Providing bandwidth guarantees to specific applications is becoming increasingly important as applications compete for shared cloud network resources. We present CloudMirror, a solution that provides bandwidth guarantees to cloud applications based on a new network abstraction and workload placement algorithm. An effective network abstraction should enable applications to easily and accurately specify their requirements, while simultaneously enabling the infrastructure to provision resources efficiently for deployed applications. Prior research has approached the bandwidth guarantee specification by using abstractions that resemble physical network topologies. We present a contrasting approach of deriving a network abstraction based on application communication structure, called Tenant Application Graph or TAG. CloudMirror also incorporates a new workload placement algorithm that efficiently meets bandwidth requirements specified by TAGs while factoring in high availability considerations. Extensive simulations using real application traces and datacenter topologies show that CloudMirror can handle 40% more bandwidth demand than the state of the art (e.g., the Oktopus system), while improving high availability from 20% to 70%.

Software-Defined Networking (SDN) promises to enable vigorous innovation, through separation of the control plane from the data plane, and to enable novel forms of network management, through a controller that uses a global view to make globally-valid decisions. The design of SDN controllers creates novel challenges; much previous work has focused on making them scalable, reliable, and efficient.

While cloud computing providers offer guaranteed allocations for resources such as CPU and memory, they do not offer any guarantees for network resources. The lack of network guarantees prevents tenants from predicting lower bounds on the performance of their applications. The research community has recognized this limitation but, unfortunately, prior solutions have significant limitations: either they are inefficient, because they are not work-conserving, or they are impractical, because they require expensive switch support or congestion-free network cores.

The network, similar to CPU and memory, is a critical and shared resource in the cloud. However, unlike other resources, it is neither shared proportionally to payment, nor do cloud providers offer minimum guarantees on network bandwidth. The reason networks are more difficult to share is because the network allocation of a virtual machine (VM) X depends not only on the VMs running on the same machine with X, but also on the other VMs that X communicates with and the cross-traffic on each link used by X. In this paper, we start from the above requirements--payment proportionality and minimum guarantees--and show that the network-specific challenges lead to fundamental tradeoffs when sharing cloud networks. We then propose a set of properties to explicitly express these tradeoffs. Finally, we present three allocation policies that allow us to navigate the tradeoff space. We evaluate their characteristics through simulation and testbed experiments to show that they can provide minimum guarantees and achieve better proportionality than existing solutions.

The network is a crucial resource in cloud computing, but in contrast to other resources such as CPU or memory, the network is currently shared in a best effort manner. However, sharing the network in a datacenter is more challenging than sharing the other resources. The key difficulty is that the network allocation for a VM X depends not only on the VMs running on the same machine with X, but also on the other VMs that X communicates with, as well as on the cross-traffic on each link used by X. In this paper, we first propose a set of desirable properties for allocating the network bandwidth in a datacenter at the VM granularity, and show that there exists a fundamental tradeoff between the ability to share congested links in proportion to payment and the ability to provide minimal bandwidth guarantees to VMs. Second, we show that the existing allocation models violate one or more of these properties, and propose a mechanism that can select different points in the aforementioned tradeoff between payment proportionality and bandwidth guarantees.

There is a growing body of research exploring new network architectures for the data center. These proposals all seek to improve the scalability and cost-effectiveness of current data center networks, but adopt very different approaches to doing so. For example, some proposals build networks entirely out of switches while others do so using a combination of switches and servers. How do these different network architectures compare? For that matter, by what metrics should we even begin to compare these architectures?

Over the past decade a variety of network architectures have been proposed to address IP's limitations in terms of flexible forwarding, security, and data distribution. Meanwhile, fueled by the explosive growth of video traffic and HTTP infrastructure (e.g., CDNs, web caches), HTTP has became the de-facto protocol for deploying new services and applications. Given these developments, we argue that these architectures should be evaluated not only with respect to IP, but also with respect to HTTP, and that HTTP could be a fertile ground (more so than IP) for deploying the newly proposed functionalities. In this paper, we take a step in this direction, and find that HTTP already provides many of the desired properties for new Internet architectures. HTTP is a content centric protocol, provides middlebox support in the form of reverse and forward proxies, and leverages DNS to decouple names from addresses. We then investigate HTTP's limitations, and propose an extension, called S-GET that provides support for low-latency applications, such as VoIP and chat.

Cloud computing environments impose new challenges on access control techniques due to multi-tenancy, the growing scale and dynamicity of hosts within the cloud infrastructure, and the increasing diversity of cloud network architectures. The majority of existing access control techniques were originally designed for enterprise environments that do not share these challenges and, as such, are poorly suited for cloud environments. In this paper, we argue that it is both sufficient and advantageous to implement access control only within the hypervisors at the end-hosts. We thus propose Cloud-Police, a system that implements a hypervisor-based access control mechanism. We argue that, not only can CloudPolice support more sophisticated access control policies, it can do so in a manner that is simpler, more scalable and more robust than existing network-based techniques.

Enterprise and data center networks consist of a large number of complex networked applications and services that depend upon each other. For this reason, they are difficult to manage and diagnose. In this paper we propose Macroscope, a new approach to extracting the dependencies of networked applications automatically by combining application process information with network level packet traces. We evaluate Macroscope on traces collected at 52 laptops within a large enterprise and show that Macroscope is accurate in finding the dependencies of networked applications. We also show that Macroscope requires less human involvement and is significantly more accurate than state of the art approaches that use only packet traces. Using our rich profiles of the application-service dependencies, we explore and uncover some interesting characteristics about this relationship. Finally, we discuss several usage scenarios that can benefit from Macroscope.

We address the problem of balancing the traffic load in multi-hop wireless networks. We consider a point-to-point communicating network with a uniform distribution of source-sink pairs. When routing along shortest paths, the nodes that are centrally located forward a disproportionate amount of traffic. This translates into increased congestion and energy consumption. However, the maximum load can be decreased if the packets follow curved paths. We show that the optimum such routing scheme can be expressed in terms of geometric optics and computed by linear programming. We then propose a practical solution, which we call Curveball Routing that achieves results not much worse than the optimum. We evaluate our solution at three levels of fidelity: a Java high-level simulator, the ns2 simulator, and the Intel Mirage Sensor Network Testbed. Simulation results using the high-level simulator show that our solution successfully avoids the crowded center of the network, and reduces the maximum load by up to 40%. At the same time, the increase of the expected path length is small, i.e., only 8 % on average. Simulation results using the ns2 simulator show that our solution can increase throughput on moderately loaded networks by up to 15%, while testbed results show a reduction in peak message load by up to 25%. Our prototype suggests that our solution is easily deployable.

Sensor networks are notoriously difficult to program, given that they encompass the complexities of both distributed and embedded systems. To address this problem, we present the design and implementation of a declarative sensor network platform, DSN: a declarative language, compiler and runtime suitable for programming a broad range of sensornet applications. We demonstrate that our approach is a natural fit for sensor networks by specifying several very different classes of traditional sensor network protocols, services and applications entirely declaratively -- these include tree and geographic routing, link estimation, data collection, event tracking, version coherency, and localization. To our knowledge, this is the first time these disparate sensornet tasks have been addressed by a single high-level programming environment. Moreover, the declarative approach accommodates the desire for architectural flexibility and simple management of limited resources. Our results suggest that the declarative approach is well-suited to sensor networks, and that it can produce concise and flexible code by focusing on what the code is doing, and not on how it is doing it.

We propose a solution to improve fairness and increase throughput in wireless networks with location information. Our approach consists of a multipath routing protocol, biased geographical routing (BGR), and two congestion control algorithms, in-network packet scatter (IPS) and end-to-end packet scatter (EPS), which leverage BGR to avoid the congested areas of the network. BGR achieves good performance while incurring a communication overhead of just 1 byte per data packet, and has a computational complexity similar to greedy geographic routing. IPS alleviates transient congestion by splitting traffic immediately before the congested areas. In contrast, EPS alleviates long term congestion by splitting the flow at the source, and performing rate control. EPS selects the paths dynamically, and uses a less aggressive congestion control mechanism on non-greedy paths to improve energy efficiency. Simulation and experimental results show that our solution achieves its objectives. Extensive ns-2 simulations show that our solution improves both fairness and throughput as compared to single path greedy routing. Our solution reduces the variance of throughput across all flows by 35%, reduction which is mainly achieved by increasing throughput of long-range flows with around 70%. Furthermore, overall network throughput increases by approximately 10% Experimental results on a 50- node testbed are consistent with our simulation results, suggesting that BGR is effective in practice.

The progress of mobile device technology unfolds a new spectrum of applications that challenges conventional infrastructure models. Most of these devices are perceived by their users as "appliances" rather than computers and accordingly the application management should be done transparently by the underlying system unlike classic applications managed explicitly by the user. Memory management on such devices should consider new types of mobile applications involving code mobility such as mobile agents, active networks and context aware applications. This paper describes a new code management technique, called "code collection " and proposes a specific code collection algorithm, the Adaptive Code Collection Algorithm (ACCAL). Code collection is a mechanism for transparently loading and discarding application components on mobile devices at runtime that is designed to permit very low memory usage and at the same time good performance by focusing memory usage on the hotspots of the application. To achieve these goals, ACCAL uses properties specific to executable code and enhances conventional data management methods such as garbage collection and caching. The results show that fine-grained code collection allows large applications to execute by using significantly less memory while inducing small execution time overhead.