Journal of Internet Services and Applications

Internet eXchange Points (IXPs) are Internet infrastructures composed of high-performance networks that allow multiple autonomous systems to exchange traffic. Given the challenges of managing the flows that cross an IXP, identifying elephant flows may help improve the quality of services provided to its participants. In this context, we leverage the new flexibility and resources of programmable data planes to identify elephant flows in IXP networks adaptively via the dynamic adjustment of thresholds. Our mechanism uses the information reported by the data plane to monitor network utilization in the control plane, calculating new thresholds based on previous flow sizes and durations percentiles and configuring them back into switches to support the local classification of flows. Thus, the thresholds are updated to make the identification process better aligned with the network behavior. The experimental results show that it is possible to identify and react to elephant flows quickly, less than 0.4ms, and efficiently, with only 98.4KB of data inserted into the network by the mechanism. In addition, the threshold updating mechanism achieved accuracy of up to 90% in our evaluation scenarios.

Elastic optical networks are a network infrastructure capable of withstanding the high demand for data traffic from high-speed networks. One of the problems that must be solved to ensure the smooth functioning of the network is called Routing, Modulation Level and Spectrum Assignment (RMLSA). This work aims to propose a new approach to this problem with an algorithm to select the guard band in an adaptive way. Two algorithms for the adaptive selection of the guard band, called Guard Band according to Use of the Network (GBUN) and Guard Band by OSNR Margin (GBOM), are presented. The GBUN algorithm performs the guard band selection based on the usage level of network. On the other hand the GBOM algorithm uses an Optical Signal to Noise Ratio (OSNR) margin for the selection of the guard band. The performances of the proposed algorithms are compared with algorithms that use fixed guard band values and the adaptive proposal AGBA. The results showed that the GBOM algorithm presented a better performance in terms of bandwidth blocking probability for the studied scenarios. In general, GBOM also presents a better energy efficiency when compared to the other algorithms.

There are increasingly more computing problems requiring lengthy parallel computations. For those without access to current cluster or grid infrastructures, a recent and proven viable solution can be found with on-demand utility computing infrastructures, such as Amazon Elastic Compute Cloud (EC2). A relevant class of such problems, Bag-of-Tasks (BoT), can be easily deployed over such infrastructures (to run on pools of virtual computers), if provided with suitable software for host allocation. BoT problems are found in several and relevant scenarios such as image rendering and software testing. In BoT jobs, tasks are mostly independent; thus, they can run in parallel with no communication among them. The number of allocated hosts is relevant as it impacts both the speedup and the cost: if too many hosts are used, the speedup is high but this may not be cost-effective; if too few are used, the cost is low but speedup falls below expectations. For each BoT job, given that there is no prior knowledge of neither the total job processing time nor the time each task takes to complete, it is hard to determine the number of hosts to allocate. Current solutions (e.g., bin-packing algorithms) are not adequate as they require knowing in advance either the time that the next task will take to execute or, for higher efficiency, the time taken by each one of the tasks in each job considered. Thus, we present an algorithm and heuristics that adaptively predicts the number of hosts to be allocated, so that the maximum speedup can be obtained while respecting a given predefined budget. The algorithm and heuristics were simulated against real and theoretical workloads. With the proposed solution, it is possible to obtain speedups in line with the number of allocated hosts, while being charged less than the predefined budget.

The vision of millions of users launching tens of millions of applications running on millions of globally scattered servers presents a new challenge for Cloud Providers: how to assign so many virtual applications to physical servers, while meeting latency needs, improving network utilization, and satisfying availability constraints. Today’s application placement puts too much burden on the cloud user, lacks scalability and inhibits the global reach of Public Clouds. The size, breadth, and dynamic nature of Public Clouds present a special challenge to the task of placement. Cloud Providers able to provide rapid decisions and frequent optimizations for placement will have significant competitive advantage. Not only will they provide the best customer experience, their costs will be lower as they make more efficient use of their network resources. However, given the calculations required, data structures and the algorithms used to process location-based decisions must be as globally scalable as the Public Clouds themselves. In our study, we define a novel data structure, the Virtual Cloud Model, for modeling global cloud resources. We adapt a well-known geometric device, Voronoi Diagrams, and combine it with near-real-time network latency information. We then solve the application placement problem and suggest an API for Cloud Providers to support both “low-latency” and “high-availability” applications. The algorithms are scalable, parallelizable and distributable.

Temporal dependence, as a synonym for burstiness, is often found in workloads (i.e., arrival flows and/or service times) in enterprise systems that use the multi-tier paradigm. Despite the fact that burstiness has deleterious effects on performance, existing modeling and benchmarking techniques do not provide an effective capacity planning for multi-tier systems with temporal dependence. In this paper, we first present strong evidence that existing models cannot capture bursty conditions and accurately predict performance. Therefore, we propose a simple and effective sizing methodology to integrate workload burstiness into models and benchmarking tools used in system sizing. This modeling methodology is based on the index of dispersion which jointly captures variability and burstiness of the service process in a single number. We report experimentation on a real testbed that validates the accuracy of our modeling technique by showing that experimental and model prediction results are in excellent agreement under both bursty and non-bursty workloads. To further support the capacity planning process under burstiness, we propose an enhanced benchmarking technique that can emulate workload burstiness in systems. We find that most existing benchmarks, like the standard TPC-W benchmark, are designed to assess system performance only under non-bursty conditions. In this work, we rectify this deficiency by introducing a new module into existing benchmarks, which allows to inject burstiness into the arrival stream in a controllable and reproducible manner by using the index of dispersion as a single turnable knob. This approach enables a better understanding of system performance degradation due to burstiness and makes a strong case for the usefulness of the proposed benchmark enhancement for capacity planning of enterprise systems.

Software-as-a-Service (SaaS) is a type of cloud computing in which a tenant rents access to a shared, typically web-based application hosted by a provider. Access control for SaaS should enable the tenant to control access to data that are located at the provider side, based on tenant-specific access control policies. Moreover, with the growing adoption of SaaS by large enterprises, access control for SaaS has to integrate with on-premise applications, inherently leading to a federated set-up. However, in the state of the art, the provider completely evaluates all policies, including the tenant policies. This (i) forces the tenant to disclose sensitive access control data and (ii) limits policy evaluation performance by having to fetch this policy-specific data. To address these challenges, we propose to decompose the tenant policies and evaluate the resulting parts near the data they require as much as possible while keeping sensitive tenant data local to the tenant environment. We call this concept policy federation. In this paper, we motivate the need for policy federation using an in-depth case study analysis in the domain of e-health and present a policy federation algorithm based on a widely-applicable attribute-based policy model. Furthermore, we show the impact of policy federation on policy evaluation time using the policies from the case study and a prototype implementation of supporting middleware. As shown, policy federation effectively succeeds in keeping the sensitive tenant data confidential and at the same time improves policy evaluation time in most cases.

With the huge number of offerings in the mobile application market, the choice of mobile applications that best fit particular objectives is challenging. Therefore, there is a demand for a platform elevating the momentum of mobile applications that can adapt their behavior according to the user’s context. This paper proposes AppaaS, a context-aware platform that provides mobile applications as a service. AppaaS uses several types of context information including location information, user profile, device profile, user ratings, and time to provision the best relevant mobile applications to such a context. AppaaS supports state preservation, where user-specific data and application status are stored for the user’s future reference. Experimental validation demonstrates that AppaaS alleviates the burden on mobile users to find applications that work best for a particular situation. It also enables application providers to dynamically control access to the functionality of their applications. Performance evaluation results show that AppaaS can employ cloud elastic resource provisioning to offer flexible scalability, while satisfying certain QoS constraints. Experimental results also support a conclusion that with little overhead handling context information, AppaaS can bring remarkable benefits to provisioning mobile applications as a service.

The smart city model is used by many organizations for large cities around the world to significantly enhance and improve the quality of life of the inhabitants, improve the utilization of city resources, and reduce operational costs. This model includes various heterogeneous technologies such as Cyber-Physical Systems (CPS), Internet of Things (IoT), Wireless Sensor Networks (WSNs), Cloud Computing, and Unmanned Aerial Vehicles (UAVs). However, in order to reach these important objectives, efficient networking and communication protocols are needed to provide the necessary coordination and control of the various system components. In this paper, we identify the networking characteristics and requirements of smart city applications, and identify the networking protocols that can be used to support the various data traffic flows that are needed between the different components. In addition, we provide illustrations of networking architectures of selected smart city systems, which include smart grid, smart home energy management, smart water, UAV and commercial aircraft safety, and pipeline monitoring and control systems.

Software defined Networks (SDNs) have drawn much attention both from academia and industry over the last few years. Despite the fact that underlying ideas already exist through areas such as P2P applications and active networks (e.g. virtual topologies and dynamic changes of the network via software), only now has the technology evolved to a point where it is possible to scale the implementations, which justifies the high interest in SDNs nowadays. In this article, the JISA Editors invite five leading scientists from three continents (Raouf Boutaba, David Hutchison, Raj Jain, Ramachandran Ramjee, and Christian Esteve Rothenberg) to give their opinions about what is really new in SDNs. The interviews cover whether big telecom and data center companies need to consider using SDNs, if the new paradigm is changing the way computer networks are understood and taught, and what are the open issues on the topic.

Video transmission over error prone channels as present in most of today’s communication channels, such as Mobile TV or some IPTV systems, is constantly subject to research. Simulation is an important instrument to evaluate performance of the overall video transmission system, but the multitude of parameters often requires large and time-consuming simulation sets. In this paper, we present a packet level mechanism for fast evaluation of error-prone H.264/AVC and SVC video transmission with application layer video quality metrics, such as PSNR. Our approach significantly reduces the overall simulation time by eliminating redundancy in the evaluation phase and utilizing the prediction structure of the video codec. The benefit of the presented packet level video quality evaluation is evaluated with an exemplary simulation setup of an IPTV service with link congestion.