job arrivals are unknown. We propose novel algorithms that work as a pre-processing step to any bin packing scheme by offering recommendations that are enforced in all packing decisions. Job overlaps are used to characterize pairwise job affinity and subsequently provide threshold based job allocation recommendations. Thresholds are calculated using lower bound theoretical analysis upon two extreme workloads (sparse and dense). Experimental evaluation using real world workloads illustrates the merits of our approach against state-of-the-art algorithms.

Workflow management is a widely studied research subject due to its criticality for the efficient execution of various processing activities towards concluding innovative applications. The ultimate goal is to eliminate the required time for delivering the final outcome considering the dependencies between workflow’s tasks. In this paper, we enhance the decision making of a scheduler with a batch oriented approach to deal with multiple workflows. A probabilistic data oriented approach combined with an infrastructure oriented scheme is provided to pay attention on dynamic environments where the underlying data are continuously updated trying to minimize the network overhead for migrating data. Workflows are mapped to the available datasets according to their data requirements, then, we combine the outcome with an optimization model upon the time and cost requirements of every placement. The performance of our model is revealed by a high number of experiments depicting the advantages in the network overhead.

One of the fundamental problems encountered by large-scale computing systems, such as clusters and cloud, is to schedule a set of jobs submitted by the users. Each job is characterized by resource demands, as well as start and completion time. Each job must be scheduled to execute on a machine having the required capacity between the start and completion time (referred as interval) of the job. Each machine is defined by a parallelism parameter g that indicates the maximum number of jobs that can be processed by the machine, in parallel. The above problem is referred to as the interval scheduling problem with bounded parallelism. The objective is to minimize the total busy time of all machines. Majority of the solutions proposed in the literature consider homogeneous set of jobs and machines that is a simplified assumption as in practice, heterogeneous jobs and machines are frequently encountered. In this article, we tackle the aforesaid problem with a set of heterogeneous jobs and machines. A major contribution of our work is that the problem is addressed in a novel way by combining a graph-based approach and a dynamic programming approach which is based on a variation of bin packing problem. A greedy algorithm is also proposed by employing only a graph-based approach at the aim to reduce the computational complexity. Experimental results show that the proposed algorithms can significantly reduce the cumulative busy interval over all machines compared with state-of-the-art algorithms proposed in the literature.

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you -go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.

Agent-to-agent communications is an important operation in multi-agent systems and their simulation. Given the data-centric nature of agent-simulations, direct agent-to-agent communication is generally an orthogonal operation to accessing shared data in the simulation. In distributed multi-agent-system simulations in particular, implementing direct agent-to-agent communication may impose serious performance degradation due to potentially large communication and synchronization overheads. In this paper, we propose an efficient agent-to-agent communication method in the context of optimistic distributed simulation of multi-agent systems. An implementation of the proposed method is demonstrated and quantitatively evaluated through its integration into the PDES-MAS simulation kernel.

Service migration between datacenters can reduce the network overhead within a cloud infrastructure; thereby, also improving the quality of service for the clients. Most of the algorithms in the literature assume that the client access pattern remains stable for a sufficiently long period so as to amortize such migrations. However, if such an assumption does not hold, these algorithms can take arbitrarily poor migration decisions that can substantially degrade system performance. In this paper, we approach the issue of performing service migrations for an unknown and dynamically changing client access pattern. We propose an online algorithm that minimizes the inter-datacenter network, taking into account the network load of migrating a service between two datacenters, as well as the fact that the client request pattern may change “quickly”, before such a migration is amortized. We provide a rigorous mathematical proof showing that the algorithm is 3.8-competitive for a cloud network structured as a tree of multiple datacenters. We briefly discuss how the algorithm can be modified to work on general graph networks with an O(log|V|) probabilistic approximation of the optimal algorithm. Finally, we present an experimental evaluation of the algorithm based on extensive simulations.

Virtual machine (VM) migration is a widely used technique in cloud computing systems to increase reliability. There are also many other reasons that a VM is migrated during its lifetime, such as reducing energy consumption, improving performance, maintenance, etc. During a live VM migration, the underlying VM continues being up until all or part of its data has been transmitted from source to destination. The remaining data are transmitted in an off-line manner by suspending the corresponding VM. The longer the off-line transmission time, the worse the performance of the respective VM. The above is because during the off-line data transmission, the VM service is down. Because a running VM's memory is subject to changes, already transmitted data pages may get dirtied and thus needing re-transmission. The decision of when suspending the VM is not a trivial task at all. The above is justified by the fact that when suspending the VM early we may result in transmitting off-line a significant amount of data degrading thus the VM's performance. On the other hand, a long waiting time to suspend the VM may result in re-transmitting a huge amount of dirty data, leading in that way to waste of resources. In this paper, we tackle the joint problem of minimizing both the total VM migration time (reflecting the resources spent during a migration) and the VM downtime (reflecting the performance degradation). The aforementioned objective functions are weighted according to the needs of the underlying cloud provider/user. To tackle the problem, we propose an online deterministic algorithm resulting in an strong competitive ratio, as well as a randomized online algorithm achieving significantly better results against the deterministic algorithm.

Data stream processing has received considerable attention from both research community and industry over the last years. Since latency is a key issue in data stream processing environments, the majority of the works existing in the literature focus on minimizing the latency experienced by the users. The aforementioned minimization takes place by assigning the data stream processing components close to data sources. Server consolidation is also a key issue for drastically reducing energy consumption in computing systems. Unfortunately, energy consumption and latency are two objective functions that may be in conflict with each other. Therefore, when the target function is to minimize energy consumption, the delay experienced by users may be considerable high, and the opposite. For the above reason there is a dire need to design strategies such that by targeting the minimization of energy consumption, there is a graceful degradation in latency, as well as the opposite. To achieve the above, we propose a Pareto-efficient algorithm that tackles the problem of data processing tasks placement simultaneously in both dimensions regarding the energy consumption and latency. The proposed algorithm outputs a set of solutions that are not dominated by any solution within the set regarding energy consumption and latency. The experimental results show that the proposed approach is superior against single-solution approaches because by targeting one objective function the other one can be gracefully degraded by choosing the appropriate solution.

Minimizing service-level agreement (SLA) violations and energy consumption through server consolidation is of paramount importance for the sustainability of cloud environments. In this paper, we propose an online method to reduce cloud SLA violations by taking into account: (a) the energy consumption of migrating virtual machines and (b) server consolidation techniques to minimize the energy consumption within the system. Rigorous mathematical competitive analysis shows that the proposed method achieves a 2.4 competitive ratio against a cognitive adversary. Our solution is superior compared to the current state of the art algorithms, such as UP-VMC, KMI, MBFD, both theoretically (competitive ratios of other alternatives are unbounded) and empirically through simulations using CloudSim. More specifically, the competitive ratios of state of the art algorithms are unbounded and our proposed methodology reduces the VM migrations and energy consumption by 85% and 45%, respectively. The aforementioned improvement comes at an expense of a small increase in terms of SLA violations. The above results are achieved without the a priori knowledge of VM utilization patterns.

Recent advances in chip design and integration technologies have led to the development of Single-Chip Cloud computers which are a microcosm of cloud datacenters. Those computers are based on Network-on-Chip (NoC) architectures with deep memory hierarchies. Developing scheduling algorithms to reduce data access latency as well as energy consumption is a major challenge for such architectures. In this paper, we propose a set of algorithms to jointly address the problem of task scheduling and data allocation in a unified approach. Moreover, we present a feasible system model for NoC based multicores considering a three-level memory hierarchy that effectively captures the energy consumed by various elements of system including: processing cores, caches, and NoC subsystem. Simulation results show the superiority of proposed algorithms compared to two state-of-the-art algorithms found in the literature. The experimental results clearly indicate that algorithms performing data and task scheduling in a joint fashion are superior against techniques implementing task and data scheduling separately.

Several virtual machine (VM) placement algorithms have been proposed and studied in the literature with various scopes such as server consolidation or network cost minimization. In most cases, decisions on VM migrations are taken without factoring in directly the data access cost by VMs. In this paper, we investigate the use of data replication in conjunction with the VM assignment problem and target on developing algorithms that decide both on which data should be replicated where and which VM must be migrated so as to minimize the network overhead among traditional cloud and mobile cloud systems. We discuss both the un-capacitated case and the more realistic case whereby datacenters (for the traditional cloud case) and micro-datacenters (for the mobile cloud case) have limited storage and computing capacity. We propose an algorithm based on hyper-graph partitioning to solve the aforementioned problem in an optimal way regarding the unconstrained case and extend it to capture storage and computing capacity constraints. Experimental evaluation shows that the proposed algorithm yields up to 53 percent network overhead reduction when compared to state-of-the-art algorithms found in the literature.

The new video coding standard HEVC (High Efficiency Video Coding) offers the desired compression performance in the era of HDTV and UHDTV, as it achieves nearly 50% bit rate saving compared to H.264/AVC. To leverage the involved computational overhead, HEVC offers three parallelization potentials namely: wavefront parallelization, tile-based and slice-based. In this paper we study slice-based parallelization of HEVC using OpenMP on the encoding part. In particular we delve on the problem of proper slice sizing to reduce load imbalances among threads. Capitalizing on existing ideas for H.264/AVC we develop a fast dynamic approach to decide on load distribution and compare it against an alternative in the HEVC literature. Through experiments with commonly used video sequences, we highlight the merits and drawbacks of the tested heuristics. We then improve upon them for the case of Low-Delay by exploiting GOP structure. The resulting algorithm is shown to clearly outperform its counterparts achieving less than 10% load imbalance in many cases.

Smart cities have given a significant impetus to manage traffic and use transport networks in an intelligent way. For the above reason, intelligent transportation systems (ITSs) and location-based services (LBSs) have become an interesting research area over the last years. Due to the rapid increase of data volume within the transportation domain, cloud environment is of paramount importance for storing, accessing, handling, and processing such huge amounts of data. A large part of data within the transportation domain is produced in the form of Global Positioning System (GPS) data. Such a kind of data is usually infrequent and noisy and achieving the quality of real-time transport applications based on GPS is a difficult task. The map-matching process, which is responsible for the accurate alignment of observed GPS positions onto a road network, plays a pivotal role in many ITS applications. Regarding accuracy, the performance of a map-matching strategy is based on the shortest path between two consecutive observed GPS positions. On the other extreme, processing shortest path queries (SPQs) incurs high computational cost. Current map-matching techniques are approached with a fixed number of parameters, i.e., the number of candidate points ( N CP ) and error circle radius (ECR), which may lead to uncertainty when identifying road segments and either low-accurate results or a large number of SPQs. Moreover, due to the sampling error, GPS data with a high-sampling period (i.e., less than 10 s) typically contains extraneous datum, which also incurs an extra number of SPQs. Due to the high computation cost incurred by SPQs, current map-matching strategies are not suitable for real-time processing. In this paper, we propose real-time map-matching (called RT-MM), which is a fully adaptive map-matching strategy based on cloud to address the key challenge of SPQs in a map-matching process for real-time GPS trajectories. The evaluation of our approach against state-of-the-art approaches is performed through simulations based on both synthetic and real-world datasets.

With an ever increasing amount of data generated by scientific experiments, social networks and mobile as well as wireless sensor networks, reducing resource consumption by big data applications becomes of paramount importance. Towards this end, filtering data close to the data sources is a common strategy in order to reduce network traffic. Assuming a network of nodes, each potentially generating data and a query in the form of a single operator to be applied in these data, the basic statement of the operator placement problem is: find the best node to place the operator so that the network traffic is minimized. In this paper we study the problem of placing a set of communicating operators exhibiting a tree structure over a tree network of nodes with capacity constraints. We take advantage of our previous work on unconstrained placement in order to develop a new approach enabling both single and group operator migrations using evictions of hosted operators if free space is required. To enhance their applicability, the algorithms work in a distributed asynchronous manner, requiring only minimal knowledge at each network node. Results from simulation experiments show that the proposed algorithms reduce considerably network overhead against their counterparts.

Agent-based middleware platforms for wireless sensor networks (WSNs) have received a lot of attention during the last years, due to their great flexibility in re-programming, monitoring, handling, and optimizing the application as well as the whole system. Even though many algorithms have been proposed for the dynamic placement of agents within the WSN, they do not take into account the coordination aspects of such migrations. This not only may result in slow convergence but also in perpetual oscillations of agent migrations, degrading application and system performance. In this paper, we propose full-coordination, semi-coordination, and non-coordination agent migration strategies, and evaluate their convergence and network overhead. We also provide proofs that convergence is guaranteed when dynamic agent placement algorithms adopt our proposed strategies. Our results show that the semi-coordination strategy is superior in terms of both network overhead and convergence rate.

Operator placement plays a key role in reducing the aggregate network overhead within a wireless sensor network (WSN) to extend battery life and the longevity of the network. Consequently, optimal algorithms for the operator placement problem (OPP) are of paramount importance to WSN performance. Unfortunately, the OPP becomes NP-complete when capacity constraints on the WSN nodes are taken into account. There are many algorithms in the literature that tackle the OPP; however, most of them consider tree-structured query graphs without limitations regarding the operators hosted by the WSN nodes. Therefore, there is a need to propose sophisticated approaches such that the problem is solved in an effective fashion. In this paper, we propose a fully distributed approach that takes into account the WSN node capacity constraints. The proposed approach is thoroughly evaluated through simulations and the results reveal that the proposed approach is superior to several state-of-the-art algorithms, such as DRA, DBA, MCFA, dFNS, and GRAL* found in the literature.

Recent embedded middleware platforms enable the structuring of an application as a set of collaborating agents deployed on various nodes of the underlying wireless sensor network (WSN). Of particular importance is the network cost incurred due to agent communication, which in turn depends on how the agents are placed within the WSN system. In this paper, we present two agent migration algorithms with the aim of minimizing the total network overhead. The first one takes independent single agent migration decisions, while the second one considers groups of agents for migration. Both algorithms work in a fully distributed fashion based on the knowledge available locally at each node, and can be used both for one-shot initial application deployment as well as for the continuous updating of agent placement. We also propose two methodologies to tackle the problem when WSN nodes have limited capacity. We show through theoretical analysis that one of our algorithms (called GRAL$\ast$) always results in an optimal placement, while for the rest of the algorithms, we derive approximation ratios pertaining to their performance. We evaluate the performance of our algorithms through a series of simulation experiments. Results show that group migration algorithms are superior compared to single agent migration algorithms with the performance difference reaching 34% for some settings.

In this paper we tackle the problem of virtual machine (VM) placement onto physical servers to jointly optimize two objective functions. The first objective is to minimize the total energy spent within a cloud due to the servers that are commissioned to satisfy the computational demands of VMs. The second objective is to minimize the total network overhead incurred due to: (a) communicational dependencies between VMs, and (b) the VM migrations performed for the transition from an old assignment scheme to a new one. We study different methodologies for solving the aforementioned problem. The first approach is based on VM packing algorithms that optimize the above objective functions separately, reaching a single solution. The other approach is to tackle simultaneously the two optimization targets and define a set of non-dominating solutions. Performance evaluation using simulation experiments reveals interesting trade-offs between energy consumption and network load.

According to the pay-per-use model adopted in clouds, the more the resources consumed by an application running in a cloud computing environment, the greater the amount of money the owner of the corresponding application will be charged. Therefore, applying intelligent solutions to minimize the resource consumption is of great importance. Because centralized solutions are deemed unsuitable for large-distributed systems or large-scale applications, we propose a fully distributed algorithm (called DRA) to overcome the scalability issues. The aforementioned problem can be solved by identifying an assignment scheme between the interacting components of an application, such as processes and virtual machines, and the computing nodes of a cloud system, such that the total amount of resources consumed by the respective application is minimized. The decisions for the transition from one assignment scheme to another one are made in a dynamic way and based only on local information. It should be stressed that DRA achieves convergence and always results in the optimal solution. We also show, through an experimental evaluation, that DRA achieves up to 55% network cost reduction when compared to the most recent algorithm in the literature.

With the development of mobile code frameworks for embedded systems, an application can be structured as a set of cooperating components (agents) that are placed on the nodes of the system in a flexible way. Reducing the network traffic caused by the application components is a crucial issue for the increase in the lifetime of wireless embedded systems, since it is widely known that the communication cost plays the most significant role in the energy consumption of embedded devices. To this end, most placement algorithms place or move an agent towards the center of gravity of the communication workload. However, if the target node does not have enough capacity, the attempt is usually aborted. In this paper, we introduce eviction-enabled algorithms that allow nodes to free capacity by forcing a locally hosted agent to move to another node, even at a loss, to accept a new and potentially more beneficial agent. To the best of our knowledge, this is the first time that agents are evicted by local hosts to enable beneficial agent migrations and eventually improve the total network cost. In this paper, we provide algorithms tackling the aforementioned problem in a fully distributed manner. We also present and discuss the results of extensive simulations, showing that eviction-enabled algorithms can outperform their counterparts by up to 300%.

We assume a wireless sensor and actuator network with nodes that can harvest energy from the environment, and an application deployed in this network, which is structured as a set of cooperating mobile components that can be placed on any node that provides the required sensor and actuator resources. We propose algorithms that take into account the energy consumption rate of agents as well as energy reserves and harvesting rate of nodes, and decide about the migration of agents in order to improve application availability. Initial evaluation results via simulation show that application availability can be greatly improved compared to having a static application placement.

Recent embedded middleware initiatives enable the structuring of an application as a set of collaborating agents deployed in the various sensing/actuating entities of the system. Of particular importance is the incurred cost due to agent communication which in terms depends on agent positions in the system. In this paper we present GRAL a grouping algorithm that migrates groups of agents with the aim of minimizing communication. The algorithm works in a distributed fashion based on knowledge available locally at each node and can be used both for one-shot initial application deployment and for the continuous updating of agent placement. Through simulation experiments under various scenarios we evaluate the algorithm, comparing the solution quality reached against the optimal obtained from exhaustive search.

Given two replication schemes Xoldand Xnew, the replica transfer scheduling problem (RTSP) aims at reaching Xnew, starting from Xold, with minimal implementation cost. In this paper we generalize the problem description to include special cases, where deadlocks can occur while in the process of implementing Xnew. We address this impediment by introducing artificial (dummy) transfers. We then prove that RTSP-decision is NP-complete and propose two kinds of heuristics. The first attempts to replace dummy transfers with valid ones, while the second minimizes the implementation cost. Experimental evaluation of the algorithms illustrates the merits of our approach.