Analytical model for the IPACT dynamic bandwidth allocation algorithm for EPONs

A

On dynamic resource allocation in systems with bursty sources

There is a trend to use computing resources in a way that is more removed from the technical constraints. Users buy compute time on machines that they do not control or necessarily know the specifics of. Conversely this means the providers of such resources have more freedom in allocating them amongst different tasks. They can use this freedom to provide more, or better, service by reallocating resources as demand for them changes. However deciding when to reallocate resources is not trivial. In order to make good reallocation decisions, this thesis constructs a series of models. Each of the models concerns a resource allocation problem in the presence of bursty sources. The focus of the modelling, however, varies. In its most basic form it considers several different job types competing over the allocation of a limited number of servers. The goal there is to minimize the (weighted) mean time jobs spend in the system. The weighting can reflect the relative importance of the different job types. Reallocation of servers between job types is in general considered to be neither free nor instantaneous. We then show how to find the optimal static allocation of servers over job types. Finding the optimal dynamic allocation of servers is formulated as solving a Markov decision process. We show that this is practically unfeasible for all but the most simple systems. Instead a number of heuristics are introduced. Some are fluid-approximation based and some are parameterless, i.e. do not require the a priori knowledge of parameters of the system. The performance of these heuristic policies is then explored in a series of simulations. A slightly different model is formulated next. Its goal is not to optimize allocation of servers over several job types, but rather between powered up and powered down states. In the powered up state servers can provide service for incoming jobs. In the powered down state servers cannot service incoming jobs but incur a profit due to power savings. Balancing power and performance is again formulated as a Markov decision process. This is not explicitly solved but instead some of the heuristics considered earlier are adapted to give dynamic policies for powering servers up and v down. Their performance is again tested in a number of simulations, including some where the arrival process is not only bursty but also non-Markovian. The third and final model considers allocation of servers over different job types again. This time the servers experience breakdowns and subsequent repairs. During a repair period the servers cannot process any incoming jobs. To reduce the complexity of this model, it is assumed that switches of servers between job types are instantaneous, albeit not necessarily free. This is modeled as a Markov decision process and we show how to find the optimal static allocation of servers. For the dynamic allocation previously considered heuristics are adapted again. Simulations then show the performance of these heuristics and the optimal static allocation in a number of scenarios.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A performance study on dynamic load balancing algorithms.

by Sau-ming Lau.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 131-134).Abstract --- p.iAcknowledgement --- p.iiiList of Tables --- p.viiiList of Figures --- p.xChapter 1 --- Introduction --- p.1Chapter 2 --- Basic Concepts and Related Work --- p.9Chapter 2.1 --- Components of Dynamic Load Balancing Algorithms --- p.10Chapter 2.2 --- Classification of Load Balancing Algorithms --- p.11Chapter 2.2.1 --- Casavant and Kuhl's Taxonomy --- p.12Chapter 3 --- System Model and Assumptions --- p.19Chapter 3.1 --- The System Model and Assumptions --- p.19Chapter 3.2 --- Survey on Cost Models --- p.21Chapter 3.2.1 --- "Eager, Lazowska, and Zahorjan's Model" --- p.22Chapter 3.2.2 --- "Shivaratri, Krueger, and Singhal's Model" --- p.23Chapter 3.3 --- Our Cost Model --- p.24Chapter 3.3.1 --- Design Philosophy --- p.24Chapter 3.3.2 --- Polling Query Cost Model --- p.25Chapter 3.3.3 --- Load State Broadcasting Cost Model --- p.26Chapter 3.3.4 --- Task Assignment Cost Model --- p.27Chapter 3.3.5 --- Task Migration Cost Model --- p.28Chapter 3.3.6 --- Execution Priority --- p.29Chapter 3.3.7 --- Simulation Parameter Values --- p.31Chapter 3.4 --- Performance Metrics --- p.33Chapter 4 --- A Performance Study on Load Information Dissemination Strategies --- p.36Chapter 4.1 --- Algorithm Descriptions --- p.37Chapter 4.1.1 --- Transfer Policy --- p.37Chapter 4.1.2 --- Information Policy --- p.40Chapter 4.1.3 --- Location Policy --- p.40Chapter 4.1.4 --- Categorization of the Algorithms --- p.43Chapter 4.2 --- Simulations and Analysis of Results --- p.43Chapter 4.2.1 --- Performance Comparisons --- p.44Chapter 4.2.2 --- Effect of Imbalance Factor on AWLT Algorithms --- p.49Chapter 4.2.3 --- Comparison of Average Performance --- p.52Chapter 4.2.4 --- Raw Simulation Results --- p.54Chapter 4.3 --- Discussions --- p.55Chapter 5 --- Resolving Processor Thrashing with Batch Assignment --- p.56Chapter 5.1 --- The GR.batch Algorithm --- p.57Chapter 5.1.1 --- The Guarantee and Reservation Protocol --- p.57Chapter 5.1.2 --- The Location Policy --- p.58Chapter 5.1.3 --- Batch Size Determination --- p.60Chapter 5.1.4 --- The Complete GR.batch Description --- p.62Chapter 5.2 --- Additional Performance Metrics --- p.66Chapter 5.3 --- Simulations and Analysis of Results --- p.67Chapter 5.4 --- Discussions --- p.73Chapter 6 --- Applying Batch Assignment to Systems with Bursty Task Arrival Patterns --- p.75Chapter 6.1 --- Bursty Workload Pattern Characterization Model --- p.76Chapter 6.2 --- Algorithm Descriptions --- p.77Chapter 6.2.1 --- The GR.batch Algorithm --- p.77Chapter 6.2.2 --- The SK .single Algorithm --- p.77Chapter 6.2.3 --- Summary of Algorithm Properties --- p.77Chapter 6.3 --- Analysis of Simulation Results --- p.77Chapter 6.3.1 --- Performance Comparison --- p.79Chapter 6.3.2 --- Time Trace --- p.80Chapter 6.4 --- Discussions --- p.80Chapter 7 --- A Preliminary Study on Task Assignment Augmented with Migration --- p.87Chapter 7.1 --- Algorithm Descriptions --- p.87Chapter 7.1.1 --- Information Policy --- p.88Chapter 7.1.2 --- Location Policy --- p.88Chapter 7.1.3 --- Transfer Policy --- p.88Chapter 7.1.4 --- The Three Load Balancing Algorithms --- p.89Chapter 7.2 --- Simulations and Analysis of Results --- p.90Chapter 7.2.1 --- Even Task Service Time --- p.90Chapter 7.2.2 --- Uneven Task Service Time --- p.94Chapter 7.3 --- Discussions --- p.99Chapter 8 --- Assignment Augmented with Migration Revisited --- p.100Chapter 8.1 --- Algorithm Descriptions --- p.100Chapter 8.1.1 --- The GR.BATCH.A Algorithm --- p.101Chapter 8.1.2 --- The SK.SINGLE.AM Algorithm --- p.101Chapter 8.1.3 --- Summary of Algorithm Properties --- p.101Chapter 8.2 --- Simulations and Analysis of Results --- p.101Chapter 8.2.1 --- Performance Comparisons --- p.102Chapter 8.2.2 --- Effect of Workload Imbalance --- p.105Chapter 8.3 --- Discussions --- p.106Chapter 9 --- Applying Batch Transfer to Heterogeneous Systems with Many Task Classes --- p.108Chapter 9.1 --- Heterogeneous System Model --- p.109Chapter 9.1.1 --- Processing Node Specification --- p.110Chapter 9.1.2 --- Task Type Specification --- p.111Chapter 9.1.3 --- Workload State Measurement --- p.112Chapter 9.1.4 --- Task Selection Candidates --- p.113Chapter 9.2 --- Algorithm Descriptions --- p.115Chapter 9.2.1 --- First Category ´ؤ The Sk .single Variations --- p.115Chapter 9.2.2 --- Second Category ´ؤ The GR. batch Variation Modeled with SSP --- p.117Chapter 9.3 --- Analysis of Simulation Results --- p.123Chapter 10 --- Conclusions and Future Work --- p.127Bibliography --- p.131Appendix A System Model Notations and Definitions --- p.131Appendix A.1 Processing Node Model --- p.131Appendix A.2 Cost Models --- p.132Appendix A.3 Load Measurement --- p.134Appendix A.4 Batch Size Determination Rules --- p.135Appendix A.5 Bursty Arrivals Modeling --- p.135Appendix A.6 Heterogeneous Systems Modeling --- p.135Appendix B Shivaratri and Krueger's Location Policy --- p.13

An Adaptive Scheme for Admission Control in ATM Networks

This paper presents a real time front-end admission control scheme for ATM networks. A call management scheme which uses the burstiness associated with traffic sources in a heterogeneous ATM environment to effect dynamic assignment of bandwidth is presented. In the proposed scheme, call acceptance is based on an on-line evaluation of the upper bound on cell loss probability which is derived from the estimated distribution of the number of calls arriving. Using this scheme, the negotiated quality of service will be assured when there is no estimation error. The control mechanism is effective when the number of calls is large, and tolerates loose bandwidth enforcement and loose policing control. The proposed approach is very effective in the connection oriented transport of ATM networks where the decision to admit new traffic is based on thea priori knowledge of the state of the route taken by the traffic

Theoretical Analysis and Evaluation of NoCs with Weighted Round-Robin Arbitration

Fast and accurate performance analysis techniques are essential in early design space exploration and pre-silicon evaluations, including software eco-system development. In particular, on-chip communication continues to play an increasingly important role as the many-core processors scale up. This paper presents the first performance analysis technique that targets networks-on-chip (NoCs) that employ weighted round-robin (WRR) arbitration. Besides fairness, WRR arbitration provides flexibility in allocating bandwidth proportionally to the importance of the traffic classes, unlike basic round-robin and priority-based arbitration. The proposed approach first estimates the effective service time of the packets in the queue due to WRR arbitration. Then, it uses the effective service time to compute the average waiting time of the packets. Next, we incorporate a decomposition technique to extend the analytical model to handle NoC of any size. The proposed approach achieves less than 5% error while executing real applications and 10% error under challenging synthetic traffic with different burstiness levels.Comment: This paper is accepted in International Conference on Computer Aided Design (ICCAD), 202

How to Choose the Relevant MAC Protocol for Wireless Smart Parking Urban Networks?

Parking sensor network is rapidly deploying around the world and is regarded as one of the first implemented urban services in smart cities. To provide the best network performance, the MAC protocol shall be adaptive enough in order to satisfy the traffic intensity and variation of parking sensors. In this paper, we study the heavy-tailed parking and vacant time models from SmartSantander, and then we apply the traffic model in the simulation with four different kinds of MAC protocols, that is, contention-based, schedule-based and two hybrid versions of them. The result shows that the packet interarrival time is no longer heavy-tailed while collecting a group of parking sensors, and then choosing an appropriate MAC protocol highly depends on the network configuration. Also, the information delay is bounded by traffic and MAC parameters which are important criteria while the timely message is required.Comment: The 11th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (2014