Joint Load Balancing Research Articles

Joint UAV Movement Control and Load Balancing Based on Indirect Control in Air-Ground-Integrated Networks

Joint UAV Movement Control and Load Balancing Based on Indirect Control in Air-Ground-Integrated Networks

SIMA: Scalable IoT Microservice Architecture With Users and Things Joint Load Balancing

SIMA: Scalable IoT Microservice Architecture With Users and Things Joint Load Balancing

Energy-Efficient Resource Allocation for D2D-V2V Communication with Load Balancing

The significance of vehicle-to everything (V2X) communication in ensuring road safety is undeniable. In addition, real-time vehicle communication requires an ample amount of spectrum resources. However, the existing spectrum resources are seriously scarce, and the utilization rate is not high, leading to high delays in V2X communication and other unfavorable factors in the case of fast-moving vehicles, bringing great safety risks to driving. Load balancing is one of the most effective methods to improve spectrum utilization. However, the existing load balancing schemes merely focus on static conditions, with a lack of joint scheduling schemes, which cannot support the communication framework of dynamic V2X. To address both of these issues, in this paper, a new communication method is proposed. In addition, this paper studies a joint load balancing scheme of mobility vehicle-to-vehicle (V2V) and user association under incomplete channel state information (CSI) and realizes the load balancing management of a cross-cell V2X network. An algorithm combining power control and resource allocation mode selection is proposed. In particular, according to different coverage areas, different allocation algorithms are adopted to maximize the overall system efficiency. The simulation results show that this strategy can maintain low latency and effectively improve the system energy efficiency of vehicle users.

Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive and computation-intensive mobile applications and services. Therefore, the Unmanned Aerial Vehicle (UAV) mobile edge computing (MEC) paradigm offers low latency communication, computation, and storage capabilities, which makes it an attractive way to mitigate these limitations by offloading them. Nevertheless, the majority of the offloading schemes let IoT devices send their intensive tasks to the connected edge server, which predictably limits the performance gain due to overload. Therefore, in this paper, besides integrating task offloading and load balancing, we study the resource allocation problem for multi-tier UAV-aided MEC systems. First, an efficient load-balancing algorithm is designed for optimizing the load among ground MEC servers through the handover process as well as hovering UAVs over the crowded areas which are still loaded due to the fixed location of the ground base stations server (GBSs). Moreover, we formulate the joint task offloading, load balancing, and resource allocation as an integer problem to minimize the system cost. Furthermore, an efficient task offloading algorithm based on deep reinforcement learning techniques is proposed to derive the offloading solution. Finally, the experimental results show that the proposed approach not only has a fast convergence performance but also has a significantly lower system cost when compared to the benchmark approaches.

Learning-based joint service caching and load balancing for MEC blockchain networks

Integrating the blockchain technology into mobile-edge computing (MEC) networks with multiple cooperative MEC servers (MECS) providing a promising solution to improving resource utilization, and helping establish a secure reward mechanism that can facilitate load balancing among MECS. In addition, intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads. In this paper, we investigate a learning-based joint service caching and load balancing policy for optimizing the communication and computation resources allocation, so as to improve the resource utilization of MEC blockchain networks. We formulate the problem as a challenging long-term network revenue maximization Markov decision process (MDP) problem. To address the highly dynamic and high dimension of system states, we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network (DQN) approach. The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

Joint energy efficiency and load balancing optimization in hybrid IP/SDN networks

Software-defined networking (SDN) is a paradigm that provides flexibility and programmability to computer networks. By introducing SDN nodes in a legacy IP network topology, network operators can benefit on higher control over the infrastructure. However, this migration is not a fast or straightforward process. Furthermore, to provide an adequate quality of service in hybrid IP/SDN networks, the coordination of both IP and SDN paradigm is fundamental. In this paper, this coordination is used to solve two optimization problems that are typically solved separately: (i) traffic load balancing and (ii) power consumption minimization. Each of these problems has opposing objectives, and thus, their joint consideration implies striking a balance between them. Therefore, this paper proposes the Hybrid Spreading Load Algorithm (HSLA) heuristic that jointly faces the problems of balancing traffic by minimizing link utilization and network’s power consumption in a hybrid IP/SDN network. HSLA is evaluated over differently sized topologies using different methods to select which nodes are migrated from IP to SDN. These evaluations reveal that alternative approaches that only address one of the objectives are outperformed by HSLA.

Joint Parallel Offloading and Load Balancing for Cooperative-MEC Systems With Delay Constraints

Mobile-edge computing (MEC) has been recognized as a promising solution to provide efficient communication and computation capabilities for mobile users (MUs). However, the problem relating to parallel offloading and load balancing with multiple cooperative MEC servers and massive delay-sensitive execution workloads remains to be investigated. In this paper, we study a joint parallel offloading and load balancing policy for such an MEC system. We formulate a long-term system average-cost (i.e., weighted sum of energy consumption and execution delays) stochastic programming problem under MU battery level stability and delay constraints. Our aims include optimizing the data sizes for uplink offloading transmissions for optimized communication and computation resource allocation among multi-users, and maximizing the computation capability utilization of cooperative MEC servers by load balancing. To solve this problem, we first design a Lyapunov-based centralized cost management algorithm (LYP-CCMA) to obtain the optimal system average-cost under the battery level stability constraints. Further, we propose two algorithms based on alternating direction method of multipliers (ADMM) to implement distributed resources allocation. Simulation results verify our analysis and demonstrate the superior performance of our proposed schemes over several baseline schemes.

Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network

Distributed link-state routing protocols, including Open Shortest Path First (OSPF) and Intermediate System–Intermediate System (IS-IS), have successfully provided robust shortest path routing for IP networks. However, shortest path routing is inflexible and sometimes results in congestion on some critical links. By separating the control plane and the data plane, the centralized control of Software Defined Networking (SDN)-based approach possesses flexible routing capabilities. Fibbing is an approach that can achieve centralized control over a network running distributed routing protocols. In a Fibbing-controlled IP network, the controller cleverly generates fake protocol messages to manipulate routers to steer the flow of the desired paths. However, introducing fake nodes destroys the structure of the loop-free property of Loop-Free Alternate (LFA) that is used to achieve fast failure recovery in IP networks. This paper addresses this issue and presents a solution to provision routing paths so a Fibbing network can still apply LFA in the network. The proposed network jointly considers load-balanced and fast failure recovery. We formulate the problem as an integer linear programming problem. The numerical results reveal that the proposed method can provide 100% survivability against any single node or single link failure.

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them concerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet satisfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA violations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application and are unsuitable to manage multi-Cloud/data center deployments required for mission critical services. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dynamic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost while satisfying some constraints on the average response time of multi-Cloud based applications. This paper proposes a joint load balancing and receding horizon capacity allocation techniques, which can be employed to handle multiple classes of requests. An extensive evaluation of the proposed solution against an Oracle with perfect knowledge of the future and well-known heuristics proposed in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case QoS constraints violation rate is 4.26 percent versus up to 17.25 percent of other approaches and can easily reduced by roughly a factor of four by exploiting the receding horizon approach). Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads. Analytical results are validated through simulation, which also analyzes the impact of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of the proposed approach under real workloads.

Joint Load Balancing and Offloading Optimization in Multiple Parked Vehicle‐Assisted Edge Computing

The introduction of mobile edge computing (MEC) in vehicular network has been a promising paradigm to improve vehicular services by offloading computation‐intensive tasks to the MEC server. To avoid the overload phenomenon in MEC server, the vast idle resources of parked vehicles can be utilized to effectively relieve the computational burden on the server. Furthermore, unbalanced load allocation may cause larger latency and energy consumption. To solve the problem, the reported works preferred to allocate workload between MEC server and single parked vehicle. In this paper, a multiple parked vehicle‐assisted edge computing (MPVEC) paradigm is first introduced. A joint load balancing and offloading optimization problem is formulated to minimize the system cost under delay constraint. In order to accomplish the offloading tasks, a multiple offloading node selection algorithm is proposed to select several appropriate PVs to collaborate with the MEC server in computing tasks. Furthermore, a workload allocation strategy based on dynamic game is presented to optimize the system performance with jointly considering the workload balance among computing nodes. Numerical results indicate that the offloading strategy in MPVEC scheme can significantly reduce the system cost and load balancing of the system can be achieved.

Secure and Optimized Load Balancing for Multitier IoT and Edge-Cloud Computing Systems

Mobile-edge computing (MEC) has emerged as a new computing paradigm with great potential to alleviate resource limitations attributed to mobile device users (MDUs) by offloading intensive computations to ubiquitous MEC server. However, most of the current offloading policies allow MDUs to transmit their tasks to the same connected small base stations (sBSs), which invariably increases latency and limits performance gain due to overload. Moreover, the security issue mitigating sensitive communication of information is not adequately addressed. Therefore, in this study, in addition to proposing a joint load balancing and computation offloading (CO) technique for MEC systems, we introduce a new security layer to circumvent potential security issues. First, a load balancing algorithm for efficient redistribution of MDUs among sBSs is proposed. In addition, a new advanced encryption standard (AES) cryptographic technique suffused with electrocardiogram (ECG) signal-based encryption and decryption key is presented as a security layer to safeguard the vulnerability of data during the transmission. Furthermore, an integrated model of load balancing, CO and security is formulated as a problem whose goal is to decrease the time and energy demands of the system. Detailed experimental results prove that our model with and without the additional security layers can save about 68.2% and 72.4% of system consumption compared to the local execution.

Optimized joint LiFi coordinated multipoint joint transmission clustering and load balancing for hybrid LiFi and WiFi networks

Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks provide a promising solution for future indoor wireless communication. Hybrid networks face the challenge of imbalanced cell load due to the uneven distribution of user equipment (UE), and they need to suppress inter-cell interference induced by the dense deployment of LiFi access points (APs). In this paper, with the aim to efficiently establish UE-AP associations and allocate resources, we propose a potential game (PG) aided design of joint user-centric LiFi coordinated multipoint (CoMP) joint transmission (JT) clustering and load balancing (LB). In our design, we formulate the joint design problem as a mixed-integer nonlinear programming (MINLP) problem, which integrates the requirement for the minimum data rate of each UE. With the aim to maximize the system throughput under proportional fairness constraints, we propose to build a PG model to solve the MINLP problem. Then we derive the Nash equilibrium based on the best-response dynamics, and thus a suboptimal solution is attained. Thanks to the space diversity gain brought by our joint LiFi CoMP-JT clustering and LB design, the signal-to-interference-plus-noise ratio can be improved. Thus, when the physical layer configurations remain the same, hybrid networks can achieve larger capacity and better reliability performances. Simulation results validate that hybrid networks using our presented design would achieve better throughput and satisfaction performances than benchmarks.

VM Scaling and Load Balancing via Cost Optimal MDP Solution

Dynamic resource allocation mechanism is an essential building block in contemporary cloud computing environment, enabling the support of the large variability of incoming requests from an enormous number of applications utilizing such cloud infrastructure. In this article, we devise a dynamic resource allocation mechanism that optimizes the application’s profit under the set of costs and revenues while maintaining performance constraints. Specifically, we devise a decision-maker (DM) agent which formulates the joint admission control, scaling and load balancing problem as a stochastic process solvable by Markov decision process (MDP) which provides the optimal policy. Accordingly, at each time instance, the DM can determine based on the system’s current state, on the set of requirements and on the set of costs, whether to add or release a VM (scale-out or scale-in, respectively), whether to admit or reject an upcoming task and if admitting it, which VM to allocate it to. We explore the value function structure and provide insights with respect to the optimal policies produced from it. To address scalability issues of the detailed MDP solution we provide an alternative solution by abstract MDP which consolidates multiple system states into a single abstract state, hence can cope with much larger systems at the expense of slight performance degradation. To demonstrate the feasibility of the suggested scheme, we designed and implemented it, alongside with two traditional auto-scalers, on the Amazon Web Services (AWS) infrastructure. We ran numerous MATLAB simulations and AWS-based experiments which provided insights and demonstrated superiority against the traditional policies we compared with.

Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks

The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes, where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multiuser multiserver VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer nonlinear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading algorithm compared to the benchmark solutions.

Joint balancing of IT and spectrum resources for selecting virtualized network function in inter-datacenter elastic optical networks.

With network functions decoupled from specific hardware, network function virtualization (NFV) is a promising technology to accelerate service provisioning in datacenter (DC) networks. In inter-datacenter elastic optical networks, each virtualized network function (VNF) is usually deployed in multiple DCs for the sake of survivability. In service provisioning, different VNF selections greatly affect IT resources in DCs as well as spectrum resources in optical networks. This paper investigates how to select appropriate VNFs for service requests to achieve joint load balancing of IT and spectrum resources. Two joint balancing factors are proposed to quantify the impact of different VNF selections on network load. Furthermore, a Joint-Optimization Selection (JOS) algorithm is designed to select VNFs in a joint load balancing manner. The performance of the proposed algorithm is evaluated in terms of resource utilization, blocking probability and average path length. Simulation results indicate that the proposed algorithm outperforms the benchmark algorithm.

Join-Idle-Queue with Service Elasticity

We consider the model of a token-based joint auto-scaling and load balancing strategy, proposed in a recent paper by Mukherjee, Dhara, Borst, and van Leeuwaarden [4] (SIGMETRICS '17), which offers an efficient scalable implementation and yet achieves asymptotically optimal steady-state delay performance and energy consumption as the number of servers N ! 1. In the above work, the asymptotic results are obtained under the assumption that the queues have fixed-size finite buffers, and therefore the fundamental question of stability of the proposed scheme with infinite buffers was left open. In this paper, we address this fundamental stability question. The system stability under the usual subcritical load assumption is not automatic. Moreover, the stability may not even hold for all N. The key challenge stems from the fact that the process lacks monotonicity, which has been the powerful primary tool for establishing stability in load balancing models. We develop a novel method to prove that the subcritically loaded system is stable for large enough N, and establish convergence of steady-state distributions to the optimal one, as N ! 1. The method goes beyond the state of the art techniques " it uses an induction-based idea and a "weak monotonicity" property of the model; this technique is of independent interest and may have broader applicability.

Joint load balancing and energy saving algorithm for virtual network embedding in infrastructure providers

Network virtualization is key to cloud services, in that it enables multiple users to share a physical infrastructure through abstraction. We propose an online virtual network (VN) embedding scheme which jointly considers load balancing and energy saving so as to maximize the profit of Infrastructure Providers (InPs). For load balancing, we propose to minimize a convex objective which penalizes mapping of VNs to overloaded resources. For energy saving, we consider two popular energy models: speed scaling and power-down. In the speed scaling model, energy consumption is modeled as a convex function of the load imposed on resources. We observe that both load-balancing and energy-saving objectives superadditively penalize high utilization/congestion at resources, and that such synergistic nature of the objectives leads to efficient joint optimization. In the power-down model, a fixed cost exists for keeping a node powered on, which is characterized by a nonconvex energy curve. In this case, we propose an iterative algorithm which explores the trade-offs between load balancing versus cost reduction from power-down of idle servers, in a controlled way. Our algorithm performs a sequential node and link mapping; in particular, for link mapping, we adopt randomized rounding with path stripping in order to obtain a constant factor approximation to the minimum penalty for link utilization. Numerical experiments show the efficacy of our algorithm in servicing VN requests of various topologies and resource requirements.

Optimal Design of Energy- and Spectral- Efficient Wireless Backhaul Hetnets: Joint Load Balancing and Interference Management

We design an energy and spectral efficiency evaluation framework for wireless backhaul heterogeneous networks by jointing the load balancing and interference management. Instead of achieve rate in the conventional association, the proposed load balancing strategy utilizes the effective rate with imperfect channel state information, which comprehensively considers the number of associated users, bandwidth allocated to wireless backhaul, and the achieve rate. Moreover, the proposed interference management combines the reverse time division duplexing with power optimization control. The formulated problem is a multi-objective optimization problem, maximizing the energy efficiency and spectral efficiency simultaneously. With the scalarization approach, the original problem is then converted into a single-objective optimization problem, but it is still a nonconvex problem and generally NP-hard. To deal with it, a novel global lower bound maximization algorithm is developed, which iteratively solves the convex approximated problem until convergence. Finally, the convex approximated problem can be decoupled into smaller sub-problems by the alternating direction method to develop a distributed algorithm. Simulation results demonstrate that the proposed algorithms can significantly improve energy and spectral-efficient performance, and are superior to other algorithms.

Optimal joint load balancing and EDCA configuration of IEEE 802.11 wireless hotspots

SummaryWireless hotspots, ie, infrastructures composed of several IEEE 802.11 access points (APs), are today the most common solution in providing Internet access to a wide and rapidly changing population of users. According to common device implementation, stations establish associations with APs based on the measured strongest Received Signal Strength Indicator level. This usually leads to an uneven distribution of users to APs, increasing the chances of local network congestion. Load balancing (LB) solutions aim at mitigating this problem controlling the distribution of users. The LB algorithms enforce stations to associate with APs that manage a low number of users and/or that have a low traffic load. Usually, LB solutions do not consider traffic priorities or they assume some a priori quality of service (QoS) configuration for users.In this study, we propose a QoS‐LB solution based on the cell breathing technique with the goal of balancing the load in IEEE 802.11e Enhanced Distributed Channel Access (EDCA) Hotspots. The proposed algorithm explores the space of possible power/EDCA configurations by using a branch and bound approach that reduces the number of analyzed configurations and, hence, the time required to find the global optimal solution. The algorithm has been analytically defined and its performance evaluated through simulations and tests in a real test bed. The results prove that the proposed solution is effective in solving the optimal QoS‐LB configuration problem in WiFi hotspots of average size.

Joint Load Balancing and Interference Mitigation in 5G Heterogeneous Networks

We study the problem of joint load balancing and interference mitigation in heterogeneous networks (HetNets) in which massive multiple-input multiple-output (MIMO) macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs) are assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of small cells density, number of base station antennas, and transmit power levels at low and high frequency bands. We further provide insights into the performance analysis and convergence of the proposed framework. The numerical results show that the proposed user association algorithm outperforms other baselines. Interestingly, we find that even at lower frequency band the performance of open access small cell is close to that of closed access at some operating points, the open access full- duplex small cell still yields higher gain as compared to the closed access at higher frequency bands. With increasing the small cell density or the wireless backhaul quality, the open access full- duplex small cells outperform and achieve a 5.6x gain in terms of cell-edge performance as compared to the closed access ones in ultra-dense networks with 350 small cell base stations per km2 .