Network-Oriented Service Controls

My Ph.D. undertook a detailed investigation of network-oriented service control (NOSC) mechanisms for providing QoS guarantees in Internet services. NOSCs operate at the network and transport layers; thus, they require neither OS changes nor difficult offline capacity analysis. Furthermore, NOSCs can be easily integrated into network protocols, firewalls, and Layer-3+ load-balancers. Because these control mechanisms are located on the path between the client and the server, the applied control will affect both the clients' future requests and the services' processing capacities. Central to my research is establishing a fundamental understanding of how arriving requests react to the applied control, and how applications react to the change in the server's load. The combination of both views---the client's and the service's---builds a complete picture detailing the performance, effectiveness, and limitations of using NOSC for controlling Internet services. My research methodology balances analytical modeling with empirical validation. Analytical modeling provides greater insight into the problem at hand, while empirical validation provides evidences for real-world large-scale deployment.


Eve: A Scalable Network Client Emulator

, , and
10th Communications and Networking Simulation Symposium
Norfolk, VA,

On the Role and Controllability of Persistent Clients in Traffic Aggregates

IEEE/ACM Transactions on Networking
Vol. 14, No. 2,

Re-synchronization and Controllability of Bursty Service Requests

, and
IEEE/ACM Transactions on Networking
Vol. 12, No. 4,

Persistent Dropping: An Efficient Control of Traffic Aggregates

Karlsruhe, Germany,

Adaptive Packet Filters

, and
San Antonio, Texas,

Design Objectives

Classic network-based QoS-management approaches focus on fair allocation of network bandwidth for different flows. They are effective for long-lived connections, e.g., multimedia streaming, where the primary component of user-perceived latency is data transfer. What has occurred with the advancement in Internet services and improvement in server and network capacity is a shift in the time and space granularity of requests: each request is now proportionally shorter in time and requires less network and server resources. At the sane time, each server must handle a much greater number of requests. Furthermore, requests are becoming more correlated, as future requests depend greatly on the successes of previous ones. A customer in an e-commerce site, for instance, will first browse for a product, add it to a shopping cart, and then check out. If NOSCs are to be effective in providing QoS support and overload protection, they must correctly deal with how requests are issued by clients and how servers process such requests. In particular, three design objectives have influenced the development of NOSCs:
o1. QoS Differentiation. QoS differentiation can be enforced across two dimensions: service and client. For the former, a server that is hosting multiple services may need to differentiate among the levels of service that each is getting. In contrast, a server may also need to differentiate between different client groups. In both cases, the server must first classify incoming requests based on the destination service or the originating client group. QoS differentiation is then achieved by appropriately throttling incoming traffic (as a whole) to match the server's capacity while giving higher priority (or weight) to traffic flows belonging to different services or client groups---a similar idea to Rate-Controlled Static Priority and DiffServ. Since each service may react differently to the applied controls, NOSCs focus on determining what and how much to control to reach their desired goal.
o2. Minimize Client-perceived Latency. From a client's perspective, delay is the most important aspect in the performance of servers and networks. NOSCs, however, cannot reduce the processing delay of requests inside an Internet service (because they do not modify the actual service). To this end, NOSCs treat the server as a black box where it can bound the client's delay using two approaches. First, it can enhance the performance of existing controls before a request is admitted into service. This is akin to using Random Early Drop (RED) as a replacement for drop tail inside Internet routers to improve the controllability of long- lived TCP connections. Second, it can perform a form of low-level admission control, where new (connection) requests are admitted only if they do not violate some performance criteria. This is similar to RSVP and measurement-based admission control, but here server capacity is the resource under control instead of network capacity.
o3. Improved Reaction to Overload. Overload protection is one of the primary goals that motivate using NOSCs. Flash crowd events (FCEs) and distributed denial of service attacks (DDoS) can easily overwhelm even the most powerful servers. NOSCs are well-suited for providing this type of overload protection since FCEs and DDoS attacks can be both detected and mitigated at the network or transport layers. Therefore, appropriately- configured NOSCs can act as the first-line of defense against server overload.
The high-level architecture of most NOSCs adheres to traditional control models, which consists of a controller and an input to be controlled. In NOSCs, network traffic, which is represented by arriving requests, is the input. The controller refers to the software (or hardware) mechanisms that change the behavior of the input as it is passed to the output. A controller can be open or closed loop. In a closed-loop design, the controller computes the difference between a target and measured output value, and uses it to fine-tune its controls. Open-loop controllers do not feed back any information from the output. More specifically, the controller can consist of four basic components:
c1. Packet classifier splits incoming requests into different classes based on client groups or destination services. Without this classification process, all incoming requests are treated equally. Therefore, the packet classifier is a crucial component for providing QoS differentiation; however, it is not necessary for providing overload protection.
c2. Enforcement filters specify how to control the incoming traffic or requests. They generally have an open-loop design. Examples of this component include random dropping and token buckets---both are used to shape the underlying traffic to meet a specific transmission rate. The enforcement components is referred to as filters since they filter out nonconforming traffic.
c3. Adaptation policies describe how these filters are manipulated based on specific input conditions. They provide the feedback loop for implementing more complex control algorithms. For instance, a rate-based dropping technique can increase the corresponding drop probability based on the load on the server to force more TCP connections to back off, thus reducing the arrival rate of incoming traffic.
c4. Monitoring signal is a key element in providing the adaptation mechanism. Queue thresholds, such as overflow and underflow, are commonly used to trigger the adaptation policy to take corrective actions. Signals reflect the health of the system as well as the effectiveness of the underlying controls.
Different designs and implementations of these four components give rise to drastically different NOSCs. Part of my research is to look at different design alternatives with the aim of finding those that are best suited---in terms of performance, robustness, and implementation---for controlling Internet services.

My Ph.D. Research Focus

My Ph.D. research followed a structured approach for studying the interactions between applied controls, the resulting reaction from the underlying traffic, and the change in client-perceived latency. Located between the client and the service---either inside a router or at the entry point of a server---the applied control has effects in two directions. In one direction, it affects the client's traffic as the controller decides what to let through and what to deny access. In the other direction, the applied control also affects the performance of the server as it dictates the maximum arrival rate (and possibly the distribution) of requests into service. Therefore, my research studied both directions. The exact problems are detailed below.
p1. Gap Between Request Arrival Models and Controls. The majority of existing studies view request arrivals at an Internet service as a train of uncorrelated events from the perspective of the applied control. Such studies have looked at snapshots of flow aggregates (under specific load conditions) to characterize the various aspects of request arrivals (generally using an ON/OFF traffic model). While this is useful in many aspects of networking research, their models are too rigid to describe how request arrivals will change as different controls are applied to incoming traffic. Describing the arrivals with a Weibull distribution, for example, does not show how future arrivals will be affected by an increase in the number of dropped requests. Without this information, one cannot accurately predict the sensitivity of arriving requests to the applied controls. This may result in having a controller that oscillates between over- and under-enforcement. There is then a need for a request arrival model that incorporates this crucial feature.  The new model must characterize the distribution of request arrivals and how it will change with different enforcement policies. This new understanding will not only improve the design of future control mechanisms, but will also allow more realistic testing before they are deployed in real systems.
p2. Inadequacy of Traditional Enforcement Mechanisms. The way that traffic is controlled has remained relatively unchanged with buffering or dropping (either randomly or using token buckets) as the defacto mechanisms. Such mechanisms have proven their effectiveness at controlling long-lived TCP connections. However, by adopting them directly for controlling Internet services, they are assumed to also be effective for short-lived requests---an assumption that must be validated. What is needed is, thus, a rigorous investigation of the traditional controls that evaluates their effectiveness, pinpoints their shortcomings, and enhances their capabilities based on the new understanding of the intrinsic behavior of arriving requests. Such new mechanisms can provide improved performance for both the client and the server.
New enforcement mechanisms are also needed. Application-level and OS- level QoS mechanisms have relied on admission control to allow only those requests that can be served while meeting their performance targets without causing the violation of targets for previously-admitted requests. Admission control tells the connecting clients that their requests will either be admitted and served in a timely manner or rejected immediately. The capability of explicitly rejecting requests is not fully available to NOSCs as they operate at the IP level.  However, NOSCs can greatly improve both the controllability and client-perceived latency by having a similar mechanism.  Especially with large and unexpected request bursts or flash crowds, explicit rejection can avoid repeated attempts by the clients to gain access to the corresponding service, which may only aggravate the overload situation.
p3. Search for the Optimality of Applied Controls. The importance of determining the relationship of the applied control and request arrivals is outlined above. There is, nonetheless, a greater need for a deep understanding of how the control mechanisms (either the traditional or newly-proposed) will affect the client-perceived latency. This is especially necessary as each control policy has a wide range of possible parameter settings. A brute-force study of all possible control policies with all possible parameter selections is a daunting task. By creating powerful (analytic) models that are capable of expressing client-perceived delay as a function of the applied control, the search from the optimal control policy is greatly simplified. The result is the ability to choose the enforcement policy that minimizes the network delay while meeting the desired control targets.
p4. No Translation Between Network Load and Server Load. Classic network control mechanisms (like WFQ and CBQ) have focused on the fair partitioning of network resources among competing traffic. The translation between network load and resource requirement, therefore, is straightforward as the length of each packet is enough to calculate the required buffer size, consumed bandwidth, and transmission overhead. When NOSCs are applied to Internet services, this direct translation is no longer valid as single requests can produce huge processing delays inside an Internet service. This is particularly important for QoS-sensitive services, where NOSCs provide a form of low-level admission control. To appropriately control arriving requests, it is crucial to have enhanced understanding---either through empirical or analytical studies---of how these requests will be processed inside an Internet service. The goal here is, thus, to provide a mechanism that translates between arriving requests (or network load) to server load (or server resource requirements). This capability can be used to appropriately configure the NOSC policies, investigate the interactions between different services, and study the true extent for which NOSCs can be used to provide QoS guarantees/differentiation.
p5. Rigid Controller Architecture. Many of the existing NOSCs are designed with a specific deployment environment in mind. This rigidity hinders further deployment and adoptability of these techniques, as there is a wide range of services that may need specific controller customization. However, the basic components of any NOSC are relatively simple and can be designed in a modular way that allows a kind of plug-and-play approach for providing service-specific customization. Combining easy-customization with enhanced understanding and improved controls as outlined above will ensure large-scale and cost-effective deployment.

Primary Contributions

My Ph.D. dissertation makes several key contributions toward advancing the state of the art in using NOSCs. They are motivated by the issues outlined earlier and are centered around the need for fundamental understanding of the relationship between the applied control, the reaction of the underlying traffic, and the resulting change in client-perceived latency.
i. Traffic Analysis. Rigorous analysis of the effects of controls on incoming traffic to an Internet service is performed. This is a crucial step for designing and evaluating any network control mechanism. Based on this empirical study, the persistent client model is introduced to capture how client requests do not simply go away when dropped. Instead, there is a long-range dependency between the applied controls and future request arrivals. The proposed model is a departure from traditional approaches where client models are based on observed traffic aggregates (mostly through traffic traces). In contrast, this model is created after careful analysis of a wide range of individual clients accessing different web services. Combining the observations of different clients, a coherent picture is built detailing the expected behavior of the aggregate behavior of arriving requests.
With this new model, several unexpected aggregate behaviors in routers and end-servers are unfolded during different load scenarios. In particular, there was a surprising discovery of re-synchronization of dropped requests. This has a strong implication on the control of bursty request arrivals. Specifically, it shows that traditional traffic policing mechanisms (e.g., Random Early Drop and Token Bucket Filters) often underestimate the effects of the enforcement policy on the underlying client- perceived latency.
Because this work relies on actual implementation and real measurements, Eve---a scalable, extensible, and programmer-friendly tool that allows for fast generation of clients to test high-capacity Internet servers ---is built. Eve is used to simulate thousands, or tens of thousands, of persistent clients with very high accuracy. It is a crucial component in the evaluation process as it integrates the proposed user-model with on- line measurements.
ii. Optimal Drop Policy. Based on the proposed client model, an analytical model is developed to relate the drop probability and the connection-establishment delay, which can easily dominate the total client-perceived latency when accessing an Internet service. This model is constructed such that it can be applied to a wide range of drop policies. With this capability, the effects of different drop policies are explored and persistent dropping, a simple and novel mechanism that minimizes the client-perceived delay and the number of retransmitted requests while achieving the same control targets as traditional control mechanisms, is proposed. In particular, persistent dropping achieves three important goals: (1) it allows routers and end-servers to quickly converge to their control targets without sacrificing fairness, (2) it minimizes the portion of client delay that is attributed to the applied controls, and (3) it is both easily implementable and computationally tractable. Two working implementations of persistent dropping are implemented that can be deployed in routers or end-servers.
iii. Reject Message Extensions for TCP Connections. As mentioned earlier, admission control, from the point of the network stack, is a high-level operation that has been used extensively by QoS mechanisms. There are many situations where it is more advantageous to both the server and the client to perform this admission control before the connection is fully established. After surveying possible solutions, it was clear that there is no universal mechanism for providing admission control at the IP level. A new IETF Internet standard is thus proposed for extending the Internet Control Message Protocol (ICMP) to provide explicit rejection capability. The choice of adding a new ICMP message, as opposed to changing the TCP stack implementation, is motivated by the need for a simple implementation that can be incrementally deployed. This new capability is an effective way for server and router to deal with clients' persistence. It also provides the client with immediate information, rather than relying on timeouts to infer the server's state.
iv. Improved Traffic Prediction. Because incoming requests to an Internet service can be inherently bursty, a new multi-stage filter is introduced to perform accurate traffic prediction of future arrivals based on the applied control policy. Traffic prediction was based on the persistent client model and was augmented with a rejection mechanism to create the Abacus Filter (AF), a new traffic policing mechanism. AF complements the persistent dropping technique by better adapting to changing arrival patterns. The deriving principle is to maximize the number of admitted requests subject to the maximum acceptable network delay constraint. By modeling the time of the next TCP retransmissions, AFs estimate the amount of available future capacity and only admits portion of a burst that, with high probability, will eventually be admitted. This way, bursty traffic or a sudden jump in network load does not affect the overall delay of successful connections.
v. Analysis of Impact of Control on Internet Services. The above contributions focus on analyzing and improving request controls inside the network. The picture is completed by rigorously analyzing the effects of controls on the corresponding Internet services. Here the multi-threaded round robin (MTRR) server model is introduced to analyze the internal dynamics of a typical Internet service. This model is a generalization of existing time-sharing process models and is more representative of a wide range of current service implementations. For this model, powerful, yet computationally-efficient, mathematical relationships are developed that capture three important elements: (1) the effects of concurrency on the performance (in terms of throughput and response time) of a service, (2) the interactions between threads/processes of different services, and (3) the relationship between requests arrival and service performance.
This analytical model is general enough to describe the interactions between the applied controls and multiple services sharing the same machine. In particular, it is used to address important issues in the design of effective QoS mechanisms: (1) predict the true impact of the applied control---expressed by the maximum allowed request arrival rate ---on the client-perceived latency, (2) estimate the corresponding performance of all running services, (3) describe the stability criterion of the underlying server, (4) find the rate limits that guarantee certain response times to different client groups (e.g., paid customers are given preferential treatment over the non-paying ones).
vi. Integrated Adaptation Mechanism. Combining the results from the above contributions, a general architecture is designed for integrating signaling information with dynamic control. This mechanism, which is called Adaptive Packet Filters (APFs), requires minimal OS modifications to facilitate its deployment and maximize its portability. It uses a simple and robust technique that can provide immediate reaction to varying load conditions. An APF implements its components as plug-in modules. This allows it to easily integrate new control filters (i.e., persistent dropping and abacus filters). The current implementation targets Layer-4 network devices and shows full overload protection as well as service differentiation.