By using different interface adapters for different methods, it is possible to construct a maximally covering web of interface adapters which incurs minimum loss during interface adaptation. We introduce a polynomial-time algorithm that can achieve this. However, we also show that minimizing the number of adapters included in a maximally covering web of interface adapters is an NP-complete problem.
@cite_2 proposes an interface adaptation framework which attempts to minimize the loss incurred by an interface adapter chain, and @cite_3 rigorously defines the mathematical background required to implement such a framework. These only consider the use of single chains of interface adapters.
@cite_3 @cite_2
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Despite providing similar functionality, multiple network services may require the use of different interfaces to access the functionality, and this problem will only become worse with the widespread deployment of ubiquitous computing environments. One way around this problem is to use interface adapters that adapt one interface into another. Chaining these adapters allows flexible interface adaptation with fewer adapters, but the loss incurred because of imperfect interface adaptation must be considered. This study outlines a matrix-based mathematical basis for analysing the chaining of lossy interface adapters. The authors also show that the problem of finding an optimal interface adapter chain is NP-complete with a reduction from 3SAT. A key feature of ubiquitous computing is service continuity which allows a user to transparently continue his task regardless of his movement. For service continuity, the underlying system needs to not only discover a service satisfying a user's request, but also provide an interface differences resolution scheme if the interface of the service found is not the same as that of the service requested. For resolving interface mismatches, one of solutions is to use an interface adapter. The most serious problem in the interface adapter-based approach is the overhead of adapter generation. There are many research efforts about adapter generation load reduction and this paper focuses on an adapter chaining scheme to reduce the number of necessary adapters among different service interfaces. We propose a construction-time adaptation loss evaluation scheme and an adapter chain construction algorithm, which finds an adapter chain with minimal adaptation loss. In this paper, we define the problem of simultaneously deploying multiple versions of a web service in the face of independently developed unsupervised clients. We then propose a solution in the form of a design technique called Chain of Adapters and argue that this approach strikes a good balance between the various requirements. We recount our experiences in automating the application of the technique and provide an initial analysis of the performance degradations it may occasion. The Chain of Adapters technique is particularly suitable for self-managed systems since it makes many version-related reconfiguration tasks safe, and thus subject to automation. Modern, complex software systems (e-commerce, IT, critical infrastructures, etc.) are increasingly required to continue operation in the face of change, to self-adapt to accommodate shifting user priorities, resource variability, changing environments, and component failures. While manual oversight benefits from global problem contexts and flexible policies, human operators are costly and prone to error. Low-level, embedded mechanisms (exceptions, time-outs, etc.) are effective and timely for error recovery, but are local in scope to the point-of-failure, application-specific, and costly to modify when adaptation objectives change. An ideal solution leverages domain expertise, provides an end-to-end system perspective, adapts the system in a timely manner, and can be engineered cost-effectively. Architecture-based self-adaptation closes the “loop of control,” using external mechanisms and the architecture model of the target system to adapt the system. An architecture model exposes important system properties and constraints, provides end-to-end problem contexts, and allows principled and automated adaptations. Existing architecture-based approaches specialize support for particular classes of systems and fixed sets of quality-of-service concerns; they are costly to develop for new systems and to evolve for new qualities. To overcome these limitations, we posit this thesis: We can provide software engineers the ability to add and evolve self-adaptation capabilities cost-effectively, for a wide range of software systems, and for multiple objectives, by defining a self-adaptation framework that factors out common adaptation mechanisms and provides explicit customization points to tailor self-adaptation capabilities for particular classes of systems, for multiple quality-of-service objectives. Our approach, embodied in a system called Rainbow, provides an engineering approach and a framework of mechanisms to monitor a target system and its environment, reflect observations into the system's architecture model, detect opportunities for improvements, select a course of action, and effect changes. The framework provides general and reusable infrastructures with well-defined customization points, a set of abstractions, and an adaptation engineering process, focusing engineers on adaptation concerns to systematically customize Rainbow to particular systems. To automate system self-adaptation, Rainbow provides a language, called Stitch, to represent routine human adaptation knowledge using a core set of adaptation concepts. The recently developed compressive sensing (CS) framework enables the design of sub-Nyquist analog-to-digital converters. Several architectures have been proposed for the acquisition of sparse signals in large swaths of bandwidth. In this paper we consider a more flexible multi-channel signal model consisting of several discontiguous channels where the occupancy of the combined bandwidth of the channels is sparse. We introduce a new compressive acquisition architecture, the compressive multiplexer (CMUX), to sample such signals. We demonstrate that our architecture is CS-feasible and suggest a simple implementation with numerous practical advantages. This paper presents a set of tools to support multimodal adaptive Web applications. The contributions include a novel solution for generating multimodal interactive applications, which can be executed in any browser-enabled device; and run-time support for obtaining multimodal adaptations at various granularity levels, which can be specified through a language for adaptation rules. The architecture is able to exploit model-based user interface descriptions and adaptation rules in order to achieve adaptive behaviour that can be triggered by dynamic changes in the context of use. We also report on an example application and a user test concerning adaptation rules changing dynamically its multimodality. This paper presents a new approach for automatically deriving worst-case resource bounds for C programs. The described technique combines ideas from amortized analysis and abstract interpretation in a unified framework to address four challenges for state-of-the-art techniques: compositionality, user interaction, generation of proof certificates, and scalability. Compositionality is achieved by incorporating the potential method of amortized analysis. It enables the derivation of global whole-program bounds with local derivation rules by naturally tracking size changes of variables in sequenced loops and function calls. The resource consumption of functions is described abstractly and a function call can be analyzed without access to the function body. User interaction is supported with a new mechanism that clearly separates qualitative and quantitative verification. A user can guide the analysis to derive complex non-linear bounds by using auxiliary variables and assertions. The assertions are separately proved using established qualitative techniques such as abstract interpretation or Hoare logic. Proof certificates are automatically generated from the local derivation rules. A soundness proof of the derivation system with respect to a formal cost semantics guarantees the validity of the certificates. Scalability is attained by an efficient reduction of bound inference to a linear optimization problem that can be solved by off-the-shelf LP solvers. The analysis framework is implemented in the publicly-available tool C4B. An experimental evaluation demonstrates the advantages of the new technique with a comparison of C4B with existing tools on challenging micro benchmarks and the analysis of more than 2900 lines of C code from the cBench benchmark suite. Coverage enhancement and low complexity are quite important aspects to support and broaden the application of machine type communication (MTC) in long term evolution (LTE). Repeating the same data across multiple subframes is widely considered as basic solution to improve the coverage. Low complexity target is mainly achieved by reducing the supported bandwidth and restricting the data processing size. Repetitions can be regarded as a new dimension of link adaptation besides the modulation and coding scheme (MCS) selection and the low complexity features are new factors impacting the link adaption as well. However, existing link adaptation mechanism in LTE system is unable to support such two-dimension link adaptation. Straightforward method suffers serious spectral efficiency loss and more power consumption and there is little other research on this issue, either. To save power and improve the spectral efficiency when performing the link adaptation for MTC, a smart method of coordinating the MCS selection and the repetition level determination with the consideration of new low complexity features is proposed in this paper. Extensive link level simulations are carried out to verify the effectiveness of proposed method. Simulation results show that proposed method could improve the spectral efficiency by around 42.1 and reduce the power consumption by around 36.8 In this paper, we make the first attempt to build a framework to simultaneously estimate semantic parts, shape, translation, and orientation of cars from single street view. Our framework contains three major contributions. Firstly, a novel domain adaptation approach based on the class consistency loss is developed to transfer our part segmentation model from the synthesized images to the real images. Secondly, we propose a novel network structure that leverages part-level features from street views and 3D losses for pose and shape estimation. Thirdly, we construct a high quality dataset that contains more than 300 different car models with physical dimensions and part-level annotations based on global and local deformations. We have conducted experiments on both synthesized data and real images. Our results show that the domain adaptation approach can bring 35.5 percentage point performance improvement in terms of mean intersection-over-union score (mIoU) comparing with the baseline network using domain randomization only. Our network for translation and orientation estimation achieves competitive performance on highly complex street views (e.g., 11 cars per image on average). Moreover, our network is able to reconstruct a list of 3D car models with part-level details from street views, which could benefit various applications such as fine-grained car recognition, vehicle re-identification, and traffic simulation. Recent empirical studies have investigated the use of source code metrics to predict the change- and defect-proneness of source code files and classes. While results showed strong correlations and good predictive power of these metrics, they do not distinguish between interface, abstract or concrete classes. In particular, interfaces declare contracts that are meant to remain stable during the evolution of a software system while the implementation in concrete classes is more likely to change. This paper aims at investigating to which extent the existing source code metrics can be used for predicting change-prone Java interfaces. We empirically investigate the correlation between metrics and the number of fine-grained source code changes in interfaces of ten Java open-source systems. Then, we evaluate the metrics to calculate models for predicting change-prone Java interfaces. Our results show that the external interface cohesion metric exhibits the strongest correlation with the number of source code changes. This metric also improves the performance of prediction models to classify Java interfaces into change-prone and not change-prone. Modern software systems are increasingly requested to be adaptive to changes in the environment in which they are embedded. Moreover, adaptation often needs to be performed automatically, through self-managed reactions enacted by the application at run time. Off-line, human-driven changes should be requested only if self-adaptation cannot be achieved successfully. To support this kind of autonomic behavior, software systems must be empowered by a rich run-time support that can monitor the relevant phenomena of the surrounding environment to detect changes, analyze the data collected to understand the possible consequences of changes, reason about the ability of the application to continue to provide the required service, and finally react if an adaptation is needed. This paper focuses on non-functional requirements, which constitute an essential component of the quality that modern software systems need to exhibit. Although the proposed approach is quite general, it is mainly exemplified in the paper in the context of service-oriented systems, where the quality of service (QoS) is regulated by contractual obligations between the application provider and its clients. We analyze the case where an application, exported as a service, is built as a composition of other services. Non-functional requirements—such as reliability and performance—heavily depend on the environment in which the application is embedded. Thus changes in the environment may ultimately adversely affect QoS satisfaction. We illustrate an approach and support tools that enable a holistic view of the design and run-time management of adaptive software systems. The approach is based on formal (probabilistic) models that are used at design time to reason about dependability of the application in quantitative terms. Models continue to exist at run time to enable continuous verification and detection of changes that require adaptation. Modern software systems are often characterized by uncertainty and changes in the environment in which they are embedded. Hence, they must be designed as adaptive systems. We propose a framework that supports adaptation to non-functional manifestations of uncertainty. Our framework allows engineers to derive, from an initial model of the system, a finite state automaton augmented with probabilities. The system is then executed by an interpreter that navigates the automaton and invokes the component implementations associated to the states it traverses. The interpreter adapts the execution by choosing among alternative possible paths of the automaton in order to maximize the system's ability to meet its non-functional requirements. To demonstrate the adaptation capabilities of the proposed approach we implemented an adaptive application inspired by an existing worldwide distributed mobile application and we discussed several adaptation scenarios. Carrier sense multiple access with enhanced collision avoidance (CSMA ECA) is a distributed MAC protocol that allows collision-free access to the medium in WLANs. The only difference between CSMA ECA and the well-known CSMA CA is that the former uses a deterministic backoff after successful transmissions. Collision-free operation is reached after a transient state during which some collisions may occur. This paper shows that the duration of the transient state can be shortened by appropriately setting the contention parameters. Standard absorbing Markov chain theory is used to describe the behaviour of the system in the transient state and to predict the expected number of slots to reach the collision-free operation. The paper also introduces CSMA E2CA, in which a deterministic backoff is used two consecutive times after a successful transmission. CSMA E2CA converges quicker to collision-free operation and delivers higher performance than CSMA ECA, specially in harsh wireless scenarios with high frame-error rates. The last part of the paper addresses scenarios with a large number of contenders. We suggest dynamic parameter adjustment techniques to accommodate a varying (and potentially high) number of contenders. The effectiveness of these adjustments in preventing collisions is validated by means of simulation. A software system interacts with its environment through interfaces. Improper handling of exceptional returns from system interfaces can cause robustness problems. Robust- ness of software systems are governed by various tempo- ral properties related to interfaces. Static verification has been shown to be effective in checking these temporal prop- erties. But manually specifying these properties is cum- bersome and requires the knowledge of interface specifica- tions, which are often either unavailable or undocumented. In this paper, we propose a novel framework to automati- cally infer system-specific interface specifications from pro- gram source code. We use a model checker to generate traces related to the interfaces. From these model check- ing traces, we infer interface specification details such as return value on success or failure. Based on these inferred specifications, we translate generically specified interface robustness rules to concrete robustness properties verifi- able by static checking. Hence the generic rules can be specified at an abstract level that needs no knowledge of the source code, system, or interfaces. We implement our framework for an existing static analyzer that employs push down model checking and apply the analyzer to the well known POSIX-API system interfaces. We found 28 robust- ness violations in 10 open source packages using our frame- work. Adaptive quality-of-service (QoS) techniques can effectively respond to time-varying channel conditions found in wireless networks. In this paper, we assess the state-of-the-art in QoS adaptive wireless systems and argue for new adaptation techniques that are better suited to respond to application-specific adaptation needs. A QoS adaptive data link control model is presented that accounts for application-specific adaptation dynamics that include adaptation time scales and adaptation policies. A centralized adaptation controller employs a novel utility-fair bandwidth allocation scheme that supports the dynamic bandwidth needs of adaptive flows over a range of operating conditions. Three wireless service classes play an integral role in accommodating a wide variety of adaptation strategies. In this paper, we discuss the design of the utility-fair allocation scheme and the interaction between the centralized adaptation controller and a set of distributed adaptation handlers, which play a key role in intelligently responding to the time-varying channel capacity experienced over the air-interface. Compressive sensing (CS) is a framework whereby one performs n non-adaptive measurements to constitute an n-dimensional vector v, with v used to recover an m-dimensional approximation ^ u to a desired m-dimensional signal u, with n ? m; this is performed under the assumption that u is sparse in the basis represented by the matrix “, the columns of which define discrete basis vectors. It has been demonstrated that with appropriate design of the compressive measurements used to define v, the decompressive mapping v ! ^ u may be performed with error kui^ uk 2 having asymptotic properties (large n and m > n) analogous to those of the best adaptive transform-coding algorithm applied in the basis “. The mapping v ! ^ constitutes an inverse problem, often solved using ‘1 regularization or related techniques. In most previous research, if multiple compressive measurements fvigi=1;M are performed, each of the associated f^ uigi=1;M are recovered one at a time, independently. In many applications the M “tasks” defined by the mappings vi ! ^ ui are not statistically independent, and it may be possible to improve the performance of the inversion if statistical inter-relationships are exploited. In this paper we address this problem within a multi-task learning setting, wherein the mapping vi ! ^ ui for each task corresponds to inferring the parameters (here, wavelet coefficients) associated with the desired signal ui, and a shared prior is placed across all of the M tasks. In this multi-task learning framework data from all M tasks contribute toward inferring a posterior on the hyperparameters, and once the shared prior is thereby inferred, the data from each of the M individual tasks is then employed to estimate the task-dependent wavelet coefficients. An empirical Bayes procedure and fast inference algorithm is developed. Example results are presented on several data sets. We propose an adaptive framework to estimate the quality of video tracking algorithms without ground-truth data. The framework is divided into two main stages, namely, the estimation of the tracker condition to identify temporal segments during which a target is lost and the measurement of the quality of the estimated track when the tracker is successful. A key novelty of the proposed framework is the capability of evaluating video trackers with multiple failures and recoveries over long sequences. Successful tracking is identified by analyzing the uncertainty of the tracker, whereas track recovery from errors is determined based on the time-reversibility constraint. The proposed approach is demonstrated on a particle filter tracker over a heterogeneous data set. Experimental results show the effectiveness and robustness of the proposed framework that improves state-of-the-art approaches in the presence of tracking challenges such as occlusions, illumination changes, and clutter and on sequences containing multiple tracking errors and recoveries. The increased complexity of performance-sensitive software systems leads to increased use of automated adaptation policies in lieu of manual performance tuning. Composition of adaptive components into larger adaptive systems, however, presents challenges that arise from potential incompatibilities among the respective adaptation policies. Consequently, unstable or poorly-tuned feedback loops may result that cause performance deterioration. This paper (i) presents a mechanism, called adaptation graph analysis, for identifying potential incompatibilities between composed adaptation policies and (ii) illustrates a general design methodology for co-adaptation that resolves such incompatibilities. Our results are demonstrated by a case study on energy minimization in multi-tier Web server farms subject to soft real-time constraints. Two independently efficient energy saving policies (an on off policy that switches machines off when not needed and a dynamic voltage scaling policy) are shown to conflict leading to increased energy consumption when combined. Our adaptation graph analysis predicts the problem, and our co-adaptation design methodology finds a solution that improves performance. Experimental results from a 17-server farm running the industry standard TPC-W e-commerce benchmark show that co-adaptation renders a cut-down in energy consumption by more than 50 , when workload is not high, while maintaining latency within acceptable bounds. The paper serves as a proof of concept of the proposed conflict-identification and resolution methodology and an invitation to further investigate a science for composing adaptive systems. In this paper, we present error-resilient Internet video transmission using path diversity and rate-distortion optimized reference picture selection. Under this scheme, the optimal packet dependency is determined adapting to network characteristics and video content, to achieve a better trade-off between coding efficiency and forming independent streams to increase error-resilience. The optimization is achieved within a rate-distortion framework, so that the expected end-to-end distortion is minimized under the given rate constraint. The expected distortion is calculated based on an accurate binary tree modeling with the effects of channel loss and error concealment taken into account. With the aid of active probing, packets are sent across multiple available paths according to a transmission policy which takes advantage of path diversity and seeks to minimize the loss rate. Experiments demonstrate that the proposed scheme provides significant diversity gain, as well as gains over video redundancy coding and the NACK mode of conventional reference picture selection. In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well.