Competitive analysis of caching in distributed databases

We consider the problem of dynamically allocating and deallocating local memory resources among multiple users in a parallel or distributed system. Given a group of independent users and a collection of interconnected local memory devices, we want to render the fragmentation of the memory resources irrelevant by allowing any user to allocate space for his or her purposes as long as there is space available anywhere in the system. In effect, we would like it to appear to the users as though they are allocating memory from a single central pool of memory, even though the space is distributed throughout the system. Our goal is to devise an on-line allocation algorithm that minimizes two cost measures: first, the fraction of unused space , which arises due to fragmentation of the memory; second, the slowdown needed by the system to service user requests, which arises due to the contention for access to the memory devices. We solve this distributed dynamic allocation problem in near-optimal fashion by devising an algorithm that allows the memory to be used to 100% of capacity despite the fragmentation and guarantees that service delays will always be within a constant factor of optimal. The algorithm is completely on-line (no foreknowledge of user activity is assumed) and can accommodate any sequence of allocations and deallocations by the users that does not violate global memory bounds. We also consider the distributed dynamic allocation problem in the more restrictive setting where the local memory devices are connected by a low-degree fixed-connection network, rather than being fully interconnected. In this case, communication costs must be more explicitly considered in our allocation algorithms. We give allocation algorithms for butterfly and hypercube networks, and prove necessary and sufficient conditions on the total amount of memory space needed for near-optimal algorithms to exist.

A 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) system uses the concept of two-tier heterogeneous networks (HetNets), where low-power and short-range femtocells are laid under macrocells to fulfill the quality of service (QoS) requirements of users and to boost overall network capacity. However, co-channel interference is one of the major issues that need to be resolved for the successful deployment of HetNets. To overcome this problem, fractional frequency reuse (FFR) schemes have been proposed that can efficiently utilize the available spectrum. Nevertheless, these schemes waste limited frequency resources owing to their static allocation and lack of following QoS requirements, network loading conditions, and service priority of users. In this paper, a QoS-based dynamic FFR (QoS-DFFR) scheme is proposed that efficiently allocates the non-occupied center-zone frequency bands, i.e., bonus bandwidth (BBW), to cell-edge users by considering their QoS requirements. Consequently, the proposed QoS-DFFR scheme can optimize cell-edge user throughput and sector throughput and reduce co-channel interference by dynamically allocating the BBW to the most demanding cell-edge users. The proposed QoS-DFFR scheme improves performance because of its ability to dynamically allocate the limited portion of the frequency bands based on the service priorities of users. The system-level simulation results show that the proposed QoS-DFFR scheme performs remarkably well in a HetNet environment. Compared with the usual FFR schemes, the proposed scheme almost doubles the cell-edge user's throughput and reduces the user's packet loss rate.

In the Data Grid environment, the primary goal of data replication is to shorten the data access time that is experienced by the job and reduce the job turnaround time as a consequence. After introducing a Data Grid architecture that supports efficient data access for the Grid job, two dynamic data replication algorithms are put forward. Combined with different Grid scheduling heuristics, the performances of the data replication algorithms are evaluated with various simulations. The simulation results demonstrate that the dynamic replication algorithms can reduce the job turnaround time remarkably. Especially the combination of Shortest Turnaround Time (STT) scheduling heuristic and Centralized Dynamic Replication (CDR) algorithm exhibits prominent performance in diverse conditions of workload and system environment.

The data grid is one of the infrastructures for data and storage resource management. Data replication can improve data access performance, fault tolerance, and reduce network transmission bandwidth. As the site storage space is limited in the grid, how to effectively use grid resources become an important challenge. This paper introduces a dynamic grid replication algorithm based on popularity support and confidence (BPSC). Through the algorithm, data and its associated copy can be placed on a suitable site, with reduced access latency. Through Optorsim, simulation results show that the algorithm can provide better performance compared with other algorithms Grid computing is an important branch of distributed computing. Grid computing constructs a virtual computer system through using a different location of the large number of heterogeneous resources (CPU and storage), provides the model to solve the problem of large scale computing and storage. The data grid is mainly the grid for data storage management. The data grid is defined as an effective combination of data and computing resources of distributed systems by Data Grid Group. The large-scale data- intensive applications, such as bioinformatics, high energy physics, space information generated vast amounts of data, these data may be TB scale, even up to the scale of PB. It is not practical to maintain large scale data at each site, and provide computing resources for visitors. At the same time, it will lead to increased access latency through a centralized way to storing and accessing these data from each site. Data replication technology is an effective method to reduce access latency in the data grid system. Data replication is one of the basic methods of distributed systems to improve the efficiency of system access, maintenance, system high availability. Data replication is to store data in the appropriate site for users to effective access to massive data. Data replication can reduce data access latency, reduce network bandwidth consumption, avoid congestion, adjust the load balancing in server-side, improve system reliability. Many data replication algorithm can reduce data access latency, improve system reliability, but which data will be copied, when the data will be copied at, where the data will be copied are the basic means of determining data replication algorithm. In this paper, we introduced a real-time dynamic data replication algorithm. The algorithm is based on the popularity of file dynamic to replicate data, and consider the storage space limitations of the site. Through optorsim simulation verified the algorithm. This paper is organized as follows: Section 2 is about related works on replication in data grids. In Section 3, system model and component in system are presented. The file

An efficient interleaving algorithm is applied to reduce the loss of ciphered information when a cipher system transmits over a wireless fading channel environment. As such, a new scheme for deciding the interleaving depth over a wireless environment is described. Simulations confirm that the proposed effective algorithm with a variable interleaving depth produces a better performance over a fading channel than a static depth algorithm with a fixed interleaving depth. Experimental results showed that the BER performance of the proposed efficient interleaving scheme was higher than that of the fixed interleaving depth scheme. Of particular note is that the dynamic allocation algorithm (DAA) reduces degraded error bits by up to 51.5%, compared with static allocation algorithm (SAA) of depth 48 in 224MHz.

Consider a distributed system consisting of n computers connected by a number of identical broadcast channels. All computers may receive messages from all channels. We distinguish between two kinds of systems: systems in which the computers may send on any channel (dynamic allocation) and system where the send port of each computer is statically allocated to a particular channel. A distributed task (application) is executed on the distributed system. A task performs execution as well as communication between its subtasks. We compare the completion time of the communication for such a task using dynamic allocation and \(k_d\) channels with the completion time using static allocation and \(k_s\) channels. Some distributed tasks will benefit very much from allowing dynamic allocation, whereas others will work fine with static allocation. In this paper we define optimal upper and lower bounds on the gain (or loss) of using dynamic allocation and \(k_d\) channels compared to static allocation and \(k_s\) channels. Our results show that, for some tasks, the gain of permitting dynamic allocation is substantial, e.g. when \(k_s = k_d = 3\), there are tasks which will complete 1.89 times faster using dynamic allocation compared to using the best possible static allocation, but there are no tasks with a higher such ratio.

In this paper, a dynamic resource allocation algorithm to satisfy the packet delay requirements for real-time services, while maximizing the system capacity in multiuser orthogonal frequency division multiplexing (OFDM) systems is discussed. Our proposed cross-layer algorithm, called Joint Dynamic Subcarrier Allocation and Flow Control (DSA-FC) algorithm, consists of two interactive components. In the medium access control (MAC) layer, the users' expected transmission rates in terms of the number of subcarriers per symbol and their corresponding transmission priorities are evaluated. With the subcarrier gain information of each user, the physical (PHY) layer subcarrier allocation is optimally designed to satisfy the users' requirements under the system signal-to-noise ratio (SNR) and power constraints. In a system where the number of active users changes dynamically, the MAC-layer congestion control and removal schemes can guarantee the quality of service (QoS) of the existing users in the system and fully utilize the bandwidth resource. The proposed algorithm combines these two components, and the numerical results show that it significantly improves the system performance in terms of the bandwidth efficiency and delay performance for real-time services.

To increase frequency efficiency in cellular communication networks, this paper describes a cell coloring based distributed frequency allocation approach (C-DFA) for all kinds of cellular networks. C-DFA has high computational efficiency and is simpler to realize than other distributed approaches. Building on C-DFA, a distributed dynamic fractional frequency allocation (DDFFA) algorithm is designed for IEEE 802.16j supported Relay Based Cellular Networks (RBCN). It is shown that: a) C-DFA can better realize frequency efficiency and network resilience compared to centralized traditional distributed frequency allocation approach. b) DDFFA can significantly increase frequency efficiency to provide high capacity and throughput to the RBCN though at the cost of extra computation and BS-BS communication. The evaluations and analysis are based on moderate and high user congestion RBCN scenarios.

In this letter, we investigate the dynamic rate allocation and forwarding strategy adaption scheme for wireless networks with potential selfish nodes. Aided by an incentive mechanism, we develop a stochastic differential equation (SDE) to portray the dynamic node selfishness in terms of node's energy resource and incentives. Then, a stochastic optimization model is employed to maximize the average network utility while bounding the node selfishness. Based on the continuous-time Lyapunov optimization theory, we solve the optimization problem and propose a dynamic rate allocation and forwarding strategy update (DRAF) algorithm to accommodate the dynamic network state. We further analyze the tracking errors between the output of DRFA algorithm and the optimal solution. Then, an adaptive-compensation rate allocation and forwarding strategy update (ACRAF) algorithm is designed, which iterates only once when network state changes. Finally, we provide a sufficient condition that the ACRAF algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis.

Unanimous action to achieve specific goals is crucial for the success of a robotic swarm. This requires clearly defined roles and precise communication between the robots of a swarm. An optimized task allocation algorithm defines the mechanism and logistics of decision-making that enable the robotic swarm to achieve such common goals. With more nodes, the traffic of messages that are required to communicate inside the swarm relatively increases to maintain decentralization. Increased traffic eliminates real-time capabilities, which is an essential aspect of a swarm system. The aim of this research is to reduce execution time while retaining efficient power consumption rates. In this research, two novel decentralized swarm communication algorithms are proposed, namely Clustered Dynamic Task Allocation–Centralized Loop (CDTA-CL) and Clustered Dynamic Task Allocation–Dual Loop (CDTA-DL), both inspired by the Clustered Dynamic Task Allocation (CDTA) algorithm. Moreover, a simulation tool was developed to simulate different swarm-clustered communication algorithms in order to calculate the total communication time and consumed power. The results of testing the proposed CDTA-DL and CDTA-CL against the CDTA attest that the proposed algorithm consumes substantially less time. Both CDTA-DL and CDTA-CL have achieved a significant speedup of 75.976% and 54.4% over CDTA, respectively.

This paper examines a cipher system for security in tactical network, plus an interleaving scheme is applied to the ciphered information to enhance the transmission performance over a fading channel. Experimental results showed that the BER performance of the proposed efficient interleaving scheme is higher than that of the fixed interleaving depth scheme. Of particular note is that the dynamic allocation algorithm (DAA, non-fixed interleaving depth) reduces degraded error bits by up to 53%, compared with static allocation algorithm (SAA, fixed interleaved depth) of depth 120 in 420MHz.

An interleaving algorithm is applied to reduce the loss of ciphered information when a cipher system transmits over a wireless security environment. As such, a new scheme for deciding the interleaving depth over a wireless mobile environment is described. Simulations confirm that the proposed dynamic allocation algorithm (DAA) with a non-fixed interleaving depth produced a better performance over a fading channel than a static allocation algorithm (SAA) with a fixed interleaving depth.KeywordsFading ChannelStream CipherTransmission PerformanceSymbol Error RateBurst ErrorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper focuses on the network of IEEE802.15.3 which exists in VBR and CBR flows, and provides a combination of dynamic and static channel time allocation algorithm.

This article investigates resource allocation in ATM networks and emphasizes bandwidth allocation. Resource allocation methods are categorized, and static and dynamic allocation strategies are compared. Dynamic allocation uses actual traffic behavior, while static allocation uses only the reference traffic condition given a priori. Examples of dynamic allocation strategies are available bit rate flow control, dynamic connection admission control, and dynamic VP bandwidth control. Dynamic resource allocation is shown to be promising for situations where the a priori reference model is unclear.

This paper proposes a multi-objective Gannet Optimization Algorithm (MOGOA) to address the issue of unbalanced train occupancy rates in railway train operation planning. MOGOA employs an adaptive multi-population co-evolutionary strategy to balance exploration and exploitation, utilizing a non-dominated sorting algorithm based on crowding distance to select parent and child samples. These samples serve as initial solutions for subsequent iterations. A novel maximin fitness function guides the iterative update of the global optimal position. MOGOA is applied to the train operation planning problem with dynamic passenger flow allocation feedback. It collaboratively optimizes the number of train operations, sections, and stops to reduce costs, balance occupancy rates, minimize travel time, and enhance travel satisfaction. The practical applicability of MOGOA in optimizing train operation plans based on dynamic passenger flow allocation is significant.