Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve energy efficiency is non-trivial in a heterogeneous system since application components are distributed across multiple devices. In this paper, we present Aves - a decision-making engine based on a holistic energy-prediction model, with which the processing tasks of applications can be placed automatically in an energy-efficient manner without programmer/user intervention. We validate the effectiveness of the model and reveal several counter-intuitive placement decisions. Our decision engine's improvements are typically 10-30%, with up to a factor 14 in the most extreme cases. We also show that Aves gives an accurate decision in comparison with real energy measurements for two sensor-based applications.

Sensor-based applications using Big Data are of increasing importance in various fields. A typical example of such use cases is building health-care applications [1], [2]. A typical scenario is where a patient's heart rate is monitored by a smartwatch. A smartphone can then analyze the gathered data and identify patterns in the patient's heart rate. However, if the data analysis is too complex to be performed on a smartphone, the computation could be offloaded to a nearby cloudlet or a remote cloud. A decision usually follows the analysis, and actuation is performed accordingly (e.g., a message is sent to either the patient or the doctor). Developing such an application is intrinsically complex, as the programmer needs to reconcile different APIs specific to different platforms.

Distributed ledgers are among the most replicated data repositories in the world. They offer data consistency, immutability, and auditability, based on the assumption that each participating node locally verifies their entire content. Although their content, currently extending up to a few hundred gigabytes, can be accommodated by dedicated commodity hard disks, downloading it, processing it, and storing it in general-purpose desktop and laptop computers can prove largely impractical. Even worse, this becomes a prohibitive restriction for smartphones, mobile devices, and resource-constrained IoT devices. In this demo, we present an implementation of Dietcoin, a Bitcoin protocol extension that allows nodes to perform secure local verification of Bitcoin transactions with small bandwidth and storage requirements. This demo presents and benchmarks the main features of Dietcoin that are important for today's cryptocurrencies and smart contract systems, but are missing in the current state-of-the-art: (i) allowing resource-constrained devices to verify the correctness of selected blocks locally without having to download the complete ledger; (ii) enabling devices to join a blockchain quickly yet securely, dropping bootstrap time from days down to a matter of seconds; (iii) providing a generic solution that can be applied to other distributed ledgers secured with Proof-of-Work.

The daily home-office commute of millions of people in crowded cities puts a strain on air quality, traveling time and noise pollution. This is especially problematic in western cities, where cars and taxis have low occupancy with daily commuters. To reduce these issues, authorities often encourage commuters to share their rides, also known as carpooling or ridesharing. To increase the ridesharing usage it is essential that commuters are efficiently matched. In this paper we present RideMatcher, a novel peer-to-peer system for matching car rides based on their routes and travel times. Unlike other ridesharing systems, RideMatcher is completely decentralized, which makes it possible to deploy it on distributed infrastructures, using fog and edge computing. Despite being decentralized, our system is able to efficiently match ridesharing users in near real-time. Our evaluations performed on a dataset with 34,837 real taxi trips from New York show that RideMatcher is able to reduce the number of taxi trips by up to 65%, the distance traveled by taxi cabs by up to 64%, and the cost of the trips by up to 66%.

Nowadays smartphones are equipped with many sensors which applications can continuously invoke to acquire real-time sensor information, such as GPS tracking. Due to the resource-constrained nature of the smartphones, it is often beneficial if the processing of the sensor data is offloaded to a remote resource. However, the decision to offload the computation depends on a multitude of factors such as the hardware capabilities of the phone, the communication energy and latency and the characteristics of the stream computations, e.g., window size, sensor frequency and operational complexity.In this paper we introduce Kea, a profiling-based computation offloading system that automatically decides whether offloading is beneficial for smartphones. The decision making is based on two criteria: the power consumption of the application and the elapsed time for processing the sensor data. Our evaluation results show that unexpected factors such as CPU frequency scaling and the network state also influence the decision-making process. In addition, we show that Kea's profiling overhead is negligible.

Sensitive personal user-data collected by Internet-of-Things (IoT) devices is vulnerable to information leaks when uploaded to third-party cloud computing infrastructures. Even though data is encrypted before being sent, to perform analyses on the received data, the computing infrastructure typically decrypts the data, and then performs computation. Therefore, during computation, data can be leaked by means of honest-but-curious system administrators. To overcome this vulnerability, homomorphic encryption enables "blind" computation directly on encrypted data, thus rendering obsolete any data leaks. However, homomorphic encryption is highly resource demanding, as it performs many compute-intensive operations during encryption, while also increasing the size of ciphertexts, which makes it unsuitable for low-powered (mobile) IoT devices. For similar reasons, performing operations on encrypted data is also a challenging task, especially when real-time decision-making is needed. In such scenarios, efficient solutions must be augmented by placing computation close to the data: at the network edge. In this paper, we introduce privacy preserving SWAN (P2-SWAN), a homomorphic-encryption enabled mobile computing framework. Even though such encryption adds significant computational overhead, our evaluation shows that it is feasible on low-powered (mobile) devices. The overhead induced on such devices is minimized due to our carefully crafted implementation. We show that performing encrypted operations achieves excellent scalability, thus only modest numbers of computing servers can handle the load for data generated by millions of devices. Furthermore, our proposed approach achieves real-time computation not only for encrypting data on mobile devices, but also for performing encrypted computation.

Storing data in the cloud is becoming a common trend, for both end-customers and data center operators. We propose a method for deciding where to host data storage tasks under the constraint of minimal greenhouse gas emission. The decision on whether to store data locally or store it remotely at a cleaner data center relies on the models for the local and remote data centers and the network connecting them. We conclude that the transport network that connects a local node and a "cleaner" remote data center plays a significant role in the decision of where to store data, and that the frequency of access of the data is an important and related factor.

Cyber physical systems, such as intelligent dikes and smart energy systems, require scalable and flexible computing infrastructures to process data from instruments and sensor networks. Infrastructure as a Service clouds provide a flexible way to allocate remote distributed resources, but lack mechanisms to dynamically configure software (dependencies) and manage application execution. This paper describes the design and implementation of the Intercloud Operating System (ICOS), which acts between applications and distributed clouds, i.e., the Intercloud. ICOS schedules, configures, and executes applications in the Intercloud while taking data dependencies, budgets, and deadlines into account. Based on our experiences with the prototype, we present considerations and additional research challenges. The research on ICOS clarifies essential concepts needed to realize a flexible and scalable on-demand execution platform for distributed applications over distributed cloud providers.

Cloud computing enables on-demand access to a shared pool of IT resources. In the case of Infrastructure as a Service (IaaS), the cloud user typically acquires Virtual Machines (VMs) from the provider. It is up to the user to decide at what time and for how long they want to use these VMs. Because of the pay-per-use nature of most clouds, there is a strong incentive to use as few resources as possible and release them quickly when they are no longer needed. Every step of the deployment process, i.e., acquiring VMs, creating network links, and installing, configuring and starting software components on them, should therefore be as fast as possible. The amount of time the deployment process takes can be influenced by the user by performing some steps in parallel or using timing knowledge of previous deployments. This paper presents four different strategies for deploying applications on cloud computing infrastructures. Performance measurements of application deployments on three public IaaS clouds are used to show the speed differences between these strategies.

XACML policy evaluation efficiency is an important factor influencing the overall system performance, especially when the number of policies grows. Some existing approaches on high performance XACML policy evaluation can support simple policies with equality comparisons and handle requests with well defined conditions. Such mechanisms do not provide the semantic correctness of combining algorithms in cases with indeterminate and not-applicable states. They ignore the critical attribute setting, a mandatory property in XACML, leading to potential missing attribute attacks. In this paper, we present a solution using data interval partition aggregation together with new decision diagram combinations, that not only optimizes the performance but also provides correctness and completeness of XACML 3.0 features, including complex logical expressions, correctness in indeterminate states processing, critical attribute setting, obligations and advices as well as complex comparison functions for multiple data types.

This paper presents on-going research to develop the Intercloud Architecture Framework (ICAF) that addresses problems in multi-provider multi-domain heterogeneous cloud based infrastructure services and applications integration and interoperability. The paper refers to existing standards in Cloud Computing, in particular, recently published NIST Cloud Computing Reference Architecture (CCRA). The proposed ICAF defines four complementary components addressing Intercloud integration and interoperability: multilayer Cloud Services Model that combines commonly adopted cloud service models, such as IaaS, PaaS, SaaS, in one multilayer model with corresponding inter-layer interfaces; Intercloud Control and Management Plane that supports cloud based applications interaction; Intercloud Federation Framework, and Intercloud Operation Framework. The paper briefly describes the architectural framework for cloud based infrastructure services provisioned on-demand being developed in the framework of the GEYSERS project that is used as a basis for building multilayer cloud services integration framework that allows optimized provisioning of both computing, storage and networking resources. The proposed architecture is intended to provide an architectural model for developing Intercloud middleware and in this way will facilitate clouds interoperability and integration.