The sixth generation of DOCSIS standard is currently under development for the provisioning of multi-Gbps services over cable networks. Building upon DOCSIS 3.1 (D3.1), DOCSIS 4.0 (D4) introduces several features including full-duplex transmission and extended-spectrum, which benefit from subcarrier-level OFDM modulation configurations to adapt to varying channel conditions. To exploit the full potential of D4, we propose a softwarized adaptive subcarrier modulation management framework. The optimization system consists of (i) a clustering mechanism that classifies CMs (Cable Modems) with a similar channel condition into the same group using a sparsified K-means algorithm and (ii) an efficient profile generation mechanism to balance achieved channel throughput and packet error rate within the same group. Then, we implement key elements of the softwarized system using a virtualized network function in our DOCSIS experimental testbed that enables the programmatic control of OFDM channels using D4 performance parameters. Our experimental results show that the proposed optimization function offers significant improvements in OFDM channel throughput over current industry management practices. Furthermore, we confirm via simulations that using a novel clustering algorithm for the classification of CM populations and a new bit-loading method measurably enhances channel performance in large-scale distributed deployment scenarios.

In this paper, we propose SPARCLE, a novel scheduling system offering network-aware polynomial-time task assignment and resource allocation algorithms for stream processing applications in dispersed computing networks. In particular, we address two major challenges. The first one concerns the assignment of both computation and transport tasks comprising a stream processing application to computing nodes and communication links of the network, respectively, to maximize the application's processing rate. The second one concerns the resource allocation of multiple stream processing applications to satisfy their requested QoS. Our experimental results on a real image stream processing application and extensive simulations show that SPARCLE can increase the application's processing rate by 9 times and 3 times, compared to the cloud computing case and state-of-the-art algorithms, respectively.

Emerging applications like virtual reality (VR), augmented reality (AR), and 360-degree video aim to exploit the unprecedentedly low latencies promised by technologies like the tactile Internet and mobile 5G networks. Yet these promises are still unrealized. In order to fulfill them, it is crucial to understand where packet delays happen, which impacts protocol performance such as throughput and latency. In this work, we empirically find that sender-side protocol stack delays can cause high end-to-end latencies, though existing solutions primarily address network delays. Unfortunately, however, current latency diagnosis tools cannot even distinguish between delays on network links and delays in the end hosts. To close this gap, we present ELEMENT, a latency diagnosis framework that decomposes end-to-end TCP latency into endhost and network delays, without requiring admin privileges at the sender or receiver.

Ensuring high availability for applications despite unpredictable cloud component failure events is a well-known problem in managing cloud infrastructure. One proposed solution uses a VM redundancy approach, reserving cloud resources for backup VMs that can substitute for primary ones in case of a failure event. However, this solution decreases the cloud resource utilization, since the backup resources usually remain idle. In this paper, we propose ECHO, a cloud resource management system that overbooks these backup VMs by optimizing the overbooking rate tradeoff between maximizing the cloud resource utilization, and thus maximizing the cloud provider's revenue; and improving application availability, thus satisfying users. Specifically, ECHO first obtains the optimal overbooking rate required to achieve a cloud provider's desired resource utilization level. It then computes the optimal (required) number of backup VMs that are required to maintain a given application availability level. Our extensive experimental and simulation results show that using ECHO can increase the number of accepted applications with satisfied availability by about 30%, while increasing the defined resource utilization at the same time.

This paper presents a fully distributed scheduling framework called CASTLE (Client-side Adaptive Scheduler That minimizes Load and Energy), which jointly optimizes the spectral efficiency of cellular networks and battery consumption of smart devices. To do so, we focus on scenarios when many smart devices compete for cellular resources in the same base station: spreading out transmissions over time so that only a few devices transmit at once improves both spectral efficiency and battery consumption. To this end, we devise two novel features in CASTLE. First, we explicitly consider inter-cell interference for accurate cellular load estimation. Based on our observations, we exploit the RSRQ (Reference Signal Received Quality) and SINR as features in a machine learning algorithm to accurately estimate the cellular load. Second, we propose a fully distributed scheduling algorithm that coordinates transmissions between clients based on the locally estimated load level at each client. Our formulation for minimizing battery consumption at each device leads to an optimized backoff-based algorithm that fits practical environments. To evaluate these features, we prototype a complete LTE system testbed consisting of mobile devices, eNodeBs, EPC (Evolved Packet Core) and application servers. Our comprehensive experimental results show that CASTLE's load estimation is up to 91% accurate, and that CASTLE achieves higher spectral efficiency with less battery consumption, compared to existing centralized scheduling algorithms as well as a distributed CSMA-like protocol. Furthermore, we develop a light-weight SDK that can expedite the deployment of CASTLE into smart devices and evaluate it in a commercial LTE network.

We present the demonstration of a fully distributed scheduling framework called CASTLE (Client-side Adaptive Scheduler That minimizes Load and Energy) that jointly optimizes the spectral efficiency of cellular networks and battery consumption of smart devices. To do so, we focus on scenarios when many smart devices compete for cellular resources in the same base station: spreading out transmissions over time so that only a few devices transmit at once and improves both spectral efficiency and battery consumption. To this end, we devise two novel features in CASTLE. First, we explicitly consider inter-cell interference for accurate cellular load estimation in our machine learning algorithm. Second, we propose a fully distributed scheduling algorithm that coordinates transmissions between clients based on the locally estimated load level at each client. Our formulation for minimizing battery consumption at each device leads to an optimized back off-based algorithm that fits practical environments. Our comprehensive experimental results show that CASTLE's load estimation is up to 91 % accurate, and that CASTLE achieves higher spectral efficiency with less battery consumption, compared to existing centralized scheduling algorithms as well as a distributed CSMA-like protocol. Furthermore,we develop a light-weight SDK that can expedite the deployment of CASTLE into smart devices and evaluate it in a commercial LTE network.

VM redundancy is the foundation of resilient cloud applications. While active-active approaches combined with load balancing and autoscaling are usually resource efficient, the stateful nature of many cloud applications often necessitates 1+1 (or 1+n) active-standby approaches. Keeping the standbys, however, could result in inefficient utilization of cloud resources. We explore an intriguing cloud-based solution, where standby VMs from active-standby applications are selectively overbooked to reduce resources reserved for failures. The approach requires careful VM placement to avoid a situation where multiple standby VMs activate simultaneously on the same host and thus cannot get the full resource entitlement. Indeed today's clouds do not have this visibility to the applications. We rectify this situation through ShadowBox, a novel redundancy-aware VM scheduler that optimizes the placement and activation of standby VMs, while assuring applications' resource entitlements. Evaluation on a large-scale cloud shows that ShadowBox can significantly improve resource utilization (i.e., more than 2.5 times than traditional approaches) while minimizing the impact on applications' entitlements.

Video streaming applications based on Peer-to-Peer (P2P) systems are popular for their scalability, which is hard to achieve with traditional client-server approaches. In particular, layered video streaming has been much-studied due to its ability to differentiate users' streaming qualities in heterogeneous user environments. Previous work, however, has shown that user misbehavior (e.g., free-riding and protocol deviation) poses a serious threat to P2P systems that are not equipped with proper incentive mechanisms. We propose a method to disincentivize such misbehavior. Our SVC-TChain is a layered P2P video streaming method based on scalable video coding (SVC), which uses the recently proposed T-Chain incentive mechanism to discourage free-riding. After introducing T-Chain, we present the first analytical framework to study SVC piece selection with multiple video layers, using it to efficiently choose SVC-TChain's optimal piece selection parameters and thus discourage deviations from the piece selection policy. Extensive experimental results show that SVC-TChain outperforms layered extensions of BiTos and Give-to-Get, two popular P2P video streaming approaches, both in the absence of user misbehavior and when some users misbehave.

In this paper, we analyze the saturation throughput of a cognitive WLAN overlaid on a primary OFDMA TDD network (e.g., LTE or WiMAX). In this scenario, after the contention among the secondary nodes, the winner node transmits its data packet in the empty resource blocks (RBs) of downlink and uplink subframes of the primary network. Regarding the OFDMA structure as well as time-scheduled resources in the primary network, the time duration of opportunities for the secondary network does not follow simple exponential on-off pattern. To model the dynamic behavior of opportunities for secondary nodes as well as contentions to exploit the opportunities, we propose an analytical model comprised of a discrete-time Markov chain and two inter-related open multi-class queueing networks (QNs). The effects of the random number of empty resource blocks at different frames as the result of random amount of download and upload data, random packet transmission time at WLAN, the dependency of the number of empty RBs at consecutive frames, and the details of 802.11 MAC protocol are included in our analytical approach. We include different resource allocations in the primary network in our analysis. Simulation results confirm the accuracy of our analytical approach in different conditions.

In this paper, we focus on a cognitive network scenario, comprised of a WLAN overlaid on a network with time-scheduled primary users. WiMAX is an example of such primary networks. For time-scheduled primary users simple On-Off traffic model with exponential durations is not valid anymore. In this scenario, the cognitive nodes (CNs) hear downlink map (DL-MAP) and thus, know the frequency and time locations of all allocated slots at each frame. Then, cognitive nodes contend with each other in order to transmit their fixed size packets based on IEEE 802.11 MAC protocol. Since the number of empty slots at each frame is variable each packet transmission in cognitive network prolongs a random number of frames. By mapping the status of each CN on an open queueing network, including the details of the contention status with the other CNs as well as the statistical distribution of empty slots we are able to derive the saturation throughput of the cognitive WLAN. Finally, we derive the saturation throughput of cognitive network versus the number of CNs as well as the packet arrival rate at WiMAX in downlink direction and confirm our analytical results by simulation.