From static to dynamic routing

We investigate how static store-and-forward routing algorithms can be transformed into efficient dynamic algorithms, that is, how algorithms that have been designed for the case that all packets are injected at the same time can be adapted to more realistic scenarios in which packets are continuously injected into the network. Besides describing specific transformations for well-known static routing algorithms, we present a black box transformation scheme applicable to every static, oblivious routing algorithm. We analyze the performance of our protocols under a stochastic and an adversarial model of packet injections. One result of our specific transformations is the first dynamic routing algorithm for leveled networks that is stable for arbitrary admissible injection rates and that works with packet buffers of size depending solely on the injection rate and the node degree, but not on the size of the network. Furthermore, we prove strong delay bounds for the packets. Our results imply, for example, that a throughput of 99% can be achieved on an n-input butterfly network with buffers of constant size while each packet is delivered in time O(log n), with high probability. Our black box transformation ensures that if the static algorithm is pure (i.e., no extra packets apart from the original packets are routed), its dynamic variant is stable up to a maximum possible injection rate. Furthermore, in the stochastic model, the routing time of a packet depends on local parameters such as the length of its routing path, rather than on the maximum possible path length, even if the static algorithm chosen for the transformation does not provide this locality feature and is not pure. In the adversarial model, the delay bound of the packets is closely related to the time bound given for the static algorithm.

Routing algorithms constitute an important area for the packet transmission in satellite networks. There are many studies about static routing algorithms in which the satellite systems are thought to be fixed on the sky. Thus the routing operation is made over a fixed topology in these studies. However in real world, the satellites move continuously in their orbits along the communications. So the dynamic routing is the most appropriate routing type for real applications. In this study, we investigated several routing algorithms and designed a new dynamic routing algorithm. Our path selections are based on the genetic algorithms through a new fitness function. Also we computed the call blocking probabilities (CBPs) of all links on the paths formed the route of a packet transmission.

In a distributed computing environment, guaranteeing the hard deadline for real-time messages is essential to ensure schedulability of real-time tasks. Since capabilities of the shared resources for transmission are limited, e.g., the buffer size is limited on network devices, it becomes a challenge to design an effective and feasible resource sharing policy based on both the demand of real-time packet transmissions and the limitation of resource capabilities. We address this challenge in two cooperative mechanisms. First, we design a static routing algorithm to find forwarding paths for packets to guarantee their hard deadlines. The routing algorithm employs a validation-based backtracking procedure capable of deriving the demand of a set of real-time packets on each shared network device, and it checks whether this demand can be met on the device. Second, we design a packet scheduler that runs on network devices to transmit messages according to our routing requirements. We implement these mechanisms on virtual software-defined network (SDN) switches and evaluate them on real hardware in a local cluster to demonstrate the feasibility and effectiveness of our routing algorithm and packet scheduler.

This paper presents a detailed study of fairness in cache sharing between threads in a chip multiprocessor (CMP) architecture. Prior work in CMP architectures has only studied throughput optimization techniques for a shared cache. The issue of fairness in cache sharing, and its relation to throughput, has not been studied. Fairness is a critical issue because the operating system (OS) thread scheduler's effectiveness depends on the hardware to provide fair cache sharing to co-scheduled threads. Without such hardware, serious problems, such as thread starvation and priority inversion, can arise and render the OS scheduler ineffective. This paper makes several contributions. First, it proposes and evaluates five cache fairness metrics that measure the degree of fairness in cache sharing, and shows that two of them correlate very strongly with the execution-time fairness. Execution-time fairness is defined as how uniform the execution times of co-scheduled threads are changed, where each change is relative to the execution time of the same thread running alone. Secondly, using the metrics, the paper proposes static and dynamic L2 cache partitioning algorithms that optimize fairness. The dynamic partitioning algorithm is easy to implement, requires little or no profiling, has low overhead, and does not restrict the cache replacement algorithm to LRU. The static algorithm, although requiring the cache to maintain LRU stack information, can help the OS thread scheduler to avoid cache thrashing. Finally, this paper studies the relationship between fairness and throughput in detail. We found that optimizing fairness usually increases throughput, while maximizing throughput does not necessarily improve fairness. Using a set of co-scheduled pairs of benchmarks, on average our algorithms improve fairness by a factor of 4/spl times/, while increasing the throughput by 15%, compared to a nonpartitioned shared cache.

A static algorithm is one that computes the result of a query about the output for a single, fixed input. For example, a static sorting algorithm is one that takes as input a set of keys, and permits queries about the relative order of these keys according to some ordering relation. A dynamic, or incremental, algorithm is one that permits queries about the output to be interleaved with operations that incrementally modify the input. For example, a dynamic sorting algorithm is one that would permit insertion or deletion of keys to be interleaved with queries about their relative ordering.

On the basis of analyzing static routing algorithm and dynamic routing algorithms, this paper focus on the link state routing of the dynamic routing algorithm, puts forward a more concise routing way in the link state routing. Dijkstra routing algorithm is suitable for calculating the shortest path from one router to other routers, but there are more point-to-point links in the computer network, Floyd routing algorithm is more suitable for calculating the shortest distance between two router, and it is more practical in the computer network.

Suboptimal device programming is frequent in non-responders to cardiac resynchronization therapy (CRT). However, the role of device optimization and the most appropriate technique are still unknown. The aim of our study was to analyse the effect of different CRT optimization techniques within a network meta-analysis. A systematic search was conducted on MEDLINE, Embase and CENTRAL for studies comparing outcomes with empirical device settings or optimization using echocardiography, static algorithms or dynamic algorithms. Studies investigating the effect of optimization in non-responders were also analysed. A total of 17 studies with 4346 patients were included in the quantitative analysis. Of the treatments and outcomes examined, a significant difference was found only between dynamic algorithms and echocardiography, with the former leading to a higher echocardiographic response rate [odds ratio (OR): 2.02, 95% confidence interval (CI) 1.21-3.35], lower heart failure hospitalization rate (OR: 0.75, 95% CI 0.57-0.99) and greater improvement in 6-minute walk test [mean difference (MD): 45.52m, 95% credible interval (CrI) 3.91-82.44m]. We found no significant difference between empirical settings, static algorithms and dynamic algorithms. Seven studies with 228 patients reported response rates after optimization in non-responders. Altogether, 34.3%-66.7% of initial non-responders showed improvement after optimization, depending on response criteria. At the time of CRT implantation, dynamic algorithms may serve as a resource-friendly alternative to echocardiographic optimization, with similar or better mid-term outcomes. However, their superiority over empirical device settings needs to be investigated in further trials. For non-responders, CRT optimization should be considered, as the majority of patients experience improvement.

Routing algorithms for large-scale sensor networks should be capable of finding energy efficient paths to prolong the lifetime of the networks in a decentralized manner. With this respect, Ant System has several proper characteristics for routing algorithm in large-scale wireless sensor networks. First, its distributed mechanism enables routing algorithm to find a solution with only local information and be robust for uncertainties in wireless sensor networks. Second, the framework of the Ant System is proper to solve dynamic problems such as routing problem. Transition probability in Ant System can be used to estimate how good a given routing path is. Capturing these features, this work proposes two Ant Systems based routing algorithms, which are AS-RWSNs (Ant System for Routing in Wireless Sensor Networks) and SAAS-RWSN (Structure-Aware AS-RWSN). The AS-RWSN applies the original Ant System to routing algorithm for wireless sensor network and SAAS-RSN upgrades AS-RWSN with considering properties of network structure such as degree of node. In SAAS-RSN, sensors with high node degree have high data traffic since they have more routing paths. Consequently, SAAS-RSN achieves an energy balance over sensor network through this routing scheme. We demonstrate the effectiveness of the proposed algorithms by comparing three existing routing algorithms.

The research, development, and deployment of wavelength division multiplexing (WDM) technology are now evolving at a rapid pace to fulfill the increasing bandwidth requirement and deploy new network services. Routing and wavelength assignment algorithms play a key role in improving the performance of wavelength-routed all-optical networks. We study networks with dynamic wavelength routing and develop accurate analytical models for evaluating the blocking performance under dynamic input traffic in different topologies. Two dynamic routing algorithms are first developed and the performances of the algorithms in single-fiber WDM networks are studied using both analytical models and simulation. We also develop efficient algorithms to optimally place a given number of wavelength converters on a path of a network. Finally we consider the effect of multiple fibers on WDM networks without wavelength conversion. We develop analytical models for evaluating the blocking performance of multifiber networks with fixed-path routing, alternate-path routing, and fixed-path least-congestion routing algorithms. The number of fibers required to provide high performance in multifiber networks with different routing algorithms are also studied.

This research paper Compares mathematically Dynamic Routing and Static Routing.This Research paper Focuses and Examines problems that arise from the fact that dynamic routing never relies on the precise, current information about the network state. It is a normal expectation that dynamic routing has to give better results than a static one. However, it takes some time to collect information about the network current state, and optimization is always done with that imprecise information. This situation is examined by a complete mathematical analysis of a simple network. We show that dynamic routing gives better results than static, as expected, but that the margin is much smaller then intuitively expected. Further analysis shows that that minor advantage can easily be lost if there is even a small error in the dynamic routing tables, and actually dynamic routing can easily become worse than static. Quantitative analysis shows that delays in building routing tables can affect dynamic routing performance unexpectedly strongly. The conclusion is that dynamic routing should not try to adapt to traffic changes very fast.

Opportunistic prioritised clustering framework for improving OODBMS performance

Along with the development of big data, knowledge updating occurs in various situations, some scholars had studied the dynamic method of updating knowledge in multigranulation decision-theoretic rough sets when adding or deleting granular structures. However, there is no study about the case of adding or deleting objects, which limits the development of multigranulation decision-theoretic rough sets. Based on the matrix method, the dynamic knowledge updating of optimistic multigranulation decision-theoretic rough set and pessimistic multigranulation decision-theoretic rough set were studied in this paper, then given the static algorithm and dynamic algorithms, and the time complexity of three algorithms was analyzed. The theory and experimental results show that two dynamic algorithms are both more effective than the static algorithm.

Community detection is of great importance for online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing the way researchers analyze those networks. For instance, real-world networks, such as Facebook, LinkedIn and Twitter, are inherently growing rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the static networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the dynamic networks. We propose DynaMo, a novel modularity-based dynamic community detection algorithm, aiming to detect communities of dynamic networks as effective as repeatedly applying static algorithms but in a more efficient way. DynaMo is an adaptive and incremental algorithm, which is designed for incrementally maximizing the modularity gain while updating the community structure of dynamic networks. In the experimental evaluation, a comprehensive comparison has been made among DynaMo, Louvain (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real-world networks and 10,000 synthetic networks. Our results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of the effectiveness, and is 2 to 5 times (by average) faster than Louvain algorithm.

Task mapping is an important issue in MPSoC design. Most recent mapping algorithms perform them at design time, an approach known as static mapping. Nonetheless, applications running in MPSoCs may execute a varying number of simultaneous tasks. In some cases, applications may be defined only after system design, enforcing a scenario that requires the use of dynamic task mapping. Static mappings have as main advantage the global view of the system, while dynamic mappings normally provide a local view, which considers only the neighborhood of the mapping task. This work aims to evaluate the pros and cons of static and dynamic mapping solutions. Due to the global system view, it is expected that static mapping algorithms achieve superior performance (w.r.t. latency, congestion, energy consumption). As dynamic scenarios are a trend in present MPSoC designs, the cost of dynamic mapping algorithms must be known, and directions to improve the quality of such algorithms should be provided without increasing execution time. This quantitative comparison between static and dynamic mapping algorithms is the main contribution of this work.

Dynamic and static algorithms for optimal placement of resources in a tree