Allocation Requests Research Articles

Development and optimization of a horizontal carrier collaboration vehicle routing model with multi-commodity request allocation

This paper proposes a new Vehicle Routing Problem (VRP) with fair Carrier Collaboration (CC) which split multi-pickup and delivery services are considered for serving customers. This study has focused on VRP and serving customers with several commodity requirements from different geographically scattered suppliers subject to constraints on the vehicle capacity. A Mixed Integer Programming (MIP) model to maximize the total profit and the fair sharing of profit among the carriers by considering the travel time minimization is developed. Each carrier with its limited capacity can have reserved requests which must be served by itself and selective requests which can be served by itself or other vehicles or not served at all. There are various applications of the proposed model in the environment which can help reducing number of vehicles serving to the customers and eliminating empty back hauls. A Genetic Algorithm (GA) is proposed to solve this problem due to its Non-deterministic Polynomial-time hard (NP-hard) nature. In addition, Variable Neighbourhood Search (VNS) method is developed for improving the quality of initial solutions. Some instances are generated at different scales to evaluate the algorithm's performance by comparing the results of an exact optimal solution with that of the proposed algorithm. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time. The algorithm is also tested for an online shopping website in Tehran, Iran. The test outcome shows that the proposed model returns a better benefit compared to the manual methods. The results of sensitivity analysis suggest that increasing the fairness coefficient among carries can led to a decrease in the total obtained profit.

Efficient virtual data center request embedding based on row-epitaxial and batched greedy algorithms

Data centers are becoming the main backbone of and centralized repository for all cloud-accessible services in on-demand cloud computing environments. In particular, virtual data centers (VDCs) facilitate the virtualization of all data center resources such as computing, memory, storage, and networking equipment as a single unit. It is necessary to use the data center efficiently to improve its profitability. The essential factor that significantly influences efficiency is the average number of VDC requests serviced by the infrastructure provider, and the optimal allocation of requests improves the acceptance rate. In existing VDC request embedding algorithms, data center performance factors such as resource utilization rate and energy consumption are not taken into consideration. This motivated us to design a strategy for improving the resource utilization rate without increasing the energy consumption. We propose novel VDC embedding methods based on row-epitaxial and batched greedy algorithms inspired by bioinformatics. These algorithms embed new requests into the VDC while reembedding previously allocated requests. Reembedding is done to consolidate the available resources in the VDC resource pool. The experimental testbed results show that our algorithms boost the data center objectives of high resource utilization (by improving the request acceptance rate), low energy consumption, and short VDC request scheduling delay, leading to an appreciable return on investment.

A queueing-theoretic model for resource allocation in one-dimensional distributed analytics network?

We consider the problem of allocating requesters of analytic tasks to resources on servers. We assume both requesters and servers are placed in a one dimensional line: [0,1) according to two Poisson processes with each server having finite capacity. Requesters communicate with servers under a noninterference wireless protocol. We consider a "Move to Right" (MTR) request allocation strategy where each requester is allocated to the nearest available server to its right. We start our analysis from a single resource per request scenario where each requester demands a single computational resource. We map this scenario to an M/M/1 queue or a bulk service M/M/1 queue depending on the server capacity. We compare the performance of the MTR strategy with the globally optimal strategy taking "expected distance traveled by a request" (request distance) as performance metric. Next, we extend our analysis to two resources per request scenario. We show that the behavior of MTR can be transformed into an equivalent fork-join queue problem. Numerical results are presented to validate the claim.

Scalable Traffic Management for Mobile Cloud Services in 5G Networks

Mobile cloud computing has been introduced to improve the performance of mobile application clients by offloading data processing and storage to cloud. By deploying the service on several cloud-enabled data centers, the service provider can optimally locate service instances on the cloud to provide qualified services at a reasonable cost. However, a centralized approach for both request allocation and response routing does not scale efficiently due to a large number of mobile clients involved in the mobile service traffic management. Moreover, the random and unpredictable wireless network performance (e.g., delay) complicates the problem further. In this paper, we present a stochastic distributed optimization framework for mobile cloud traffic management in 5G networks. The framework takes the impact of random wireless network characteristics into account. Utilizing the alternating direction method of multipliers, the optimization problem is decomposed into independent subproblems, which are solved in a parallel fashion on distributed agents and coordinated through dual variables. The convergence issue under the stochastic setting is addressed, and the numerical tests validate the effectiveness of the proposed algorithm.

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

Virtual Machine Selection Algorithm Based on User Requirements in Mobile Cloud Computing Environment

The article analyzes the advantages of mobile cloud technologies and problems emerging during the use of those. The network infrastructure created based on cloudlets at the second level of mobile cloud computing with hierarchical structure is analyzed. At the same time, the article explores the issues of satisfaction of demand of mobile equipment for computing and memory resources by using these technologies. The article presents one solution for the allocation of mobile user requests in virtual machines created in cloudlets located near base stations of wireless metropolitan area networks (WMAN) in a balanced way by considering the technical capacity of those. Alongside, the article considers the solution of user problem during designated time and the issue of determining virtual machines satisfying other requirements. For this purpose, different characteristics of the stated problem, virtual machines, as well as communication channels between a user and virtual machines are considered. By using possible values determining the importance of cloudlets, conditions for loading software applications of a user to a virtual machine are explored and an appropriate method is proposed.

Sustainable Service Allocation Using a Metaheuristic Technique in a Fog Server for Industrial Applications

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.

BARAN

Virtual channels are employed to improve the throughput under high traffic loads in Networks-on-Chips (NoCs). However, they can impose non-negligible overheads on performance by prolonging clock cycle time, especially under low traffic loads where the impact of virtual channels on performance is trivial. In this article, we propose a novel architecture, called BARAN , that can either improve on-chip network performance or reduce its power consumption (depending on the specific implementation chosen), not both at the same time, when virtual channels are underutilized; that is, the average number of virtual channel allocation requests per cycle is lower than the number of total virtual channels. We also introduce a reconfigurable arbitration logic within the BARAN architecture that can be configured to have multiple latencies and, hence, multiple slack times. The increased slack times are then used to reduce the supply voltage of the routers or increase their clock frequency in order to reduce power consumption or improve the performance of the whole NoC system. The power-centric design of BARAN reduces NoC power consumption by 43.4% and 40.6% under CMP and GPU workloads, on average, respectively, compared to a baseline architecture while imposing negligible area and performance overheads. The performance-centric design of BARAN reduces the average packet latency by 45.4% and 42.1%, on average, under CMP and GPU workloads, respectively, compared to the baseline architecture while increasing power consumption by 39.7% and 43.7%, on average. Moreover, the performance-centric BARAN postpones the network saturation rate by 11.5% under uniform random traffic compared to the baseline architecture.

Exploring Analytical Models for Proactive Resource Management in Highly Mobile Environments

In order to provide ubiquitous communication, seamless connectivity is now required in all environments including highly mobile networks. By using vertical handover techniques it is possible to provide uninterrupted communication as connections are dynamically switched between wireless networks as users move around. However, in a highly mobile environment, traditional reactive approaches to handover are inadequate. Therefore, proactive handover techniques, in which mobile nodes attempt to determine the best time and place to handover to local networks, are actively being investigated in the context of next-generation mobile networks. Using this approach, it is possible to enhance channel allocation and resource management by using probabilistic mechanisms; because, it is possible to explicitly detect contention for resources. This paper presents a proactive approach for resource allocation in highly mobile networks and analyzed the user contention for common resources such as radio channels in highly mobile wireless networks. The proposed approach uses an analytical modelling approach to model the contention and results are obtained showing enhanced system performance. Based on these results an operational space has been explored and are shown to be useful for emerging future networks such as 5G by allowing base stations to calculate the probability of contention based on the demand for network resources. This study indicates that the proactive model enhances handover and resource allocation for highly mobile networks. This paper analyzed the effects of and alpha and beta, in effect, how these parameters affect the proactive resource allocation requests in the contention queue has been modelled for any given scenario from the conference paper "Exploring analytical models to maintain quality-of-service for resource management using a proactive approach in highly mobile environments".

Improving the performance of hybrid multiple access scheme in millimeter wave wireless personal area networks

IEEE 802.15.3c standard for millimeter wave (mmWave) wireless PAN (WPAN) specifies a hybrid medium access control (MAC) protocol consisting of contention based and contention free channel access schemes, with corresponding durations termed as contention access period (CAP) and channel time allocation period (CTAP). While CTAP is used for the transmission of isochronous data streams, CAP is used for the transmission of control messages such as channel time allocation (CTA) requests. In this paper, we propose an improvement for the hybrid MAC protocol specified by 802.15.3c, considering transmission of asynchronous data streams over the uplink from the devices (DEVs) to the pico net coordinator (PNC). The proposed scheme advocates transmission of asynchronous data frames during CAP as well as CTAP. The CTA requests which are normally transmitted during CAP would be piggybacked on the data frames. Using the theory of discrete time Markov chain (DTMC), we present an analytical model to evaluate the network throughput and average frame delay under the proposed scheme. The results establish that the proposed scheme improves the network throughput while reducing the average delay considerably. The analytical results are corroborated by extensive simulation studies.

Genetic algorithms for satellite scheduling problems

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA European Space Agency and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.

DWARM: A wear-aware memory management scheme for in-memory file systems

Emerging non-volatile memories (NVMs) are promised to revolutionize storage systems by providing fast, persistent data accesses on memory bus. A hybrid NVM/DRAM architecture that combines faster, volatile DRAM with slightly slower, denser NVM can harness the characteristics of both technologies. In order to fully take advantage of NVM, state-of-the-art in-memory file systems are designed to provide high performance and strong consistency guarantees. However, the free space management schemes of existing in-memory file systems can easily cause “hot spots” when updating data structures on NVM, leading to significant skewness in terms of writes to each data page.In this paper, we propose dynamic wear-aware range management (DWARM) scheme, a novel free space management technique for in-memory file systems. This scheme achieves wear-leveling with high performance for allocation/deallocation. The essential idea is to allocate less-written pages for each allocation request. Specifically, this scheme works by associating a write counter with each data page and updating the counters in the file write path. We build an “index” structure to fast locate the pages that have received less writes. The index divides the NVM pages into different subranges according to the write counters. Allocation always starts from the minimal subrange. Also, we propose Adaptive Wear Range Determination Algorithm to adjust the wear ranges dynamically. To accelerate lookup, we keep the index in DRAM and avoid the overhead of strong consistency by rebuilding the index in case of system failure. Experimental results show that this scheme can provide 4.9× to 158.1× wear-leveling improvement compared to the state-of-the-art memory management schemes. For application workloads, the DWARM strategy can improve the lifetime of NVM by up to 125×, 39×, and 25×, compared with the standard memory management schemes of PMFS, NOVA and SIMFS.

Power and resource allocation for MIMO based two-tier heterogeneous network using small cell clustering algorithm

Resource allocation problem in the two-tier heterogen eous MIMO network plays a vital role in avoidance of interference between the mobile users connected to the host directly and through the backhaul link. Two different frequency bandwidths are proposed to avoid the interference generated between the users placed in the interference region in the small cell. The mobile user frequency range is allotted for the link between small cell base station, macrocell base station and mobile user and millimeter frequency bandwidth is utilized between the small cell base station, small cell cluster heads and small cell users. The inter-small cell clustering helps the small cell base station node to select the number of antennas for communication and to send the request for allocation of bandwidth to eliminate the inter-cell congestion. The method was implemented in NS2 simulator, and the performance matrices were measured to analyze the efficiency and quality of service of the proposed topology.

Multibroker-Based Service Provisioning in Multidomain SD-EONs: Why and How Should the Brokers Cooperate With Each Other?

It is known that software-defined elastic optical networks (SD-EONs) facilitate optical networking that provides better network programmability, more powerful manageability, and more flexible service provisioning capability. Moreover, the hierarchical architecture of multibroker-based multidomain SD-EONs cannot only improve the network scalability but also maintain the autonomy of each administrative domain. In this paper, we study why and how the brokers should cooperate with each other to provision interdomain lightpaths in multibroker-based multidomain SD-EONs. We first formulate a cooperative market in which the brokers negotiate about their market shares (i.e., the opportunities to provision interdomain lightpaths) and seek for a mutual agreement with Nash bargaining [1] . Then, we design a mathematical model to describe the market as well as the brokers’ behaviors in it. An effective algorithm is derived from the model to solve the Nash bargaining problem for allocating lightpath requests among the brokers. The proposed algorithm also addresses the resource collision during request provisioning and can achieve collision-free request allocation. Extensive simulations verify the effectiveness of our proposal.

Design and Optimization for VoD Services With Adaptive Multicast and Client Caching

Multicast and prefix caching are two promising approaches to provide scalable video-on-demand (VoD) services. We contribute to solve the adaptive multicast delivery and the cache allocation problem for VoD services with client caching under asynchronous client requests. We first propose a client caching enabled adaptive multicast delivery scheme, which combines the stream merginglike method with prefix caching and adaptive modulation and coding, to fully exploit the multicast opportunity among asynchronous requests from heterogeneous clients. Next, we derive the bandwidth consumption of the proposed delivery scheme under a given prefix cache allocation and Poisson arrival request. We then optimize the cache allocation to minimize the average bandwidth consumption, and the problem is proved to be convex and solved effectively. Finally, three extreme cases are studied to provide closed-form solutions and more insight on the cache allocation and the delivery scheme for VoD systems.

Pre-channel scheduling and Priority-based reservation in medium access control for industrial wireless sensor network applications

Numerous technical advancements and the pervasive controlling schemes in the Industrial Wireless Sensor Network (IWSN) with the capability of interoperability among the nodes facilitate the reliable communication. With the increase of participating sensors size, the memory and energy consumption reduce the lifetime adversely. To alleviate these issues, this paper creates an energy efficient data management and routing architecture based on the data fusion techniques in IEEE 802.15.4. The data classification through proposed Multi-Stage Classification (MSC) prior to priority assignment techniques makes the immediate decision regarding the controlling actions. The split up of data using Reservation-based Medium Access Control (RMAC) raises the resource allocation requests according to the data type. The built up of the relationship between the number of slots and the data arrival enhances the resource utilization performance for each slot. The guarantee of transfer of immediate transmission with the priority supports the critical conditions with high energy efficiency.

A Markov Reward based Resource-Latency Aware Heuristic for the Virtual Network Embedding Problem

An ever increasing use of virtualization in various emerging scenarios, e.g.: Cloud Computing, Software Defined Networks, Data Streaming Processing, asks Infrastructure Providers (InPs) to optimize the allocation of the virtual network requests (VNRs) into a substrate network while satisfying QoS requirements. In this work, we propose MCRM, a two-stage virtual network embedding (VNE) algorithm with delay and placement constraints. Our solution revolves around a novel notion of similarity between virtual and physical nodes. To this end, taking advantage of Markov Reward theory, we define a set of metrics for each physical and virtual node which captures the amount of resources in a node neighborhood as well as the degree of proximity among nodes. By defining a notion of similarity between nodes we then simply map virtual nodes to the most similar physical node in the substrate network. We have thoroughly evaluated our algorithm through simulation. Our experiments show that MCRM achieves good performance results in terms of blocking probability and revenues for the InP, as well as a high and uniform utilization of resources, while satisfying the delay and placement requirements.

Mallacc

Recent work shows that dynamic memory allocation consumes nearly 7% of all cycles in Google datacenters. With the trend towards increased specialization of hardware, we propose Mallacc, an in-core hardware accelerator designed for broad use across a number of high-performance, modern memory allocators. The design of Mallacc is quite different from traditional throughput-oriented hardware accelerators. Because memory allocation requests tend to be very frequent, fast, and interspersed inside other application code, accelerators must be optimized for latency rather than throughput and area overheads must be kept to a bare minimum. Mallacc accelerates the three primary operations of a typical memory allocation request: size class computation, retrieval of a free memory block, and sampling of memory usage. Our results show that malloc latency can be reduced by up to 50% with a hardware cost of less than 1500 um2 of silicon area, less than 0.006% of a typical high-performance processor core.

Mallacc

Recent work shows that dynamic memory allocation consumes nearly 7% of all cycles in Google datacenters. With the trend towards increased specialization of hardware, we propose Mallacc, an in-core hardware accelerator designed for broad use across a number of high-performance, modern memory allocators. The design of Mallacc is quite different from traditional throughput-oriented hardware accelerators. Because memory allocation requests tend to be very frequent, fast, and interspersed inside other application code, accelerators must be optimized for latency rather than throughput and area overheads must be kept to a bare minimum. Mallacc accelerates the three primary operations of a typical memory allocation request: size class computation, retrieval of a free memory block, and sampling of memory usage. Our results show that malloc latency can be reduced by up to 50% with a hardware cost of less than 1500 um2 of silicon area, less than 0.006% of a typical high-performance processor core.

An Efficient All Shapes Busy List Processor Allocation Algorithm for 3D Mesh Multicomputers

Contiguous processor allocation is useful for security and accounting reasons. This is due to the allocated jobs are separated from one another, where each sub-mesh of processors is allocated to an exclusive job request, and the allocated sub-mesh has the same size and shape of the requested job. The size and shape constraint leads to high processor fragmentation. Most recent contiguous allocation strategies suggested for 3D mesh-connected multiconputers try all possible orientations of an allocation request when allocation fails for the requested orientation, which reduces processor fragmentation and hence improves system performance. However, none of them considers all shapes of the request in the process of allocation. To generalize this restricted rotation, we propose, in this paper, a new contiguous allocation strategy for 3D mesh-connected multicomputers, referred to as All Shapes Busy List (ASBL for short), which takes into consideration all possible contiguous request shapes when attempting allocation for a job request. ASBL depends on the list of allocated sub-meshes, in the method suggested in (Bani-Mohammad et al., 2006), for selecting an allocated sub-mesh. The performance of the proposed ASBL allocation strategy has been evaluated considering several important scheduling strategies under a variety of system loads based on different job size distributions. The simulation results have shown that the ASBL allocation strategy improves system performance in terms of parameters such as the average turnaround time of jobs and system utilization under all scheduling strategies considered.