The landscape of modern computing systems is shifting towards architectures built by combining available services under the “everything as a service” paradigm. These architectures are deployed on distributed cloud-edge infrastructures, aiming to provide innovative services to a wide range of users. However, it is crucial for these systems to address environmental sustainability concerns. This poses challenges in operating such systems in open, dynamic, and uncertain environments while minimizing their energy consumption. To tackle these challenges, we propose a decentralized service assembly approach that ensures the assembly is energetically self-sustainable by relying on locally harvested and stored energy. In our contribution, we introduce a general service selection template that enables the derivation of different selection policies. These policies guide the construction and maintenance of the service assembly. To evaluate their effectiveness in meeting the sustainability requirements, we conduct a comprehensive set of simulation experiments, providing valuable insights.

Antifragility is one of the terms that have recently emerged with the aim of indicating a direction that should be pursued toward the objective of designing Information and Communications Technology systems that remain trustworthy despite their dynamic and evolving operating context. We present a characterization of antifragility, aiming to clarify from a conceptual viewpoint the implications of its adoption as a design guideline and its relationships with other approaches sharing a similar objective. To this end, we discuss the inclusion of antifragility (and related concepts) within the well-known dependability taxonomy, which was proposed a few decades ago with the goal of providing a reference framework to reason about the different facets of the general concern of designing dependable systems. From our conceptual characterization, we then derive a possible path toward the engineering of antifragile systems.

In the last few years, a large number of real-time analytics applications rely on the Data Stream Processing (DSP) so to extract, in a timely manner, valuable information from distributed sources. Moreover, to efficiently handle the increasing amount of data, recent trends exploit the emerging presence of edge/Fog computing resources so to decentralize the execution of DSP applications. Since determining the Optimal DSP Placement (for short, ODP) is an NP-hard problem, we need efficient heuristics that can identify a good application placement on the computing infrastructure in a feasible amount of time, even for large problem instances. In this paper, we present several DSP placement heuristics that consider the heterogeneity of computing and network resources; we divide them in two main groups: model-based and model-free. The former employ different strategies for efficiently solving the ODP model. The latter implement, for the problem at hand, some of the well-known meta-heuristics, namely greedy first-fit, local search, and tabu search. By leveraging on ODP, we conduct a thorough experimental evaluation, aimed to assess the heuristics' efficiency and efficacy under different configurations of infrastructure size, application topology, and optimization objectives.

Modern applications, e.g., for pervasive computing scenarios, are increasingly reliant on systems built from multiple distributed components, which must be suitably composed to meet some specified functional and non-functional requirements. A key challenge is how to efficiently and effectively manage such complex systems. The use of self-management capabilities has been suggested as a possible way to address this challenge. To cope with the scalability and robustness issues of large distributed systems, self-management should ideally be architected in a decentralized way, where the overall system behavior emerges from local decisions and interactions. Within this context, we propose GOPRIME, a fully decentralized middleware solution for the adaptive self-assembly of distributed services. The GOPRIME goal is to build and maintain an assembly of services that, besides functional requirements, fulfils also global quality-of-service and structural requirements. The key aspect of GOPRIME is the use of a gossip protocol to achieve decentralized information dissemination and decision making. To show the validity of our approach, we present results from the experimentation of a prototype implementation of GOPRIME in a mobile health application, and an extensive set of simulation experiments that assess the effectiveness of GOPRIME in terms of scalability, robustness and convergence speed.

Fog computing is rapidly changing the distributed computing landscape by extending the Cloud computing paradigm to include wide-spread resources located at the network edges. This diffused infrastructure is well suited for the implementation of data stream processing (DSP) applications, by possibly exploiting local computing resources. Storm is an open source, scalable, and fault-tolerant DSP system designed for locally distributed clusters. We made it suitable to operate in a geographically distributed and highly variable environment; to this end, we extended Storm with new components that allow to execute a distributed QoS-aware scheduler and give self-adaptation capabilities to the system. In this paper we provide a thorough experimental evaluation of the proposed solution using two sets of DSP applications: the former is characterized by a simple topology with different requirements; the latter comprises some well known applications (i.e., Word Count, Log Processing). The results show that the distributed QoS-aware scheduler outperforms the centralized default one, improving the application performance and enhancing the system with runtime adaptation capabilities. However, complex topologies involving many operators may cause some instability that can decrease the DSP application availability.

Architecting software systems according to the service-oriented paradigm and designing runtime self-adaptable systems are two relevant research areas in today's software engineering. In this paper, we address issues that lie at the intersection of these two important fields. First, we present a characterization of the problem space of self-adaptation for service-oriented systems, thus providing a frame of reference where our and other approaches can be classified. Then, we present MOSES, a methodology and a software tool implementing it to support QoS-driven adaptation of a service-oriented system. It works in a specific region of the identified problem space, corresponding to the scenario where a service-oriented system architected as a composite service needs to sustain a traffic of requests generated by several users. MOSES integrates within a unified framework different adaptation mechanisms. In this way it achieves greater flexibility in facing various operating environments and the possibly conflicting QoS requirements of several concurrent users. Experimental results obtained with a prototype implementation of MOSES show the effectiveness of the proposed approach.

Runtime adaptation is recognized as a viable way for a service-oriented system to meet QoS requirements in its volatile operating environment. In this paper we propose a methodology to drive the adaptation of such a system, that integrates within a unified framework different adaptation mechanisms, to achieve a greater flexibility in facing different operating environments and the possibly conflicting QoS requirements of several concurrent users. To determine the most suitable adaptation action(s), the methodology is based on the formulation and solution of a linear programming problem, which is derived from a behavioral model of the system updated at runtime by a monitoring activity. Numerical experiments show the effectiveness of our approach. Besides the methodology, we also present a prototype tool that implements it.

In the service oriented paradigm applications are created as a composition of independently developed Web services. Since the same service may be offered by different providers with different non-functional Quality of Service (QoS) attributes, a selection process is needed to identify the constituent services for a given composite service that best meet the users QoS requirements. In this paper, we consider a broker that offers a composite service with multiple QoS classes to several users each generating a flow of requests over time. We propose a service selection scheme which optimizes the end-to-end aggregated QoS of all incoming flows of requests by means of a simple linear programming problem which scales as the number of users, request volumes and/or services grows. This approach differs from most of the current proposals which may not scale well since: a) requests, even from the same user, are handled independently from one another; and b) the selection process often requires the solution of an NP-hard problem.

The high heterogeneity and variability of mobile computing environments can adversely affect the performance of applications running in these environments. To tackle this problem, adaptation techniques can be exploited. Adaptation based on code mobility is a possible solution, as it allows to dynamically modify the load of the hosting nodes and the internode traffic, to adapt to the changing characteristics of computing nodes and network links. In this paper we propose a modeling framework to analyze the performance effectiveness of code mobility based adaptation in a mobile computing environment. A distinguishing feature of our framework is the modeling of both physical and logical mobility as something that can be plugged into a pre-existing architecture model, to ease the analysis of the performance impact of both different physical mobility scenarios, and of different adaptation strategies based on code mobility. To enhance the framework usability, we have adopted UML as modeling language, remaining fully compliant with the latest UML 2.0 specification and with the standard UML Profile for Schedulability, Performance and Time Specification.

Navigational tools that assist travelling people when they visit new areas are becoming increasingly common. Some of these tools are designed to be used with small portable devices (palmtops and also cellular phones) to give users more flexibility in their utilization (e.g. while walking in a town). The limited storage capacity of such devices makes it unrealistic to think that all the useful information can be stored in such devices, apart from the most basic one, like road maps and location of some points of interests. Other more detailed information should be loaded on the fly when needed, e.g., by using a wireless connection to some remote information service. However, this could cause unacceptably high latency to get the information. Another important factor that must be taken into account is the energy consumption caused by the access to the remote information service through a wireless link. Hence, the design of an information service that integrates the capabilities of palmtop-based navigational tools must be carefully designed around the goals of reducing both the perceived latency and the consumed energy. To this purpose, we investigate the use of caching and prefetching techniques. We present analytical and numerical results obtained by defining a probabilistic model of the typical utilization scenario of the considered service and corresponding cache management policies.

Adaptable software architectures (SA) have been suggested as a viable solution for the design of distributed applications that operate in a mobile computing environment to cope with the high heterogeneity and variability of this environment. Mobile code techniques can be used to implement this kind of SA since they allow us to dynamically modify the load of the hosting nodes and the internode traffic to adapt to the resources available in the nodes and to the condition of the (often wireless) network link. However, moving code among nodes has a cost (e.g., in terms of network traffic and consumed energy for mobile nodes), so designing an adaptable SA based on mobile code techniques requires a careful analysis to determine its effectiveness from the early design stages. In this respect, our main contribution consists of a methodology, called ASAP (adaptable software architectures performance), to automatically derive, starting from a design model of a mobility-based SA, a Markov model whose solution provides insights about the most effective adaptation strategy based on code mobility in a given execution environment. We assume that the SA model is expressed using the Unified Modeling Language (UML) because of its widespread use in software design, also suggesting some extension to this formalism to better express the "mobility structure" of the application, i.e., which are the mobile components, and the possible targets of their movement.

Periodic data broadcasting on a wireless channel has been proposed as an effective data dissemination technique for mobile users. With this technique, users access data by simply monitoring the channel until the required data appear in the broadcast. Hence, this access mode may be advantageous in a wireless environment with respect to the traditional client-server access mode, since it consumes less bandwidth and avoids energy-expensive requests sending. Potential drawbacks of this access mode are the latency caused by the wait for the required data in the broadcast, and the energy consumption caused by active listening on the wireless channel. Some techniques have been proposed to alleviate these two drawbacks. In particular, the use of prefetching has been proposed to reduce the latency experienced by a user. However, prefetching could have a negative effect on energy consumption, since it could actually increase the number of channel accesses. We define a general analytical model to investigate the impact of prefetching on both latency and energy consumption in a wireless broadcast data delivery system, with the goal of determining policies characterized by a good tradeoff between the two goals of reducing latency and saving energy, and then compare by simulation two policies derived from the model results with a policy proposed in the literature.

Article Modeling and evaluation of prefetching policies for context-aware information services Share on Authors: Vittoria de Nitto Personè Dipartimento di Informatica, Sistemi e Produzione, Università di Roma "Tor Vergata", Italy Dipartimento di Informatica, Sistemi e Produzione, Università di Roma "Tor Vergata", ItalyView Profile , Vincenzo Grassi Dipartimento di Informatica, Sistemi e Produzione, Università di Roma "Tor Vergata", Italy Dipartimento di Informatica, Sistemi e Produzione, Università di Roma "Tor Vergata", ItalyView Profile , Antonio Morlupi Dipartimento di Informatica, Sistemi e Produzione, Università di Roma "Tor Vergata", Italy Dipartimento di Informatica, Sistemi e Produzione, Università di Roma "Tor Vergata", ItalyView Profile Authors Info & Claims MobiCom '98: Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networkingOctober 1998 Pages 55–65https://doi.org/10.1145/288235.288249Published:25 October 1998 19citation687DownloadsMetricsTotal Citations19Total Downloads687Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The probability distribution of the overhead caused by the use of the checkpointing rollback recovery technique is evaluated in both cases of a single critical task and of an overall transaction-oriented system. This distribution is obtained in Laplace-Stieltjes transform form, from which all the moments can be easily calculated. Alternatively, inversion methods can be used to evaluate the distribution. The authors propose checkpointing strategies based on the above distribution in order to optimize performance criteria motivated, in the case of critical tasks, by real time constraints, and in the case of transaction-oriented systems, by the need of guaranteeing the users about the maximum system unavailability.>

Fault-tolerant computer systems may be evaluated by calculating their cumulative performance (e.g., number of processes jobs) over a finite mission time. A method for calculating the cumulative performance distribution assuming that the system fault-repair behavior can be modeled by a homogeneous Markov process is described. The method proposed for calculating the probability distribution of the accumulated reward over a finite mission is applicable to models of repairable and nonrepairable systems. The related solution algorithm shows a low polynomial computational complexity. The mathematical model is introduced, and the results already presented in the literature are surveyed. A comprehensive analysis of the time and space complexity of the proposed solution algorithm is presented. A numerical example is given.>

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