Jingjie Jiang

Maximized Cellular Traffic Offloading via Device-to-Device Content Sharing

In next-generation LTE-advanced cellular networks, device-to-device (D2D) communication has emerged as an effective way to offload cellular traffic and improve system performance. Conventionally, a device exclusively relies on cellular communication to retrieve the content it desires. With D2D communication, however, if the same piece of content is available in the vicinity of the device, the content can be directly retrieved from one of its neighbouring devices. Naturally, the key problem becomes how to maximize content sharing via D2D communication. Existing works on content sharing are mainly concerned with a multi-hop communication setting, while works on D2D communication have primarily focused on the communication aspects, including interference avoidance and energy efficiency. In this paper, we study the problem of maximizing cellular traffic offloading with D2D communication, by selectively caching popular content locally, and by exploring maximal matching for sender-receiver pairs. Specifically, we consider an interference-aware communication model and formulate selective caching as a Knapsack problem, and sender-receiver matching as a maximum weighted matching problem in a bipartite graph. We propose decentralized algorithms to solve both problems, and our simulation results demonstrate that our algorithms are effective in maximizing cellular traffic offloading.

Rado: A Randomized Auction Approach for Data Offloading via D2D Communication

Despite the growing deployment of 4G networks, the capacity of cellular networks is still insufficient to satisfy the ever-increasing bandwidth demand of mobile applications. Given the common interest of mobile users, Device-to-Device (D2D) communication has emerged as a promising solution to offload cellular traffic and enable proximity-based services. One of the main detriments for D2D communication is the lack of incentive for mobile users to share their content, since such sharing inevitably consumes limited resources and potentially jeopardizes user privacy. In this paper, we study the incentive problem in D2D communications. Specifically, we model the incentive in offloading scenario as an auction game. A trading network is constructed between an eNB and users, in which auctions are conducted to group offloading users and determine proper rewards. We further design a randomized auction mechanism to guarantee system efficiency and truthfulness. Extensive experiments verify the effectiveness of our mechanism in that it achieves a significant performance gain in comparison with baseline methods.

Rally: Device-to-Device Content Sharing in LTE Networks as a Game

Even with modern physical-layer technologies in LTE networks, the capacity of cellular networks is still far from sufficient to satisfy the insatiable bandwidth demand of mobile applications. Owing to common interests among mobile users, Device-to-Device (D2D) communication has emerged as a viable alternative to offload cellular traffic, with the promise of substantially alleviating the need for cellular network bandwidth. In this paper, we first carry out an extensive theoretical analysis based on a game theoretic approach, and show that the objective of maximized cellular offloading is equivalent to maximizing the social welfare in a trading network, where the content to be shared is the commodity, and mobile users are buyers or sellers. We next design Rally, a set of distributed strategies that can converge to a sub game perfect Nash equilibrium in the content sharing game. Both our theoretical analyses and simulation results have shown the effectiveness of Rally, in that it can indeed maximize cellular traffic offloading through D2D communication.

Maximizing container-based network isolation in parallel computing clusters

Data-parallel applications, especially those associated with user-facing web services, have struggled to enhance their worst case performance. It is therefore important to improve the minimum amount of resources guaranteed for applications in a cluster. Existing cluster management frameworks, however, provide isolation for computation resources (such as CPU) only, and are oblivious to network isolation guarantees. In this paper, we design, implement and evaluate Libra, a new cluster management framework that helps to maximize the isolation guarantee for the bandwidth requirements from applications. We start with a theoretical analysis of the network sharing problem, which contains two key steps: container placement and bandwidth allocation. By collecting the status of access links and the bandwidth demand of applications, we coordinate the placement of containers to minimize the system bottleneck such that the bandwidth guarantee for applications can be optimized. We further embrace host-based rate limiting to ensure such maximized bandwidth guarantee can be reached without hurting network utilization. Both our testbed-based experiments and large-scale simulations demonstrate that Libra significantly improves the network isolation guarantee: in comparison with existing cluster managers and network schedulers, the performance gain is more than 105.59%. Meanwhile, it improves application performance by 57.71% and maintains high network utilization.

Symbiosis: Network-aware task scheduling in data-parallel frameworks

Even with the recent proliferation of in-memory computation in data-parallel frameworks (such as Spark), transfers over the network are still time-consuming. Similar to computation, network transfers serve as main roadblocks as we try to minimize job completion times. Existing schedulers were designed as isolated solutions that focused on computation or network performance only. Without any coordination, the utilization of computation and network resources may become unbalanced, leading to a reduced level of overall resource utilization. In this paper, we design, implement, and evaluate Symbiosis, a network-aware task scheduler designed to coordinate computation-bound and network-bound tasks in a large cluster, so that resources are utilized in a more balanced fashion. Symbiosis is an online scheduler that predicts resource imbalance before launching tasks, and correct such imbalance by co-locating computation-bound and network-bound tasks in the same executor process. As a guiding principle, it is engineered to be practically implemented within and to complement existing data-parallel frameworks. We have implemented Symbiosis within Spark, and carried out our experiments on a 100-node cluster. We show convincing evidence that Symbiosis reduces job completion times by 11.9% in comparison to Sparks current scheduler with little overhead.

Tailor: Trimming Coflow Completion Times in Datacenter Networks

Tasks in a data-parallel job communicate with each other through a number of concurrent flows, which is described as a coflow. These flows are correlated in the sense that the performance of a coflow is dictated by the flow that takes the longest time to complete. Minimizing coflow completion times, however, turns out to be a challenge, given the correlation across flows and how they are routed collectively through a datacenter network. In this paper, we propose Tailor, a simple yet effective mechanism with the objective of trimming the coflow completion times in a datacenter network. To achieve our objective, Tailor takes advantage of OpenFlow in a software-defined datacenter network. By monitoring and rerouting live flows to links with lighter loads, Tailor guarantees that the coflow completion time is minimized dynamically and converges to its lower bound. Our experimental results in both Mininet and large-scale simulations have shown that Tailor is much more effective than flow-level schemes when it comes to reducing coflow completion times. It also outperforms existing scheduling-only coflow mechanisms and achieves similar performance with the state-of-the-art hybrid mechanism, yet with much lower complexity.

Custody: Towards Data-Aware Resource Sharing in Cloud-Based Big Data Processing

With the advent of big data processing frameworks, the performance of data-parallel applications is heavily affected by the time it takes to read input data, making it important to improve data locality. Existing methods in achieving data locality have primarily focused on selecting machines to place tasks of applications. Nevertheless, the set of machines that an application can choose from is determined by a cluster manager, which is oblivious to the location of data in existing resource sharing frameworks. In this paper, we design, implement and evaluate Custody, a new cluster management framework that helps to maximize data locality by allocating the executor processes with local access to data to those applications in need. Custody achieves this objective by dynamically collecting runtime information of an applications input data and by effectively allocating executors among and within applications through theoretic analyses of the data-aware resource sharing problem. With significantly better data locality, Custody avoids unnecessary network transfers and thus expedites job completion times. Our experimental results on a 100-node cluster demonstrate that Custody can improve the data locality for input tasks by 36.9% in comparison with Sparks default cluster manager. Meanwhile, it reduces the job completion times by 14.9% due to fewer network transfers.

Circa: Offloading collaboratively in the same vicinity with iBeacons

Code offloading to remote infrastructures has been a common practice for mobile users who seek extra power or computing resources to perform computation-intensive tasks. Existing works, however, have so far mainly focused on code offloading from a single mobile device to remote cloud servers, which restricts the potential of code offloading only to devices with available Internet access. In this paper, we propose Circa, a new framework that demonstrates the feasibility of code offloading among multiple mobile devices in close proximity to one another, leveraging the presence of iBeacons. Our objective is to eliminate the costs incurred by running virtual machine instances in the cloud, and the need to connect remotely to the cloud as well. With the assistance of iBeacons, devices in the same vicinity can discover and support one another through collaborative code offloading with short-range communication, obviating the need for centralized servers. We have implemented Circa on the iOS platform and validated its feasibility using iOS devices. According to our experimental results, with more than two collaborators, Circa is capable of reducing the total execution time of an offloaded task substantially, while preserving satisfactory performance of the mobile application.

Maximizing link utilization with coflow-aware scheduling in datacenter networks

Link utilization has received extensive attention since datacenters become the most prevalent platform for data-parallel computing applications. A specific job of such applications involves communication among multiple machines. The coflow abstraction depicts such communication and captures application performance through corresponding network requirements. Existing techniques to improve link utilization, however, either restrict themselves to work conservation, or merely focus on flow-level metrics and ignore coflow-level performance. In this paper, we address the coflow-aware scheduling problem with the objective of maximizing link utilization. Through theoretic analyses, we formulate the coflow-aware scheduling problem as a NP-hard open shop scheduling problem with heterogeneous concurrency. Despite the hardness of this problem, we design Maluca, a hierarchical scheduling framework to conduct both inter- and intra-link scheduling. Malucas algorithm is not only starvation-free and work-conserving, but also 2-approximate in terms of link utilization. Extensive simulation results demonstrate that Maluca outperforms both per-flow and coflow schemes in terms of link utilization, and achieves similar coflow performance in comparison with the state-of-art coflow scheduling schemes.

Chronos: Meeting coflow deadlines in data center networks

Guaranteed performance for data-parallel applications is important for both service providers and cloud data centers that host such services. A job of data-parallel applications involves communication among multiple machines to transmit intermediate results. Such communication comprises a collection of parallel flows, which is abstracted as a coflow in recent proposals. In this paper, we study the problem of meeting deadlines for coflows in data center networks. Existing flow-level scheduling schemes are insufficient to guarantee the coflow-level performance, since a coflow can meet its deadline only when all its constituent flows finish on time. Due to the scarce bandwidth on the network bottleneck, it is vital to coordinate concurrent coflows to meet as many deadlines as possible. We present Chronos, a scheduling framework that captures the correlation of flows belonging to a coflow, and handles the resource allocation among multiple concurrent coflows. Chronos is work-conserving and starvation-free without integrating complicated admission control mechanisms. We show via extensive simulations on ns3 that Chronos can make 1.6× more coflows meet their deadlines compared to flow-level schemes.