Telecommunications companies (telcos) and Internet Service Providers (ISPs) monitor the traffic passing through their networks for the purposes of network evaluation and planning for future growth. Most monitoring techniques currently use a form of packet sampling. However, exhaustive monitoring is a preferable solution because it ensures accurate traffic characterization and also allows encoding operations, such as compression and encryption, to be performed. To overcome the very high computational cost of exhaustive monitoring and encoding of data, this thesis suggests exploiting parallelism. By utilizing a parallel cluster in conjunction with load balancing techniques, a simulation is created to distribute the load across the parallel processors. It is shown that a very scalable system, capable of supporting a fairly high data rate can potentially be designed and implemented. A complete system is then implemented in the form of a transparent Ethernet bridge, ensuring that the system can be deployed into a network without any change to the network. The system focuses its encoding efforts on obtaining the maximum compression rate and, to that end, utilizes the concept of streams, which attempts to separate data packets into individual flows that are correlated and whose redundancy can be removed through compression. Experiments show that compression rates are favourable and confirms good throughput rates and high scalability.

Over the last several years, there has been tremendous growth in online gaming (i. e. playing games over the internet). The Massively Multiplayer Online Role Playing Game (MMORPG) is one type of online game. An MMORPG is played within a virtual world. Users have an in-game representation, called an avatar, that they control. Typically there are over a thousand avatars in the virtual world at one time. Users use client software to connect to an MMORPG server over the internet. If just one server is used then the number of avatars that can be supported in the virtual world at one time is severely limited. In order to overcome this, a multi-server approach is needed. Unlike traditional load balancing and partitioning schemes, which generally use task partitioning, data partitioning is required in this case. This thesis investigates schemes for partitioning and load balancing MMORPG applications on a network of processors. In particular, three different schemes were developed andexamined. These are: Static Av, Static MS and Dynamic MS. Static Avassigns avatars to each server, one at a time, as they enter thesimulation. Static MS assigns equal sized portions of the map of thevirtual world to each server. An avatar is assigned to the server thatowns the part of the map that the avatar is "standing"on. Dynamic MS divides the map into many more segments than there are servers. The map segments are dynamicallydistributed among the servers based on the results of aload balancing algorithm. The thesis details the algorithms and the performance associated with each of the schemes. In summary, Static Av does not perform well, whereas Static MS and Dynamic MS can be used to parallelize MMORPGapplications. To the best of our knowledge, this is thefirst published work that looks at the issue ofparallelizing and load balancing such applications.

To address the overload conditions caused by the increasing network traffic volume, recent literature in the network intrusion detection and prevention field has proposed the use of clusters of network intrusion detection and prevention systems (NIDPSs). We observe that simple traffic distribution schemes are usually used for NIDPS clusters. These schemes have two major drawbacks: (1) the loss of correlation information caused by the traffic distribution because correlated flows are not sent to the same NIDPS and (2) the unbalanced loads of the NIDPSs. The first drawback severely affects the ability to detect intrusions that require analysis of correlated flows. The second drawback greatly increases the chance of overloading an NIDPS even when loads of the others are low. In this thesis, we address these two drawbacks. In particular, we propose two novel traffic distribution systems: the Correlation-Based Load Balancer and the Correlation-Based Load Manager as two different solutions to the NIDPS traffic distribution problem. On the one hand, the Load Balancer and the Load Manager both consider the current loads of the NIDPSs while distributing traffic to provide fine-grained load balancing and dynamic load distribution, respectively. On the other hand, both systems take into account traffic correlation in their distributions, thereby significantly reducing the loss of correlation information during their distribution of traffic. We have implemented prototypes of both systems and evaluated them using extensive simulations and real traffic traces. Overall, the evaluation results show that both systems have low overhead in terms of the delays introduced to the packets. More importantly, compared to the naive hash-based distribution, the Load Balancer significantly improves the anomaly-based detection accuracy of DDoS attacks and port scans -- the two major attacks that require the analysis of correlated flows -- meanwhile, the Load Manager successfully maintains the anomaly-based detection accuracy of these two major attacks of the NIDPSs.

In this dissertation, we studied methods for improving efficiency and effectiveness of multipath routing in computer networks. We showed that multipath routing can improve network performance for failure recovery, load balancing, Quality of Service (QoS), and energy consumption. We presented a method for reducing the overhead of computing dynamic path metrics, one of the obstacles for implementing dynamic multipath routing in real world networks. In the first part, we proposed a method for building disjoint multipaths that could be used for local failure recovery as well as for multipath routing. Proactive failure recovery schemes have been recently proposed for continuous service of delay-sensitive applications during failure transients at the cost of extra infrastructural support in the form of routing table entries, extra addresses, etc. These extra infrastructure supports could be exploited to build alternative disjoint paths in those frameworks, while keeping the lengths of the alternative paths close to those of the primary paths. The evaluations showed that it was possible to extend the proactive failure recovery schemes to provide support for nearly-disjoint paths which could be employed in multipath routing for load balancing and QoS. In the second part, we proposed a method for reducing overhead of measuring dynamic link state information for multipath routing, specifically path delays used in Wardrop routing. Even when dynamic routing could be shown to offer convergence properties without oscillations, it has not been widely adopted. One of reasons was that the expected cost of keeping the link metrics updated at various nodes in the network. We proposed threshold-based updates to propagate the link state only when the currently measured link state differs from the last updated state consider- ably. Threshold-based updates were shown through analysis and simulations to offer bounded guarantees on path quality while significantly reducing the cost of propagating the dynamic link metric information. The simulation studies indicated that threshold based updates can reduce the number of link updates by up to 90-95% in some cases. In the third part, we proposed methods of using multipath routing for reducing energy consumption in computer networks. Two different approaches have been advocated earlier, from traffic engineering and topology control to hardware-based approaches. We proposed solutions at two different time scales. On a finer time granularity, we employed a method of forwarding through alternate paths to enable longer sleep schedules of links. The proposed schemes achieved more energy saving by increasing the usage of active links and the down time of sleeping links as well as avoiding too frequent link state changes. To the best of our knowledge, this was the first technique combining a routing scheme with hardware scheme to save energy consumption in networks. In our evaluation, alternative forwarding reduced energy consumption by 10% on top of a hardware-based sleeping scheme. On a longer time granularity, we proposed a technique that combined multipath routing with topology control. The proposed scheme achieved increased energy savings by maximizing the link utilization on a reduced topology where the number of active nodes and links are minimized. The proposed technique reduced energy consumption by an additional 17% over previous schemes with single/shortest path routing.

Network Processors are multicore processors capable of processing network packets at wire speeds of multi-Gbps. Due to their high performance and programmability, these processors have become the main computing elements in many demanding network processing equipments like enterprise, edge and core routers. With the ever increasing use of the internet, the processing demands of these routers have also increased. As a result, the number and complexity of the cores in network processors have also increased. Hence, efficiently managing these cores has become very challenging. This dissertation discusses two main issues related to efficient usage of large number of parallel cores in network processors: (1) How to allocate work to the processing cores to optimize performance? (2) How to meet the desired performance requirement power efficiently? This dissertation presents the design of a hash based scheduler to distribute packets to cores. The scheduler exploits multiple dimensions of locality to improve performance while minimizing out of order delivery of packets. This scheduler is designed to work seamlessly when the number of cores allocated to a service is changed. The design of a resource allocator is also presented which allocates different number of cores to services with changing traffic behavior. To improve the power efficiency, a traffic aware power management scheme is presented which exploits variations in traffic rates to save power. The results of simulation studies are presented to evaluate the proposals using real and synthetic network traces. These experiments show that the proposed packet scheduler can improve performance by as much as 40% by improving locality. It is also observed that traffic variations can be exploited to save significant power by turning off the unused cores or by running them at lower frequencies. Improving performance of the individual cores by careful scheduling also helps to reduce the power consumption because the same amount of work can now be done with fewer cores with improved performance. The proposals made in this dissertation show promising improvements over the previous work. Hashing based schedulers have very low overhead and are very suitable for data rates of 100 Gbps and even beyond. / text

Server farms are popular architectures for computing infrastructures such as supercomputing centers, data centers and web server farms. As server farms become larger and their workloads more complex, designing efficient policies for managing the resources in server farms via trial-and error becomes intractable. In this thesis, we employ stochastic modeling and analysis techniques to understand the performance of such complex systems and to guide design of policies to optimize the performance. There is a rich literature on applying stochastic modeling to diverse application areas such as telecommunication networks, inventory management, production systems, and call centers, but there are numerous disconnects between the workloads and architectures of these traditional applications of stochastic modeling and how compute server farms operate, necessitating new analytical tools. To cite a few: (i) Unlike call durations, supercomputing jobs and file sizes have high variance in service requirements and this critically affects the optimality and performance of scheduling policies. (ii) Most existing analysis of server farms focuses on the First-Come- First-Served (FCFS) scheduling discipline, while time sharing servers (e.g., web and database servers) are better modeled by the Processor- Sharing (PS) scheduling discipline. (in) Time sharing systems typically exhibit thrashing (resource contention) which limits the achievable concurrency level, but traditional models of time sharing systems ignore this fundamental phenomenon. (iv) Recently, minimizing energy consumption has become an important metric in managing server farms. State-of-the-art servers come with multiple knobs to control energy consumption, but traditional queueing models don’t take the metric of energy consumption into account. In this thesis we attempt to bridge some of these disconnects by bringing the stochastic modeling and analysis literature closer to the realities of today’s compute server farms. We introduce new queueing models for computing server farms, develop new stochastic analysis techniques to evaluate and understand these queueing models, and use the analysis to propose resource management algorithms to optimize their performance.

This work focuses on scheduling of MPI jobs when executing in shared-memory multiprocessors (SMPs). The objective was to obtain the best performance in response time in multiprogrammed multiprocessors systems using batch systems, assuming all the jobs have the same priority. To achieve that purpose, the benefits of supporting malleability on MPI jobs to reduce fragmentation and consequently improve the performance of the system were studied. The contributions made in this work can be summarized as follows:· Virtual malleability: A mechanism where a job is assigned a dynamic processor partition, where the number of processes is greater than the number of processors. The partition size is modified at runtime, according to external requirements such as the load of the system, by varying the multiprogramming level, making the job contend for resources with itself. In addition to this, a mechanism which decides at runtime if applying local or global process queues to an application depending on the load balancing between processes of it. · A job scheduling policy, that takes decisions such as how many processes to start with and the maximum multiprogramming degree based on the type and number of applications running and queued. Moreover, as soon as a job finishes execution and where there are queued jobs, this algorithm analyzes whether it is better to start execution of another job immediately or just wait until there are more resources available. · A new alternative to backfilling strategies for the problema of window execution time expiring. Virtual malleability is applied to the backfilled job, reducing its partition size but without aborting or suspending it as in traditional backfilling. The evaluation of this thesis has been done using a practical approach. All the proposals were implemented, modifying the three scheduling levels: queuing system, processor scheduler and runtime library. The impact of the contributions were studied under several types of workloads, varying machine utilization, communication and, balance degree of the applications, multiprogramming level, and job size. Results showed that it is possible to offer malleability over MPI jobs. An application obtained better performance when contending for the resources with itself than with other applications, especially in workloads with high machine utilization. Load imbalance was taken into account obtaining better performance if applying the right queue type to each application independently.The job scheduling policy proposed exploited virtual malleability by choosing at the beginning of execution some parameters like the number of processes and maximum multiprogramming level. It performed well under bursty workloads with low to medium machine utilizations. However as the load increases, virtual malleability was not enough. That is because, when the machine is heavily loaded, the jobs, once shrunk are not able to expand, so they must be executed all the time with a partition smaller than the job size, thus degrading performance. Thus, at this point the job scheduling policy concentrated just in moldability.Fragmentation was alleviated also by applying backfilling techniques to the job scheduling algorithm. Virtual malleability showed to be an interesting improvement in the window expiring problem. Backfilled jobs even on a smaller partition, can continue execution reducing memory swapping generated by aborts/suspensions In this way the queueing system is prevented from reinserting the backfilled job in the queue and re-executing it in the future.

Nowadays the popularity of WLANs is growing very rapidly. The basic solution for the connection of WLAN users to the global internet network is using leased lines or the other standard data transmission networks. But there may occur situations, when the only network for the data transmission we can use for the connection to the internet, is GSM network. In such a case, we are proposing to connect the users of WAN/WLAN networks by using GSM network, which has complete coverage in the territory of Lithuania. To guarantee the sufficient data transfer rate, we use bonding of several parallel GPRS/EDGE/UMTS channels into one total channel. In such a way we increase the reliability and data transfer rate of the total mobile data transmission channel. The parallel channels are bonded into one total channel by using either the hardware (Cisco) routers, or the software routers, configured using network administration tools of Linux operating system. In this work we give all the tools and configuration settings necessary for the realization of such a system. The results of QoS characteristics measurements are given too. We measure packet delay and data transfer rate. The majority of measurements are made by using several parallel GPRS channels because of wide spread GPRS technologies. The key accent of the system is free selection of the physical layer (OSI model) of the parallel channel. So we can bond either several GPRS, or EDGE, or UMTS channels.

This thesis is concerned with the usability of Raspberry Pi as the access point in the mobilebroadband network environment. The first part of the thesis is dedicated to Raspberry Pi itself;hardware required to set up WLAN and WAN; and to the analysis of suitable solutions forbandwidth aggregation, particularly the load balancing of mobile broadband connections andtheir aggregation into one logical link. The second part deals with the implementation of thesesolutions and subsequently with their testing and verification. The evaluation of results gives aninteresting outcome. Load balancing has proven to be resilient and feasible solution forbandwidth aggregation in the mobile broadband network environment where the speed, packetloss and jitter are of main concern. The second scenario, where the connections are bundled intoone logical link, has turned out to give variable results. Its performance is susceptible to thechanges in the mobile broadband network as the packets across the links in the bundle alternatein the round-robin fashion.

In this thesis we propose a cloud Input Shaper and Dynamic Resource Controller to provide application-level quality of service guarantees in cloud computing environments. The Input Shaper splits the cloud into two areas: one for shaped traffic that achieves quality of service targets, and one for overflow traffic that may not achieve the targets. The Dynamic Resource Controller profiles customers’ applications, then calculates and allocates the resources required by the applications to achieve given quality of service targets. The Input Shaper then shapes the rate of incoming requests to ensure that the applications achieve their quality of service targets based on the amount of allocated resources. To evaluate our system we create a new benchmark application that is suitable for use in cloud computing environments. It is designed to reflect the current design of cloud based applications and can dynamically scale each application tier to handle large and varying workload levels. In addition, the client emulator that drives the benchmark also mimics realistic user behaviors such as browsing from multiple tabs, using JavaScript, and has variable thinking and typing speeds. We show that a cloud management system evaluated using previous benchmarks could violate its estimated quality of service achievement rate by over 20%. The Input Shaper and Dynamic Resource Controller system consist of an application performance modeler, a resource allocator, decision engine, and an Apache HTTP server module to reshape the rate of incoming web requests. By dynamically allocating resources to applications, we show that their response times can be improved by as much as 30%. Also, the amount of resources required to host applications can be decreased by 20% while achieving quality of service objectives. The Input Shaper can reduce VMs’ resource utilization variances by 88%, and reduce the number of servers by 45%.