Evaluating load balancing policies for performance and energy-efficiency

Nowadays, more and more increasingly hard computations are performed in challenging fields like weather forecasting, oil and gas exploration, and cryptanalysis. Many of such computations can be implemented using a computer cluster with a large number of servers. Incoming computation requests are then, via a so-called load balancing policy, distributed over the servers to ensure optimal performance. Additionally, being able to switch-off some servers during low period of workload, gives potential to reduced energy consumption. Therefore, load balancing forms, albeit indirectly, a trade-off between performance and energy consumption. In this paper, we introduce a syntax for load-balancing policies to dynamically select a server for each request based on relevant criteria, including the number of jobs queued in servers, power states of servers, and transition delays between power states of servers. To evaluate many policies, we implement two load balancers in: (i) iDSL, a language and tool-chain for evaluating service-oriented systems, and (ii) a simulation framework in AnyLogic. Both implementations are successfully validated by comparison of the results.Comment: In Proceedings QAPL'16, arXiv:1610.0769

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation.  This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Internet of Things Cloud: Architecture and Implementation

The Internet of Things (IoT), which enables common objects to be intelligent and interactive, is considered the next evolution of the Internet. Its pervasiveness and abilities to collect and analyze data which can be converted into information have motivated a plethora of IoT applications. For the successful deployment and management of these applications, cloud computing techniques are indispensable since they provide high computational capabilities as well as large storage capacity. This paper aims at providing insights about the architecture, implementation and performance of the IoT cloud. Several potential application scenarios of IoT cloud are studied, and an architecture is discussed regarding the functionality of each component. Moreover, the implementation details of the IoT cloud are presented along with the services that it offers. The main contributions of this paper lie in the combination of the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT) servers to offer IoT services in the architecture of the IoT cloud with various techniques to guarantee high performance. Finally, experimental results are given in order to demonstrate the service capabilities of the IoT cloud under certain conditions.Comment: 19pages, 4figures, IEEE Communications Magazin

Energy efficiency of dynamic management of virtual cluster with heterogeneous hardware

Cloud computing is an essential part of today's computing world. Continuously increasing amount of computation with varying resource requirements is placed in large data centers. The variation among computing tasks, both in their resource requirements and time of processing, makes it possible to optimize the usage of physical hardware by applying cloud technologies. In this work, we develop a prototype system for load-based management of virtual machines in an OpenStack computing cluster. Our prototype is based on an idea of 'packing' idle virtual machines into special park servers optimized for this purpose. We evaluate the method by running real high-energy physics analysis software in an OpenStack test cluster and by simulating the same principle using the Cloudsim simulator software. The results show a clear improvement, 9-48 %, in the total energy efficiency when using our method together with resource overbooking and heterogeneous hardware.Peer reviewe

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms

Smart Cloud Engine and Solution Based on Knowledge Base

AbstractComplexity of cloud infrastructures needs models and tools for process management, configuration, scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolution, and enabling the reasoning on structures by implementing strategies of efficient smart cloud management and intelligence. The solution proposed provides formal verification tools and intelligence for cloud control. It can be easily integrated with any cloud configuration manager, cloud orchestrator, and monitoring tool, since the connections with these tools are performed by using REST calls and XML files. It has been validated in the large ICARO Cloud project with a national cloud service provider