Richard T. B. Ma

On cooperative settlement between content, transit and eyeball internet service providers

Internet service providers (ISPs) depend on one another to provide global network services. However, the profit-seeking nature of the ISPs leads to selfish behaviors that result in inefficiencies and disputes in the network. This concern is at the heart of the “network neutrality” debate, which also asks for an appropriate compensation structure that satisfies all types of ISPs. Our previous work showed in a general network model that the Shapley value has several desirable properties, and that if applied as the profit model, selfish ISPs would yield globally optimal routing and interconnecting decisions. In this paper, we use a more detailed and realistic network model with three classes of ISPs: content, transit, and eyeball. This additional detail enables us to delve much deeper into the implications of a Shapley settlement mechanism. We derive closed-form Shapley values for more structured ISP topologies and develop a dynamic programming procedure to compute the Shapley values under more diverse Internet topologies. We also identify the implications on the bilateral compensation between ISPs and the pricing structures for differentiated services. In practice, these results provide guidelines for solving disputes between ISPs and for establishing regulatory protocols for differentiated services and the industry.

Interconnecting eyeballs to content: a shapley value perspective on isp peering and settlement

Internet service providers (ISPs) must interconnect to provide global Internet connectivity to users. The payment structure of these interconnections are often negotiated and maintained via bilateral agreements. Current differences of opinion in the appropriate revenue model in the Internet has on occasion caused ISPs to de-peer from one another, hindering network connectivity and availability. Our previous work demonstrates that the Shapley value has several desirable properties, and that if applied as the revenue model, selfish ISPs would yield globally optimal routing and interconnecting decisions. In this paper, we focus our investigation of Shapley value in networks with two basic classes of ISP: content and eyeball. In particular, we analyze the revenue distribution between ISPs with elastic and inelastic customer demands, and calculate the bilateral payments between ISPs that implement the Shapley revenue. Our results illustrate how ISP revenues are influenced by different demand models. In particular, the marginal revenue lost by de-peering for an eyeball ISP with inelastic demand is inversely proportional to the square of its degree of connectivity to content ISPs. In practice, these results provide a guideline for ISPs, even in peering relationships, to negotiate bilateral payments and for regulatory institutions to design pricing regulations.

Internet economics: the use of Shapley value for ISP settlement

Within the current Internet, autonomous ISPs implement bilateral agreements, with each ISP establishing agreements that suit its own local objective to maximize its profit. Peering agreements based on local views and bilateral settlements, while expedient, encourage selfish routing strategies and discriminatory interconnections. From a more global perspective, such settlements reduce aggregate profits, limit the stability of routes, and discourage potentially useful peering/connectivity arrangements, thereby unnecessarily balkanizing the Internet. We show that if the distribution of profits is enforced at a global level, then there exist profit-sharing mechanisms derived from the coalition games concept of Shapley value and its extensions that will encourage these selfish ISPs who seek to maximize their own profits to converge to a Nash equilibrium. We show that these profit-sharing schemes exhibit several fairness properties that support the argument that this distribution of profits is desirable. In addition, at the Nash equilibrium point, the routing and connecting/peering strategies maximize aggregate network profits and encourage ISP connectivity so as to limit balkanization.

Modeling and Analysis of Generalized Slotted-Aloha MAC Protocols in Cooperative, Competitive and Adversarial Environments

Aloha [1] and its slotted variant [2] are commonly deployed Medium Access Control (MAC) protocols in environments where multiple transmitting devices compete for a medium, yet may have difficulty sensing each other’s presence. This paper models and evaluates the throughput that can be achieved in a system where nodes compete for bandwidth using a generalized version of slotted- Aloha protocols. We evaluate the channel utilization and fairness of these types of protocols for a variety of node objectives, including maximizing aggregate throughput of the channel, each node greedily maximizing its own throughput, and attacker nodes that attempt to jam the channel. If all nodes are selfish and greedily attempt to maximize their own throughputs, a situation similar to the traditional Prisoner’s Dilemma[3] arises. Our results reveal that under heavy loads, greedy strategies reduce the utilization, and that attackers cannot do much better than attacking during randomly selected slots.

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources, and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of Jackson open queueing networks and is capable of handling arbitrary operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.

The public option: a nonregulatory alternative to network neutrality

Network neutrality and the role of regulation on the Internet have been heavily debated in recent times. Among the various definitions of network neutrality, we focus on the one that prohibits paid prioritization of content. We develop a model of the Internet ecosystem in terms of three primary players: consumers, ISPs, and content providers. We analyze this issue from the point of view of the consumer and target the desired system state that maximizes consumer utility. By analyzing various structures of an ISP market, we obtain different conclusions on the desirability of regulation. We also introduce the notion of a Public Option ISP, an ISP that carries traffic in a network-neutral manner. We find: 1) in a monopolistic scenario, network-neutral regulations might benefit consumers, however the introduction of a Public Option ISP is even better as it aligns the interests of the monopolistic ISP with the consumer utility; and 2) in an oligopolistic scenario, the presence of a Public Option ISP is again preferable to network-neutral regulations, although the presence of competing nonneutral ISPs provides the most desirable situation for the consumers.

Incentive P2P networks: a protocol to encourage information sharing and contribution

The rapid growth of decentralized and unstructured peer-topeer (P2P) networks [3, 7] points to a new efficient paradigm for information exchange on the Internet. A P2P network may exhibit a power-law topology [5] such that it can propagate queries quickly and, if implemented efficiently [7], it can locate objects in time, where is the number of nodes in the network. However, there are remaining problems with the P2P paradigm which complicates its deployment. Freeriding and tragedy of the commons are two major problems. As shown in [1], nearly 70% of Gnutella users do not share any file with the P2P community and nearly 50% of all search responses come from the top 1% of content sharing nodes. Therefore, nodes that share resources are always congested and the tragedy of the commons [2] occurs. Another problem is that many users misreport their connection types so as to discourage others from going to their sites for file download. Worse yet, there is no service differentiation between users who do not share any information and users who contribute significantly to the P2P community. The objective of this paper is to design a mechanism that provides incentives for users to share information and offers preferential service to users who contribute to the P2P community. In particular, we address the following questions:

The public option: a non-regulatory alternative to network neutrality

Network neutrality and the role of regulation on the Internet have been heavily debated in recent times. Among the various definitions of network neutrality, we focus on the one that prohibits paid prioritization of content. We develop a model of the Internet ecosystem in terms of three primary players: consumers, ISPs, and content providers. We analyze this issue from the point of view of the consumer and target the desired system state that maximizes consumer utility. By analyzing various structures of an ISP market, we obtain different conclusions on the desirability of regulation. We also introduce the notion of a Public Option ISP, an ISP that carries traffic in a network-neutral manner. We find: in a monopolistic scenario, network-neutral regulations might benefit consumers, however the introduction of a Public Option ISP is even better as it aligns the interests of the monopolistic ISP with the consumer utility; and in an oligopolistic scenario, the presence of a Public Option ISP is again preferable to network-neutral regulations, although the presence of competing nonneutral ISPs provides the most desirable situation for the consumers.

Monet: A User-Oriented Behavior-Based Malware Variants Detection System for Android

Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around 1 million new malware samples per quarter, and it was reported that over 98% of these new malware samples are in fact “derivatives” (or variants) from existing malware families. In this paper, we first show that runtime behaviors of malware’s core functionalities are in fact similar within a malware family. Hence, we propose a framework to combine “runtime behavior” with “static structures” to detect malware variants. We present the design and implementation of Monet, which has a client and a backend server module. The client module is a lightweight, in-device app for behavior monitoring and signature generation, and we realize this using two novel interception techniques. The backend server is responsible for large scale malware detection. We collect 3723 malware samples and top 500 benign apps to carry out extensive experiments of detecting malware variants and defending against malware transformation. Our experiments show that Monet can achieve around 99% accuracy in detecting malware variants. Furthermore, it can defend against ten different obfuscation and transformation techniques, while only incurs around 7% performance overhead and about 3% battery overhead. More importantly, Monet will automatically alert users with intrusion details so to prevent further malicious behaviors.

Smooth Task Migration in Apache Storm

Task migration happens when distributed data processing systems scale in real-time. To handle the task migration process more gracefully, we propose three task migration methods: (i) worker level migration, (ii) executor level migration, and (iii) executor level migration with reliable messaging. We implement our migration methods on Apache Storm. Our experiments show that, compared with Storms original migration implementation, our methods significantly reduce the performance degradation and the number of task failures during each migration.