Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applicationsComment: ICSE 201

Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

Cloud and mobile infrastructure monitoring for latency and bandwidth sensitive applications

This PhD thesis involves the study of cloud computing infrastructures (from the networking perspective) to assess the feasibility of applications gaining increasing popularity over recent years, including multimedia and telemedicine applications, demanding low, bounded latency and sufficient bandwidth. I also focus on the case of telemedicine, where remote imaging applications (for example, telepathology or telesurgery) need to achieve a low and stable latency for the remote transmission of images, and also for the remote control of such equipment. Another important use case for telemedicine is denoted as remote computation, which involves the offloading of image processing to help diagnosis; also in this case, bandwidth and latency requirements should be enforced to ensure timely results, although they are less strict compared to the previous scenario. Nowadays, the capability of gaining access to IT resources in a rapid and on-demand fashion, according to a pay-as-you-go model, has made the cloud computing a key-enabler for innovative multimedia and telemedicine services. However, the partial obscurity of cloud performance, and also security concerns are still hindering the adoption of cloud infrastructure. To ensure that the requirements of applications running on the cloud are satisfied, there is the need to design and evaluate proper methodologies, according to the metric of interest. Moreover, some kinds of applications have specific requirements that cannot be satisfied by the current cloud infrastructure. In particular, since the cloud computing involves communication to remote servers, two problems arise: firstly, the core network infrastructure can be overloaded, considering the massive amount of data that has to flow through it to allow clients to reach the datacenters; secondly, the latency resulting from this remote interaction between clients and servers is increased. For these, and many other cases also beyond the field of telemedicine, the Edge and Fog computing paradigms were introduced. In these new paradigms, the IT resources are deployed not only in the core cloud datacenters, but also at the edge of the network, either in the telecom operator access network or even leveraging other users' devices. The proximity of resources to end-users allows to alleviate the burden on the core network and at the same time to reduce latency towards users. Indeed, the latency from users to remote cloud datacenters encompasses delays from the access and core networks, as well as the intra-datacenter delay. Therefore, this latency is expected to be higher than that required to interconnect users to edge servers, which in the envisioned paradigm are deployed in the access network, that is, nearby final users. Therefore, the edge latency is expected to be reduced to only a portion of the overall cloud delay. Moreover, the edge and central resources can be used in conjunction, and therefore attention to core cloud monitoring is of capital importance even when edge architectures will have a widespread adoption, which is not the case yet. While a lot of research work has been presented for monitoring several network-related metrics, such as bandwidth, latency, jitter and packet loss, less attention was given to the monitoring of latency in cloud and edge cloud infrastructures. In detail, while some works target cloud-latency monitoring, the evaluation is lacking a fine-grained analysis of latency considering spatial and temporal trends. Furthermore, the widespread adoption of mobile devices, and the Internet of Things paradigm further accelerate the shift towards the cloud paradigm for the additional benefits it can provide in this context, allowing energy savings and augmenting the computation capabilities of these devices, creating a new scenario denoted as mobile cloud. This scenario poses additional challenges for its bandwidth constraints, accentuating the need for tailored methodologies that can ensure that the crucial requirements of the aforementioned applications can be met by the current infrastructure. In this sense, there is still a gap of works monitoring bandwidth-related metrics in mobile networks, especially when performing in-the-wild assessment targeting actual mobile networks and operators. Moreover, even the few works testing real scenarios typically consider only one provider in one country for a limited period of time, lacking an in-depth assessment of bandwidth variability over space and time. In this thesis, I therefore consider monitoring methodologies for challenging scenarios, focusing on latency perceived by customers of public cloud providers, and bandwidth in mobile broadband networks. Indeed, as described, achieving low latency is a critical requirement for core cloud infrastructures, while providing enough bandwidth is still challenging in mobile networks compared to wired settings, even with the adoption of 4G mobile broadband networks, expecting to overcome this issue only with the widespread availability of 5G connections (with half of total traffic expected to come from 5G networks by 2026). Therefore, in the research activities carried on during my PhD, I focused on monitoring latency and bandwidth on cloud and mobile infrastructures, assessing to which extent the current public cloud infrastructure and mobile network make multimedia and telemedicine applications (as well as others having similar requirements) feasible

CASPR: Judiciously Using the Cloud for Wide-Area Packet Recovery

We revisit a classic networking problem -- how to recover from lost packets in the best-effort Internet. We propose CASPR, a system that judiciously leverages the cloud to recover from lost or delayed packets. CASPR supplements and protects best-effort connections by sending a small number of coded packets along the highly reliable but expensive cloud paths. When receivers detect packet loss, they recover packets with the help of the nearby data center, not the sender, thus providing quick and reliable packet recovery for latency-sensitive applications. Using a prototype implementation and its deployment on the public cloud and the PlanetLab testbed, we quantify the benefits of CASPR in providing fast, cost effective packet recovery. Using controlled experiments, we also explore how these benefits translate into improvements up and down the network stack

Research on Efficiency and Security for Emerging Distributed Applications

Distributed computing has never stopped its advancement since the early years of computer systems. In recent years, edge computing has emerged as an extension of cloud computing. The main idea of edge computing is to provide hardware resources in proximity to the end devices, thereby offering low network latency and high network bandwidth. However, as an emerging distributed computing paradigm, edge computing currently lacks effective system support. To this end, this dissertation studies the ways of building system support for edge computing. We first study how to support the existing, non-edge-computing applications in edge computing environments. This research leads to the design of a platform called SMOC that supports executing mobile applications on edge servers. We consider mobile applications in this project because there are a great number of mobile applications in the market and we believe that mobile-edge computing will become an important edge computing paradigm in the future. SMOC supports executing ARM-based mobile applications on x86 edge servers by establishing a running environment identical to that of the mobile device at the edge. It also exploits hardware virtualization on the mobile device to protect user input. Next, we investigate how to facilitate the development of edge applications with system support. This study leads to the design of an edge computing framework called EdgeEngine, which consists of a middleware running on top of the edge computing infrastructure and a powerful, concise programming interface. Developers can implement edge applications with minimal programming effort through the programming interface, and the middleware automatically fulfills the routine tasks, such as data dispatching, task scheduling, lock management, etc., in a highly efficient way. Finally, we envision that consensus will be an important building block for many edge applications, because we consider the consensus problem to be the most important fundamental problem in distributed computing while edge computing is an emerging distributed computing paradigm. Therefore, we investigate how to support the edge applications that rely on consensus, helping them achieve good performance. This study leads to the design of a novel, Paxos-based consensus protocol called Nomad, which rapidly orders the messages received by the edge. Nomad can quickly adapt to the workload changes across the edge computing system, and it incorporates a backend cloud to resolve the conflicts in a timely manner. By doing so, Nomad reduces the user-perceived latency as much as possible, outperforming the existing consensus protocols